System

Abstract

A system is provided.SOLUTION: The system includes a means for a user to input a symptom, a means for a terminal to transmit input data to a server, a means for the server to analyze the data and determine the symptom, a means for the server to generate information on a countermeasure method or an appropriate medical institution on the basis of the determination result, and a means for providing the generated information to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Describe the "problem that the invention aims to solve" and the "means for solving the problem."

[0005] Long waiting times at hospitals cause significant stress and waste of time for patients. Even with appointments, patients often have to wait, creating a need for efficient medical care in crowded hospitals. In addition, the workload of doctors is increasing, making it urgent to streamline medical care. At the same time, insufficient follow-up after treatment has led to recurrences and inadequate health management for patients. This invention aims to solve these issues, reduce hospital waiting times, and streamline doctors' work, thereby improving patient experience and health management. [Means for solving the problem]

[0006] This system includes a means for a user to input symptoms and transmit the data to a server, a means for the server to analyze the data and determine the symptoms, and a means for the server to generate information on countermeasures or appropriate medical institutions based on the determination results and provide it to the user. It also includes a means for the user to re-input new symptom data, a means for transmitting the data to the server, a means for the server to integrate the new data with the initial data and perform a diagnosis, a means for transmitting the diagnosis results and treatment plan to medical professionals, and a means for the medical professionals to provide treatment based on the diagnosis results and treatment plan. The system also includes a means for the server to store the medical data, a means for generating medication reminders and lifestyle advice based on the stored data, a means for transmitting the data to the user's terminal, and a means for the terminal to present the transmitted information to the user. These features can reduce waiting times at hospitals, improve the efficiency of doctors' work, and provide continuous support after treatment.

[0007] A "user" is an individual who uses the system to input their own health status and symptoms.

[0008] A "terminal" is a device that allows a user to input symptom data and send it to a server, and refers to an electronic device such as a smartphone or tablet.

[0009] A "server" is a computer system that analyzes received data, generates a judgment result, and notifies the user or medical personnel.

[0010] "Symptom input" refers to the act of a user inputting their own health condition or physical abnormalities into a dedicated application.

[0011] "Data transmission" refers to the process in which the terminal sends the entered symptom data to the server.

[0012] "Data analysis" refers to the process of determining the severity of symptoms and appropriate countermeasures based on the symptom data received by the server.

[0013] The "judgment result" is the determination of whether the symptoms are mild or severe, appropriate measures to take, and information on medical institutions obtained as a result of the server's data analysis.

[0014] "Countermeasures" are specific instructions for action, including prescriptions and lifestyle advice, that are provided to users when the symptoms are mild and can be treated at home.

[0015] "Medical institution information" is information about appropriate medical facilities that will be notified to the user if the patient's condition is serious and specialized treatment is required.

[0016] "Diagnosis" is the process by which the server identifies a medical condition and its cause based on new symptom data and existing data.

[0017] A "treatment plan" is a method of prescribing appropriate medical procedures and medications that the server generates based on the diagnosis results.

[0018] "Medical treatment" refers to medical procedures that medical professionals perform on users based on the diagnosis results and treatment guidelines provided by the server.

[0019] "Medical data" refers to a series of information related to medical treatment, such as medical treatment results, prescription details, and treatment progress.

[0020] "Medication reminder" is a function that notifies users to take their medication at a specific time based on instructions generated by the server.

[0021] "Lifestyle advice" is specific lifestyle guidance for maintaining and improving health that is provided to the user by the server based on medical examination results and medical data.

[0022] The above are definitions of important terms included in the claims. [Brief explanation of the drawings]

[0023] [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

[0024] 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.

[0025] First, the terms used in the following description will be explained.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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."

[0031] [First embodiment]

[0032] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0033] 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.

[0034] 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).

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0040] 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.

[0041] 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.

[0042] 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.

[0043] 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."

[0044] This invention is a system for reducing waiting times at hospitals and improving the efficiency of medical care. This system works in cooperation with users, terminals, and servers, and provides simple diagnosis at home, primary medical care at hospitals, and continuous follow-up after medical care.

[0045] Simple testing at home

[0046] 1. User: Enters symptoms into a dedicated application at home. For example, "I have a headache and a slight fever."

[0047] 2. Terminal: Sends the entered symptom data to the server.

[0048] 3. Server: The AI ​​analyzes the symptoms based on the received data, compares the symptom data with a medical database, and determines whether the symptoms are mild or severe.

[0049] 4. Server: Based on the assessment results, it generates information on countermeasures and appropriate medical institutions. For example, if the symptoms are mild, it generates suggestions for home treatment and over-the-counter medications, and if the symptoms are severe, it generates information on the nearest hospital.

[0050] 5. Server: Sends the generated information to the user's device.

[0051] 6. Terminal: Receives information from the server and displays it to the user, who can then take action.

[0052] Primary medical care during outpatient visits

[0053] 1. User: After arriving at the hospital, the user re-enters their latest symptoms into the dedicated application. For example, they might enter "fever over 39 degrees and worsening headache."

[0054] 2. Device: Sends new symptom data to the server.

[0055] 3. Server: The new data received is integrated with the initial data, and the AI ​​performs a diagnosis again.

[0056] 4. Server: Generates diagnostic results and treatment guidelines and sends them to the medical institution's terminal. For example, it generates a diagnostic result such as "high possibility of influenza A" and a treatment guideline such as "prescribe a specific medication."

[0057] 5. Terminal: The information sent to the medical institution's terminal is displayed to the doctor, who uses it as a reference when providing medical care.

[0058] Ongoing support after treatment

[0059] 1. Server: Stores medical data, including medical results, prescription details, and treatment progress.

[0060] 2. Server: Based on the stored data, it generates medication reminders and lifestyle advice, such as "take your medicine at 8 a.m. every day" or "get enough rest."

[0061] 3. Server: Sends reminders and advice to the user's device.

[0062] 4. Terminal: Displays the transmitted information to the user and provides reminders and advice via voice or text.

[0063] 5. User: Follow the reminders and advice from the AI, take your medication, improve your lifestyle habits, and practice continuous health management.

[0064] As a specific example, a user inputs "I have a headache and a slight fever," and the server determines that it is a "mild cold," recommending that the user "take over-the-counter fever reducers and drink plenty of fluids." After arriving at the hospital, the user re-inputs that "the fever is worsening and has exceeded 39 degrees," and the server diagnoses that "it is likely influenza A," suggesting that "specific tests and antiviral medications should be prescribed." The doctor then provides treatment based on this information, and after the treatment, the doctor reminds the user to "take antiviral medication every day at 8 a.m. and rest."

[0065] The above is an embodiment of the present invention. This system significantly reduces waiting times at hospitals, improves the work efficiency of doctors, and effectively manages the health of patients.

[0066] The processing flow will be explained below.

[0067] Simple testing at home

[0068] Step 1:

[0069] The user enters symptoms into a dedicated app. For example, they might enter, "I have a headache and a slight fever."

[0070] Step 2:

[0071] The terminal transmits the input symptom data to the server.

[0072] Step 3:

[0073] The AI ​​analyzes the symptom data received by the server, compares it with a medical database, and determines whether the symptoms are mild or severe.

[0074] Step 4:

[0075] Based on the results of the assessment, the server generates information on countermeasures and appropriate medical institutions. For example, if the symptoms are mild, it will generate suggestions for home treatment and over-the-counter medications, and if the symptoms are severe, it will generate information on the nearest hospital.

[0076] Step 5:

[0077] The server transmits the generated information to the user's terminal.

[0078] Step 6:

[0079] The device receives the information from the server and displays it to the user, who can then take appropriate action.

[0080] Primary medical care during outpatient visits

[0081] Step 1:

[0082] After arriving at the hospital, the user re-enters their latest symptoms into the dedicated application. For example, they might enter "fever of over 39 degrees and worsening headache."

[0083] Step 2:

[0084] The terminal transmits new symptom data to the server.

[0085] Step 3:

[0086] The server integrates the new data received with the initial data, and the AI ​​performs a diagnosis again.

[0087] Step 4:

[0088] The server generates a diagnosis and a treatment plan and sends them to the medical institution's terminal. For example, it generates a diagnosis such as "high possibility of influenza A" and a treatment plan such as "prescribe a specific medication."

[0089] Step 5:

[0090] The terminal displays the information sent to the medical institution's terminal to the doctor, who then uses this information to provide actual medical treatment.

[0091] Ongoing support after treatment

[0092] Step 1:

[0093] The server stores medical data, including medical results, prescription details, and treatment progress.

[0094] Step 2:

[0095] The server generates medication reminders and lifestyle advice based on the stored data, such as "take your medicine at 8 a.m. every day" or "get enough rest."

[0096] Step 3:

[0097] The server sends reminders and advice to the user's terminal.

[0098] Step 4:

[0099] The device displays the sent information to the user and provides reminders and advice via voice or text.

[0100] Step 5:

[0101] Users follow reminders and advice from the AI ​​to take their medication and improve their lifestyle habits, and practice continuous health management.

[0102] The above is a concrete explanation of the program processing of the system step by step.

[0103] Example 1

[0104] 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."

[0105] Modern medical institutions face challenges such as long waiting times for patients and overwork for doctors due to the need to process vast amounts of information. Furthermore, there is a lack of effective methods for patients to self-diagnose at home or follow up after visiting the hospital. This creates a need for efficient and prompt medical care for both patients and medical institutions.

[0106] 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.

[0107] In this invention, the server includes: means for a user to input symptoms; means for a terminal to transmit the input data to the server; means for analyzing the data and determining the symptoms using an AI model; means for generating a countermeasure or information on an appropriate medical institution based on the determination result; means for transmitting the generated information to the user's terminal and for the terminal to display the information to the user; means for the user to input symptom data again; means for integrating new data with the initial data and performing a diagnosis again using the AI ​​model; means for transmitting the diagnosis result and treatment plan to medical professionals; means for saving the medical data and updating the database; means for generating medication reminders and lifestyle advice based on the saved data; and means for the terminal to display the reminders and advice to the user. This enables reduction of waiting times at hospitals, efficiency of medical operations, and continuous health management of patients.

[0108] 1. "User" refers to an individual who uses the system to input symptoms.

[0109] 2. "Terminal" refers to the electronic device used by the user to input symptoms and transmit the data to the server.

[0110] 3. "Server" refers to the data processing system that receives data sent by users and analyzes and generates data using AI models.

[0111] 4. "Data" refers to information such as symptom information entered by the user, countermeasures generated by the server, and diagnostic results.

[0112] 5. "AI model" refers to the artificial intelligence algorithm used by the server to analyze data, determine symptoms, and generate countermeasures.

[0113] 6. "Determination result" refers to the diagnosis result obtained after the server analyzes the symptoms using the AI ​​model.

[0114] 7. "Countermeasures" refers to medical measures generated by the server based on the assessment results.

[0115] 8. "Medical Institution" refers to the appropriate hospital, clinic, or other medical facility that the User should visit.

[0116] 9. "Diagnosis result" refers to the specific disease name and symptom evaluation results provided by the server based on data analysis.

[0117] 10. "Treatment policy" refers to the treatment method or prescription that medical professionals should implement based on diagnostic results.

[0118] 11. "Medical personnel" refers to medical professionals such as doctors and nurses who provide medical care based on diagnostic results and treatment plans.

[0119] 12. "Medical data" refers to information such as medical examination results, prescription details, and treatment progress.

[0120] 13. "Database" refers to an electronic record device where the server stores medical data.

[0121] 14. "Medication Reminder" means a notification or alert that encourages a user to take their medication.

[0122] 15. "Lifestyle Advice" means lifestyle advice generated by the Server for the purpose of assisting the User in managing their health.

[0123] This system is designed to reduce waiting times and improve the efficiency of medical care, especially in hospitals. The system utilizes a user's device, a server, and an AI model to provide simple diagnosis at home, primary care during outpatient visits, and continuous follow-up after treatment.

[0124] Hardware and Software

[0125] User device: A device (smartphone, tablet, PC, etc.) on which the user inputs symptoms. A dedicated application is installed.

[0126] Server: A central server for receiving and processing data. It is connected to a medical database and uses AI models for data analysis.

[0127] AI models: Generative AI models such as OpenAI's GPT-4 are used to analyze data, make judgments, and generate advice.

[0128] Simple testing at home

[0129] 1. The user opens the dedicated application and enters their symptoms in text format. For example, they might enter, "I have a headache and a slight fever."

[0130] 2. The device encrypts the input data and sends it to the server.

[0131] 3. The server analyzes the received data using an AI model to determine symptoms, and compares them with a medical database to determine whether the symptoms are mild or severe.

[0132] 4. Based on the results of the assessment, the server generates information on countermeasures and appropriate medical institutions (for example, "take over-the-counter fever reducers and drink plenty of fluids").

[0133] 5. The server sends the generated information to the terminal.

[0134] 6. The device displays the information to the user, who then takes appropriate action.

[0135] Primary medical care during outpatient visits

[0136] 1. After arriving at the hospital, the user re-enters their latest symptoms into the dedicated application. For example, they might enter "fever over 39 degrees and worsening headache."

[0137] 2. The device sends new data to the server.

[0138] 3. The server integrates the new data with the initial data and performs a diagnosis again using the AI ​​model.

[0139] 4. The server generates the diagnosis and treatment plan, for example, "It is highly likely that the patient has influenza A. Specific tests and prescription of antiviral drugs are required," and sends this information to the medical institution's terminal.

[0140] 5. The terminal displays the information sent to the medical institution's terminal to the doctor, who uses this information as a reference when providing treatment.

[0141] Ongoing support after treatment

[0142] 1. The server stores medical data (medical results, prescription details, treatment progress, etc.) in a database.

[0143] 2. The server uses the stored data to generate medication reminders and lifestyle advice using AI models, such as reminders to "take your medication at 8 a.m. every day" and "get enough rest."

[0144] 3. The server sends the generated reminders and advice to the device.

[0145] 4. The device will provide reminders and advice to the user, either through voice or text. For example, "It's 8 AM. Don't forget to take your medicine."

[0146] 5. The user follows the reminders and advice to take their medication and improve their lifestyle.

[0147] Examples of concrete examples and prompts

[0148] For example, if a user inputs "I have a headache and a slight fever" into a dedicated application, the server will determine that it is a "mild cold" and recommend that the user "take over-the-counter fever reducers and drink plenty of fluids." If, after arriving at the hospital, the user again inputs that "the fever has risen above 39 degrees and is getting worse," the server will diagnose that "it is likely influenza A" and suggest "specific tests and the prescription of antiviral medication." The doctor will then provide treatment based on this information, and after the consultation will remind the user to "take antiviral medication every day at 8 a.m. and rest."

[0149] An example prompt is:

[0150] Example: "I have a headache and a slight fever. What should I do?"

[0151] Example server-generated response: "Your symptoms have been diagnosed as a mild cold. We recommend taking over-the-counter fever-reducing medication, drinking plenty of fluids, and getting plenty of rest."

[0152] This system will reduce waiting times at hospitals, improve the efficiency of medical operations, and enable continuous health management for patients.

[0153] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0154] Simple testing at home

[0155] Step 1:

[0156] The user opens a dedicated application and enters their symptoms (e.g., "I have a headache and a slight fever").

[0157] Input: User's symptom information

[0158] Output: User-entered symptom data

[0159] Step 2:

[0160] The terminal receives the input symptom data, encrypts the data, and transmits it to the server.

[0161] Input: User-entered symptom data

[0162] Output: Encrypted symptom data

[0163] Step 3:

[0164] The server receives the encrypted data, decrypts it, and then analyzes it using an AI model (e.g., GPT-4) to determine symptoms.

[0165] Input: Encrypted symptom data

[0166] Data processing: Data decryption

[0167] Data Computing: Data Analysis with AI Models

[0168] Output: Symptom determination result (e.g., "Mild cold")

[0169] Step 4:

[0170] Based on the results of the assessment, the server generates information on countermeasures and appropriate medical institutions.

[0171] Input: Symptom assessment result

[0172] Data calculation: Countermeasure methods and medical institution information generation

[0173] Output: Information on countermeasures and medical institutions (e.g., "Take over-the-counter fever reducers and drink plenty of fluids")

[0174] Step 5:

[0175] The server transmits the generated information to the terminal.

[0176] Input: Countermeasures and medical institution information

[0177] Output: Countermeasures and medical institution information sent

[0178] Step 6:

[0179] The terminal receives the information and displays it to the user.

[0180] Input: Countermeasures and medical institution information sent from the server

[0181] Output: Information displayed to the user (e.g., "Take over-the-counter fever reducers and drink plenty of fluids")

[0182] Primary medical care during outpatient visits

[0183] Step 1:

[0184] After arriving at the hospital, the user re-enters their latest symptoms (e.g., "fever over 39 degrees and worsening headache") into a dedicated application.

[0185] Input: Latest symptom data

[0186] Output: Re-entered symptom data

[0187] Step 2:

[0188] The terminal receives the re-entered symptom data and transmits it to the server.

[0189] Input: Re-entered symptom data

[0190] Output: Submitted symptom data

[0191] Step 3:

[0192] The server integrates the new data received with the initial data, analyzes the data again using the AI ​​model, and updates the diagnosis.

[0193] Input: Re-entered symptom data, initial symptom data

[0194] Data processing: Data integration

[0195] Data Computation: Reanalyzing Data with AI Models

[0196] Output: Updated diagnosis (e.g., "Probably influenza A").

[0197] Step 4:

[0198] The server generates a treatment plan based on the diagnosis results and sends it to the medical institution's terminal.

[0199] Input: Updated diagnostic results

[0200] Data Computation: Treatment Plan Generation

[0201] Output: The treatment plan sent (e.g., "In case of influenza A, specific tests and antiviral medications are required.")

[0202] Step 5:

[0203] The terminal receives the information sent to the medical institution's terminal and displays it to the doctor.

[0204] Input: Submitted treatment plan

[0205] Output: Treatment plan displayed to the doctor

[0206] Ongoing support after treatment

[0207] Step 1:

[0208] When the consultation is completed, the server stores data such as the consultation results, prescription details, and treatment progress in a database.

[0209] Input: medical results, prescription details, treatment progress data

[0210] Output: Saved medical data

[0211] Step 2:

[0212] The server uses AI models to generate medication reminders and lifestyle advice based on the stored medical data.

[0213] Input: Stored medical data

[0214] Data Computing: Generating Medication Reminders and Lifestyle Advice

[0215] Output: Generated medication reminders and lifestyle advice

[0216] Step 3:

[0217] The server transmits the generated reminders and advice to the terminal.

[0218] Input: Generated medication reminders and lifestyle advice

[0219] Output: Reminders and advice sent

[0220] Step 4:

[0221] The device receives reminders and advice and displays them to the user. Reminders are given via voice notification or text display.

[0222] Input: Reminders and advice sent

[0223] Output: Reminders and advice displayed to the user (e.g., "It's 8 AM. Take your antiviral medication.")

[0224] Step 5:

[0225] Users follow the reminders and advice from the device to take appropriate medication and improve their lifestyle habits.

[0226] Input: Reminders and advice

[0227] Output: Medication and lifestyle improvement behavior

[0228] (Application example 1)

[0229] 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."

[0230] In conventional medical systems, after users receive medical treatment at a hospital, the payment procedures for medical expenses are often complicated and time-consuming. Furthermore, there are insufficient medication reminders and lifestyle advice after treatment, making it difficult to provide continuous health management. This prevents users from receiving appropriate follow-up after treatment, leading to a decrease in medical efficiency.

[0231] 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.

[0232] In this invention, the server includes: means for the user to input symptoms; means for the terminal to send the input data to the server; means for the server to analyze the data and determine the symptoms; means for the server to generate information on countermeasures or appropriate medical institutions based on the determination results; means for providing the generated information to the user; means for the server to calculate medical expenses after treatment; means for the server to automatically debit the medical expenses from the user's electronic payment account; and means for sending a payment confirmation notice to the user's terminal. This enables automatic payment of medical expenses after treatment and prompt follow-up.

[0233] - "User" means an individual who uses the system to input their symptoms and receive medical treatment or medical services.

[0234] A "terminal" is an electronic device operated by a user to input data and receive medical information. Examples include smartphones and tablets.

[0235] A "server" is a computer system that receives, stores, and analyzes data sent from a terminal and provides necessary information to users and medical professionals.

[0236] "Data" refers to information entered by the user, such as symptom information, medical treatment results, and payment information, which the server analyzes and stores.

[0237] "Analysis" refers to the act of using certain algorithms and AI technology to determine symptoms and calculate medical expenses based on the data received by the server.

[0238] "Symptom determination" refers to the act of the server diagnosing what the symptom is based on the symptom information entered by the user.

[0239] "Countermeasures" are advice on specific actions the user should take or medicines that should be used based on the symptom assessment results.

[0240] "Information about medical institutions" refers to information about hospitals and clinics that are appropriate for the user, including medical appointments and medical treatment details.

[0241] "Generated information" refers to the diagnosis and advice provided to users and medical professionals based on the results of analysis and judgment by the server.

[0242] A "payment confirmation notification" is a message sent by the server to notify the user when the server has successfully debited the user's electronic payment account.

[0243] An "electronic payment account" is an account used by a user to make financial payments online, including a credit card or digital wallet.

[0244] A "medication reminder" is a notification sent by the server to the user to urge him or her to take medicine regularly.

[0245] "Lifestyle advice" refers to guidance and suggestions about daily life provided by the server to maintain and improve the user's health.

[0246] This invention realizes automatic payment of medical expenses and continuous follow-up in a system in which a user inputs symptoms and manages medical information.

[0247] System Configuration

[0248] This system consists of a user's device, a server, and an electronic payment service. The user's device is typically a smartphone or tablet, on which a dedicated application is installed. The server is a computer system for storing and analyzing data and generating notifications. The electronic payment service is an online payment platform that automatically debits the user's medical expenses.

[0249] System Operation

[0250] 1. Enter symptoms

[0251] The user uses a dedicated application at home or at a designated location to input their current symptoms, for example, "I have a headache and a slight fever." The device then sends the input data to the server.

[0252] 2. Data analysis and judgment

[0253] The server analyzes the received symptom data and determines whether the symptom is mild or severe by using a generative AI model to compare the symptom data with a medical database.

[0254] 3. Information generation and provision

[0255] Based on the results of the assessment, the server generates information on countermeasures and appropriate medical institutions and sends it to the user's device. For example, if the symptoms are mild, it suggests home treatment methods and over-the-counter medications. If the symptoms are severe, it provides information on the nearest hospital.

[0256] 4. Automatic medical payments

[0257] After the medical treatment is completed, the server calculates the medical fee based on the medical information, and then automatically deducts the medical fee from the user's electronic payment account, such as a credit card or digital wallet.

[0258] 5. Sending notifications

[0259] The server sends a payment confirmation notice to the user's terminal, and the user confirms that the payment has been completed.

[0260] 6. Ongoing follow-up

[0261] The server stores the medical data and generates medication reminders and lifestyle advice based on the data. These reminders and advice are periodically sent to the user's device.

[0262] Specific examples

[0263] Consider a case where a user inputs "I have a fever and headache" and is diagnosed with "influenza" at the hospital. The user then inputs information about the antiviral medication they are taking into the app. After the consultation, the server automatically calculates the medical expenses and deducts them from the user's electronic payment account. A notification of payment completion is sent to the user's device, and thereafter, medication reminders are sent periodically.

[0264] Prompt Sentence Examples

[0265] The user enters that they have a fever and headache and is diagnosed with influenza at the hospital. They then enter information about the antiviral medication they are taking into the app. After the consultation, payment is automatically completed and the information is notified to the user. From then on, they will receive a daily medication reminder at the specified time.

[0266] The system automates medical bill payment procedures and ensures users can follow up after medical treatment, improving user convenience and greatly improving the efficiency of the entire medical system.

[0267] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0268] Step 1:

[0269] The user uses a dedicated application at home or a designated location to input their current symptoms. The input data is specific symptom information such as "I have a headache and a slight fever." The device then sends this information to the server.

[0270] Input: User-entered symptom information

[0271] Output: Symptom information is sent from the device to the server

[0272] Step 2:

[0273] The server analyzes the symptom data received from the device. This analysis utilizes a generative AI model and compares the symptom data with a medical database. At this stage, an initial determination is made as to whether the symptoms are mild or severe.

[0274] Input: Symptom information from the terminal

[0275] Output: Initial diagnosis result (mild or severe)

[0276] Step 3:

[0277] Based on the initial diagnosis, the server generates information on necessary countermeasures or appropriate medical institutions. For example, if the symptoms are mild, the server may recommend taking over-the-counter fever reducers and drinking plenty of fluids. If the symptoms are severe, the server may generate information on the nearest hospital.

[0278] Input: Initial judgment result

[0279] Output: Information on countermeasures and medical institutions

[0280] Step 4:

[0281] The server sends the generated information about countermeasures and medical institutions to the user's device, which receives it and displays it to the user.

[0282] Input: Information on countermeasures and medical institutions

[0283] Output: Countermeasures and information about medical institutions are displayed on the user's device

[0284] Step 5:

[0285] When the user arrives at the hospital to continue treatment, they re-enter their latest symptom data. For example, they might enter information such as "fever over 39 degrees and worsening headache." The device then sends the new symptom data to the server again.

[0286] Input: Latest symptom data

[0287] Output: New symptom data is sent from the device to the server.

[0288] Step 6:

[0289] The server integrates the new symptom data with the initial data and again uses AI to perform a detailed diagnosis.

[0290] Input: New symptom data and initial data

[0291] Output: Detailed diagnostic results and treatment plan

[0292] Step 7:

[0293] The server generates a diagnosis and treatment plan and sends it to the medical professional's device, which receives and displays this information. The doctor uses this information as a reference when providing treatment.

[0294] Input: Detailed diagnosis and treatment plan

[0295] Output: Information is displayed on the medical personnel's terminal.

[0296] Step 8:

[0297] After the medical treatment is completed, the server calculates the medical costs based on the medical information, including the details of the treatment and the prescribed medication.

[0298] Input: Medical information

[0299] Output: Calculated medical costs

[0300] Step 9:

[0301] The server automatically debits the calculated medical costs from the user's electronic payment account, using a pre-registered credit card or digital wallet.

[0302] Input: Calculated medical expenses

[0303] Output: Withdrawal completion result

[0304] Step 10:

[0305] The server sends a payment confirmation notice to the user's terminal, which the user receives and confirms that the payment has been completed.

[0306] Input: Withdrawal completion result

[0307] Output: Payment confirmation notification

[0308] Step 11:

[0309] The server stores medical data and generates medication reminders and lifestyle advice based on that data.

[0310] Input: Medical data

[0311] Output: Medication reminders and lifestyle advice

[0312] Step 12:

[0313] The server sends the generated medication reminders and lifestyle advice to the user's device, which receives them and periodically notifies them. The user receives these notifications and acts according to the instructions.

[0314] Input: Medication reminders and lifestyle advice

[0315] Output: A notification will be displayed periodically on the user's device.

[0316] 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.

[0317] This invention is a system for reducing waiting times at hospitals and improving medical efficiency, and also provides support that takes into account the user's emotional state. This system works in cooperation with the user, terminal, server, and emotion engine, and provides simple diagnosis at home, primary medical treatment at the hospital, and continuous follow-up after treatment.

[0318] Simple testing at home

[0319] 1. User: At home, the user inputs their symptoms into a dedicated application, and emotional data is also collected by the emotion recognition system. For example, if the user inputs "I have a headache and a slight fever," their emotional state is recorded as "anxiety" or "stress."

[0320] 2. Terminal: Sends the input symptom data and emotion data to the server.

[0321] 3. Server: Based on the received data, the AI ​​analyzes the symptoms and emotional state. It compares the symptom data with a medical database to determine whether the symptoms are mild or severe, and analyzes the emotional data with an emotion engine to determine the emotional state.

[0322] 4. Server: Based on the assessment results, the server generates information on countermeasures and appropriate medical institutions. For example, if the symptoms are mild, the server generates suggestions for home treatment and over-the-counter medications, and if the symptoms are severe, it generates information on the nearest hospital. Based on the emotional data, the server also adds countermeasures according to the user's emotional state.

[0323] 5. Server: Sends the generated information to the user's device.

[0324] 6. Terminal: Receives information from the server and displays it to the user, who can then take action.

[0325] Primary medical care during outpatient visits

[0326] 1. User: After arriving at the hospital, the user re-enters their latest symptoms into the dedicated application, and the emotion recognition system also collects their latest emotional data. For example, if the user enters "fever over 39 degrees and worsening headache," their emotional state is recorded as "strong anxiety" or "tension."

[0327] 2. Device: Sends new symptom data and emotion data to the server.

[0328] 3. Server: The AI ​​combines the new data received with the initial data and performs another diagnosis. At the same time, the emotion engine analyzes the latest emotional state.

[0329] 4. Server: Generates diagnosis results and treatment plans and sends them to the medical institution's terminal. For example, it generates a diagnosis result such as "high possibility of influenza A" and a treatment plan such as "prescribe a specific medication." Response methods based on the patient's emotional state are also incorporated into the treatment plan.

[0330] 5. Terminal: The information sent to the medical institution's terminal is displayed to the doctor, who uses it as a reference when providing medical care.

[0331] Ongoing support after treatment

[0332] 1. Server: Stores medical data and emotional data. The stored data includes medical results, prescription details, treatment progress, emotional state, etc.

[0333] 2. Server: Based on the stored data, it generates medication reminders and lifestyle advice. For example, it generates advice such as "Take your medicine at 8:00 a.m. every day" or "Get enough rest." At the same time, it also includes mental health advice based on the patient's emotional state.

[0334] 3. Server: Sends reminders and advice to the user's device.

[0335] 4. Terminal: Displays the transmitted information to the user and provides reminders and advice via voice or text.

[0336] 5. User: Follow the reminders and advice from the AI, take your medication, improve your lifestyle habits, and practice continuous health management.

[0337] For example, if a user inputs "I have a headache and a slight fever" and the emotional data collected indicates "anxiety," the server will determine that it is a "mild cold" and recommend that the user take an over-the-counter fever reducer and drink plenty of fluids, while also providing emotional care advice such as listening to music that promotes relaxation. If, after arriving at the hospital, the user re-inputs that their fever has risen above 39 degrees and worsened, and the emotional state is collected as "strong anxiety," the server will diagnose that the patient is likely to have influenza A and suggest that they undergo specific tests and prescribe antiviral medication, along with emotional care guidelines such as setting aside time to explain the situation in detail to the doctor. After the consultation, the server will remind the patient to take antiviral medication every day at 8 a.m. and rest, and to practice deep breathing as a relaxation technique.

[0338] The above is an embodiment of the present invention. This system significantly reduces waiting times at hospitals, improves the work efficiency of doctors, and effectively manages the health and mental health of patients.

[0339] The processing flow will be explained below.

[0340] Simple testing at home

[0341] Step 1:

[0342] The user inputs their symptoms into a dedicated app. For example, they might input "I have a headache and a slight fever," and the emotion recognition system will collect their emotional state as "anxiety."

[0343] Step 2:

[0344] The terminal transmits the input symptom data and emotion data to the server.

[0345] Step 3:

[0346] The AI ​​analyzes the symptom data received by the server, compares it with a medical database, and determines whether the symptoms are mild or severe.

[0347] Step 4:

[0348] The server analyzes the emotional data with an emotion engine to determine the emotional state.

[0349] Step 5:

[0350] The server generates information on countermeasures and appropriate medical institutions based on the symptoms and emotional state. For example, if the symptoms are mild, it generates home treatment methods (e.g., over-the-counter medication suggestions), and if the symptoms are severe, it generates information on the nearest hospital. Based on the emotional state, it also suggests emotional care measures (e.g., relaxation methods).

[0351] Step 6:

[0352] The server transmits the generated information to the user's terminal.

[0353] Step 7:

[0354] The device receives the information from the server and displays it to the user, who can then take appropriate action.

[0355] Primary medical care during outpatient visits

[0356] Step 1:

[0357] After arriving at the hospital, the user again enters their latest symptoms into the dedicated application. For example, they might enter "fever over 39 degrees and worsening headache," and the emotion recognition system records their emotional state as "strong anxiety."

[0358] Step 2:

[0359] The device sends new symptom data and emotion data to the server.

[0360] Step 3:

[0361] The server integrates the new data received with the initial data, and the AI ​​performs a second diagnosis. At the same time, the emotion engine analyzes the latest emotional state.

[0362] Step 4:

[0363] The server generates a diagnosis and a treatment plan. For example, it generates a diagnosis such as "high possibility of influenza A" and a treatment plan such as "prescribe a specific medication." The treatment plan also includes a response method based on the patient's emotional state (e.g., setting aside time to explain the situation in detail to the doctor).

[0364] Step 5:

[0365] The server sends the diagnosis results and treatment plan to the medical institution's terminal.

[0366] Step 6:

[0367] The device displays the received information to the doctor, who then uses it as a reference when providing treatment.

[0368] Ongoing support after treatment

[0369] Step 1:

[0370] The server stores medical data and emotional data, including medical results, prescription details, treatment progress, and emotional state.

[0371] Step 2:

[0372] The server generates medication reminders and lifestyle advice based on the stored data. For example, it might generate advice such as "take your medicine at 8 a.m. every day" or "get enough rest." At the same time, it also includes mental care advice based on the user's emotional state (e.g., repeatedly taking deep breaths as a relaxation technique).

[0373] Step 3:

[0374] The server sends reminders and advice to the user's terminal.

[0375] Step 4:

[0376] The device displays the sent information to the user and provides reminders and advice via voice or text.

[0377] Step 5:

[0378] Users follow reminders and advice from the AI ​​to take their medication and improve their lifestyle habits, and practice continuous health management.

[0379] The above is a concrete explanation of each step of the program processing of a system that combines an emotion engine.

[0380] Example 2

[0381] 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."

[0382] In conventional medical systems, sufficient pre-diagnosis and emotional care are not provided before the user arrives at the hospital, causing extended diagnosis times. Furthermore, the user's emotional state is not taken into consideration during waiting times at the hospital or follow-up care after treatment, resulting in inappropriate care being provided. As a result, medical efficiency and user satisfaction have not been sufficiently improved.

[0383] 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.

[0384] In this invention, the server includes means for analyzing symptoms using a generative AI model based on received data, means for analyzing emotional states using an emotion engine, and means for generating countermeasures, information on appropriate medical institutions, and emotional care suggestions based on the results of the assessment. This makes it possible to comprehensively analyze the user's symptoms and emotional state, provide appropriate countermeasures and information on medical institutions, and also suggest emotional care. This reduces waiting times at hospitals, improves doctor work efficiency, and effectively manages the health and mental health of patients.

[0385] 1. "User" is a person who uses the system to input symptoms and receive suggested treatments and medical information.

[0386] 2. "Symptoms" are data that indicate the health condition or abnormality that the user feels.

[0387] 3. "Emotional state" is data that indicates the user's current emotional or mental state.

[0388] 4. "Terminal" means a device used by a User to access the System, such as a smartphone or tablet.

[0389] 5. "Server" means a computer system that receives data sent by users and analyzes and provides information.

[0390] 6. A "generative AI model" is an artificial intelligence algorithm used for natural language processing and symptom analysis.

[0391] 7. "Emotion Engine" is a software module for analyzing the user's emotional state.

[0392] 8. "Medical database" means a collection of medical information used for symptom analysis and diagnosis.

[0393] 9. "Countermeasures" are general treatments or measures to deal with the user's symptoms.

[0394] 10. "Information about medical institutions" means information about appropriate medical facilities that a user should visit.

[0395] 11. "Emotional care" refers to suggestions for psychological support and relaxation tailored to the user's emotional state.

[0396] 12. "Medical data" refers to medical information generated by physicians, such as medical examination results and treatment details.

[0397] 13. "Remind" is a notification that prompts the user to take medication or take other action to manage their health.

[0398] 14. "Lifestyle Advice" means suggestions for improving the user's lifestyle to maintain their health.

[0399] MODE FOR CARRYING OUT THE INVENTION

[0400] The present invention is a system that collects and analyzes a user's symptom input and emotional state, and provides appropriate measures and information on medical institutions. This system is composed of a user, a terminal, a server, a generative AI model, and an emotion engine. An embodiment of this system is described in detail below.

[0401] Hardware and Software Configuration

[0402] The system uses the following major hardware and software:

[0403] User device: A device such as a smartphone or tablet that is used by installing a dedicated application.

[0404] Server: A computer system that receives, analyzes, and stores data sent by users.

[0405] Generative AI models: Artificial intelligence algorithms that perform natural language processing and symptom analysis. For example, we use AI models such as GPT-3.

[0406] Emotion engine: A software module for analyzing the user's emotional state. For example, it uses the Microsoft Azure Emotion Analysis API.

[0407] Medical database: A collection of medical information used for symptom analysis and diagnosis, such as UMLS.

[0408] System Embodiments

[0409] Simple testing at home

[0410] 1. The user enters their symptoms into a dedicated application at home. For example, they might enter, "I have a headache and a slight fever." Emotional data is also collected using an emotion recognition system. Automatically captured images and audio analysis identify the symptoms as "anxiety" or "stress."

[0411] 2. The device sends the input symptom data and emotion data to the server.

[0412] 3. The server uses a generative AI model based on the received data to analyze symptoms and emotional state. The symptom data is compared with a medical database. At the same time, an emotion engine is used to analyze the emotional state.

[0413] 4. The server determines whether the symptoms are mild or severe and generates information on countermeasures and appropriate medical institutions. For example, if the symptoms are mild, it suggests home treatment methods and over-the-counter medications, while if the symptoms are severe, it provides information on the nearest medical institution. It also generates advice on emotional care, such as "listening to music that promotes relaxation."

[0414] 5. The server sends the generated information to the user's terminal.

[0415] 6. The device receives the information from the server and displays it to the user, who can then take action based on it.

[0416] Primary medical care during outpatient visits

[0417] 1. After arriving at the hospital, the user re-enters their latest symptoms into the dedicated application. For example, they might enter "fever over 39 degrees and worsening headache." The application also collects their latest emotional data, which is recognized as "strong anxiety" or "tension."

[0418] 2. The device sends the new symptom data and emotion data to the server.

[0419] 3. The server integrates the new data with the initial data, performs a diagnosis again using the generative AI model, and analyzes the latest emotional state using the emotion engine.

[0420] 4. The server generates a diagnosis and a treatment plan and sends it to the medical institution's terminal. For example, it may diagnose "high possibility of influenza A" and suggest "prescribing antiviral drugs." It also includes a plan for emotional care.

[0421] 5. The terminal displays the information sent to the medical institution's terminal to the doctor, who then uses it as a reference when providing medical care.

[0422] Ongoing support after treatment

[0423] 1. The server stores medical and emotional data, including medical results, prescriptions, treatment progress, and emotional state.

[0424] 2. The server generates medication reminders, lifestyle advice, and emotional care suggestions based on the stored data, such as "take your medicine at 8 a.m. every day" and "get enough rest."

[0425] 3. The server sends the generated reminders and advice to the user's device.

[0426] 4. The device presents the sent information to the user and provides reminders and advice via voice or text.

[0427] 5. Users follow the reminders and advice from the AI ​​to take their medication and improve their lifestyle habits, thereby practicing continuous health management.

[0428] Specific examples

[0429] If the user inputs "I have a headache and a slight fever" into the dedicated application and the emotional data indicates "anxiety," the server will determine that it is a "mild cold" and suggest "taking an over-the-counter fever reducer and drinking plenty of fluids." It will also provide emotional care advice such as "listening to music that promotes relaxation."

[0430] If the patient re-enters that their fever is worsening and exceeds 39 degrees Celsius after arriving at the hospital, and their emotional state is recorded as "strong anxiety," the server will diagnose that they have a high probability of having influenza A and suggest prescribing antiviral medication. It also includes emotional care policies such as allowing time for the patient to explain the situation in detail to the doctor.

[0431] After the consultation, the patient is reminded to "take antiviral medication every day at 8:00 a.m. and rest" and "take repeated deep breaths as a relaxation technique."

[0432] The above is an embodiment of the present invention. This system reduces waiting times at hospitals, improves the work efficiency of doctors, and effectively manages the health and mental health of patients.

[0433] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0434] Simple testing at home

[0435] Step 1:

[0436] The user enters their symptoms into a dedicated application at home. For example, they might type "I have a headache and a slight fever" into a text box. The application also uses a camera and microphone to collect emotional data through an emotion recognition system. For example, it can extract emotional data such as "anxiety" or "stress" from automatically captured images or recorded audio.

[0437] Input: Symptom text ("headache and slight fever"), face photo or voice data

[0438] Output: Symptom data ("headache and slight fever"), emotion data ("anxiety")

[0439] Step 2:

[0440] The device sends the input symptom data and emotion data to a server, for example, by using the network function of a smartphone.

[0441] Input: Symptom data, emotion data

[0442] Output: Send data to the server

[0443] Step 3:

[0444] The server analyzes the symptoms using a generative AI model based on the received symptom data. For example, it analyzes the symptom data using natural language processing (NLP) and compares it with a medical database. At the same time, it analyzes the emotion data using an emotion engine.

[0445] Input: Symptom data, emotion data

[0446] Output: Analysis results (symptom severity, emotional state)

[0447] Step 4:

[0448] The server determines whether the symptoms are mild or severe and generates appropriate countermeasures and information on medical institutions. If the symptoms are mild, it suggests over-the-counter medications and home treatment, and if the symptoms are severe, it provides information on the nearest medical institution. It also generates countermeasures based on the user's emotional state.

[0449] Input: Analysis results (symptom severity, emotional state)

[0450] Output: Countermeasures, medical institution information, emotional care suggestions

[0451] Step 5:

[0452] The server transmits the generated information to the user's terminal, for example, by using a notification function.

[0453] Input: Countermeasures, medical institution information, emotional care suggestions

[0454] Output: Send data to the user's device

[0455] Step 6:

[0456] The device receives information from the server and displays it to the user using a dedicated application, which displays the information in text, images, and audio.

[0457] Input: Countermeasures, medical institution information, emotional care suggestions

[0458] Output: What is displayed to the user

[0459] Primary medical care during outpatient visits

[0460] Step 1:

[0461] After arriving at the hospital, the user re-enters their latest symptoms into the dedicated application. For example, they might enter "fever over 39 degrees and worsening headache." New emotion data is also collected. The emotion recognition system extracts these as "strong anxiety" or "tension."

[0462] Input: Latest symptom text ("Fever over 39 degrees and worsening headache"), face photo or voice data

[0463] Output: New symptom data ("Fever over 39 degrees, headache getting worse"), New emotion data ("Strong anxiety")

[0464] Step 2:

[0465] The device sends new symptom data and emotion data to the server via the smartphone's network function.

[0466] Input: New symptom data, new emotion data

[0467] Output: Send data to the server

[0468] Step 3:

[0469] The server then integrates the new data received with the initial data and performs a re-diagnosis using the generative AI model, while simultaneously analyzing the new emotion data with the emotion engine.

[0470] Input: New symptom data, new emotion data, initial data

[0471] Output: Integrated diagnosis results (specific disease name and severity), latest emotional state

[0472] Step 4:

[0473] The server generates an integrated diagnosis and treatment plan and sends it to the medical institution's terminal. For example, it generates information such as "high possibility of influenza A" or "prescribe a specific medication." It also includes emotional care guidelines.

[0474] Input: Integrated diagnosis results, latest emotional state

[0475] Output: Treatment policy, emotional care response policy

[0476] Step 5:

[0477] The terminal displays the information sent to the medical institution's terminal to the doctor, who then uses it as a reference when providing medical care.

[0478] Input: Treatment policy, emotional care response policy

[0479] Output: Display information to doctor

[0480] Ongoing support after treatment

[0481] Step 1:

[0482] The server stores medical and emotional data, including medical results, prescriptions, treatment progress, and emotional states.

[0483] Input: medical results, prescription details, treatment progress, emotional state

[0484] Output: Data storage

[0485] Step 2:

[0486] The server generates medication reminders, lifestyle advice, and emotional care suggestions based on the stored data, such as "take your medicine at 8 a.m. every day" and "get enough rest."

[0487] Input: Saved data

[0488] Output: Medication reminders, lifestyle advice, emotional care suggestions

[0489] Step 3:

[0490] The server transmits the generated reminders and advice to the user's terminal.

[0491] Input: Medication reminders, lifestyle advice, emotional care suggestions

[0492] Output: Send data to the user's device

[0493] Step 4:

[0494] The device presents the transmitted information to the user and provides reminders and advice via voice or text.

[0495] Input: Medication reminders, lifestyle advice, emotional care suggestions

[0496] Output: What is displayed to the user

[0497] Step 5:

[0498] Users follow the reminders and advice from the AI ​​to take their medication and improve their lifestyle habits, such as taking their medicine at 8 a.m. or taking deep breaths repeatedly.

[0499] Input: Medication reminders, lifestyle advice, emotional care suggestions

[0500] Output: Action execution

[0501] (Application example 2)

[0502] 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."

[0503] The current medical system has problems such as long waiting times at hospitals, which reduces the operational efficiency of medical institutions. There is also a lack of care that takes into account the emotional state of patients, which means that psychological support is not provided adequately. This can lead to lower patient satisfaction and a decline in the quality of medical care. Therefore, there is a need for a comprehensive support system that not only assesses symptoms but also takes into account emotional states.

[0504] 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 the user to input symptoms, means for the terminal to send the input data to the server, means for the server to analyze the data and determine the symptoms, means for the server to generate countermeasures or information on appropriate medical institutions based on the determination results, means for providing the generated information to the user, and means for adding countermeasures according to the emotional state to the generated countermeasures and information on medical institutions. This reduces waiting times at hospitals and enables comprehensive medical support that takes the user's emotional state into consideration.

[0505] A "user" is an individual who utilizes the system to input symptoms and emotional states.

[0506] A "symptom" is a health problem or discomfort that a user experiences.

[0507] "Emotional state" refers to a user's emotional state or state of mind.

[0508] A "terminal" is a device through which a user inputs data and receives information from a server.

[0509] A "server" is a central control device that receives data sent by users and performs analysis and information generation.

[0510] "Diagnosis results" refer to the health status assessment results issued by the server after analyzing the data.

[0511] "Countermeasures" are actions or measures that the user should take, which are generated by the server based on the judgment results.

[0512] "Information about medical institutions" refers to information generated by the server about appropriate medical institutions that the user should visit.

[0513] "Emotion recognition function" refers to a function for assessing and analyzing a user's emotional state.

[0514] A "remind" is a notification or alert that prompts a user to take a specific action.

[0515] "Mental care" refers to advice and support to support the user's mental health.

[0516] "Analysis" refers to the process by which the server processes the data received from the user and evaluates their symptoms and emotional state.

[0517] "Data" is a general term for symptom information and emotional information entered by the user, as well as diagnostic results and countermeasures generated by the server.

[0518] "Medical personnel" refers to doctors, nurses, and other medical professionals who treat users based on diagnosis results and treatment plans.

[0519] This invention is a system for reducing waiting times at hospitals and improving medical efficiency, and also provides support that takes into account the user's emotional state. This system works in cooperation with the user, terminal, server, and emotion engine, and provides simple diagnosis at home, primary medical treatment at the hospital, and continuous follow-up after treatment.

[0520] Simple testing at home

[0521] Users can input their symptoms at home using a dedicated application, and at the same time, emotional data is collected using an emotion recognition system, such as inputting information like "headache" and "anxiety."

[0522] The terminal transmits the input symptom data and emotion data to the server.

[0523] The server then uses AI models to analyze the symptoms and emotional state of the patient based on the received data. The symptom data is compared with a medical database, and the emotional data is analyzed by an emotion engine. For example, a diagnosis such as "mild cold" or "moderate anxiety" is generated.

[0524] Based on the results of the assessment, the server generates information on countermeasures and appropriate medical institutions. In addition, countermeasures tailored to the user's emotional state are also added based on the emotional data.

[0525] The terminal provides this information to the user.

[0526] Primary medical care during outpatient visits

[0527] After arriving at the hospital, the user enters their symptoms into the dedicated application again, and the emotion recognition system collects the latest emotional data. For example, the user enters information such as "fever over 39 degrees" and "strong anxiety."

[0528] The device sends new symptom data and emotion data to the server.

[0529] The server combines the new data with the initial data and performs a new diagnosis using the AI ​​model. At the same time, the emotion engine analyzes the patient's latest emotional state. For example, a diagnosis such as "High probability of influenza A" or "Highly stressed" is generated.

[0530] The server generates diagnostic results and a treatment plan, which are then sent to the medical institution's terminal. Treatment plans also incorporate measures based on the patient's emotional state.

[0531] The terminal displays the information sent to the medical institution's terminal to the doctor, making it useful for the doctor during medical treatment.

[0532] Ongoing support after treatment

[0533] The server stores medical and emotional data and generates medication reminders and lifestyle advice based on this data, such as "take your medicine at 8 a.m." and "take deep breaths."

[0534] The server sends reminders and advice to the user's terminal.

[0535] The device displays this information to the user and provides reminders and advice via voice and text.

[0536] Users follow reminders and advice from the AI, take necessary measures, and continuously manage their health.

[0537] Specific examples of hardware and software used

[0538] Hardware: Smartphones, tablets, hospital computer terminals, etc.

[0539] Software: Dedicated applications (smartphone apps), Flask (web framework), AI models, medical databases, emotion recognition APIs, etc.

[0540] Specific examples

[0541] If a user inputs "headache and slight fever" and collects emotional data such as "anxiety," the server generates countermeasures such as "mild cold" and "listening to music that promotes relaxation." If, after arriving at the hospital, the user re-inputs "fever of 39 degrees or higher" and "strong anxiety," the server will diagnose "high possibility of influenza A" and suggest "specific tests and prescription of antiviral drugs." It also includes emotional care policies such as "setting aside time to talk frequently with a doctor."

[0542] Prompt Sentence Examples

[0543] "Based on the data entered by the user, analyze the symptoms and emotional state and suggest appropriate advice and emotional support. Below is the user's input data. Symptoms: headache and slight fever. Emotion: anxiety."

[0544] This system will significantly reduce waiting times at hospitals, improve the operational efficiency of medical institutions, and effectively manage the health and mental health of patients.

[0545] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0546] (Program processing flow)

[0547] Step 1:

[0548] The user inputs their symptoms and emotional state into a dedicated smartphone application, for example, entering "headache" and "anxiety," and this input data is collected by the application.

[0549] input:

[0550] Symptoms and emotional state from users

[0551] output:

[0552] Symptom data and emotion data entered

[0553] Step 2:

[0554] The device sends the input symptom data and emotion data to the server, and the application converts this data into an appropriate format and sends it to the server using a communication protocol.

[0555] input:

[0556] Symptom and emotion data

[0557] output:

[0558] Data sent to the server

[0559] Step 3:

[0560] The server analyzes the received data. First, it compares the received symptom data with a medical database to determine the symptoms. At the same time, it analyzes the emotion data using an emotion engine to evaluate the emotional state.

[0561] input:

[0562] Symptom and emotion data submitted

[0563] Data processing and calculation:

[0564] Symptom analysis using AI models

[0565] Emotional state analysis using emotion recognition systems

[0566] output:

[0567] Symptom assessment results and emotional state assessment results

[0568] Step 4:

[0569] The server generates countermeasures and information on appropriate medical institutions based on the assessment results. Furthermore, based on the collected emotional data, it adds countermeasures according to the user's emotional state.

[0570] input:

[0571] Symptom assessment results and emotional state assessment results

[0572] Data processing and calculation:

[0573] Generate appropriate countermeasures based on the judgment results

[0574] AI model proposes optimal medical institutions

[0575] Add measures according to emotional state

[0576] output:

[0577] Countermeasures, information on medical institutions, and suggestions for emotional care

[0578] Step 5:

[0579] The server transmits the generated information to the user's terminal, encodes the data according to a transmission protocol, and transmits it to the user's terminal.

[0580] input:

[0581] Countermeasures, information on medical institutions, and suggestions for emotional care

[0582] output:

[0583] Data sent to the user terminal

[0584] Step 6:

[0585] The terminal receives the information from the server and displays it to the user. The application decodes the received data and displays it in a format that is easy for the user to understand.

[0586] input:

[0587] Countermeasures, medical institution information, and emotional care suggestions sent from the server

[0588] output:

[0589] Information displayed on the screen

[0590] Operation:

[0591] The user takes action based on the presented countermeasures

[0592] Step 7:

[0593] After arriving at the hospital, the user again enters their symptoms into a dedicated smartphone application, and the emotion recognition system also collects the latest emotional data. For example, the user enters information such as "fever over 39 degrees" and "strong anxiety."

[0594] input:

[0595] Latest symptom and emotion data

[0596] output:

[0597] Re-entered symptom and emotion data

[0598] Step 8:

[0599] The device sends new symptom data and emotion data to the server.

[0600] input:

[0601] New symptom and emotion data

[0602] output:

[0603] Data sent to the server

[0604] Step 9:

[0605] The server combines the new data with the initial data and performs a re-diagnosis using the AI ​​model, while the emotion engine simultaneously analyzes the latest emotional state.

[0606] input:

[0607] Integration of initial data and new data

[0608] Data processing and calculation:

[0609] AI re-diagnoses symptoms using integrated data

[0610] Latest emotional state analysis with emotion engine

[0611] output:

[0612] Post-integration diagnostic results and emotional assessment

[0613] Step 10:

[0614] The server generates diagnostic results and a treatment plan, which it then sends to the medical institution's terminal. The server also incorporates measures based on the patient's emotional state into the treatment plan.

[0615] input:

[0616] Post-integration diagnostic results and emotional assessment

[0617] Data processing and calculation:

[0618] Generation of treatment plans based on diagnostic results

[0619] Adding ways to respond depending on emotional state

[0620] output:

[0621] Treatment plan and diagnosis results sent to medical institutions

[0622] Step 11:

[0623] The terminal displays the information sent to the medical institution's terminal to the doctor, who then provides medical treatment based on this information.

[0624] input:

[0625] Treatment plan and diagnosis results sent to medical institutions

[0626] output:

[0627] Information displayed on the doctor's terminal

[0628] Operation:

[0629] The doctor will provide medical treatment based on the diagnosis and treatment plan.

[0630] Step 12:

[0631] The server stores medical data and emotion data, and generates medication reminders and lifestyle advice based on this data and sends them to the user's device.

[0632] input:

[0633] Medical and emotional data

[0634] Data processing and calculation:

[0635] Generates medication reminders and lifestyle advice based on stored data

[0636] output:

[0637] Reminders and advice, including generated mental health advice

[0638] Step 13:

[0639] The device displays the sent reminders and advice to the user, and provides voice and text reminders, allowing the user to follow the instructions and take the necessary measures.

[0640] input:

[0641] Reminders and advice sent from the server

[0642] output:

[0643] On-screen and audio notifications

[0644] Operation:

[0645] Continuous health management by users following reminders and advice

[0646] 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.

[0647] 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.

[0648] 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.

[0649] [Second embodiment]

[0650] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0651] 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.

[0652] 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).

[0653] 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.

[0654] 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.

[0655] 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).

[0656] 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.

[0657] 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.

[0658] 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.

[0659] 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.

[0660] 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.

[0661] 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."

[0662] This invention is a system for reducing waiting times at hospitals and improving the efficiency of medical care. This system works in cooperation with users, terminals, and servers, and provides simple diagnosis at home, primary medical care at hospitals, and continuous follow-up after medical care.

[0663] Simple testing at home

[0664] 1. User: Enters symptoms into a dedicated application at home. For example, "I have a headache and a slight fever."

[0665] 2. Terminal: Sends the entered symptom data to the server.

[0666] 3. Server: The AI ​​analyzes the symptoms based on the received data, compares the symptom data with a medical database, and determines whether the symptoms are mild or severe.

[0667] 4. Server: Based on the assessment results, it generates information on countermeasures and appropriate medical institutions. For example, if the symptoms are mild, it generates suggestions for home treatment and over-the-counter medications, and if the symptoms are severe, it generates information on the nearest hospital.

[0668] 5. Server: Sends the generated information to the user's device.

[0669] 6. Terminal: Receives information from the server and displays it to the user, who can then take action.

[0670] Primary medical care during outpatient visits

[0671] 1. User: After arriving at the hospital, the user re-enters their latest symptoms into the dedicated application. For example, they might enter "fever over 39 degrees and worsening headache."

[0672] 2. Device: Sends new symptom data to the server.

[0673] 3. Server: The new data received is integrated with the initial data, and the AI ​​performs a diagnosis again.

[0674] 4. Server: Generates diagnostic results and treatment guidelines and sends them to the medical institution's terminal. For example, it generates a diagnostic result such as "high possibility of influenza A" and a treatment guideline such as "prescribe a specific medication."

[0675] 5. Terminal: The information sent to the medical institution's terminal is displayed to the doctor, who uses it as a reference when providing medical care.

[0676] Ongoing support after treatment

[0677] 1. Server: Stores medical data, including medical results, prescription details, and treatment progress.

[0678] 2. Server: Based on the stored data, it generates medication reminders and lifestyle advice, such as "take your medicine at 8 a.m. every day" or "get enough rest."

[0679] 3. Server: Sends reminders and advice to the user's device.

[0680] 4. Terminal: Displays the transmitted information to the user and provides reminders and advice via voice or text.

[0681] 5. User: Follow the reminders and advice from the AI, take your medication, improve your lifestyle habits, and practice continuous health management.

[0682] As a specific example, a user inputs "I have a headache and a slight fever," and the server determines that it is a "mild cold," recommending that the user "take over-the-counter fever reducers and drink plenty of fluids." After arriving at the hospital, the user re-inputs that "the fever is worsening and has exceeded 39 degrees," and the server diagnoses that "it is likely influenza A," suggesting that "specific tests and antiviral medications should be prescribed." The doctor then provides treatment based on this information, and after the treatment, the doctor reminds the user to "take antiviral medication every day at 8 a.m. and rest."

[0683] The above is an embodiment of the present invention. This system significantly reduces waiting times at hospitals, improves the work efficiency of doctors, and effectively manages the health of patients.

[0684] The processing flow will be explained below.

[0685] Simple testing at home

[0686] Step 1:

[0687] The user enters symptoms into a dedicated app. For example, they might enter, "I have a headache and a slight fever."

[0688] Step 2:

[0689] The terminal transmits the input symptom data to the server.

[0690] Step 3:

[0691] The AI ​​analyzes the symptom data received by the server, compares it with a medical database, and determines whether the symptoms are mild or severe.

[0692] Step 4:

[0693] Based on the results of the assessment, the server generates information on countermeasures and appropriate medical institutions. For example, if the symptoms are mild, it will generate suggestions for home treatment and over-the-counter medications, and if the symptoms are severe, it will generate information on the nearest hospital.

[0694] Step 5:

[0695] The server transmits the generated information to the user's terminal.

[0696] Step 6:

[0697] The device receives the information from the server and displays it to the user, who can then take appropriate action.

[0698] Primary medical care during outpatient visits

[0699] Step 1:

[0700] After arriving at the hospital, the user re-enters their latest symptoms into the dedicated application. For example, they might enter "fever of over 39 degrees and worsening headache."

[0701] Step 2:

[0702] The terminal transmits new symptom data to the server.

[0703] Step 3:

[0704] The server integrates the new data received with the initial data, and the AI ​​performs a diagnosis again.

[0705] Step 4:

[0706] The server generates a diagnosis and a treatment plan and sends them to the medical institution's terminal. For example, it generates a diagnosis such as "high possibility of influenza A" and a treatment plan such as "prescribe a specific medication."

[0707] Step 5:

[0708] The terminal displays the information sent to the medical institution's terminal to the doctor, who then uses this information to provide actual medical treatment.

[0709] Ongoing support after treatment

[0710] Step 1:

[0711] The server stores medical data, including medical results, prescription details, and treatment progress.

[0712] Step 2:

[0713] The server generates medication reminders and lifestyle advice based on the stored data, such as "take your medicine at 8 a.m. every day" or "get enough rest."

[0714] Step 3:

[0715] The server sends reminders and advice to the user's terminal.

[0716] Step 4:

[0717] The device displays the sent information to the user and provides reminders and advice via voice or text.

[0718] Step 5:

[0719] Users follow reminders and advice from the AI ​​to take their medication and improve their lifestyle habits, and practice continuous health management.

[0720] The above is a concrete explanation of the program processing of the system step by step.

[0721] Example 1

[0722] 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."

[0723] Modern medical institutions face challenges such as long waiting times for patients and overwork for doctors due to the need to process vast amounts of information. Furthermore, there is a lack of effective methods for patients to self-diagnose at home or follow up after visiting the hospital. This creates a need for efficient and prompt medical care for both patients and medical institutions.

[0724] 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.

[0725] In this invention, the server includes: means for a user to input symptoms; means for a terminal to transmit the input data to the server; means for analyzing the data and determining the symptoms using an AI model; means for generating a countermeasure or information on an appropriate medical institution based on the determination result; means for transmitting the generated information to the user's terminal and for the terminal to display the information to the user; means for the user to input symptom data again; means for integrating new data with the initial data and performing a diagnosis again using the AI ​​model; means for transmitting the diagnosis result and treatment plan to medical professionals; means for saving the medical data and updating the database; means for generating medication reminders and lifestyle advice based on the saved data; and means for the terminal to display the reminders and advice to the user. This enables reduction of waiting times at hospitals, efficiency of medical operations, and continuous health management of patients.

[0726] 1. "User" refers to an individual who uses the system to input symptoms.

[0727] 2. "Terminal" refers to the electronic device used by the user to input symptoms and transmit the data to the server.

[0728] 3. "Server" refers to the data processing system that receives data sent by users and analyzes and generates data using AI models.

[0729] 4. "Data" refers to information such as symptom information entered by the user, countermeasures generated by the server, and diagnostic results.

[0730] 5. "AI model" refers to the artificial intelligence algorithm used by the server to analyze data, determine symptoms, and generate countermeasures.

[0731] 6. "Determination result" refers to the diagnosis result obtained after the server analyzes the symptoms using the AI ​​model.

[0732] 7. "Countermeasures" refers to medical measures generated by the server based on the assessment results.

[0733] 8. "Medical Institution" refers to the appropriate hospital, clinic, or other medical facility that the User should visit.

[0734] 9. "Diagnosis result" refers to the specific disease name and symptom evaluation results provided by the server based on data analysis.

[0735] 10. "Treatment policy" refers to the treatment method or prescription that medical professionals should implement based on diagnostic results.

[0736] 11. "Medical personnel" refers to medical professionals such as doctors and nurses who provide medical care based on diagnostic results and treatment plans.

[0737] 12. "Medical data" refers to information such as medical examination results, prescription details, and treatment progress.

[0738] 13. "Database" refers to an electronic record device where the server stores medical data.

[0739] 14. "Medication Reminder" means a notification or alert that encourages a user to take their medication.

[0740] 15. "Lifestyle Advice" means lifestyle advice generated by the Server for the purpose of assisting the User in managing their health.

[0741] This system is designed to reduce waiting times and improve the efficiency of medical care, especially in hospitals. The system utilizes a user's device, a server, and an AI model to provide simple diagnosis at home, primary care during outpatient visits, and continuous follow-up after treatment.

[0742] Hardware and Software

[0743] User device: A device (smartphone, tablet, PC, etc.) on which the user inputs symptoms. A dedicated application is installed.

[0744] Server: A central server for receiving and processing data. It is connected to a medical database and uses AI models for data analysis.

[0745] AI models: Generative AI models such as OpenAI's GPT-4 are used to analyze data, make judgments, and generate advice.

[0746] Simple testing at home

[0747] 1. The user opens the dedicated application and enters their symptoms in text format. For example, they might enter, "I have a headache and a slight fever."

[0748] 2. The device encrypts the input data and sends it to the server.

[0749] 3. The server analyzes the received data using an AI model to determine symptoms, and compares them with a medical database to determine whether the symptoms are mild or severe.

[0750] 4. Based on the results of the assessment, the server generates information on countermeasures and appropriate medical institutions (for example, "take over-the-counter fever reducers and drink plenty of fluids").

[0751] 5. The server sends the generated information to the terminal.

[0752] 6. The device displays the information to the user, who then takes appropriate action.

[0753] Primary medical care during outpatient visits

[0754] 1. After arriving at the hospital, the user re-enters their latest symptoms into the dedicated application. For example, they might enter "fever over 39 degrees and worsening headache."

[0755] 2. The device sends new data to the server.

[0756] 3. The server integrates the new data with the initial data and performs a diagnosis again using the AI ​​model.

[0757] 4. The server generates the diagnosis and treatment plan, for example, "It is highly likely that the patient has influenza A. Specific tests and prescription of antiviral drugs are required," and sends this information to the medical institution's terminal.

[0758] 5. The terminal displays the information sent to the medical institution's terminal to the doctor, who uses this information as a reference when providing treatment.

[0759] Ongoing support after treatment

[0760] 1. The server stores medical data (medical results, prescription details, treatment progress, etc.) in a database.

[0761] 2. The server uses the stored data to generate medication reminders and lifestyle advice using AI models, such as reminders to "take your medication at 8 a.m. every day" and "get enough rest."

[0762] 3. The server sends the generated reminders and advice to the device.

[0763] 4. The device will provide reminders and advice to the user, either through voice or text. For example, "It's 8 AM. Don't forget to take your medicine."

[0764] 5. The user follows the reminders and advice to take their medication and improve their lifestyle.

[0765] Examples of concrete examples and prompts

[0766] For example, if a user inputs "I have a headache and a slight fever" into a dedicated application, the server will determine that it is a "mild cold" and recommend that the user "take over-the-counter fever reducers and drink plenty of fluids." If, after arriving at the hospital, the user again inputs that "the fever has risen above 39 degrees and is getting worse," the server will diagnose that "it is likely influenza A" and suggest "specific tests and the prescription of antiviral medication." The doctor will then provide treatment based on this information, and after the consultation will remind the user to "take antiviral medication every day at 8 a.m. and rest."

[0767] An example prompt is:

[0768] Example: "I have a headache and a slight fever. What should I do?"

[0769] Example server-generated response: "Your symptoms have been diagnosed as a mild cold. We recommend taking over-the-counter fever-reducing medication, drinking plenty of fluids, and getting plenty of rest."

[0770] This system will reduce waiting times at hospitals, improve the efficiency of medical operations, and enable continuous health management for patients.

[0771] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0772] Simple testing at home

[0773] Step 1:

[0774] The user opens a dedicated application and enters their symptoms (e.g., "I have a headache and a slight fever").

[0775] Input: User's symptom information

[0776] Output: User-entered symptom data

[0777] Step 2:

[0778] The terminal receives the input symptom data, encrypts the data, and transmits it to the server.

[0779] Input: User-entered symptom data

[0780] Output: Encrypted symptom data

[0781] Step 3:

[0782] The server receives the encrypted data, decrypts it, and then analyzes it using an AI model (e.g., GPT-4) to determine symptoms.

[0783] Input: Encrypted symptom data

[0784] Data processing: Data decryption

[0785] Data Computing: Data Analysis with AI Models

[0786] Output: Symptom determination result (e.g., "Mild cold")

[0787] Step 4:

[0788] Based on the results of the assessment, the server generates information on countermeasures and appropriate medical institutions.

[0789] Input: Symptom assessment result

[0790] Data calculation: Countermeasure methods and medical institution information generation

[0791] Output: Information on countermeasures and medical institutions (e.g., "Take over-the-counter fever reducers and drink plenty of fluids")

[0792] Step 5:

[0793] The server transmits the generated information to the terminal.

[0794] Input: Countermeasures and medical institution information

[0795] Output: Countermeasures and medical institution information sent

[0796] Step 6:

[0797] The terminal receives the information and displays it to the user.

[0798] Input: Countermeasures and medical institution information sent from the server

[0799] Output: Information displayed to the user (e.g., "Take over-the-counter fever reducers and drink plenty of fluids")

[0800] Primary medical care during outpatient visits

[0801] Step 1:

[0802] After arriving at the hospital, the user re-enters their latest symptoms (e.g., "fever over 39 degrees and worsening headache") into a dedicated application.

[0803] Input: Latest symptom data

[0804] Output: Re-entered symptom data

[0805] Step 2:

[0806] The terminal receives the re-entered symptom data and transmits it to the server.

[0807] Input: Re-entered symptom data

[0808] Output: Submitted symptom data

[0809] Step 3:

[0810] The server integrates the new data received with the initial data, analyzes the data again using the AI ​​model, and updates the diagnosis.

[0811] Input: Re-entered symptom data, initial symptom data

[0812] Data processing: Data integration

[0813] Data Computation: Reanalyzing Data with AI Models

[0814] Output: Updated diagnosis (e.g., "Probably influenza A").

[0815] Step 4:

[0816] The server generates a treatment plan based on the diagnosis results and sends it to the medical institution's terminal.

[0817] Input: Updated diagnostic results

[0818] Data Computation: Treatment Plan Generation

[0819] Output: The treatment plan sent (e.g., "In case of influenza A, specific tests and antiviral medications are required.")

[0820] Step 5:

[0821] The terminal receives the information sent to the medical institution's terminal and displays it to the doctor.

[0822] Input: Submitted treatment plan

[0823] Output: Treatment plan displayed to the doctor

[0824] Ongoing support after treatment

[0825] Step 1:

[0826] When the consultation is completed, the server stores data such as the consultation results, prescription details, and treatment progress in a database.

[0827] Input: medical results, prescription details, treatment progress data

[0828] Output: Saved medical data

[0829] Step 2:

[0830] The server uses AI models to generate medication reminders and lifestyle advice based on the stored medical data.

[0831] Input: Stored medical data

[0832] Data Computing: Generating Medication Reminders and Lifestyle Advice

[0833] Output: Generated medication reminders and lifestyle advice

[0834] Step 3:

[0835] The server transmits the generated reminders and advice to the terminal.

[0836] Input: Generated medication reminders and lifestyle advice

[0837] Output: Reminders and advice sent

[0838] Step 4:

[0839] The device receives reminders and advice and displays them to the user. Reminders are given via voice notification or text display.

[0840] Input: Reminders and advice sent

[0841] Output: Reminders and advice displayed to the user (e.g., "It's 8 AM. Take your antiviral medication.")

[0842] Step 5:

[0843] Users follow the reminders and advice from the device to take appropriate medication and improve their lifestyle habits.

[0844] Input: Reminders and advice

[0845] Output: Medication and lifestyle improvement behavior

[0846] (Application example 1)

[0847] 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."

[0848] In conventional medical systems, after users receive medical treatment at a hospital, the payment procedures for medical expenses are often complicated and time-consuming. Furthermore, there are insufficient medication reminders and lifestyle advice after treatment, making it difficult to provide continuous health management. This prevents users from receiving appropriate follow-up after treatment, leading to a decrease in medical efficiency.

[0849] 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.

[0850] In this invention, the server includes: means for the user to input symptoms; means for the terminal to send the input data to the server; means for the server to analyze the data and determine the symptoms; means for the server to generate information on countermeasures or appropriate medical institutions based on the determination results; means for providing the generated information to the user; means for the server to calculate medical expenses after treatment; means for the server to automatically debit the medical expenses from the user's electronic payment account; and means for sending a payment confirmation notice to the user's terminal. This enables automatic payment of medical expenses after treatment and prompt follow-up.

[0851] - "User" means an individual who uses the system to input their symptoms and receive medical treatment or medical services.

[0852] A "terminal" is an electronic device operated by a user to input data and receive medical information. Examples include smartphones and tablets.

[0853] A "server" is a computer system that receives, stores, and analyzes data sent from a terminal and provides necessary information to users and medical professionals.

[0854] "Data" refers to information entered by the user, such as symptom information, medical treatment results, and payment information, which the server analyzes and stores.

[0855] "Analysis" refers to the act of using certain algorithms and AI technology to determine symptoms and calculate medical expenses based on the data received by the server.

[0856] "Symptom determination" refers to the act of the server diagnosing what the symptom is based on the symptom information entered by the user.

[0857] "Countermeasures" are advice on specific actions the user should take or medicines that should be used based on the symptom assessment results.

[0858] "Information about medical institutions" refers to information about hospitals and clinics that are appropriate for the user, including medical appointments and medical treatment details.

[0859] "Generated information" refers to the diagnosis and advice provided to users and medical professionals based on the results of analysis and judgment by the server.

[0860] A "payment confirmation notification" is a message sent by the server to notify the user when the server has successfully debited the user's electronic payment account.

[0861] An "electronic payment account" is an account used by a user to make financial payments online, including a credit card or digital wallet.

[0862] A "medication reminder" is a notification sent by the server to the user to urge him or her to take medicine regularly.

[0863] "Lifestyle advice" refers to guidance and suggestions about daily life provided by the server to maintain and improve the user's health.

[0864] This invention realizes automatic payment of medical expenses and continuous follow-up in a system in which a user inputs symptoms and manages medical information.

[0865] System Configuration

[0866] This system consists of a user's device, a server, and an electronic payment service. The user's device is typically a smartphone or tablet, on which a dedicated application is installed. The server is a computer system for storing and analyzing data and generating notifications. The electronic payment service is an online payment platform that automatically debits the user's medical expenses.

[0867] System Operation

[0868] 1. Enter symptoms

[0869] The user uses a dedicated application at home or at a designated location to input their current symptoms, for example, "I have a headache and a slight fever." The device then sends the input data to the server.

[0870] 2. Data analysis and judgment

[0871] The server analyzes the received symptom data and determines whether the symptom is mild or severe by using a generative AI model to compare the symptom data with a medical database.

[0872] 3. Information generation and provision

[0873] Based on the results of the assessment, the server generates information on countermeasures and appropriate medical institutions and sends it to the user's device. For example, if the symptoms are mild, it suggests home treatment methods and over-the-counter medications. If the symptoms are severe, it provides information on the nearest hospital.

[0874] 4. Automatic medical payments

[0875] After the medical treatment is completed, the server calculates the medical fee based on the medical information, and then automatically deducts the medical fee from the user's electronic payment account, such as a credit card or digital wallet.

[0876] 5. Sending notifications

[0877] The server sends a payment confirmation notice to the user's terminal, and the user confirms that the payment has been completed.

[0878] 6. Ongoing follow-up

[0879] The server stores the medical data and generates medication reminders and lifestyle advice based on the data. These reminders and advice are periodically sent to the user's device.

[0880] Specific examples

[0881] Consider a case where a user inputs "I have a fever and headache" and is diagnosed with "influenza" at the hospital. The user then inputs information about the antiviral medication they are taking into the app. After the consultation, the server automatically calculates the medical expenses and deducts them from the user's electronic payment account. A notification of payment completion is sent to the user's device, and thereafter, medication reminders are sent periodically.

[0882] Prompt Sentence Examples

[0883] The user enters that they have a fever and headache and is diagnosed with influenza at the hospital. They then enter information about the antiviral medication they are taking into the app. After the consultation, payment is automatically completed and the information is notified to the user. From then on, they will receive a daily medication reminder at the specified time.

[0884] The system automates medical bill payment procedures and ensures users can follow up after medical treatment, improving user convenience and greatly improving the efficiency of the entire medical system.

[0885] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0886] Step 1:

[0887] The user uses a dedicated application at home or a designated location to input their current symptoms. The input data is specific symptom information such as "I have a headache and a slight fever." The device then sends this information to the server.

[0888] Input: User-entered symptom information

[0889] Output: Symptom information is sent from the device to the server

[0890] Step 2:

[0891] The server analyzes the symptom data received from the device. This analysis utilizes a generative AI model and compares the symptom data with a medical database. At this stage, an initial determination is made as to whether the symptoms are mild or severe.

[0892] Input: Symptom information from the terminal

[0893] Output: Initial diagnosis result (mild or severe)

[0894] Step 3:

[0895] Based on the initial diagnosis, the server generates information on necessary countermeasures or appropriate medical institutions. For example, if the symptoms are mild, the server may recommend taking over-the-counter fever reducers and drinking plenty of fluids. If the symptoms are severe, the server may generate information on the nearest hospital.

[0896] Input: Initial judgment result

[0897] Output: Information on countermeasures and medical institutions

[0898] Step 4:

[0899] The server sends the generated information about countermeasures and medical institutions to the user's device, which receives it and displays it to the user.

[0900] Input: Information on countermeasures and medical institutions

[0901] Output: Countermeasures and information about medical institutions are displayed on the user's device

[0902] Step 5:

[0903] When the user arrives at the hospital to continue treatment, they re-enter their latest symptom data. For example, they might enter information such as "fever over 39 degrees and worsening headache." The device then sends the new symptom data to the server again.

[0904] Input: Latest symptom data

[0905] Output: New symptom data is sent from the device to the server.

[0906] Step 6:

[0907] The server integrates the new symptom data with the initial data and again uses AI to perform a detailed diagnosis.

[0908] Input: New symptom data and initial data

[0909] Output: Detailed diagnostic results and treatment plan

[0910] Step 7:

[0911] The server generates a diagnosis and treatment plan and sends it to the medical professional's device, which receives and displays this information. The doctor uses this information as a reference when providing treatment.

[0912] Input: Detailed diagnosis and treatment plan

[0913] Output: Information is displayed on the medical personnel's terminal.

[0914] Step 8:

[0915] After the medical treatment is completed, the server calculates the medical costs based on the medical information, including the details of the treatment and the prescribed medication.

[0916] Input: Medical information

[0917] Output: Calculated medical costs

[0918] Step 9:

[0919] The server automatically debits the calculated medical costs from the user's electronic payment account, using a pre-registered credit card or digital wallet.

[0920] Input: Calculated medical expenses

[0921] Output: Withdrawal completion result

[0922] Step 10:

[0923] The server sends a payment confirmation notice to the user's terminal, which the user receives and confirms that the payment has been completed.

[0924] Input: Withdrawal completion result

[0925] Output: Payment confirmation notification

[0926] Step 11:

[0927] The server stores medical data and generates medication reminders and lifestyle advice based on that data.

[0928] Input: Medical data

[0929] Output: Medication reminders and lifestyle advice

[0930] Step 12:

[0931] The server sends the generated medication reminders and lifestyle advice to the user's device, which receives them and periodically notifies them. The user receives these notifications and acts according to the instructions.

[0932] Input: Medication reminders and lifestyle advice

[0933] Output: A notification will be displayed periodically on the user's device.

[0934] 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.

[0935] This invention is a system for reducing waiting times at hospitals and improving medical efficiency, and also provides support that takes into account the user's emotional state. This system works in cooperation with the user, terminal, server, and emotion engine, and provides simple diagnosis at home, primary medical treatment at the hospital, and continuous follow-up after treatment.

[0936] Simple testing at home

[0937] 1. User: At home, the user inputs their symptoms into a dedicated application, and emotional data is also collected by the emotion recognition system. For example, if the user inputs "I have a headache and a slight fever," their emotional state is recorded as "anxiety" or "stress."

[0938] 2. Terminal: Sends the input symptom data and emotion data to the server.

[0939] 3. Server: Based on the received data, the AI ​​analyzes the symptoms and emotional state. It compares the symptom data with a medical database to determine whether the symptoms are mild or severe, and analyzes the emotional data with an emotion engine to determine the emotional state.

[0940] 4. Server: Based on the assessment results, the server generates information on countermeasures and appropriate medical institutions. For example, if the symptoms are mild, the server generates suggestions for home treatment and over-the-counter medications, and if the symptoms are severe, it generates information on the nearest hospital. Based on the emotional data, the server also adds countermeasures according to the user's emotional state.

[0941] 5. Server: Sends the generated information to the user's device.

[0942] 6. Terminal: Receives information from the server and displays it to the user, who can then take action.

[0943] Primary medical care during outpatient visits

[0944] 1. User: After arriving at the hospital, the user re-enters their latest symptoms into the dedicated application, and the emotion recognition system also collects their latest emotional data. For example, if the user enters "fever over 39 degrees and worsening headache," their emotional state is recorded as "strong anxiety" or "tension."

[0945] 2. Device: Sends new symptom data and emotion data to the server.

[0946] 3. Server: The AI ​​combines the new data received with the initial data and performs another diagnosis. At the same time, the emotion engine analyzes the latest emotional state.

[0947] 4. Server: Generates diagnosis results and treatment plans and sends them to the medical institution's terminal. For example, it generates a diagnosis result such as "high possibility of influenza A" and a treatment plan such as "prescribe a specific medication." Response methods based on the patient's emotional state are also incorporated into the treatment plan.

[0948] 5. Terminal: The information sent to the medical institution's terminal is displayed to the doctor, who uses it as a reference when providing medical care.

[0949] Ongoing support after treatment

[0950] 1. Server: Stores medical data and emotional data. The stored data includes medical results, prescription details, treatment progress, emotional state, etc.

[0951] 2. Server: Based on the stored data, it generates medication reminders and lifestyle advice. For example, it generates advice such as "Take your medicine at 8:00 a.m. every day" or "Get enough rest." At the same time, it also includes mental health advice based on the patient's emotional state.

[0952] 3. Server: Sends reminders and advice to the user's device.

[0953] 4. Terminal: Displays the transmitted information to the user and provides reminders and advice via voice or text.

[0954] 5. User: Follow the reminders and advice from the AI, take your medication, improve your lifestyle habits, and practice continuous health management.

[0955] For example, if a user inputs "I have a headache and a slight fever" and the emotional data collected indicates "anxiety," the server will determine that it is a "mild cold" and recommend that the user take an over-the-counter fever reducer and drink plenty of fluids, while also providing emotional care advice such as listening to music that promotes relaxation. If, after arriving at the hospital, the user re-inputs that their fever has risen above 39 degrees and worsened, and the emotional state is collected as "strong anxiety," the server will diagnose that the patient is likely to have influenza A and suggest that they undergo specific tests and prescribe antiviral medication, along with emotional care guidelines such as setting aside time to explain the situation in detail to the doctor. After the consultation, the server will remind the patient to take antiviral medication every day at 8 a.m. and rest, and to practice deep breathing as a relaxation technique.

[0956] The above is an embodiment of the present invention. This system significantly reduces waiting times at hospitals, improves the work efficiency of doctors, and effectively manages the health and mental health of patients.

[0957] The processing flow will be explained below.

[0958] Simple testing at home

[0959] Step 1:

[0960] The user inputs their symptoms into a dedicated app. For example, they might input "I have a headache and a slight fever," and the emotion recognition system will collect their emotional state as "anxiety."

[0961] Step 2:

[0962] The terminal transmits the input symptom data and emotion data to the server.

[0963] Step 3:

[0964] The AI ​​analyzes the symptom data received by the server, compares it with a medical database, and determines whether the symptoms are mild or severe.

[0965] Step 4:

[0966] The server analyzes the emotional data with an emotion engine to determine the emotional state.

[0967] Step 5:

[0968] The server generates information on countermeasures and appropriate medical institutions based on the symptoms and emotional state. For example, if the symptoms are mild, it generates home treatment methods (e.g., over-the-counter medication suggestions), and if the symptoms are severe, it generates information on the nearest hospital. Based on the emotional state, it also suggests emotional care measures (e.g., relaxation methods).

[0969] Step 6:

[0970] The server transmits the generated information to the user's terminal.

[0971] Step 7:

[0972] The device receives the information from the server and displays it to the user, who can then take appropriate action.

[0973] Primary medical care during outpatient visits

[0974] Step 1:

[0975] After arriving at the hospital, the user again enters their latest symptoms into the dedicated application. For example, they might enter "fever over 39 degrees and worsening headache," and the emotion recognition system records their emotional state as "strong anxiety."

[0976] Step 2:

[0977] The device sends new symptom data and emotion data to the server.

[0978] Step 3:

[0979] The server integrates the new data received with the initial data, and the AI ​​performs a second diagnosis. At the same time, the emotion engine analyzes the latest emotional state.

[0980] Step 4:

[0981] The server generates a diagnosis and a treatment plan. For example, it generates a diagnosis such as "high possibility of influenza A" and a treatment plan such as "prescribe a specific medication." The treatment plan also includes a response method based on the patient's emotional state (e.g., setting aside time to explain the situation in detail to the doctor).

[0982] Step 5:

[0983] The server sends the diagnosis results and treatment plan to the medical institution's terminal.

[0984] Step 6:

[0985] The device displays the received information to the doctor, who then uses it as a reference when providing treatment.

[0986] Ongoing support after treatment

[0987] Step 1:

[0988] The server stores medical data and emotional data, including medical results, prescription details, treatment progress, and emotional state.

[0989] Step 2:

[0990] The server generates medication reminders and lifestyle advice based on the stored data. For example, it might generate advice such as "take your medicine at 8 a.m. every day" or "get enough rest." At the same time, it also includes mental care advice based on the user's emotional state (e.g., repeatedly taking deep breaths as a relaxation technique).

[0991] Step 3:

[0992] The server sends reminders and advice to the user's terminal.

[0993] Step 4:

[0994] The device displays the sent information to the user and provides reminders and advice via voice or text.

[0995] Step 5:

[0996] Users follow reminders and advice from the AI ​​to take their medication and improve their lifestyle habits, and practice continuous health management.

[0997] The above is a concrete explanation of each step of the program processing of a system that combines an emotion engine.

[0998] Example 2

[0999] 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."

[1000] In conventional medical systems, sufficient pre-diagnosis and emotional care are not provided before the user arrives at the hospital, causing extended diagnosis times. Furthermore, the user's emotional state is not taken into consideration during waiting times at the hospital or follow-up care after treatment, resulting in inappropriate care being provided. As a result, medical efficiency and user satisfaction have not been sufficiently improved.

[1001] 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.

[1002] In this invention, the server includes means for analyzing symptoms using a generative AI model based on received data, means for analyzing emotional states using an emotion engine, and means for generating countermeasures, information on appropriate medical institutions, and emotional care suggestions based on the results of the assessment. This makes it possible to comprehensively analyze the user's symptoms and emotional state, provide appropriate countermeasures and information on medical institutions, and also suggest emotional care. This reduces waiting times at hospitals, improves doctor work efficiency, and effectively manages the health and mental health of patients.

[1003] 1. "User" is a person who uses the system to input symptoms and receive suggested treatments and medical information.

[1004] 2. "Symptoms" are data that indicate the health condition or abnormality that the user feels.

[1005] 3. "Emotional state" is data that indicates the user's current emotional or mental state.

[1006] 4. "Terminal" means a device used by a User to access the System, such as a smartphone or tablet.

[1007] 5. "Server" means a computer system that receives data sent by users and analyzes and provides information.

[1008] 6. A "generative AI model" is an artificial intelligence algorithm used for natural language processing and symptom analysis.

[1009] 7. "Emotion Engine" is a software module for analyzing the user's emotional state.

[1010] 8. "Medical database" means a collection of medical information used for symptom analysis and diagnosis.

[1011] 9. "Countermeasures" are general treatments or measures to deal with the user's symptoms.

[1012] 10. "Information about medical institutions" means information about appropriate medical facilities that a user should visit.

[1013] 11. "Emotional care" refers to suggestions for psychological support and relaxation tailored to the user's emotional state.

[1014] 12. "Medical data" refers to medical information generated by physicians, such as medical examination results and treatment details.

[1015] 13. "Remind" is a notification that prompts the user to take medication or take other action to manage their health.

[1016] 14. "Lifestyle Advice" means suggestions for improving the user's lifestyle to maintain their health.

[1017] MODE FOR CARRYING OUT THE INVENTION

[1018] The present invention is a system that collects and analyzes a user's symptom input and emotional state, and provides appropriate measures and information on medical institutions. This system is composed of a user, a terminal, a server, a generative AI model, and an emotion engine. An embodiment of this system is described in detail below.

[1019] Hardware and Software Configuration

[1020] The system uses the following major hardware and software:

[1021] User device: A device such as a smartphone or tablet that is used by installing a dedicated application.

[1022] Server: A computer system that receives, analyzes, and stores data sent by users.

[1023] Generative AI models: Artificial intelligence algorithms that perform natural language processing and symptom analysis. For example, we use AI models such as GPT-3.

[1024] Emotion engine: A software module for analyzing the user's emotional state. For example, it uses the Microsoft Azure Emotion Analysis API.

[1025] Medical database: A collection of medical information used for symptom analysis and diagnosis, such as UMLS.

[1026] System Embodiments

[1027] Simple testing at home

[1028] 1. The user enters their symptoms into a dedicated application at home. For example, they might enter, "I have a headache and a slight fever." Emotional data is also collected using an emotion recognition system. Automatically captured images and audio analysis identify the symptoms as "anxiety" or "stress."

[1029] 2. The device sends the input symptom data and emotion data to the server.

[1030] 3. The server uses a generative AI model based on the received data to analyze symptoms and emotional state. The symptom data is compared with a medical database. At the same time, an emotion engine is used to analyze the emotional state.

[1031] 4. The server determines whether the symptoms are mild or severe and generates information on countermeasures and appropriate medical institutions. For example, if the symptoms are mild, it suggests home treatment methods and over-the-counter medications, while if the symptoms are severe, it provides information on the nearest medical institution. It also generates advice on emotional care, such as "listening to music that promotes relaxation."

[1032] 5. The server sends the generated information to the user's terminal.

[1033] 6. The device receives the information from the server and displays it to the user, who can then take action based on it.

[1034] Primary medical care during outpatient visits

[1035] 1. After arriving at the hospital, the user re-enters their latest symptoms into the dedicated application. For example, they might enter "fever over 39 degrees and worsening headache." The application also collects their latest emotional data, which is recognized as "strong anxiety" or "tension."

[1036] 2. The device sends the new symptom data and emotion data to the server.

[1037] 3. The server integrates the new data with the initial data, performs a diagnosis again using the generative AI model, and analyzes the latest emotional state using the emotion engine.

[1038] 4. The server generates a diagnosis and a treatment plan and sends it to the medical institution's terminal. For example, it may diagnose "high possibility of influenza A" and suggest "prescribing antiviral drugs." It also includes a plan for emotional care.

[1039] 5. The terminal displays the information sent to the medical institution's terminal to the doctor, who then uses it as a reference when providing medical care.

[1040] Ongoing support after treatment

[1041] 1. The server stores medical and emotional data, including medical results, prescriptions, treatment progress, and emotional state.

[1042] 2. The server generates medication reminders, lifestyle advice, and emotional care suggestions based on the stored data, such as "take your medicine at 8 a.m. every day" and "get enough rest."

[1043] 3. The server sends the generated reminders and advice to the user's device.

[1044] 4. The device presents the sent information to the user and provides reminders and advice via voice or text.

[1045] 5. Users follow the reminders and advice from the AI ​​to take their medication and improve their lifestyle habits, thereby practicing continuous health management.

[1046] Specific examples

[1047] If the user inputs "I have a headache and a slight fever" into the dedicated application and the emotional data indicates "anxiety," the server will determine that it is a "mild cold" and suggest "taking an over-the-counter fever reducer and drinking plenty of fluids." It will also provide emotional care advice such as "listening to music that promotes relaxation."

[1048] If the patient re-enters that their fever is worsening and exceeds 39 degrees Celsius after arriving at the hospital, and their emotional state is recorded as "strong anxiety," the server will diagnose that they have a high probability of having influenza A and suggest prescribing antiviral medication. It also includes emotional care policies such as allowing time for the patient to explain the situation in detail to the doctor.

[1049] After the consultation, the patient is reminded to "take antiviral medication every day at 8:00 a.m. and rest" and "take repeated deep breaths as a relaxation technique."

[1050] The above is an embodiment of the present invention. This system reduces waiting times at hospitals, improves the work efficiency of doctors, and effectively manages the health and mental health of patients.

[1051] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1052] Simple testing at home

[1053] Step 1:

[1054] The user enters their symptoms into a dedicated application at home. For example, they might type "I have a headache and a slight fever" into a text box. The application also uses a camera and microphone to collect emotional data through an emotion recognition system. For example, it can extract emotional data such as "anxiety" or "stress" from automatically captured images or recorded audio.

[1055] Input: Symptom text ("headache and slight fever"), face photo or voice data

[1056] Output: Symptom data ("headache and slight fever"), emotion data ("anxiety")

[1057] Step 2:

[1058] The device sends the input symptom data and emotion data to a server, for example, by using the network function of a smartphone.

[1059] Input: Symptom data, emotion data

[1060] Output: Send data to the server

[1061] Step 3:

[1062] The server analyzes the symptoms using a generative AI model based on the received symptom data. For example, it analyzes the symptom data using natural language processing (NLP) and compares it with a medical database. At the same time, it analyzes the emotion data using an emotion engine.

[1063] Input: Symptom data, emotion data

[1064] Output: Analysis results (symptom severity, emotional state)

[1065] Step 4:

[1066] The server determines whether the symptoms are mild or severe and generates appropriate countermeasures and information on medical institutions. If the symptoms are mild, it suggests over-the-counter medications and home treatment, and if the symptoms are severe, it provides information on the nearest medical institution. It also generates countermeasures based on the user's emotional state.

[1067] Input: Analysis results (symptom severity, emotional state)

[1068] Output: Countermeasures, medical institution information, emotional care suggestions

[1069] Step 5:

[1070] The server transmits the generated information to the user's terminal, for example, by using a notification function.

[1071] Input: Countermeasures, medical institution information, emotional care suggestions

[1072] Output: Send data to the user's device

[1073] Step 6:

[1074] The device receives information from the server and displays it to the user using a dedicated application, which displays the information in text, images, and audio.

[1075] Input: Countermeasures, medical institution information, emotional care suggestions

[1076] Output: What is displayed to the user

[1077] Primary medical care during outpatient visits

[1078] Step 1:

[1079] After arriving at the hospital, the user re-enters their latest symptoms into the dedicated application. For example, they might enter "fever over 39 degrees and worsening headache." New emotion data is also collected. The emotion recognition system extracts these as "strong anxiety" or "tension."

[1080] Input: Latest symptom text ("Fever over 39 degrees and worsening headache"), face photo or voice data

[1081] Output: New symptom data ("Fever over 39 degrees, headache getting worse"), New emotion data ("Strong anxiety")

[1082] Step 2:

[1083] The device sends new symptom data and emotion data to the server via the smartphone's network function.

[1084] Input: New symptom data, new emotion data

[1085] Output: Send data to the server

[1086] Step 3:

[1087] The server then integrates the new data received with the initial data and performs a re-diagnosis using the generative AI model, while simultaneously analyzing the new emotion data with the emotion engine.

[1088] Input: New symptom data, new emotion data, initial data

[1089] Output: Integrated diagnosis results (specific disease name and severity), latest emotional state

[1090] Step 4:

[1091] The server generates an integrated diagnosis and treatment plan and sends it to the medical institution's terminal. For example, it generates information such as "high possibility of influenza A" or "prescribe a specific medication." It also includes emotional care guidelines.

[1092] Input: Integrated diagnosis results, latest emotional state

[1093] Output: Treatment policy, emotional care response policy

[1094] Step 5:

[1095] The terminal displays the information sent to the medical institution's terminal to the doctor, who then uses it as a reference when providing medical care.

[1096] Input: Treatment policy, emotional care response policy

[1097] Output: Display information to doctor

[1098] Ongoing support after treatment

[1099] Step 1:

[1100] The server stores medical and emotional data, including medical results, prescriptions, treatment progress, and emotional states.

[1101] Input: medical results, prescription details, treatment progress, emotional state

[1102] Output: Data storage

[1103] Step 2:

[1104] The server generates medication reminders, lifestyle advice, and emotional care suggestions based on the stored data, such as "take your medicine at 8 a.m. every day" and "get enough rest."

[1105] Input: Saved data

[1106] Output: Medication reminders, lifestyle advice, emotional care suggestions

[1107] Step 3:

[1108] The server transmits the generated reminders and advice to the user's terminal.

[1109] Input: Medication reminders, lifestyle advice, emotional care suggestions

[1110] Output: Send data to the user's device

[1111] Step 4:

[1112] The device presents the transmitted information to the user and provides reminders and advice via voice or text.

[1113] Input: Medication reminders, lifestyle advice, emotional care suggestions

[1114] Output: What is displayed to the user

[1115] Step 5:

[1116] Users follow the reminders and advice from the AI ​​to take their medication and improve their lifestyle habits, such as taking their medicine at 8 a.m. or taking deep breaths repeatedly.

[1117] Input: Medication reminders, lifestyle advice, emotional care suggestions

[1118] Output: Action execution

[1119] (Application example 2)

[1120] 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."

[1121] The current medical system has problems such as long waiting times at hospitals, which reduces the operational efficiency of medical institutions. There is also a lack of care that takes into account the emotional state of patients, which means that psychological support is not provided adequately. This can lead to lower patient satisfaction and a decline in the quality of medical care. Therefore, there is a need for a comprehensive support system that not only assesses symptoms but also takes into account emotional states.

[1122] 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 the user to input symptoms, means for the terminal to send the input data to the server, means for the server to analyze the data and determine the symptoms, means for the server to generate countermeasures or information on appropriate medical institutions based on the determination results, means for providing the generated information to the user, and means for adding countermeasures according to the emotional state to the generated countermeasures and information on medical institutions. This reduces waiting times at hospitals and enables comprehensive medical support that takes the user's emotional state into consideration.

[1123] A "user" is an individual who utilizes the system to input symptoms and emotional states.

[1124] A "symptom" is a health problem or discomfort that a user experiences.

[1125] "Emotional state" refers to a user's emotional state or state of mind.

[1126] A "terminal" is a device through which a user inputs data and receives information from a server.

[1127] A "server" is a central control device that receives data sent by users and performs analysis and information generation.

[1128] "Diagnosis results" refer to the health status assessment results issued by the server after analyzing the data.

[1129] "Countermeasures" are actions or measures that the user should take, which are generated by the server based on the judgment results.

[1130] "Information about medical institutions" refers to information generated by the server about appropriate medical institutions that the user should visit.

[1131] "Emotion recognition function" refers to a function for assessing and analyzing a user's emotional state.

[1132] A "remind" is a notification or alert that prompts a user to take a specific action.

[1133] "Mental care" refers to advice and support to support the user's mental health.

[1134] "Analysis" refers to the process by which the server processes the data received from the user and evaluates their symptoms and emotional state.

[1135] "Data" is a general term for symptom information and emotional information entered by the user, as well as diagnostic results and countermeasures generated by the server.

[1136] "Medical personnel" refers to doctors, nurses, and other medical professionals who treat users based on diagnosis results and treatment plans.

[1137] This invention is a system for reducing waiting times at hospitals and improving medical efficiency, and also provides support that takes into account the user's emotional state. This system works in cooperation with the user, terminal, server, and emotion engine, and provides simple diagnosis at home, primary medical treatment at the hospital, and continuous follow-up after treatment.

[1138] Simple testing at home

[1139] Users can input their symptoms at home using a dedicated application, and at the same time, emotional data is collected using an emotion recognition system, such as inputting information like "headache" and "anxiety."

[1140] The terminal transmits the input symptom data and emotion data to the server.

[1141] The server then uses AI models to analyze the symptoms and emotional state of the patient based on the received data. The symptom data is compared with a medical database, and the emotional data is analyzed by an emotion engine. For example, a diagnosis such as "mild cold" or "moderate anxiety" is generated.

[1142] Based on the results of the assessment, the server generates information on countermeasures and appropriate medical institutions. In addition, countermeasures tailored to the user's emotional state are also added based on the emotional data.

[1143] The terminal provides this information to the user.

[1144] Primary medical care during outpatient visits

[1145] After arriving at the hospital, the user enters their symptoms into the dedicated application again, and the emotion recognition system collects the latest emotional data. For example, the user enters information such as "fever over 39 degrees" and "strong anxiety."

[1146] The device sends new symptom data and emotion data to the server.

[1147] The server combines the new data with the initial data and performs a new diagnosis using the AI ​​model. At the same time, the emotion engine analyzes the patient's latest emotional state. For example, a diagnosis such as "High probability of influenza A" or "Highly stressed" is generated.

[1148] The server generates diagnostic results and a treatment plan, which are then sent to the medical institution's terminal. Treatment plans also incorporate measures based on the patient's emotional state.

[1149] The terminal displays the information sent to the medical institution's terminal to the doctor, making it useful for the doctor during medical treatment.

[1150] Ongoing support after treatment

[1151] The server stores medical and emotional data and generates medication reminders and lifestyle advice based on this data, such as "take your medicine at 8 a.m." and "take deep breaths."

[1152] The server sends reminders and advice to the user's terminal.

[1153] The device displays this information to the user and provides reminders and advice via voice and text.

[1154] Users follow reminders and advice from the AI, take necessary measures, and continuously manage their health.

[1155] Specific examples of hardware and software used

[1156] Hardware: Smartphones, tablets, hospital computer terminals, etc.

[1157] Software: Dedicated applications (smartphone apps), Flask (web framework), AI models, medical databases, emotion recognition APIs, etc.

[1158] Specific examples

[1159] If a user inputs "headache and slight fever" and collects emotional data such as "anxiety," the server generates countermeasures such as "mild cold" and "listening to music that promotes relaxation." If, after arriving at the hospital, the user re-inputs "fever of 39 degrees or higher" and "strong anxiety," the server will diagnose "high possibility of influenza A" and suggest "specific tests and prescription of antiviral drugs." It also includes emotional care policies such as "setting aside time to talk frequently with a doctor."

[1160] Prompt Sentence Examples

[1161] "Based on the data entered by the user, analyze the symptoms and emotional state and suggest appropriate advice and emotional support. Below is the user's input data. Symptoms: headache and slight fever. Emotion: anxiety."

[1162] This system will significantly reduce waiting times at hospitals, improve the operational efficiency of medical institutions, and effectively manage the health and mental health of patients.

[1163] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1164] (Program processing flow)

[1165] Step 1:

[1166] The user inputs their symptoms and emotional state into a dedicated smartphone application, for example, entering "headache" and "anxiety," and this input data is collected by the application.

[1167] input:

[1168] Symptoms and emotional state from users

[1169] output:

[1170] Symptom data and emotion data entered

[1171] Step 2:

[1172] The device sends the input symptom data and emotion data to the server, and the application converts this data into an appropriate format and sends it to the server using a communication protocol.

[1173] input:

[1174] Symptom and emotion data

[1175] output:

[1176] Data sent to the server

[1177] Step 3:

[1178] The server analyzes the received data. First, it compares the received symptom data with a medical database to determine the symptoms. At the same time, it analyzes the emotion data using an emotion engine to evaluate the emotional state.

[1179] input:

[1180] Symptom and emotion data submitted

[1181] Data processing and calculation:

[1182] Symptom analysis using AI models

[1183] Emotional state analysis using emotion recognition systems

[1184] output:

[1185] Symptom assessment results and emotional state assessment results

[1186] Step 4:

[1187] The server generates countermeasures and information on appropriate medical institutions based on the assessment results. Furthermore, based on the collected emotional data, it adds countermeasures according to the user's emotional state.

[1188] input:

[1189] Symptom assessment results and emotional state assessment results

[1190] Data processing and calculation:

[1191] Generate appropriate countermeasures based on the judgment results

[1192] AI model proposes optimal medical institutions

[1193] Add measures according to emotional state

[1194] output:

[1195] Countermeasures, information on medical institutions, and suggestions for emotional care

[1196] Step 5:

[1197] The server transmits the generated information to the user's terminal, encodes the data according to a transmission protocol, and transmits it to the user's terminal.

[1198] input:

[1199] Countermeasures, information on medical institutions, and suggestions for emotional care

[1200] output:

[1201] Data sent to the user terminal

[1202] Step 6:

[1203] The terminal receives the information from the server and displays it to the user. The application decodes the received data and displays it in a format that is easy for the user to understand.

[1204] input:

[1205] Countermeasures, medical institution information, and emotional care suggestions sent from the server

[1206] output:

[1207] Information displayed on the screen

[1208] Operation:

[1209] The user takes action based on the presented countermeasures

[1210] Step 7:

[1211] After arriving at the hospital, the user again enters their symptoms into a dedicated smartphone application, and the emotion recognition system also collects the latest emotional data. For example, the user enters information such as "fever over 39 degrees" and "strong anxiety."

[1212] input:

[1213] Latest symptom and emotion data

[1214] output:

[1215] Re-entered symptom and emotion data

[1216] Step 8:

[1217] The device sends new symptom data and emotion data to the server.

[1218] input:

[1219] New symptom and emotion data

[1220] output:

[1221] Data sent to the server

[1222] Step 9:

[1223] The server combines the new data with the initial data and performs a re-diagnosis using the AI ​​model, while the emotion engine simultaneously analyzes the latest emotional state.

[1224] input:

[1225] Integration of initial data and new data

[1226] Data processing and calculation:

[1227] AI re-diagnoses symptoms using integrated data

[1228] Latest emotional state analysis with emotion engine

[1229] output:

[1230] Post-integration diagnostic results and emotional assessment

[1231] Step 10:

[1232] The server generates diagnostic results and a treatment plan, which it then sends to the medical institution's terminal. The server also incorporates measures based on the patient's emotional state into the treatment plan.

[1233] input:

[1234] Post-integration diagnostic results and emotional assessment

[1235] Data processing and calculation:

[1236] Generation of treatment plans based on diagnostic results

[1237] Adding ways to respond depending on emotional state

[1238] output:

[1239] Treatment plan and diagnosis results sent to medical institutions

[1240] Step 11:

[1241] The terminal displays the information sent to the medical institution's terminal to the doctor, who then provides medical treatment based on this information.

[1242] input:

[1243] Treatment plan and diagnosis results sent to medical institutions

[1244] output:

[1245] Information displayed on the doctor's terminal

[1246] Operation:

[1247] The doctor will provide medical treatment based on the diagnosis and treatment plan.

[1248] Step 12:

[1249] The server stores medical data and emotion data, and generates medication reminders and lifestyle advice based on this data and sends them to the user's device.

[1250] input:

[1251] Medical and emotional data

[1252] Data processing and calculation:

[1253] Generates medication reminders and lifestyle advice based on stored data

[1254] output:

[1255] Reminders and advice, including generated mental health advice

[1256] Step 13:

[1257] The device displays the sent reminders and advice to the user, and provides voice and text reminders, allowing the user to follow the instructions and take the necessary measures.

[1258] input:

[1259] Reminders and advice sent from the server

[1260] output:

[1261] On-screen and audio notifications

[1262] Operation:

[1263] Continuous health management by users following reminders and advice

[1264] 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.

[1265] 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.

[1266] 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.

[1267] [Third embodiment]

[1268] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1269] 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.

[1270] 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).

[1271] 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.

[1272] 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.

[1273] 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).

[1274] 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.

[1275] 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.

[1276] 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.

[1277] 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.

[1278] 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.

[1279] 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."

[1280] This invention is a system for reducing waiting times at hospitals and improving the efficiency of medical care. This system works in cooperation with users, terminals, and servers, and provides simple diagnosis at home, primary medical care at hospitals, and continuous follow-up after medical care.

[1281] Simple testing at home

[1282] 1. User: Enters symptoms into a dedicated application at home. For example, "I have a headache and a slight fever."

[1283] 2. Terminal: Sends the entered symptom data to the server.

[1284] 3. Server: The AI ​​analyzes the symptoms based on the received data, compares the symptom data with a medical database, and determines whether the symptoms are mild or severe.

[1285] 4. Server: Based on the assessment results, it generates information on countermeasures and appropriate medical institutions. For example, if the symptoms are mild, it generates suggestions for home treatment and over-the-counter medications, and if the symptoms are severe, it generates information on the nearest hospital.

[1286] 5. Server: Sends the generated information to the user's device.

[1287] 6. Terminal: Receives information from the server and displays it to the user, who can then take action.

[1288] Primary medical care during outpatient visits

[1289] 1. User: After arriving at the hospital, the user re-enters their latest symptoms into the dedicated application. For example, they might enter "fever over 39 degrees and worsening headache."

[1290] 2. Device: Sends new symptom data to the server.

[1291] 3. Server: The new data received is integrated with the initial data, and the AI ​​performs a diagnosis again.

[1292] 4. Server: Generates diagnostic results and treatment guidelines and sends them to the medical institution's terminal. For example, it generates a diagnostic result such as "high possibility of influenza A" and a treatment guideline such as "prescribe a specific medication."

[1293] 5. Terminal: The information sent to the medical institution's terminal is displayed to the doctor, who uses it as a reference when providing medical care.

[1294] Ongoing support after treatment

[1295] 1. Server: Stores medical data, including medical results, prescription details, and treatment progress.

[1296] 2. Server: Based on the stored data, it generates medication reminders and lifestyle advice, such as "take your medicine at 8 a.m. every day" or "get enough rest."

[1297] 3. Server: Sends reminders and advice to the user's device.

[1298] 4. Terminal: Displays the transmitted information to the user and provides reminders and advice via voice or text.

[1299] 5. User: Follow the reminders and advice from the AI, take your medication, improve your lifestyle habits, and practice continuous health management.

[1300] As a specific example, a user inputs "I have a headache and a slight fever," and the server determines that it is a "mild cold," recommending that the user "take over-the-counter fever reducers and drink plenty of fluids." After arriving at the hospital, the user re-inputs that "the fever is worsening and has exceeded 39 degrees," and the server diagnoses that "it is likely influenza A," suggesting that "specific tests and antiviral medications should be prescribed." The doctor then provides treatment based on this information, and after the treatment, the doctor reminds the user to "take antiviral medication every day at 8 a.m. and rest."

[1301] The above is an embodiment of the present invention. This system significantly reduces waiting times at hospitals, improves the work efficiency of doctors, and effectively manages the health of patients.

[1302] The processing flow will be explained below.

[1303] Simple testing at home

[1304] Step 1:

[1305] The user enters symptoms into a dedicated app. For example, they might enter, "I have a headache and a slight fever."

[1306] Step 2:

[1307] The terminal transmits the input symptom data to the server.

[1308] Step 3:

[1309] The AI ​​analyzes the symptom data received by the server, compares it with a medical database, and determines whether the symptoms are mild or severe.

[1310] Step 4:

[1311] Based on the results of the assessment, the server generates information on countermeasures and appropriate medical institutions. For example, if the symptoms are mild, it will generate suggestions for home treatment and over-the-counter medications, and if the symptoms are severe, it will generate information on the nearest hospital.

[1312] Step 5:

[1313] The server transmits the generated information to the user's terminal.

[1314] Step 6:

[1315] The device receives the information from the server and displays it to the user, who can then take appropriate action.

[1316] Primary medical care during outpatient visits

[1317] Step 1:

[1318] After arriving at the hospital, the user re-enters their latest symptoms into the dedicated application. For example, they might enter "fever of over 39 degrees and worsening headache."

[1319] Step 2:

[1320] The terminal transmits new symptom data to the server.

[1321] Step 3:

[1322] The server integrates the new data received with the initial data, and the AI ​​performs a diagnosis again.

[1323] Step 4:

[1324] The server generates a diagnosis and a treatment plan and sends them to the medical institution's terminal. For example, it generates a diagnosis such as "high possibility of influenza A" and a treatment plan such as "prescribe a specific medication."

[1325] Step 5:

[1326] The terminal displays the information sent to the medical institution's terminal to the doctor, who then uses this information to provide actual medical treatment.

[1327] Ongoing support after treatment

[1328] Step 1:

[1329] The server stores medical data, including medical results, prescription details, and treatment progress.

[1330] Step 2:

[1331] The server generates medication reminders and lifestyle advice based on the stored data, such as "take your medicine at 8 a.m. every day" or "get enough rest."

[1332] Step 3:

[1333] The server sends reminders and advice to the user's terminal.

[1334] Step 4:

[1335] The device displays the sent information to the user and provides reminders and advice via voice or text.

[1336] Step 5:

[1337] Users follow reminders and advice from the AI ​​to take their medication and improve their lifestyle habits, and practice continuous health management.

[1338] The above is a concrete explanation of the program processing of the system step by step.

[1339] Example 1

[1340] 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."

[1341] Modern medical institutions face challenges such as long waiting times for patients and overwork for doctors due to the need to process vast amounts of information. Furthermore, there is a lack of effective methods for patients to self-diagnose at home or follow up after visiting the hospital. This creates a need for efficient and prompt medical care for both patients and medical institutions.

[1342] 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.

[1343] In this invention, the server includes: means for a user to input symptoms; means for a terminal to transmit the input data to the server; means for analyzing the data and determining the symptoms using an AI model; means for generating a countermeasure or information on an appropriate medical institution based on the determination result; means for transmitting the generated information to the user's terminal and for the terminal to display the information to the user; means for the user to input symptom data again; means for integrating new data with the initial data and performing a diagnosis again using the AI ​​model; means for transmitting the diagnosis result and treatment plan to medical professionals; means for saving the medical data and updating the database; means for generating medication reminders and lifestyle advice based on the saved data; and means for the terminal to display the reminders and advice to the user. This enables reduction of waiting times at hospitals, efficiency of medical operations, and continuous health management of patients.

[1344] 1. "User" refers to an individual who uses the system to input symptoms.

[1345] 2. "Terminal" refers to the electronic device used by the user to input symptoms and transmit the data to the server.

[1346] 3. "Server" refers to the data processing system that receives data sent by users and analyzes and generates data using AI models.

[1347] 4. "Data" refers to information such as symptom information entered by the user, countermeasures generated by the server, and diagnostic results.

[1348] 5. "AI model" refers to the artificial intelligence algorithm used by the server to analyze data, determine symptoms, and generate countermeasures.

[1349] 6. "Determination result" refers to the diagnosis result obtained after the server analyzes the symptoms using the AI ​​model.

[1350] 7. "Countermeasures" refers to medical measures generated by the server based on the assessment results.

[1351] 8. "Medical Institution" refers to the appropriate hospital, clinic, or other medical facility that the User should visit.

[1352] 9. "Diagnosis result" refers to the specific disease name and symptom evaluation results provided by the server based on data analysis.

[1353] 10. "Treatment policy" refers to the treatment method or prescription that medical professionals should implement based on diagnostic results.

[1354] 11. "Medical personnel" refers to medical professionals such as doctors and nurses who provide medical care based on diagnostic results and treatment plans.

[1355] 12. "Medical data" refers to information such as medical examination results, prescription details, and treatment progress.

[1356] 13. "Database" refers to an electronic record device where the server stores medical data.

[1357] 14. "Medication Reminder" means a notification or alert that encourages a user to take their medication.

[1358] 15. "Lifestyle Advice" means lifestyle advice generated by the Server for the purpose of assisting the User in managing their health.

[1359] This system is designed to reduce waiting times and improve the efficiency of medical care, especially in hospitals. The system utilizes a user's device, a server, and an AI model to provide simple diagnosis at home, primary care during outpatient visits, and continuous follow-up after treatment.

[1360] Hardware and Software

[1361] User device: A device (smartphone, tablet, PC, etc.) on which the user inputs symptoms. A dedicated application is installed.

[1362] Server: A central server for receiving and processing data. It is connected to a medical database and uses AI models for data analysis.

[1363] AI models: Generative AI models such as OpenAI's GPT-4 are used to analyze data, make judgments, and generate advice.

[1364] Simple testing at home

[1365] 1. The user opens the dedicated application and enters their symptoms in text format. For example, they might enter, "I have a headache and a slight fever."

[1366] 2. The device encrypts the input data and sends it to the server.

[1367] 3. The server analyzes the received data using an AI model to determine symptoms, and compares them with a medical database to determine whether the symptoms are mild or severe.

[1368] 4. Based on the results of the assessment, the server generates information on countermeasures and appropriate medical institutions (for example, "take over-the-counter fever reducers and drink plenty of fluids").

[1369] 5. The server sends the generated information to the terminal.

[1370] 6. The device displays the information to the user, who then takes appropriate action.

[1371] Primary medical care during outpatient visits

[1372] 1. After arriving at the hospital, the user re-enters their latest symptoms into the dedicated application. For example, they might enter "fever over 39 degrees and worsening headache."

[1373] 2. The device sends new data to the server.

[1374] 3. The server integrates the new data with the initial data and performs a diagnosis again using the AI ​​model.

[1375] 4. The server generates the diagnosis and treatment plan, for example, "It is highly likely that the patient has influenza A. Specific tests and prescription of antiviral drugs are required," and sends this information to the medical institution's terminal.

[1376] 5. The terminal displays the information sent to the medical institution's terminal to the doctor, who uses this information as a reference when providing treatment.

[1377] Ongoing support after treatment

[1378] 1. The server stores medical data (medical results, prescription details, treatment progress, etc.) in a database.

[1379] 2. The server uses the stored data to generate medication reminders and lifestyle advice using AI models, such as reminders to "take your medication at 8 a.m. every day" and "get enough rest."

[1380] 3. The server sends the generated reminders and advice to the device.

[1381] 4. The device will provide reminders and advice to the user, either through voice or text. For example, "It's 8 AM. Don't forget to take your medicine."

[1382] 5. The user follows the reminders and advice to take their medication and improve their lifestyle.

[1383] Examples of concrete examples and prompts

[1384] For example, if a user inputs "I have a headache and a slight fever" into a dedicated application, the server will determine that it is a "mild cold" and recommend that the user "take over-the-counter fever reducers and drink plenty of fluids." If, after arriving at the hospital, the user again inputs that "the fever has risen above 39 degrees and is getting worse," the server will diagnose that "it is likely influenza A" and suggest "specific tests and the prescription of antiviral medication." The doctor will then provide treatment based on this information, and after the consultation will remind the user to "take antiviral medication every day at 8 a.m. and rest."

[1385] An example prompt is:

[1386] Example: "I have a headache and a slight fever. What should I do?"

[1387] Example server-generated response: "Your symptoms have been diagnosed as a mild cold. We recommend taking over-the-counter fever-reducing medication, drinking plenty of fluids, and getting plenty of rest."

[1388] This system will reduce waiting times at hospitals, improve the efficiency of medical operations, and enable continuous health management for patients.

[1389] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1390] Simple testing at home

[1391] Step 1:

[1392] The user opens a dedicated application and enters their symptoms (e.g., "I have a headache and a slight fever").

[1393] Input: User's symptom information

[1394] Output: User-entered symptom data

[1395] Step 2:

[1396] The terminal receives the input symptom data, encrypts the data, and transmits it to the server.

[1397] Input: User-entered symptom data

[1398] Output: Encrypted symptom data

[1399] Step 3:

[1400] The server receives the encrypted data, decrypts it, and then analyzes it using an AI model (e.g., GPT-4) to determine symptoms.

[1401] Input: Encrypted symptom data

[1402] Data processing: Data decryption

[1403] Data Computing: Data Analysis with AI Models

[1404] Output: Symptom determination result (e.g., "Mild cold")

[1405] Step 4:

[1406] Based on the results of the assessment, the server generates information on countermeasures and appropriate medical institutions.

[1407] Input: Symptom assessment result

[1408] Data calculation: Countermeasure methods and medical institution information generation

[1409] Output: Information on countermeasures and medical institutions (e.g., "Take over-the-counter fever reducers and drink plenty of fluids")

[1410] Step 5:

[1411] The server transmits the generated information to the terminal.

[1412] Input: Countermeasures and medical institution information

[1413] Output: Countermeasures and medical institution information sent

[1414] Step 6:

[1415] The terminal receives the information and displays it to the user.

[1416] Input: Countermeasures and medical institution information sent from the server

[1417] Output: Information displayed to the user (e.g., "Take over-the-counter fever reducers and drink plenty of fluids")

[1418] Primary medical care during outpatient visits

[1419] Step 1:

[1420] After arriving at the hospital, the user re-enters their latest symptoms (e.g., "fever over 39 degrees and worsening headache") into a dedicated application.

[1421] Input: Latest symptom data

[1422] Output: Re-entered symptom data

[1423] Step 2:

[1424] The terminal receives the re-entered symptom data and transmits it to the server.

[1425] Input: Re-entered symptom data

[1426] Output: Submitted symptom data

[1427] Step 3:

[1428] The server integrates the new data received with the initial data, analyzes the data again using the AI ​​model, and updates the diagnosis.

[1429] Input: Re-entered symptom data, initial symptom data

[1430] Data processing: Data integration

[1431] Data Computation: Reanalyzing Data with AI Models

[1432] Output: Updated diagnosis (e.g., "Probably influenza A").

[1433] Step 4:

[1434] The server generates a treatment plan based on the diagnosis results and sends it to the medical institution's terminal.

[1435] Input: Updated diagnostic results

[1436] Data Computation: Treatment Plan Generation

[1437] Output: The treatment plan sent (e.g., "In case of influenza A, specific tests and antiviral medications are required.")

[1438] Step 5:

[1439] The terminal receives the information sent to the medical institution's terminal and displays it to the doctor.

[1440] Input: Submitted treatment plan

[1441] Output: Treatment plan displayed to the doctor

[1442] Ongoing support after treatment

[1443] Step 1:

[1444] When the consultation is completed, the server stores data such as the consultation results, prescription details, and treatment progress in a database.

[1445] Input: medical results, prescription details, treatment progress data

[1446] Output: Saved medical data

[1447] Step 2:

[1448] The server uses AI models to generate medication reminders and lifestyle advice based on the stored medical data.

[1449] Input: Stored medical data

[1450] Data Computing: Generating Medication Reminders and Lifestyle Advice

[1451] Output: Generated medication reminders and lifestyle advice

[1452] Step 3:

[1453] The server transmits the generated reminders and advice to the terminal.

[1454] Input: Generated medication reminders and lifestyle advice

[1455] Output: Reminders and advice sent

[1456] Step 4:

[1457] The device receives reminders and advice and displays them to the user. Reminders are given via voice notification or text display.

[1458] Input: Reminders and advice sent

[1459] Output: Reminders and advice displayed to the user (e.g., "It's 8 AM. Take your antiviral medication.")

[1460] Step 5:

[1461] Users follow the reminders and advice from the device to take appropriate medication and improve their lifestyle habits.

[1462] Input: Reminders and advice

[1463] Output: Medication and lifestyle improvement behavior

[1464] (Application example 1)

[1465] 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."

[1466] In conventional medical systems, after users receive medical treatment at a hospital, the payment procedures for medical expenses are often complicated and time-consuming. Furthermore, there are insufficient medication reminders and lifestyle advice after treatment, making it difficult to provide continuous health management. This prevents users from receiving appropriate follow-up after treatment, leading to a decrease in medical efficiency.

[1467] 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.

[1468] In this invention, the server includes: means for the user to input symptoms; means for the terminal to send the input data to the server; means for the server to analyze the data and determine the symptoms; means for the server to generate information on countermeasures or appropriate medical institutions based on the determination results; means for providing the generated information to the user; means for the server to calculate medical expenses after treatment; means for the server to automatically debit the medical expenses from the user's electronic payment account; and means for sending a payment confirmation notice to the user's terminal. This enables automatic payment of medical expenses after treatment and prompt follow-up.

[1469] - "User" means an individual who uses the system to input their symptoms and receive medical treatment or medical services.

[1470] A "terminal" is an electronic device operated by a user to input data and receive medical information. Examples include smartphones and tablets.

[1471] A "server" is a computer system that receives, stores, and analyzes data sent from a terminal and provides necessary information to users and medical professionals.

[1472] "Data" refers to information entered by the user, such as symptom information, medical treatment results, and payment information, which the server analyzes and stores.

[1473] "Analysis" refers to the act of using certain algorithms and AI technology to determine symptoms and calculate medical expenses based on the data received by the server.

[1474] "Symptom determination" refers to the act of the server diagnosing what the symptom is based on the symptom information entered by the user.

[1475] "Countermeasures" are advice on specific actions the user should take or medicines that should be used based on the symptom assessment results.

[1476] "Information about medical institutions" refers to information about hospitals and clinics that are appropriate for the user, including medical appointments and medical treatment details.

[1477] "Generated information" refers to the diagnosis and advice provided to users and medical professionals based on the results of analysis and judgment by the server.

[1478] A "payment confirmation notification" is a message sent by the server to notify the user when the server has successfully debited the user's electronic payment account.

[1479] An "electronic payment account" is an account used by a user to make financial payments online, including a credit card or digital wallet.

[1480] A "medication reminder" is a notification sent by the server to the user to urge him or her to take medicine regularly.

[1481] "Lifestyle advice" refers to guidance and suggestions about daily life provided by the server to maintain and improve the user's health.

[1482] This invention realizes automatic payment of medical expenses and continuous follow-up in a system in which a user inputs symptoms and manages medical information.

[1483] System Configuration

[1484] This system consists of a user's device, a server, and an electronic payment service. The user's device is typically a smartphone or tablet, on which a dedicated application is installed. The server is a computer system for storing and analyzing data and generating notifications. The electronic payment service is an online payment platform that automatically debits the user's medical expenses.

[1485] System Operation

[1486] 1. Enter symptoms

[1487] The user uses a dedicated application at home or at a designated location to input their current symptoms, for example, "I have a headache and a slight fever." The device then sends the input data to the server.

[1488] 2. Data analysis and judgment

[1489] The server analyzes the received symptom data and determines whether the symptom is mild or severe by using a generative AI model to compare the symptom data with a medical database.

[1490] 3. Information generation and provision

[1491] Based on the results of the assessment, the server generates information on countermeasures and appropriate medical institutions and sends it to the user's device. For example, if the symptoms are mild, it suggests home treatment methods and over-the-counter medications. If the symptoms are severe, it provides information on the nearest hospital.

[1492] 4. Automatic medical payments

[1493] After the medical treatment is completed, the server calculates the medical fee based on the medical information, and then automatically deducts the medical fee from the user's electronic payment account, such as a credit card or digital wallet.

[1494] 5. Sending notifications

[1495] The server sends a payment confirmation notice to the user's terminal, and the user confirms that the payment has been completed.

[1496] 6. Ongoing follow-up

[1497] The server stores the medical data and generates medication reminders and lifestyle advice based on the data. These reminders and advice are periodically sent to the user's device.

[1498] Specific examples

[1499] Consider a case where a user inputs "I have a fever and headache" and is diagnosed with "influenza" at the hospital. The user then inputs information about the antiviral medication they are taking into the app. After the consultation, the server automatically calculates the medical expenses and deducts them from the user's electronic payment account. A notification of payment completion is sent to the user's device, and thereafter, medication reminders are sent periodically.

[1500] Prompt Sentence Examples

[1501] The user enters that they have a fever and headache and is diagnosed with influenza at the hospital. They then enter information about the antiviral medication they are taking into the app. After the consultation, payment is automatically completed and the information is notified to the user. From then on, they will receive a daily medication reminder at the specified time.

[1502] The system automates medical bill payment procedures and ensures users can follow up after medical treatment, improving user convenience and greatly improving the efficiency of the entire medical system.

[1503] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1504] Step 1:

[1505] The user uses a dedicated application at home or a designated location to input their current symptoms. The input data is specific symptom information such as "I have a headache and a slight fever." The device then sends this information to the server.

[1506] Input: User-entered symptom information

[1507] Output: Symptom information is sent from the device to the server

[1508] Step 2:

[1509] The server analyzes the symptom data received from the device. This analysis utilizes a generative AI model and compares the symptom data with a medical database. At this stage, an initial determination is made as to whether the symptoms are mild or severe.

[1510] Input: Symptom information from the terminal

[1511] Output: Initial diagnosis result (mild or severe)

[1512] Step 3:

[1513] Based on the initial diagnosis, the server generates information on necessary countermeasures or appropriate medical institutions. For example, if the symptoms are mild, the server may recommend taking over-the-counter fever reducers and drinking plenty of fluids. If the symptoms are severe, the server may generate information on the nearest hospital.

[1514] Input: Initial judgment result

[1515] Output: Information on countermeasures and medical institutions

[1516] Step 4:

[1517] The server sends the generated information about countermeasures and medical institutions to the user's device, which receives it and displays it to the user.

[1518] Input: Information on countermeasures and medical institutions

[1519] Output: Countermeasures and information about medical institutions are displayed on the user's device

[1520] Step 5:

[1521] When the user arrives at the hospital to continue treatment, they re-enter their latest symptom data. For example, they might enter information such as "fever over 39 degrees and worsening headache." The device then sends the new symptom data to the server again.

[1522] Input: Latest symptom data

[1523] Output: New symptom data is sent from the device to the server.

[1524] Step 6:

[1525] The server integrates the new symptom data with the initial data and again uses AI to perform a detailed diagnosis.

[1526] Input: New symptom data and initial data

[1527] Output: Detailed diagnostic results and treatment plan

[1528] Step 7:

[1529] The server generates a diagnosis and treatment plan and sends it to the medical professional's device, which receives and displays this information. The doctor uses this information as a reference when providing treatment.

[1530] Input: Detailed diagnosis and treatment plan

[1531] Output: Information is displayed on the medical personnel's terminal.

[1532] Step 8:

[1533] After the medical treatment is completed, the server calculates the medical costs based on the medical information, including the details of the treatment and the prescribed medication.

[1534] Input: Medical information

[1535] Output: Calculated medical costs

[1536] Step 9:

[1537] The server automatically debits the calculated medical costs from the user's electronic payment account, using a pre-registered credit card or digital wallet.

[1538] Input: Calculated medical expenses

[1539] Output: Withdrawal completion result

[1540] Step 10:

[1541] The server sends a payment confirmation notice to the user's terminal, which the user receives and confirms that the payment has been completed.

[1542] Input: Withdrawal completion result

[1543] Output: Payment confirmation notification

[1544] Step 11:

[1545] The server stores medical data and generates medication reminders and lifestyle advice based on that data.

[1546] Input: Medical data

[1547] Output: Medication reminders and lifestyle advice

[1548] Step 12:

[1549] The server sends the generated medication reminders and lifestyle advice to the user's device, which receives them and periodically notifies them. The user receives these notifications and acts according to the instructions.

[1550] Input: Medication reminders and lifestyle advice

[1551] Output: A notification will be displayed periodically on the user's device.

[1552] 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.

[1553] This invention is a system for reducing waiting times at hospitals and improving medical efficiency, and also provides support that takes into account the user's emotional state. This system works in cooperation with the user, terminal, server, and emotion engine, and provides simple diagnosis at home, primary medical treatment at the hospital, and continuous follow-up after treatment.

[1554] Simple testing at home

[1555] 1. User: At home, the user inputs their symptoms into a dedicated application, and emotional data is also collected by the emotion recognition system. For example, if the user inputs "I have a headache and a slight fever," their emotional state is recorded as "anxiety" or "stress."

[1556] 2. Terminal: Sends the input symptom data and emotion data to the server.

[1557] 3. Server: Based on the received data, the AI ​​analyzes the symptoms and emotional state. It compares the symptom data with a medical database to determine whether the symptoms are mild or severe, and analyzes the emotional data with an emotion engine to determine the emotional state.

[1558] 4. Server: Based on the assessment results, the server generates information on countermeasures and appropriate medical institutions. For example, if the symptoms are mild, the server generates suggestions for home treatment and over-the-counter medications, and if the symptoms are severe, it generates information on the nearest hospital. Based on the emotional data, the server also adds countermeasures according to the user's emotional state.

[1559] 5. Server: Sends the generated information to the user's device.

[1560] 6. Terminal: Receives information from the server and displays it to the user, who can then take action.

[1561] Primary medical care during outpatient visits

[1562] 1. User: After arriving at the hospital, the user re-enters their latest symptoms into the dedicated application, and the emotion recognition system also collects their latest emotional data. For example, if the user enters "fever over 39 degrees and worsening headache," their emotional state is recorded as "strong anxiety" or "tension."

[1563] 2. Device: Sends new symptom data and emotion data to the server.

[1564] 3. Server: The AI ​​combines the new data received with the initial data and performs another diagnosis. At the same time, the emotion engine analyzes the latest emotional state.

[1565] 4. Server: Generates diagnosis results and treatment plans and sends them to the medical institution's terminal. For example, it generates a diagnosis result such as "high possibility of influenza A" and a treatment plan such as "prescribe a specific medication." Response methods based on the patient's emotional state are also incorporated into the treatment plan.

[1566] 5. Terminal: The information sent to the medical institution's terminal is displayed to the doctor, who uses it as a reference when providing medical care.

[1567] Ongoing support after treatment

[1568] 1. Server: Stores medical data and emotional data. The stored data includes medical results, prescription details, treatment progress, emotional state, etc.

[1569] 2. Server: Based on the stored data, it generates medication reminders and lifestyle advice. For example, it generates advice such as "Take your medicine at 8:00 a.m. every day" or "Get enough rest." At the same time, it also includes mental health advice based on the patient's emotional state.

[1570] 3. Server: Sends reminders and advice to the user's device.

[1571] 4. Terminal: Displays the transmitted information to the user and provides reminders and advice via voice or text.

[1572] 5. User: Follow the reminders and advice from the AI, take your medication, improve your lifestyle habits, and practice continuous health management.

[1573] For example, if a user inputs "I have a headache and a slight fever" and the emotional data collected indicates "anxiety," the server will determine that it is a "mild cold" and recommend that the user take an over-the-counter fever reducer and drink plenty of fluids, while also providing emotional care advice such as listening to music that promotes relaxation. If, after arriving at the hospital, the user re-inputs that their fever has risen above 39 degrees and worsened, and the emotional state is collected as "strong anxiety," the server will diagnose that the patient is likely to have influenza A and suggest that they undergo specific tests and prescribe antiviral medication, along with emotional care guidelines such as setting aside time to explain the situation in detail to the doctor. After the consultation, the server will remind the patient to take antiviral medication every day at 8 a.m. and rest, and to practice deep breathing as a relaxation technique.

[1574] The above is an embodiment of the present invention. This system significantly reduces waiting times at hospitals, improves the work efficiency of doctors, and effectively manages the health and mental health of patients.

[1575] The processing flow will be explained below.

[1576] Simple testing at home

[1577] Step 1:

[1578] The user inputs their symptoms into a dedicated app. For example, they might input "I have a headache and a slight fever," and the emotion recognition system will collect their emotional state as "anxiety."

[1579] Step 2:

[1580] The terminal transmits the input symptom data and emotion data to the server.

[1581] Step 3:

[1582] The AI ​​analyzes the symptom data received by the server, compares it with a medical database, and determines whether the symptoms are mild or severe.

[1583] Step 4:

[1584] The server analyzes the emotional data with an emotion engine to determine the emotional state.

[1585] Step 5:

[1586] The server generates information on countermeasures and appropriate medical institutions based on the symptoms and emotional state. For example, if the symptoms are mild, it generates home treatment methods (e.g., over-the-counter medication suggestions), and if the symptoms are severe, it generates information on the nearest hospital. Based on the emotional state, it also suggests emotional care measures (e.g., relaxation methods).

[1587] Step 6:

[1588] The server transmits the generated information to the user's terminal.

[1589] Step 7:

[1590] The device receives the information from the server and displays it to the user, who can then take appropriate action.

[1591] Primary medical care during outpatient visits

[1592] Step 1:

[1593] After arriving at the hospital, the user again enters their latest symptoms into the dedicated application. For example, they might enter "fever over 39 degrees and worsening headache," and the emotion recognition system records their emotional state as "strong anxiety."

[1594] Step 2:

[1595] The device sends new symptom data and emotion data to the server.

[1596] Step 3:

[1597] The server integrates the new data received with the initial data, and the AI ​​performs a second diagnosis. At the same time, the emotion engine analyzes the latest emotional state.

[1598] Step 4:

[1599] The server generates a diagnosis and a treatment plan. For example, it generates a diagnosis such as "high possibility of influenza A" and a treatment plan such as "prescribe a specific medication." The treatment plan also includes a response method based on the patient's emotional state (e.g., setting aside time to explain the situation in detail to the doctor).

[1600] Step 5:

[1601] The server sends the diagnosis results and treatment plan to the medical institution's terminal.

[1602] Step 6:

[1603] The device displays the received information to the doctor, who then uses it as a reference when providing treatment.

[1604] Ongoing support after treatment

[1605] Step 1:

[1606] The server stores medical data and emotional data, including medical results, prescription details, treatment progress, and emotional state.

[1607] Step 2:

[1608] The server generates medication reminders and lifestyle advice based on the stored data. For example, it might generate advice such as "take your medicine at 8 a.m. every day" or "get enough rest." At the same time, it also includes mental care advice based on the user's emotional state (e.g., repeatedly taking deep breaths as a relaxation technique).

[1609] Step 3:

[1610] The server sends reminders and advice to the user's terminal.

[1611] Step 4:

[1612] The device displays the sent information to the user and provides reminders and advice via voice or text.

[1613] Step 5:

[1614] Users follow reminders and advice from the AI ​​to take their medication and improve their lifestyle habits, and practice continuous health management.

[1615] The above is a concrete explanation of each step of the program processing of a system that combines an emotion engine.

[1616] Example 2

[1617] 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."

[1618] In conventional medical systems, sufficient pre-diagnosis and emotional care are not provided before the user arrives at the hospital, causing extended diagnosis times. Furthermore, the user's emotional state is not taken into consideration during waiting times at the hospital or follow-up care after treatment, resulting in inappropriate care being provided. As a result, medical efficiency and user satisfaction have not been sufficiently improved.

[1619] 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.

[1620] In this invention, the server includes means for analyzing symptoms using a generative AI model based on received data, means for analyzing emotional states using an emotion engine, and means for generating countermeasures, information on appropriate medical institutions, and emotional care suggestions based on the results of the assessment. This makes it possible to comprehensively analyze the user's symptoms and emotional state, provide appropriate countermeasures and information on medical institutions, and also suggest emotional care. This reduces waiting times at hospitals, improves doctor work efficiency, and effectively manages the health and mental health of patients.

[1621] 1. "User" is a person who uses the system to input symptoms and receive suggested treatments and medical information.

[1622] 2. "Symptoms" are data that indicate the health condition or abnormality that the user feels.

[1623] 3. "Emotional state" is data that indicates the user's current emotional or mental state.

[1624] 4. "Terminal" means a device used by a User to access the System, such as a smartphone or tablet.

[1625] 5. "Server" means a computer system that receives data sent by users and analyzes and provides information.

[1626] 6. A "generative AI model" is an artificial intelligence algorithm used for natural language processing and symptom analysis.

[1627] 7. "Emotion Engine" is a software module for analyzing the user's emotional state.

[1628] 8. "Medical database" means a collection of medical information used for symptom analysis and diagnosis.

[1629] 9. "Countermeasures" are general treatments or measures to deal with the user's symptoms.

[1630] 10. "Information about medical institutions" means information about appropriate medical facilities that a user should visit.

[1631] 11. "Emotional care" refers to suggestions for psychological support and relaxation tailored to the user's emotional state.

[1632] 12. "Medical data" refers to medical information generated by physicians, such as medical examination results and treatment details.

[1633] 13. "Remind" is a notification that prompts the user to take medication or take other action to manage their health.

[1634] 14. "Lifestyle Advice" means suggestions for improving the user's lifestyle to maintain their health.

[1635] MODE FOR CARRYING OUT THE INVENTION

[1636] The present invention is a system that collects and analyzes a user's symptom input and emotional state, and provides appropriate measures and information on medical institutions. This system is composed of a user, a terminal, a server, a generative AI model, and an emotion engine. An embodiment of this system is described in detail below.

[1637] Hardware and Software Configuration

[1638] The system uses the following major hardware and software:

[1639] User device: A device such as a smartphone or tablet that is used by installing a dedicated application.

[1640] Server: A computer system that receives, analyzes, and stores data sent by users.

[1641] Generative AI models: Artificial intelligence algorithms that perform natural language processing and symptom analysis. For example, we use AI models such as GPT-3.

[1642] Emotion engine: A software module for analyzing the user's emotional state. For example, it uses the Microsoft Azure Emotion Analysis API.

[1643] Medical database: A collection of medical information used for symptom analysis and diagnosis, such as UMLS.

[1644] System Embodiments

[1645] Simple testing at home

[1646] 1. The user enters their symptoms into a dedicated application at home. For example, they might enter, "I have a headache and a slight fever." Emotional data is also collected using an emotion recognition system. Automatically captured images and audio analysis identify the symptoms as "anxiety" or "stress."

[1647] 2. The device sends the input symptom data and emotion data to the server.

[1648] 3. The server uses a generative AI model based on the received data to analyze symptoms and emotional state. The symptom data is compared with a medical database. At the same time, an emotion engine is used to analyze the emotional state.

[1649] 4. The server determines whether the symptoms are mild or severe and generates information on countermeasures and appropriate medical institutions. For example, if the symptoms are mild, it suggests home treatment methods and over-the-counter medications, while if the symptoms are severe, it provides information on the nearest medical institution. It also generates advice on emotional care, such as "listening to music that promotes relaxation."

[1650] 5. The server sends the generated information to the user's terminal.

[1651] 6. The device receives the information from the server and displays it to the user, who can then take action based on it.

[1652] Primary medical care during outpatient visits

[1653] 1. After arriving at the hospital, the user re-enters their latest symptoms into the dedicated application. For example, they might enter "fever over 39 degrees and worsening headache." The application also collects their latest emotional data, which is recognized as "strong anxiety" or "tension."

[1654] 2. The device sends the new symptom data and emotion data to the server.

[1655] 3. The server integrates the new data with the initial data, performs a diagnosis again using the generative AI model, and analyzes the latest emotional state using the emotion engine.

[1656] 4. The server generates a diagnosis and a treatment plan and sends it to the medical institution's terminal. For example, it may diagnose "high possibility of influenza A" and suggest "prescribing antiviral drugs." It also includes a plan for emotional care.

[1657] 5. The terminal displays the information sent to the medical institution's terminal to the doctor, who then uses it as a reference when providing medical care.

[1658] Ongoing support after treatment

[1659] 1. The server stores medical and emotional data, including medical results, prescriptions, treatment progress, and emotional state.

[1660] 2. The server generates medication reminders, lifestyle advice, and emotional care suggestions based on the stored data, such as "take your medicine at 8 a.m. every day" and "get enough rest."

[1661] 3. The server sends the generated reminders and advice to the user's device.

[1662] 4. The device presents the sent information to the user and provides reminders and advice via voice or text.

[1663] 5. Users follow the reminders and advice from the AI ​​to take their medication and improve their lifestyle habits, thereby practicing continuous health management.

[1664] Specific examples

[1665] If the user inputs "I have a headache and a slight fever" into the dedicated application and the emotional data indicates "anxiety," the server will determine that it is a "mild cold" and suggest "taking an over-the-counter fever reducer and drinking plenty of fluids." It will also provide emotional care advice such as "listening to music that promotes relaxation."

[1666] If the patient re-enters that their fever is worsening and exceeds 39 degrees Celsius after arriving at the hospital, and their emotional state is recorded as "strong anxiety," the server will diagnose that they have a high probability of having influenza A and suggest prescribing antiviral medication. It also includes emotional care policies such as allowing time for the patient to explain the situation in detail to the doctor.

[1667] After the consultation, the patient is reminded to "take antiviral medication every day at 8:00 a.m. and rest" and "take repeated deep breaths as a relaxation technique."

[1668] The above is an embodiment of the present invention. This system reduces waiting times at hospitals, improves the work efficiency of doctors, and effectively manages the health and mental health of patients.

[1669] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1670] Simple testing at home

[1671] Step 1:

[1672] The user enters their symptoms into a dedicated application at home. For example, they might type "I have a headache and a slight fever" into a text box. The application also uses a camera and microphone to collect emotional data through an emotion recognition system. For example, it can extract emotional data such as "anxiety" or "stress" from automatically captured images or recorded audio.

[1673] Input: Symptom text ("headache and slight fever"), face photo or voice data

[1674] Output: Symptom data ("headache and slight fever"), emotion data ("anxiety")

[1675] Step 2:

[1676] The device sends the input symptom data and emotion data to a server, for example, by using the network function of a smartphone.

[1677] Input: Symptom data, emotion data

[1678] Output: Send data to the server

[1679] Step 3:

[1680] The server analyzes the symptoms using a generative AI model based on the received symptom data. For example, it analyzes the symptom data using natural language processing (NLP) and compares it with a medical database. At the same time, it analyzes the emotion data using an emotion engine.

[1681] Input: Symptom data, emotion data

[1682] Output: Analysis results (symptom severity, emotional state)

[1683] Step 4:

[1684] The server determines whether the symptoms are mild or severe and generates appropriate countermeasures and information on medical institutions. If the symptoms are mild, it suggests over-the-counter medications and home treatment, and if the symptoms are severe, it provides information on the nearest medical institution. It also generates countermeasures based on the user's emotional state.

[1685] Input: Analysis results (symptom severity, emotional state)

[1686] Output: Countermeasures, medical institution information, emotional care suggestions

[1687] Step 5:

[1688] The server transmits the generated information to the user's terminal, for example, by using a notification function.

[1689] Input: Countermeasures, medical institution information, emotional care suggestions

[1690] Output: Send data to the user's device

[1691] Step 6:

[1692] The device receives information from the server and displays it to the user using a dedicated application, which displays the information in text, images, and audio.

[1693] Input: Countermeasures, medical institution information, emotional care suggestions

[1694] Output: What is displayed to the user

[1695] Primary medical care during outpatient visits

[1696] Step 1:

[1697] After arriving at the hospital, the user re-enters their latest symptoms into the dedicated application. For example, they might enter "fever over 39 degrees and worsening headache." New emotion data is also collected. The emotion recognition system extracts these as "strong anxiety" or "tension."

[1698] Input: Latest symptom text ("Fever over 39 degrees and worsening headache"), face photo or voice data

[1699] Output: New symptom data ("Fever over 39 degrees, headache getting worse"), New emotion data ("Strong anxiety")

[1700] Step 2:

[1701] The device sends new symptom data and emotion data to the server via the smartphone's network function.

[1702] Input: New symptom data, new emotion data

[1703] Output: Send data to the server

[1704] Step 3:

[1705] The server then integrates the new data received with the initial data and performs a re-diagnosis using the generative AI model, while simultaneously analyzing the new emotion data with the emotion engine.

[1706] Input: New symptom data, new emotion data, initial data

[1707] Output: Integrated diagnosis results (specific disease name and severity), latest emotional state

[1708] Step 4:

[1709] The server generates an integrated diagnosis and treatment plan and sends it to the medical institution's terminal. For example, it generates information such as "high possibility of influenza A" or "prescribe a specific medication." It also includes emotional care guidelines.

[1710] Input: Integrated diagnosis results, latest emotional state

[1711] Output: Treatment policy, emotional care response policy

[1712] Step 5:

[1713] The terminal displays the information sent to the medical institution's terminal to the doctor, who then uses it as a reference when providing medical care.

[1714] Input: Treatment policy, emotional care response policy

[1715] Output: Display information to doctor

[1716] Ongoing support after treatment

[1717] Step 1:

[1718] The server stores medical and emotional data, including medical results, prescriptions, treatment progress, and emotional states.

[1719] Input: medical results, prescription details, treatment progress, emotional state

[1720] Output: Data storage

[1721] Step 2:

[1722] The server generates medication reminders, lifestyle advice, and emotional care suggestions based on the stored data, such as "take your medicine at 8 a.m. every day" and "get enough rest."

[1723] Input: Saved data

[1724] Output: Medication reminders, lifestyle advice, emotional care suggestions

[1725] Step 3:

[1726] The server transmits the generated reminders and advice to the user's terminal.

[1727] Input: Medication reminders, lifestyle advice, emotional care suggestions

[1728] Output: Send data to the user's device

[1729] Step 4:

[1730] The device presents the transmitted information to the user and provides reminders and advice via voice or text.

[1731] Input: Medication reminders, lifestyle advice, emotional care suggestions

[1732] Output: What is displayed to the user

[1733] Step 5:

[1734] Users follow the reminders and advice from the AI ​​to take their medication and improve their lifestyle habits, such as taking their medicine at 8 a.m. or taking deep breaths repeatedly.

[1735] Input: Medication reminders, lifestyle advice, emotional care suggestions

[1736] Output: Action execution

[1737] (Application example 2)

[1738] 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."

[1739] The current medical system has problems such as long waiting times at hospitals, which reduces the operational efficiency of medical institutions. There is also a lack of care that takes into account the emotional state of patients, which means that psychological support is not provided adequately. This can lead to lower patient satisfaction and a decline in the quality of medical care. Therefore, there is a need for a comprehensive support system that not only assesses symptoms but also takes into account emotional states.

[1740] 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 the user to input symptoms, means for the terminal to send the input data to the server, means for the server to analyze the data and determine the symptoms, means for the server to generate countermeasures or information on appropriate medical institutions based on the determination results, means for providing the generated information to the user, and means for adding countermeasures according to the emotional state to the generated countermeasures and information on medical institutions. This reduces waiting times at hospitals and enables comprehensive medical support that takes the user's emotional state into consideration.

[1741] A "user" is an individual who utilizes the system to input symptoms and emotional states.

[1742] A "symptom" is a health problem or discomfort that a user experiences.

[1743] "Emotional state" refers to a user's emotional state or state of mind.

[1744] A "terminal" is a device through which a user inputs data and receives information from a server.

[1745] A "server" is a central control device that receives data sent by users and performs analysis and information generation.

[1746] "Diagnosis results" refer to the health status assessment results issued by the server after analyzing the data.

[1747] "Countermeasures" are actions or measures that the user should take, which are generated by the server based on the judgment results.

[1748] "Information about medical institutions" refers to information generated by the server about appropriate medical institutions that the user should visit.

[1749] "Emotion recognition function" refers to a function for assessing and analyzing a user's emotional state.

[1750] A "remind" is a notification or alert that prompts a user to take a specific action.

[1751] "Mental care" refers to advice and support to support the user's mental health.

[1752] "Analysis" refers to the process by which the server processes the data received from the user and evaluates their symptoms and emotional state.

[1753] "Data" is a general term for symptom information and emotional information entered by the user, as well as diagnostic results and countermeasures generated by the server.

[1754] "Medical personnel" refers to doctors, nurses, and other medical professionals who treat users based on diagnosis results and treatment plans.

[1755] This invention is a system for reducing waiting times at hospitals and improving medical efficiency, and also provides support that takes into account the user's emotional state. This system works in cooperation with the user, terminal, server, and emotion engine, and provides simple diagnosis at home, primary medical treatment at the hospital, and continuous follow-up after treatment.

[1756] Simple testing at home

[1757] Users can input their symptoms at home using a dedicated application, and at the same time, emotional data is collected using an emotion recognition system, such as inputting information like "headache" and "anxiety."

[1758] The terminal transmits the input symptom data and emotion data to the server.

[1759] The server then uses AI models to analyze the symptoms and emotional state of the patient based on the received data. The symptom data is compared with a medical database, and the emotional data is analyzed by an emotion engine. For example, a diagnosis such as "mild cold" or "moderate anxiety" is generated.

[1760] Based on the results of the assessment, the server generates information on countermeasures and appropriate medical institutions. In addition, countermeasures tailored to the user's emotional state are also added based on the emotional data.

[1761] The terminal provides this information to the user.

[1762] Primary medical care during outpatient visits

[1763] After arriving at the hospital, the user enters their symptoms into the dedicated application again, and the emotion recognition system collects the latest emotional data. For example, the user enters information such as "fever over 39 degrees" and "strong anxiety."

[1764] The device sends new symptom data and emotion data to the server.

[1765] The server combines the new data with the initial data and performs a new diagnosis using the AI ​​model. At the same time, the emotion engine analyzes the patient's latest emotional state. For example, a diagnosis such as "High probability of influenza A" or "Highly stressed" is generated.

[1766] The server generates diagnostic results and a treatment plan, which are then sent to the medical institution's terminal. Treatment plans also incorporate measures based on the patient's emotional state.

[1767] The terminal displays the information sent to the medical institution's terminal to the doctor, making it useful for the doctor during medical treatment.

[1768] Ongoing support after treatment

[1769] The server stores medical and emotional data and generates medication reminders and lifestyle advice based on this data, such as "take your medicine at 8 a.m." and "take deep breaths."

[1770] The server sends reminders and advice to the user's terminal.

[1771] The device displays this information to the user and provides reminders and advice via voice and text.

[1772] Users follow reminders and advice from the AI, take necessary measures, and continuously manage their health.

[1773] Specific examples of hardware and software used

[1774] Hardware: Smartphones, tablets, hospital computer terminals, etc.

[1775] Software: Dedicated applications (smartphone apps), Flask (web framework), AI models, medical databases, emotion recognition APIs, etc.

[1776] Specific examples

[1777] If a user inputs "headache and slight fever" and collects emotional data such as "anxiety," the server generates countermeasures such as "mild cold" and "listening to music that promotes relaxation." If, after arriving at the hospital, the user re-inputs "fever of 39 degrees or higher" and "strong anxiety," the server will diagnose "high possibility of influenza A" and suggest "specific tests and prescription of antiviral drugs." It also includes emotional care policies such as "setting aside time to talk frequently with a doctor."

[1778] Prompt Sentence Examples

[1779] "Based on the data entered by the user, analyze the symptoms and emotional state and suggest appropriate advice and emotional support. Below is the user's input data. Symptoms: headache and slight fever. Emotion: anxiety."

[1780] This system will significantly reduce waiting times at hospitals, improve the operational efficiency of medical institutions, and effectively manage the health and mental health of patients.

[1781] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1782] (Program processing flow)

[1783] Step 1:

[1784] The user inputs their symptoms and emotional state into a dedicated smartphone application, for example, entering "headache" and "anxiety," and this input data is collected by the application.

[1785] input:

[1786] Symptoms and emotional state from users

[1787] output:

[1788] Symptom data and emotion data entered

[1789] Step 2:

[1790] The device sends the input symptom data and emotion data to the server, and the application converts this data into an appropriate format and sends it to the server using a communication protocol.

[1791] input:

[1792] Symptom and emotion data

[1793] output:

[1794] Data sent to the server

[1795] Step 3:

[1796] The server analyzes the received data. First, it compares the received symptom data with a medical database to determine the symptoms. At the same time, it analyzes the emotion data using an emotion engine to evaluate the emotional state.

[1797] input:

[1798] Symptom and emotion data submitted

[1799] Data processing and calculation:

[1800] Symptom analysis using AI models

[1801] Emotional state analysis using emotion recognition systems

[1802] output:

[1803] Symptom assessment results and emotional state assessment results

[1804] Step 4:

[1805] The server generates countermeasures and information on appropriate medical institutions based on the assessment results. Furthermore, based on the collected emotional data, it adds countermeasures according to the user's emotional state.

[1806] input:

[1807] Symptom assessment results and emotional state assessment results

[1808] Data processing and calculation:

[1809] Generate appropriate countermeasures based on the judgment results

[1810] AI model proposes optimal medical institutions

[1811] Add measures according to emotional state

[1812] output:

[1813] Countermeasures, information on medical institutions, and suggestions for emotional care

[1814] Step 5:

[1815] The server transmits the generated information to the user's terminal, encodes the data according to a transmission protocol, and transmits it to the user's terminal.

[1816] input:

[1817] Countermeasures, information on medical institutions, and suggestions for emotional care

[1818] output:

[1819] Data sent to the user terminal

[1820] Step 6:

[1821] The terminal receives the information from the server and displays it to the user. The application decodes the received data and displays it in a format that is easy for the user to understand.

[1822] input:

[1823] Countermeasures, medical institution information, and emotional care suggestions sent from the server

[1824] output:

[1825] Information displayed on the screen

[1826] Operation:

[1827] The user takes action based on the presented countermeasures

[1828] Step 7:

[1829] After arriving at the hospital, the user again enters their symptoms into a dedicated smartphone application, and the emotion recognition system also collects the latest emotional data. For example, the user enters information such as "fever over 39 degrees" and "strong anxiety."

[1830] input:

[1831] Latest symptom and emotion data

[1832] output:

[1833] Re-entered symptom and emotion data

[1834] Step 8:

[1835] The device sends new symptom data and emotion data to the server.

[1836] input:

[1837] New symptom and emotion data

[1838] output:

[1839] Data sent to the server

[1840] Step 9:

[1841] The server combines the new data with the initial data and performs a re-diagnosis using the AI ​​model, while the emotion engine simultaneously analyzes the latest emotional state.

[1842] input:

[1843] Integration of initial data and new data

[1844] Data processing and calculation:

[1845] AI re-diagnoses symptoms using integrated data

[1846] Latest emotional state analysis with emotion engine

[1847] output:

[1848] Post-integration diagnostic results and emotional assessment

[1849] Step 10:

[1850] The server generates diagnostic results and a treatment plan, which it then sends to the medical institution's terminal. The server also incorporates measures based on the patient's emotional state into the treatment plan.

[1851] input:

[1852] Post-integration diagnostic results and emotional assessment

[1853] Data processing and calculation:

[1854] Generation of treatment plans based on diagnostic results

[1855] Adding ways to respond depending on emotional state

[1856] output:

[1857] Treatment plan and diagnosis results sent to medical institutions

[1858] Step 11:

[1859] The terminal displays the information sent to the medical institution's terminal to the doctor, who then provides medical treatment based on this information.

[1860] input:

[1861] Treatment plan and diagnosis results sent to medical institutions

[1862] output:

[1863] Information displayed on the doctor's terminal

[1864] Operation:

[1865] The doctor will provide medical treatment based on the diagnosis and treatment plan.

[1866] Step 12:

[1867] The server stores medical data and emotion data, and generates medication reminders and lifestyle advice based on this data and sends them to the user's device.

[1868] input:

[1869] Medical and emotional data

[1870] Data processing and calculation:

[1871] Generates medication reminders and lifestyle advice based on stored data

[1872] output:

[1873] Reminders and advice, including generated mental health advice

[1874] Step 13:

[1875] The device displays the sent reminders and advice to the user, and provides voice and text reminders, allowing the user to follow the instructions and take the necessary measures.

[1876] input:

[1877] Reminders and advice sent from the server

[1878] output:

[1879] On-screen and audio notifications

[1880] Operation:

[1881] Continuous health management by users following reminders and advice

[1882] 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.

[1883] 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.

[1884] 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.

[1885] [Fourth embodiment]

[1886] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1887] 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.

[1888] 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).

[1889] 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.

[1890] 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.

[1891] 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).

[1892] 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.

[1893] 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.

[1894] 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.

[1895] 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.

[1896] 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.

[1897] 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.

[1898] 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."

[1899] This invention is a system for reducing waiting times at hospitals and improving the efficiency of medical care. This system works in cooperation with users, terminals, and servers, and provides simple diagnosis at home, primary medical care at hospitals, and continuous follow-up after medical care.

[1900] Simple testing at home

[1901] 1. User: Enters symptoms into a dedicated application at home. For example, "I have a headache and a slight fever."

[1902] 2. Terminal: Sends the entered symptom data to the server.

[1903] 3. Server: The AI ​​analyzes the symptoms based on the received data, compares the symptom data with a medical database, and determines whether the symptoms are mild or severe.

[1904] 4. Server: Based on the assessment results, it generates information on countermeasures and appropriate medical institutions. For example, if the symptoms are mild, it generates suggestions for home treatment and over-the-counter medications, and if the symptoms are severe, it generates information on the nearest hospital.

[1905] 5. Server: Sends the generated information to the user's device.

[1906] 6. Terminal: Receives information from the server and displays it to the user, who can then take action.

[1907] Primary medical care during outpatient visits

[1908] 1. User: After arriving at the hospital, the user re-enters their latest symptoms into the dedicated application. For example, they might enter "fever over 39 degrees and worsening headache."

[1909] 2. Device: Sends new symptom data to the server.

[1910] 3. Server: The new data received is integrated with the initial data, and the AI ​​performs a diagnosis again.

[1911] 4. Server: Generates diagnostic results and treatment guidelines and sends them to the medical institution's terminal. For example, it generates a diagnostic result such as "high possibility of influenza A" and a treatment guideline such as "prescribe a specific medication."

[1912] 5. Terminal: The information sent to the medical institution's terminal is displayed to the doctor, who uses it as a reference when providing medical care.

[1913] Ongoing support after treatment

[1914] 1. Server: Stores medical data, including medical results, prescription details, and treatment progress.

[1915] 2. Server: Based on the stored data, it generates medication reminders and lifestyle advice, such as "take your medicine at 8 a.m. every day" or "get enough rest."

[1916] 3. Server: Sends reminders and advice to the user's device.

[1917] 4. Terminal: Displays the transmitted information to the user and provides reminders and advice via voice or text.

[1918] 5. User: Follow the reminders and advice from the AI, take your medication, improve your lifestyle habits, and practice continuous health management.

[1919] As a specific example, a user inputs "I have a headache and a slight fever," and the server determines that it is a "mild cold," recommending that the user "take over-the-counter fever reducers and drink plenty of fluids." After arriving at the hospital, the user re-inputs that "the fever is worsening and has exceeded 39 degrees," and the server diagnoses that "it is likely influenza A," suggesting that "specific tests and antiviral medications should be prescribed." The doctor then provides treatment based on this information, and after the treatment, the doctor reminds the user to "take antiviral medication every day at 8 a.m. and rest."

[1920] The above is an embodiment of the present invention. This system significantly reduces waiting times at hospitals, improves the work efficiency of doctors, and effectively manages the health of patients.

[1921] The processing flow will be explained below.

[1922] Simple testing at home

[1923] Step 1:

[1924] The user enters symptoms into a dedicated app. For example, they might enter, "I have a headache and a slight fever."

[1925] Step 2:

[1926] The terminal transmits the input symptom data to the server.

[1927] Step 3:

[1928] The AI ​​analyzes the symptom data received by the server, compares it with a medical database, and determines whether the symptoms are mild or severe.

[1929] Step 4:

[1930] Based on the results of the assessment, the server generates information on countermeasures and appropriate medical institutions. For example, if the symptoms are mild, it will generate suggestions for home treatment and over-the-counter medications, and if the symptoms are severe, it will generate information on the nearest hospital.

[1931] Step 5:

[1932] The server transmits the generated information to the user's terminal.

[1933] Step 6:

[1934] The device receives the information from the server and displays it to the user, who can then take appropriate action.

[1935] Primary medical care during outpatient visits

[1936] Step 1:

[1937] After arriving at the hospital, the user re-enters their latest symptoms into the dedicated application. For example, they might enter "fever of over 39 degrees and worsening headache."

[1938] Step 2:

[1939] The terminal transmits new symptom data to the server.

[1940] Step 3:

[1941] The server integrates the new data received with the initial data, and the AI ​​performs a diagnosis again.

[1942] Step 4:

[1943] The server generates a diagnosis and a treatment plan and sends them to the medical institution's terminal. For example, it generates a diagnosis such as "high possibility of influenza A" and a treatment plan such as "prescribe a specific medication."

[1944] Step 5:

[1945] The terminal displays the information sent to the medical institution's terminal to the doctor, who then uses this information to provide actual medical treatment.

[1946] Ongoing support after treatment

[1947] Step 1:

[1948] The server stores medical data, including medical results, prescription details, and treatment progress.

[1949] Step 2:

[1950] The server generates medication reminders and lifestyle advice based on the stored data, such as "take your medicine at 8 a.m. every day" or "get enough rest."

[1951] Step 3:

[1952] The server sends reminders and advice to the user's terminal.

[1953] Step 4:

[1954] The device displays the sent information to the user and provides reminders and advice via voice or text.

[1955] Step 5:

[1956] Users follow reminders and advice from the AI ​​to take their medication and improve their lifestyle habits, and practice continuous health management.

[1957] The above is a concrete explanation of the program processing of the system step by step.

[1958] Example 1

[1959] 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."

[1960] Modern medical institutions face challenges such as long waiting times for patients and overwork for doctors due to the need to process vast amounts of information. Furthermore, there is a lack of effective methods for patients to self-diagnose at home or follow up after visiting the hospital. This creates a need for efficient and prompt medical care for both patients and medical institutions.

[1961] 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.

[1962] In this invention, the server includes: means for a user to input symptoms; means for a terminal to transmit the input data to the server; means for analyzing the data and determining the symptoms using an AI model; means for generating a countermeasure or information on an appropriate medical institution based on the determination result; means for transmitting the generated information to the user's terminal and for the terminal to display the information to the user; means for the user to input symptom data again; means for integrating new data with the initial data and performing a diagnosis again using the AI ​​model; means for transmitting the diagnosis result and treatment plan to medical professionals; means for saving the medical data and updating the database; means for generating medication reminders and lifestyle advice based on the saved data; and means for the terminal to display the reminders and advice to the user. This enables reduction of waiting times at hospitals, efficiency of medical operations, and continuous health management of patients.

[1963] 1. "User" refers to an individual who uses the system to input symptoms.

[1964] 2. "Terminal" refers to the electronic device used by the user to input symptoms and transmit the data to the server.

[1965] 3. "Server" refers to the data processing system that receives data sent by users and analyzes and generates data using AI models.

[1966] 4. "Data" refers to information such as symptom information entered by the user, countermeasures generated by the server, and diagnostic results.

[1967] 5. "AI model" refers to the artificial intelligence algorithm used by the server to analyze data, determine symptoms, and generate countermeasures.

[1968] 6. "Determination result" refers to the diagnosis result obtained after the server analyzes the symptoms using the AI ​​model.

[1969] 7. "Countermeasures" refers to medical measures generated by the server based on the assessment results.

[1970] 8. "Medical Institution" refers to the appropriate hospital, clinic, or other medical facility that the User should visit.

[1971] 9. "Diagnosis result" refers to the specific disease name and symptom evaluation results provided by the server based on data analysis.

[1972] 10. "Treatment policy" refers to the treatment method or prescription that medical professionals should implement based on diagnostic results.

[1973] 11. "Medical personnel" refers to medical professionals such as doctors and nurses who provide medical care based on diagnostic results and treatment plans.

[1974] 12. "Medical data" refers to information such as medical examination results, prescription details, and treatment progress.

[1975] 13. "Database" refers to an electronic record device where the server stores medical data.

[1976] 14. "Medication Reminder" means a notification or alert that encourages a user to take their medication.

[1977] 15. "Lifestyle Advice" means lifestyle advice generated by the Server for the purpose of assisting the User in managing their health.

[1978] This system is designed to reduce waiting times and improve the efficiency of medical care, especially in hospitals. The system utilizes a user's device, a server, and an AI model to provide simple diagnosis at home, primary care during outpatient visits, and continuous follow-up after treatment.

[1979] Hardware and Software

[1980] User device: A device (smartphone, tablet, PC, etc.) on which the user inputs symptoms. A dedicated application is installed.

[1981] Server: A central server for receiving and processing data. It is connected to a medical database and uses AI models for data analysis.

[1982] AI models: Generative AI models such as OpenAI's GPT-4 are used to analyze data, make judgments, and generate advice.

[1983] Simple testing at home

[1984] 1. The user opens the dedicated application and enters their symptoms in text format. For example, they might enter, "I have a headache and a slight fever."

[1985] 2. The device encrypts the input data and sends it to the server.

[1986] 3. The server analyzes the received data using an AI model to determine symptoms, and compares them with a medical database to determine whether the symptoms are mild or severe.

[1987] 4. Based on the results of the assessment, the server generates information on countermeasures and appropriate medical institutions (for example, "take over-the-counter fever reducers and drink plenty of fluids").

[1988] 5. The server sends the generated information to the terminal.

[1989] 6. The device displays the information to the user, who then takes appropriate action.

[1990] Primary medical care during outpatient visits

[1991] 1. After arriving at the hospital, the user re-enters their latest symptoms into the dedicated application. For example, they might enter "fever over 39 degrees and worsening headache."

[1992] 2. The device sends new data to the server.

[1993] 3. The server integrates the new data with the initial data and performs a diagnosis again using the AI ​​model.

[1994] 4. The server generates the diagnosis and treatment plan, for example, "It is highly likely that the patient has influenza A. Specific tests and prescription of antiviral drugs are required," and sends this information to the medical institution's terminal.

[1995] 5. The terminal displays the information sent to the medical institution's terminal to the doctor, who uses this information as a reference when providing treatment.

[1996] Ongoing support after treatment

[1997] 1. The server stores medical data (medical results, prescription details, treatment progress, etc.) in a database.

[1998] 2. The server uses the stored data to generate medication reminders and lifestyle advice using AI models, such as reminders to "take your medication at 8 a.m. every day" and "get enough rest."

[1999] 3. The server sends the generated reminders and advice to the device.

[2000] 4. The device will provide reminders and advice to the user, either through voice or text. For example, "It's 8 AM. Don't forget to take your medicine."

[2001] 5. The user follows the reminders and advice to take their medication and improve their lifestyle.

[2002] Examples of concrete examples and prompts

[2003] For example, if a user inputs "I have a headache and a slight fever" into a dedicated application, the server will determine that it is a "mild cold" and recommend that the user "take over-the-counter fever reducers and drink plenty of fluids." If, after arriving at the hospital, the user again inputs that "the fever has risen above 39 degrees and is getting worse," the server will diagnose that "it is likely influenza A" and suggest "specific tests and the prescription of antiviral medication." The doctor will then provide treatment based on this information, and after the consultation will remind the user to "take antiviral medication every day at 8 a.m. and rest."

[2004] An example prompt is:

[2005] Example: "I have a headache and a slight fever. What should I do?"

[2006] Example server-generated response: "Your symptoms have been diagnosed as a mild cold. We recommend taking over-the-counter fever-reducing medication, drinking plenty of fluids, and getting plenty of rest."

[2007] This system will reduce waiting times at hospitals, improve the efficiency of medical operations, and enable continuous health management for patients.

[2008] The flow of the identification process in the first embodiment will be described with reference to FIG.

[2009] Simple testing at home

[2010] Step 1:

[2011] The user opens a dedicated application and enters their symptoms (e.g., "I have a headache and a slight fever").

[2012] Input: User's symptom information

[2013] Output: User-entered symptom data

[2014] Step 2:

[2015] The terminal receives the input symptom data, encrypts the data, and transmits it to the server.

[2016] Input: User-entered symptom data

[2017] Output: Encrypted symptom data

[2018] Step 3:

[2019] The server receives the encrypted data, decrypts it, and then analyzes it using an AI model (e.g., GPT-4) to determine symptoms.

[2020] Input: Encrypted symptom data

[2021] Data processing: Data decryption

[2022] Data Computing: Data Analysis with AI Models

[2023] Output: Symptom determination result (e.g., "Mild cold")

[2024] Step 4:

[2025] Based on the results of the assessment, the server generates information on countermeasures and appropriate medical institutions.

[2026] Input: Symptom assessment result

[2027] Data calculation: Countermeasure methods and medical institution information generation

[2028] Output: Information on countermeasures and medical institutions (e.g., "Take over-the-counter fever reducers and drink plenty of fluids")

[2029] Step 5:

[2030] The server transmits the generated information to the terminal.

[2031] Input: Countermeasures and medical institution information

[2032] Output: Countermeasures and medical institution information sent

[2033] Step 6:

[2034] The terminal receives the information and displays it to the user.

[2035] Input: Countermeasures and medical institution information sent from the server

[2036] Output: Information displayed to the user (e.g., "Take over-the-counter fever reducers and drink plenty of fluids")

[2037] Primary medical care during outpatient visits

[2038] Step 1:

[2039] After arriving at the hospital, the user re-enters their latest symptoms (e.g., "fever over 39 degrees and worsening headache") into a dedicated application.

[2040] Input: Latest symptom data

[2041] Output: Re-entered symptom data

[2042] Step 2:

[2043] The terminal receives the re-entered symptom data and transmits it to the server.

[2044] Input: Re-entered symptom data

[2045] Output: Submitted symptom data

[2046] Step 3:

[2047] The server integrates the new data received with the initial data, analyzes the data again using the AI ​​model, and updates the diagnosis.

[2048] Input: Re-entered symptom data, initial symptom data

[2049] Data processing: Data integration

[2050] Data Computation: Reanalyzing Data with AI Models

[2051] Output: Updated diagnosis (e.g., "Probably influenza A").

[2052] Step 4:

[2053] The server generates a treatment plan based on the diagnosis results and sends it to the medical institution's terminal.

[2054] Input: Updated diagnostic results

[2055] Data Computation: Treatment Plan Generation

[2056] Output: The treatment plan sent (e.g., "In case of influenza A, specific tests and antiviral medications are required.")

[2057] Step 5:

[2058] The terminal receives the information sent to the medical institution's terminal and displays it to the doctor.

[2059] Input: Submitted treatment plan

[2060] Output: Treatment plan displayed to the doctor

[2061] Ongoing support after treatment

[2062] Step 1:

[2063] When the consultation is completed, the server stores data such as the consultation results, prescription details, and treatment progress in a database.

[2064] Input: medical results, prescription details, treatment progress data

[2065] Output: Saved medical data

[2066] Step 2:

[2067] The server uses AI models to generate medication reminders and lifestyle advice based on the stored medical data.

[2068] Input: Stored medical data

[2069] Data Computing: Generating Medication Reminders and Lifestyle Advice

[2070] Output: Generated medication reminders and lifestyle advice

[2071] Step 3:

[2072] The server transmits the generated reminders and advice to the terminal.

[2073] Input: Generated medication reminders and lifestyle advice

[2074] Output: Reminders and advice sent

[2075] Step 4:

[2076] The device receives reminders and advice and displays them to the user. Reminders are given via voice notification or text display.

[2077] Input: Reminders and advice sent

[2078] Output: Reminders and advice displayed to the user (e.g., "It's 8 AM. Take your antiviral medication.")

[2079] Step 5:

[2080] Users follow the reminders and advice from the device to take appropriate medication and improve their lifestyle habits.

[2081] Input: Reminders and advice

[2082] Output: Medication and lifestyle improvement behavior

[2083] (Application example 1)

[2084] 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."

[2085] In conventional medical systems, after users receive medical treatment at a hospital, the payment procedures for medical expenses are often complicated and time-consuming. Furthermore, there are insufficient medication reminders and lifestyle advice after treatment, making it difficult to provide continuous health management. This prevents users from receiving appropriate follow-up after treatment, leading to a decrease in medical efficiency.

[2086] 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.

[2087] In this invention, the server includes: means for the user to input symptoms; means for the terminal to send the input data to the server; means for the server to analyze the data and determine the symptoms; means for the server to generate information on countermeasures or appropriate medical institutions based on the determination results; means for providing the generated information to the user; means for the server to calculate medical expenses after treatment; means for the server to automatically debit the medical expenses from the user's electronic payment account; and means for sending a payment confirmation notice to the user's terminal. This enables automatic payment of medical expenses after treatment and prompt follow-up.

[2088] - "User" means an individual who uses the system to input their symptoms and receive medical treatment or medical services.

[2089] A "terminal" is an electronic device operated by a user to input data and receive medical information. Examples include smartphones and tablets.

[2090] A "server" is a computer system that receives, stores, and analyzes data sent from a terminal and provides necessary information to users and medical professionals.

[2091] "Data" refers to information entered by the user, such as symptom information, medical treatment results, and payment information, which the server analyzes and stores.

[2092] "Analysis" refers to the act of using certain algorithms and AI technology to determine symptoms and calculate medical expenses based on the data received by the server.

[2093] "Symptom determination" refers to the act of the server diagnosing what the symptom is based on the symptom information entered by the user.

[2094] "Countermeasures" are advice on specific actions the user should take or medicines that should be used based on the symptom assessment results.

[2095] "Information about medical institutions" refers to information about hospitals and clinics that are appropriate for the user, including medical appointments and medical treatment details.

[2096] "Generated information" refers to the diagnosis and advice provided to users and medical professionals based on the results of analysis and judgment by the server.

[2097] A "payment confirmation notification" is a message sent by the server to notify the user when the server has successfully debited the user's electronic payment account.

[2098] An "electronic payment account" is an account used by a user to make financial payments online, including a credit card or digital wallet.

[2099] A "medication reminder" is a notification sent by the server to the user to urge him or her to take medicine regularly.

[2100] "Lifestyle advice" refers to guidance and suggestions about daily life provided by the server to maintain and improve the user's health.

[2101] This invention realizes automatic payment of medical expenses and continuous follow-up in a system in which a user inputs symptoms and manages medical information.

[2102] System Configuration

[2103] This system consists of a user's device, a server, and an electronic payment service. The user's device is typically a smartphone or tablet, on which a dedicated application is installed. The server is a computer system for storing and analyzing data and generating notifications. The electronic payment service is an online payment platform that automatically debits the user's medical expenses.

[2104] System Operation

[2105] 1. Enter symptoms

[2106] The user uses a dedicated application at home or at a designated location to input their current symptoms, for example, "I have a headache and a slight fever." The device then sends the input data to the server.

[2107] 2. Data analysis and judgment

[2108] The server analyzes the received symptom data and determines whether the symptom is mild or severe by using a generative AI model to compare the symptom data with a medical database.

[2109] 3. Information generation and provision

[2110] Based on the results of the assessment, the server generates information on countermeasures and appropriate medical institutions and sends it to the user's device. For example, if the symptoms are mild, it suggests home treatment methods and over-the-counter medications. If the symptoms are severe, it provides information on the nearest hospital.

[2111] 4. Automatic medical payments

[2112] After the medical treatment is completed, the server calculates the medical fee based on the medical information, and then automatically deducts the medical fee from the user's electronic payment account, such as a credit card or digital wallet.

[2113] 5. Sending notifications

[2114] The server sends a payment confirmation notice to the user's terminal, and the user confirms that the payment has been completed.

[2115] 6. Ongoing follow-up

[2116] The server stores the medical data and generates medication reminders and lifestyle advice based on the data. These reminders and advice are periodically sent to the user's device.

[2117] Specific examples

[2118] Consider a case where a user inputs "I have a fever and headache" and is diagnosed with "influenza" at the hospital. The user then inputs information about the antiviral medication they are taking into the app. After the consultation, the server automatically calculates the medical expenses and deducts them from the user's electronic payment account. A notification of payment completion is sent to the user's device, and thereafter, medication reminders are sent periodically.

[2119] Prompt Sentence Examples

[2120] The user enters that they have a fever and headache and is diagnosed with influenza at the hospital. They then enter information about the antiviral medication they are taking into the app. After the consultation, payment is automatically completed and the information is notified to the user. From then on, they will receive a daily medication reminder at the specified time.

[2121] The system automates medical bill payment procedures and ensures users can follow up after medical treatment, improving user convenience and greatly improving the efficiency of the entire medical system.

[2122] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2123] Step 1:

[2124] The user uses a dedicated application at home or a designated location to input their current symptoms. The input data is specific symptom information such as "I have a headache and a slight fever." The device then sends this information to the server.

[2125] Input: User-entered symptom information

[2126] Output: Symptom information is sent from the device to the server

[2127] Step 2:

[2128] The server analyzes the symptom data received from the device. This analysis utilizes a generative AI model and compares the symptom data with a medical database. At this stage, an initial determination is made as to whether the symptoms are mild or severe.

[2129] Input: Symptom information from the terminal

[2130] Output: Initial diagnosis result (mild or severe)

[2131] Step 3:

[2132] Based on the initial diagnosis, the server generates information on necessary countermeasures or appropriate medical institutions. For example, if the symptoms are mild, the server may recommend taking over-the-counter fever reducers and drinking plenty of fluids. If the symptoms are severe, the server may generate information on the nearest hospital.

[2133] Input: Initial judgment result

[2134] Output: Information on countermeasures and medical institutions

[2135] Step 4:

[2136] The server sends the generated information about countermeasures and medical institutions to the user's device, which receives it and displays it to the user.

[2137] Input: Information on countermeasures and medical institutions

[2138] Output: Countermeasures and information about medical institutions are displayed on the user's device

[2139] Step 5:

[2140] When the user arrives at the hospital to continue treatment, they re-enter their latest symptom data. For example, they might enter information such as "fever over 39 degrees and worsening headache." The device then sends the new symptom data to the server again.

[2141] Input: Latest symptom data

[2142] Output: New symptom data is sent from the device to the server.

[2143] Step 6:

[2144] The server integrates the new symptom data with the initial data and again uses AI to perform a detailed diagnosis.

[2145] Input: New symptom data and initial data

[2146] Output: Detailed diagnostic results and treatment plan

[2147] Step 7:

[2148] The server generates a diagnosis and treatment plan and sends it to the medical professional's device, which receives and displays this information. The doctor uses this information as a reference when providing treatment.

[2149] Input: Detailed diagnosis and treatment plan

[2150] Output: Information is displayed on the medical personnel's terminal.

[2151] Step 8:

[2152] After the medical treatment is completed, the server calculates the medical costs based on the medical information, including the details of the treatment and the prescribed medication.

[2153] Input: Medical information

[2154] Output: Calculated medical costs

[2155] Step 9:

[2156] The server automatically debits the calculated medical costs from the user's electronic payment account, using a pre-registered credit card or digital wallet.

[2157] Input: Calculated medical expenses

[2158] Output: Withdrawal completion result

[2159] Step 10:

[2160] The server sends a payment confirmation notice to the user's terminal, which the user receives and confirms that the payment has been completed.

[2161] Input: Withdrawal completion result

[2162] Output: Payment confirmation notification

[2163] Step 11:

[2164] The server stores medical data and generates medication reminders and lifestyle advice based on that data.

[2165] Input: Medical data

[2166] Output: Medication reminders and lifestyle advice

[2167] Step 12:

[2168] The server sends the generated medication reminders and lifestyle advice to the user's device, which receives them and periodically notifies them. The user receives these notifications and acts according to the instructions.

[2169] Input: Medication reminders and lifestyle advice

[2170] Output: A notification will be displayed periodically on the user's device.

[2171] 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.

[2172] This invention is a system for reducing waiting times at hospitals and improving medical efficiency, and also provides support that takes into account the user's emotional state. This system works in cooperation with the user, terminal, server, and emotion engine, and provides simple diagnosis at home, primary medical treatment at the hospital, and continuous follow-up after treatment.

[2173] Simple testing at home

[2174] 1. User: At home, the user inputs their symptoms into a dedicated application, and emotional data is also collected by the emotion recognition system. For example, if the user inputs "I have a headache and a slight fever," their emotional state is recorded as "anxiety" or "stress."

[2175] 2. Terminal: Sends the input symptom data and emotion data to the server.

[2176] 3. Server: Based on the received data, the AI ​​analyzes the symptoms and emotional state. It compares the symptom data with a medical database to determine whether the symptoms are mild or severe, and analyzes the emotional data with an emotion engine to determine the emotional state.

[2177] 4. Server: Based on the assessment results, the server generates information on countermeasures and appropriate medical institutions. For example, if the symptoms are mild, the server generates suggestions for home treatment and over-the-counter medications, and if the symptoms are severe, it generates information on the nearest hospital. Based on the emotional data, the server also adds countermeasures according to the user's emotional state.

[2178] 5. Server: Sends the generated information to the user's device.

[2179] 6. Terminal: Receives information from the server and displays it to the user, who can then take action.

[2180] Primary medical care during outpatient visits

[2181] 1. User: After arriving at the hospital, the user re-enters their latest symptoms into the dedicated application, and the emotion recognition system also collects their latest emotional data. For example, if the user enters "fever over 39 degrees and worsening headache," their emotional state is recorded as "strong anxiety" or "tension."

[2182] 2. Device: Sends new symptom data and emotion data to the server.

[2183] 3. Server: The AI ​​combines the new data received with the initial data and performs another diagnosis. At the same time, the emotion engine analyzes the latest emotional state.

[2184] 4. Server: Generates diagnosis results and treatment plans and sends them to the medical institution's terminal. For example, it generates a diagnosis result such as "high possibility of influenza A" and a treatment plan such as "prescribe a specific medication." Response methods based on the patient's emotional state are also incorporated into the treatment plan.

[2185] 5. Terminal: The information sent to the medical institution's terminal is displayed to the doctor, who uses it as a reference when providing medical care.

[2186] Ongoing support after treatment

[2187] 1. Server: Stores medical data and emotional data. The stored data includes medical results, prescription details, treatment progress, emotional state, etc.

[2188] 2. Server: Based on the stored data, it generates medication reminders and lifestyle advice. For example, it generates advice such as "Take your medicine at 8:00 a.m. every day" or "Get enough rest." At the same time, it also includes mental health advice based on the patient's emotional state.

[2189] 3. Server: Sends reminders and advice to the user's device.

[2190] 4. Terminal: Displays the transmitted information to the user and provides reminders and advice via voice or text.

[2191] 5. User: Follow the reminders and advice from the AI, take your medication, improve your lifestyle habits, and practice continuous health management.

[2192] For example, if a user inputs "I have a headache and a slight fever" and the emotional data collected indicates "anxiety," the server will determine that it is a "mild cold" and recommend that the user take an over-the-counter fever reducer and drink plenty of fluids, while also providing emotional care advice such as listening to music that promotes relaxation. If, after arriving at the hospital, the user re-inputs that their fever has risen above 39 degrees and worsened, and the emotional state is collected as "strong anxiety," the server will diagnose that the patient is likely to have influenza A and suggest that they undergo specific tests and prescribe antiviral medication, along with emotional care guidelines such as setting aside time to explain the situation in detail to the doctor. After the consultation, the server will remind the patient to take antiviral medication every day at 8 a.m. and rest, and to practice deep breathing as a relaxation technique.

[2193] The above is an embodiment of the present invention. This system significantly reduces waiting times at hospitals, improves the work efficiency of doctors, and effectively manages the health and mental health of patients.

[2194] The processing flow will be explained below.

[2195] Simple testing at home

[2196] Step 1:

[2197] The user inputs their symptoms into a dedicated app. For example, they might input "I have a headache and a slight fever," and the emotion recognition system will collect their emotional state as "anxiety."

[2198] Step 2:

[2199] The terminal transmits the input symptom data and emotion data to the server.

[2200] Step 3:

[2201] The AI ​​analyzes the symptom data received by the server, compares it with a medical database, and determines whether the symptoms are mild or severe.

[2202] Step 4:

[2203] The server analyzes the emotional data with an emotion engine to determine the emotional state.

[2204] Step 5:

[2205] The server generates information on countermeasures and appropriate medical institutions based on the symptoms and emotional state. For example, if the symptoms are mild, it generates home treatment methods (e.g., over-the-counter medication suggestions), and if the symptoms are severe, it generates information on the nearest hospital. Based on the emotional state, it also suggests emotional care measures (e.g., relaxation methods).

[2206] Step 6:

[2207] The server transmits the generated information to the user's terminal.

[2208] Step 7:

[2209] The device receives the information from the server and displays it to the user, who can then take appropriate action.

[2210] Primary medical care during outpatient visits

[2211] Step 1:

[2212] After arriving at the hospital, the user again enters their latest symptoms into the dedicated application. For example, they might enter "fever over 39 degrees and worsening headache," and the emotion recognition system records their emotional state as "strong anxiety."

[2213] Step 2:

[2214] The device sends new symptom data and emotion data to the server.

[2215] Step 3:

[2216] The server integrates the new data received with the initial data, and the AI ​​performs a second diagnosis. At the same time, the emotion engine analyzes the latest emotional state.

[2217] Step 4:

[2218] The server generates a diagnosis and a treatment plan. For example, it generates a diagnosis such as "high possibility of influenza A" and a treatment plan such as "prescribe a specific medication." The treatment plan also includes a response method based on the patient's emotional state (e.g., setting aside time to explain the situation in detail to the doctor).

[2219] Step 5:

[2220] The server sends the diagnosis results and treatment plan to the medical institution's terminal.

[2221] Step 6:

[2222] The device displays the received information to the doctor, who then uses it as a reference when providing treatment.

[2223] Ongoing support after treatment

[2224] Step 1:

[2225] The server stores medical data and emotional data, including medical results, prescription details, treatment progress, and emotional state.

[2226] Step 2:

[2227] The server generates medication reminders and lifestyle advice based on the stored data. For example, it might generate advice such as "take your medicine at 8 a.m. every day" or "get enough rest." At the same time, it also includes mental care advice based on the user's emotional state (e.g., repeatedly taking deep breaths as a relaxation technique).

[2228] Step 3:

[2229] The server sends reminders and advice to the user's terminal.

[2230] Step 4:

[2231] The device displays the sent information to the user and provides reminders and advice via voice or text.

[2232] Step 5:

[2233] Users follow reminders and advice from the AI ​​to take their medication and improve their lifestyle habits, and practice continuous health management.

[2234] The above is a concrete explanation of each step of the program processing of a system that combines an emotion engine.

[2235] Example 2

[2236] 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."

[2237] In conventional medical systems, sufficient pre-diagnosis and emotional care are not provided before the user arrives at the hospital, causing extended diagnosis times. Furthermore, the user's emotional state is not taken into consideration during waiting times at the hospital or follow-up care after treatment, resulting in inappropriate care being provided. As a result, medical efficiency and user satisfaction have not been sufficiently improved.

[2238] 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.

[2239] In this invention, the server includes means for analyzing symptoms using a generative AI model based on received data, means for analyzing emotional states using an emotion engine, and means for generating countermeasures, information on appropriate medical institutions, and emotional care suggestions based on the results of the assessment. This makes it possible to comprehensively analyze the user's symptoms and emotional state, provide appropriate countermeasures and information on medical institutions, and also suggest emotional care. This reduces waiting times at hospitals, improves doctor work efficiency, and effectively manages the health and mental health of patients.

[2240] 1. "User" is a person who uses the system to input symptoms and receive suggested treatments and medical information.

[2241] 2. "Symptoms" are data that indicate the health condition or abnormality that the user feels.

[2242] 3. "Emotional state" is data that indicates the user's current emotional or mental state.

[2243] 4. "Terminal" means a device used by a User to access the System, such as a smartphone or tablet.

[2244] 5. "Server" means a computer system that receives data sent by users and analyzes and provides information.

[2245] 6. A "generative AI model" is an artificial intelligence algorithm used for natural language processing and symptom analysis.

[2246] 7. "Emotion Engine" is a software module for analyzing the user's emotional state.

[2247] 8. "Medical database" means a collection of medical information used for symptom analysis and diagnosis.

[2248] 9. "Countermeasures" are general treatments or measures to deal with the user's symptoms.

[2249] 10. "Information about medical institutions" means information about appropriate medical facilities that a user should visit.

[2250] 11. "Emotional care" refers to suggestions for psychological support and relaxation tailored to the user's emotional state.

[2251] 12. "Medical data" refers to medical information generated by physicians, such as medical examination results and treatment details.

[2252] 13. "Remind" is a notification that prompts the user to take medication or take other action to manage their health.

[2253] 14. "Lifestyle Advice" means suggestions for improving the user's lifestyle to maintain their health.

[2254] MODE FOR CARRYING OUT THE INVENTION

[2255] The present invention is a system that collects and analyzes a user's symptom input and emotional state, and provides appropriate measures and information on medical institutions. This system is composed of a user, a terminal, a server, a generative AI model, and an emotion engine. An embodiment of this system is described in detail below.

[2256] Hardware and Software Configuration

[2257] The system uses the following major hardware and software:

[2258] User device: A device such as a smartphone or tablet that is used by installing a dedicated application.

[2259] Server: A computer system that receives, analyzes, and stores data sent by users.

[2260] Generative AI models: Artificial intelligence algorithms that perform natural language processing and symptom analysis. For example, we use AI models such as GPT-3.

[2261] Emotion engine: A software module for analyzing the user's emotional state. For example, it uses the Microsoft Azure Emotion Analysis API.

[2262] Medical database: A collection of medical information used for symptom analysis and diagnosis, such as UMLS.

[2263] System Embodiments

[2264] Simple testing at home

[2265] 1. The user enters their symptoms into a dedicated application at home. For example, they might enter, "I have a headache and a slight fever." Emotional data is also collected using an emotion recognition system. Automatically captured images and audio analysis identify the symptoms as "anxiety" or "stress."

[2266] 2. The device sends the input symptom data and emotion data to the server.

[2267] 3. The server uses a generative AI model based on the received data to analyze symptoms and emotional state. The symptom data is compared with a medical database. At the same time, an emotion engine is used to analyze the emotional state.

[2268] 4. The server determines whether the symptoms are mild or severe and generates information on countermeasures and appropriate medical institutions. For example, if the symptoms are mild, it suggests home treatment methods and over-the-counter medications, while if the symptoms are severe, it provides information on the nearest medical institution. It also generates advice on emotional care, such as "listening to music that promotes relaxation."

[2269] 5. The server sends the generated information to the user's terminal.

[2270] 6. The device receives the information from the server and displays it to the user, who can then take action based on it.

[2271] Primary medical care during outpatient visits

[2272] 1. After arriving at the hospital, the user re-enters their latest symptoms into the dedicated application. For example, they might enter "fever over 39 degrees and worsening headache." The application also collects their latest emotional data, which is recognized as "strong anxiety" or "tension."

[2273] 2. The device sends the new symptom data and emotion data to the server.

[2274] 3. The server integrates the new data with the initial data, performs a diagnosis again using the generative AI model, and analyzes the latest emotional state using the emotion engine.

[2275] 4. The server generates a diagnosis and a treatment plan and sends it to the medical institution's terminal. For example, it may diagnose "high possibility of influenza A" and suggest "prescribing antiviral drugs." It also includes a plan for emotional care.

[2276] 5. The terminal displays the information sent to the medical institution's terminal to the doctor, who then uses it as a reference when providing medical care.

[2277] Ongoing support after treatment

[2278] 1. The server stores medical and emotional data, including medical results, prescriptions, treatment progress, and emotional state.

[2279] 2. The server generates medication reminders, lifestyle advice, and emotional care suggestions based on the stored data, such as "take your medicine at 8 a.m. every day" and "get enough rest."

[2280] 3. The server sends the generated reminders and advice to the user's device.

[2281] 4. The device presents the sent information to the user and provides reminders and advice via voice or text.

[2282] 5. Users follow the reminders and advice from the AI ​​to take their medication and improve their lifestyle habits, thereby practicing continuous health management.

[2283] Specific examples

[2284] If the user inputs "I have a headache and a slight fever" into the dedicated application and the emotional data indicates "anxiety," the server will determine that it is a "mild cold" and suggest "taking an over-the-counter fever reducer and drinking plenty of fluids." It will also provide emotional care advice such as "listening to music that promotes relaxation."

[2285] If the patient re-enters that their fever is worsening and exceeds 39 degrees Celsius after arriving at the hospital, and their emotional state is recorded as "strong anxiety," the server will diagnose that they have a high probability of having influenza A and suggest prescribing antiviral medication. It also includes emotional care policies such as allowing time for the patient to explain the situation in detail to the doctor.

[2286] After the consultation, the patient is reminded to "take antiviral medication every day at 8:00 a.m. and rest" and "take repeated deep breaths as a relaxation technique."

[2287] The above is an embodiment of the present invention. This system reduces waiting times at hospitals, improves the work efficiency of doctors, and effectively manages the health and mental health of patients.

[2288] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2289] Simple testing at home

[2290] Step 1:

[2291] The user enters their symptoms into a dedicated application at home. For example, they might type "I have a headache and a slight fever" into a text box. The application also uses a camera and microphone to collect emotional data through an emotion recognition system. For example, it can extract emotional data such as "anxiety" or "stress" from automatically c...

Claims

1. a means for a user to input symptoms; A means for transmitting input data from the terminal to a server; A means for the server to analyze the data and determine symptoms; A means for the server to generate countermeasures or information on appropriate medical institutions based on the judgment result; means for providing the generated information to a user; A system including:

2. a means for the user to re-enter symptom data; means for the terminal to transmit new data to the server; a means for the server to integrate the new data with the initial data and perform a diagnosis; a means for transmitting diagnostic results and treatment plans to medical personnel; A means for medical professionals to provide medical care based on diagnostic results and treatment plans, The system of claim 1 , comprising:

3. A means for the server to store medical data; a means for generating medication reminders and lifestyle advice based on the stored data by the server; means for transmitting medication reminders and lifestyle advice to a user's terminal; means for the terminal to present the transmitted information to a user; The system of claim 1 , comprising:

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