system

Abstract

Provide a system. A means for inputting medical interview information; means for transmitting the input medical interview information to a server; A means for transmitting the received medical interview information to an artificial intelligence engine for analysis by the server; A means for an artificial intelligence engine to analyze the medical interview information and output recommended actions and possible disease names corresponding to the symptoms; A means for the server to receive the analysis results and access the medical institution database to acquire and transmit appropriate medical institution information; A means for presenting the acquired medical institution information and recommended actions to the user; A system including:

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] When patients feel unwell, it is usually difficult for them to find an appropriate medical institution, and it often takes a long time before they can be seen. They may also attempt self-diagnosis based solely on information on the internet, risking receiving inappropriate medical treatment. Furthermore, particularly in cases of highly urgent symptoms, a prompt and appropriate response is required, but many current systems are unable to adequately meet this need. Medical institutions also face difficulties in attracting appropriate patients, and until now, there has been no way to efficiently provide patients with the most appropriate medical institution. There is a need for a system that can improve these situations and enable patients to quickly and appropriately find a medical institution and receive medical treatment. [Means for solving the problem]

[0005] The present invention provides a system in which a patient inputs symptoms at any location and an artificial intelligence engine analyzes the information based on the medical interview. Specifically, the system includes a means for inputting medical interview information, a means for transmitting the input medical interview information to a server, a means for the server to transmit the received medical interview information to an artificial intelligence engine for analysis, a means for the artificial intelligence engine to analyze the medical interview information and output recommended actions corresponding to the symptoms and possible disease names, a means for the server to receive the analysis results, access a medical institution database to obtain and transmit appropriate medical institution information, and a means for presenting the obtained medical institution information and recommended actions to the user. Further features of the system include a means for the server to access an over-the-counter drug database, obtain appropriate over-the-counter drug information based on the analysis results, and provide it to the user, and a means for coordinating with the medical institution's reservation system to make a medical appointment for the user. This allows patients to quickly and appropriately find a medical institution and receive a consultation, thereby contributing to the appropriate customer acquisition of medical institutions.

[0006] "Medical interview information" refers to information that a patient provides, including their symptoms, details of their physical condition, past medical history, allergy information, and medications they are currently taking.

[0007] A "terminal" is an electronic device that has the function of inputting and transmitting medical interview information and receiving and displaying display information, and includes smartphones, tablets, personal computers, etc.

[0008] A "server" is a computer system that receives medical interview information sent from a terminal, sends the information to an artificial intelligence engine for analysis, manages the analysis results, and provides necessary information on medical institutions and over-the-counter drugs.

[0009] An "artificial intelligence engine" is a system consisting of algorithms and software that analyzes the information provided and outputs recommended actions to address symptoms and possible illnesses.

[0010] A "medical institution database" is a database that stores data from various medical institutions and allows users to search and access the data to provide appropriate medical institution information.

[0011] The "over-the-counter drug database" is a database that stores data on pharmaceuticals that can be purchased on the market and provides appropriate over-the-counter drug information according to symptoms.

[0012] "Recommended actions" are information that instructs patients on what actions to take based on the analysis results of the artificial intelligence engine, such as visiting a specific medical institution or using a specific over-the-counter medication.

[0013] A "reservation system" is a system that manages and processes reservations at medical institutions and has the functionality to allow patients to make reservations online.

[0014] "Analysis results" are data containing diagnostic information and recommended actions generated by the artificial intelligence engine. [Brief explanation of the drawings]

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

[0016] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0018] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0019] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0020] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0021] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0023] [First embodiment]

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

[0025] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0032] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0035] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0036] This system allows patients to input their symptoms anywhere, and an AI engine analyzes the information to recommend appropriate medical institutions and treatment methods. This system covers the entire process, from inputting medical history information to analysis, referring appropriate medical institutions, confirming appointments, suggesting over-the-counter medications, and providing follow-up care.

[0037] System configuration

[0038] The system consists of the following main components:

[0039] 1. Means of inputting medical interview information

[0040] 2. Means of sending medical interview information

[0041] 3. Analysis of medical interview information

[0042] 4. Means of accessing medical institution databases

[0043] 5. Access to the over-the-counter drug database

[0044] 6. Medical institution reservation methods

[0045] 7. Follow-up measures after consultation

[0046] Explanation of program processing

[0047] 1. Enter medical interview information

[0048] Through the application, users enter their symptoms into a questionnaire, for example, "fever, sore throat, cough, general fatigue," and this information is stored on the device.

[0049] 2. Sending medical interview information

[0050] The terminal sends the entered medical interview information to the server. The data sent is structured in JSON format, for example, as shown below.

[0051] json

[0052] {

[0053] "temperature": "38 degrees",

[0054] "symptoms": ["sore throat", "cough", "general fatigue"]

[0055] }

[0056] 3. Analysis of medical interview information

[0057] The server sends the received medical interview information to the AI ​​engine, which analyzes the information and obtains the following diagnosis results:

[0058] json

[0059] {

[0060] "possible_conditions": ["acute pharyngitis", "influenza"],

[0061] "recommendations": ["Seek medical help", "Use over-the-counter pain relievers"]

[0062] }

[0063] The server receives the analysis results and uses them for the next process.

[0064] 4. Referral to medical institutions

[0065] Based on the analysis results, the server accesses the medical institution database and creates a list of nearby appropriate medical institutions. For example, the following list is generated:

[0066] json

[0067] [

[0068] {"name": "Internal Medicine Clinic, Chuo Ward, Tokyo", "address": "Chuo Ward, Tokyo ~", "contact": "0123-456-789"},

[0069] {"name": "Tokyo Medical Center", "address": "Minato-ku, Tokyo", "contact": "0987-654-321"}

[0070] ]

[0071] The server sends this information to the terminal and presents it to the user.

[0072] 5. Over-the-counter medication suggestions

[0073] The server accesses the over-the-counter drug database and retrieves appropriate over-the-counter drug information based on the analysis results. For example, the following over-the-counter drug information can be obtained:

[0074] json

[0075] [

[0076] {"name": "Acetaminophen", "type": "Antipyretic analgesic", "usage": "One tablet, three times a day"}

[0077] ]

[0078] This information is also provided to the user.

[0079] 6. Medical appointments

[0080] The user selects a medical institution from the provided list of medical institutions and enters reservation information. The terminal sends a reservation request to the server. For example, the format is as follows:

[0081] json

[0082] {

[0083] "clinic_id": "1",

[0084] "user_id": "1001",

[0085] "appointment_date": "2023-10-15",

[0086] "appointment_time": "10:00"

[0087] }

[0088] The server passes this information to the medical institution's system to confirm the appointment, after which a notification of the appointment is sent to the terminal.

[0089] 7. Follow-up after consultation

[0090] After the consultation, the user enters progress information into the app. The device then sends this information to the server. The server then uses the AI ​​engine to analyze the progress information again and recommends a follow-up visit if necessary. If a follow-up visit is necessary, the user is referred to a medical institution and an appointment is made.

[0091] In this way, this system analyzes patient interview information and quickly introduces appropriate medical institutions and over-the-counter medications, simplifies the appointment process, and provides follow-up after the consultation, thereby providing consistent medical support to patients.

[0092] The processing flow will be explained below.

[0093] Step 1:

[0094] The user opens the application. The user enters their symptom information into the input screen. For example, they enter "fever, sore throat, cough, general fatigue." Once they have completed the input, they press the "Submit" button.

[0095] Step 2:

[0096] The device collects the medical interview information entered by the user, structures the information in JSON format, and prepares it for transmission.

[0097] Step 3:

[0098] The terminal sends the formatted and structured medical interview information to the server. The following JSON format is used as an example of the data to be sent:

[0099] json

[0100] {

[0101] "temperature": "38 degrees",

[0102] "symptoms": ["sore throat", "cough", "general fatigue"]

[0103] }

[0104] Step 4:

[0105] The server receives the medical interview information sent from the terminal, checks the integrity of the received data, and stores it in a database.

[0106] Step 5:

[0107] The server sends the stored medical interview information to the AI ​​engine, which then calls the AI ​​engine's API and creates a request to analyze the medical interview information.

[0108] Step 6:

[0109] The AI ​​engine analyzes the medical interview information. The analysis results include recommended actions to take based on the symptoms and possible illnesses. For example, the analysis results shown below are output in JSON format.

[0110] json

[0111] {

[0112] "possible_conditions": ["acute pharyngitis", "influenza"],

[0113] "recommendations": ["Seek medical help", "Use over-the-counter pain relievers"]

[0114] }

[0115] Step 7:

[0116] The server receives the analysis results from the AI ​​engine and proceeds to the next step based on the received analysis results.

[0117] Step 8:

[0118] The server accesses a medical institution database based on the analysis results, searches for medical institutions that match the disease name in the analysis results, and obtains information on nearby medical institutions.

[0119] Step 9:

[0120] The server generates the medical institution information it has acquired in list format. For example, the following list is generated in JSON format:

[0121] json

[0122] [

[0123] {"name": "Internal Medicine Clinic, Chuo Ward, Tokyo", "address": "Chuo Ward, Tokyo ~", "contact": "0123-456-789"},

[0124] {"name": "Tokyo Medical Center", "address": "Minato-ku, Tokyo", "contact": "0987-654-321"}

[0125] ]

[0126] This medical institution information is sent to the terminal and presented to the user.

[0127] Step 10:

[0128] The server accesses the over-the-counter drug database and retrieves appropriate over-the-counter drug information based on the analysis results. The retrieved over-the-counter drug information is structured in JSON format and sent to the terminal as follows:

[0129] json

[0130] [

[0131] {"name": "Acetaminophen", "type": "Antipyretic analgesic", "usage": "One tablet, three times a day"}

[0132] ]

[0133] The terminal presents over-the-counter drug information to the user.

[0134] Step 11:

[0135] The user selects a medical institution from the provided list of medical institutions. Based on the information about the selected medical institution, the user inputs reservation information, for example, specifying the desired date and time for the examination.

[0136] Step 12:

[0137] The device sends the user's reservation request to the server. The following JSON format is used as an example of the data sent:

[0138] json

[0139] {

[0140] "clinic_id": "1",

[0141] "user_id": "1001",

[0142] "appointment_date": "2023-10-15",

[0143] "appointment_time": "10:00"

[0144] }

[0145] Step 13:

[0146] The server receives the reservation request and confirms the reservation in cooperation with the medical institution's reservation system. The reservation information is sent to the medical institution, which confirms the reservation. The server then sends the reservation confirmation information to the terminal and notifies the user.

[0147] Step 14:

[0148] After the examination, the user enters progress information into the app. For example, they enter information such as "the fever continues" or "the general feeling of fatigue is increasing." Once the information is entered, the device sends the progress information to the server.

[0149] Step 15:

[0150] Based on the progress information received by the server, the AI ​​engine is requested to perform another analysis. The AI ​​engine analyzes the progress information and evaluates the need for a follow-up visit. If a follow-up visit is recommended, the server searches again for a medical institution that requires a follow-up visit and introduces it to the user. It also completes the follow-up appointment procedure.

[0151] These are the specific processing steps of the program for this system. Through this series of processes, users can quickly and accurately find a medical institution and receive appropriate examinations and follow-up care.

[0152] Example 1

[0153] 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."

[0154] Conventional medical consultation systems are often limited to allowing patients to input their symptoms and introducing appropriate medical institutions and treatment methods, and lack sufficient functionality to assess the need for follow-up or re-examination after the consultation. Furthermore, they are unable to consistently suggest over-the-counter medications or schedule appointments at medical institutions, making it difficult to provide consistent medical support to patients.

[0155] 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.

[0156] In this invention, the server includes: means for inputting medical interview information; means for transmitting the input medical interview information to the server; means for the server to transmit the received medical interview information to an artificial intelligence engine for analysis; means for the artificial intelligence engine to analyze the medical interview information and output recommended actions corresponding to the symptoms and possible disease names; means for the server to receive the analysis results and access a medical institution database to obtain and transmit appropriate medical institution information; means for presenting the obtained medical institution information and recommended actions to the user; means for the server to transmit appointment information to a medical institution selected by the user and confirm the appointment in cooperation with the medical institution's system; and means for the user to input progress information after the consultation, and for the server to have the artificial intelligence engine analyze it again and recommend a re-examination. This makes it possible to provide consistent medical support to patients and evaluate the need for follow-up and re-examination after the consultation.

[0157] "Medical interview information" is information that a user inputs through an application regarding their own health condition and symptoms.

[0158] "Server" refers to the central system that receives, analyzes, and processes the medical interview information.

[0159] The "artificial intelligence engine" is a software module that analyzes the input medical interview information and outputs recommended actions and possible disease names corresponding to the symptoms.

[0160] The "medical institution database" is a database that stores information about medical institutions, and is used by the server to access and acquire appropriate medical institution information.

[0161] The "over-the-counter drug database" is a database that stores information about various over-the-counter drugs, and is used by the server to access and obtain appropriate over-the-counter drug information.

[0162] The "reservation system" is an online system that works in conjunction with medical institutions to process reservations.

[0163] "Progress information" is information about the health condition and progress of symptoms that the user inputs through the application after the medical examination.

[0164] "Recommended actions" refer to appropriate actions suggested to the user as a result of the AI ​​engine analyzing the medical interview information.

[0165] Defining these terms will help you understand the system better.

[0166] The present invention is a system in which a patient inputs their symptoms anywhere, an artificial intelligence engine analyzes the information, and introduces appropriate medical institutions and treatment methods. Specific embodiments for carrying out the present invention are described in detail below.

[0167] First, users enter their symptoms using a dedicated application installed on their smartphone, tablet, or other device. During this process, users enter specific symptoms such as fever, sore throat, cough, and general fatigue using text boxes and drop-down menus. For example, if a user enters "fever, sore throat, cough, and general fatigue," each piece of information is stored in a local database on the device.

[0168] Next, the device serializes the saved medical interview information into JSON format and sends it to the server via the communication module. At this time, the following format is used as an example of the data sent.

[0169] json

[0170] {

[0171] "temperature": "38 degrees",

[0172] "symptoms": ["sore throat", "cough", "general fatigue"]

[0173] }

[0174] The server makes an API request to send the received medical interview information to the AI ​​engine. The AI ​​engine analyzes the medical interview information and outputs recommended actions and possible illnesses corresponding to the symptoms. For example, the following diagnosis results may be obtained:

[0175] json

[0176] {

[0177] "possible_conditions": ["acute pharyngitis", "influenza"],

[0178] "recommendations": ["Seek medical help", "Use over-the-counter pain relievers"]

[0179] }

[0180] The server receives the analysis results and accesses the medical institution database to create a list of appropriate nearby medical institutions. This list is converted into JSON format and sent to the device. The device then presents the appropriate medical institution information to the user based on the received information. The user can view the list on the application and select as needed.

[0181] In addition, the server also accesses the over-the-counter drug database and obtains appropriate over-the-counter drug information based on the analysis results. For example, the following over-the-counter drug information can be obtained:

[0182] json

[0183] {

[0184] "name": "Acetaminophen",

[0185] "type": "Antipyretic analgesic",

[0186] "usage": "One tablet, three times a day"

[0187] }

[0188] The acquired over-the-counter drug information is also provided to the user, who can check the over-the-counter drug information on the application.

[0189] When the user selects an appropriate medical institution from the list of medical institutions and enters reservation information, the terminal sends a reservation request to the server. The server connects this information to the reservation system and confirms the reservation. A reservation confirmation notification is then sent to the terminal, allowing the user to confirm the reservation details.

[0190] After the consultation, the user enters progress information into the application. This progress information is sent from the device to the server, where it is analyzed again using an artificial intelligence engine to determine whether a follow-up visit is necessary. If necessary, the server will recommend a follow-up visit and assist with the referral and appointment procedures at a medical institution.

[0191] Specific examples

[0192] For example, a user opens the application and enters their symptoms as "fever, sore throat, cough, and general fatigue." The system then collects this information, and an artificial intelligence engine analyzes it. The system suggests the possibility of "acute pharyngitis" or "influenza," and recommends nearby medical institutions such as an "internal medicine clinic" or "medical center." It also suggests "acetaminophen" as an appropriate over-the-counter medication. If the user selects "internal medicine clinic" and sets the appointment date to "2023-10-15," the system confirms the appointment and notifies the user.

[0193] Example prompts for generative AI models

[0194] "I have a fever, a sore throat, and a severe cough. I also feel fatigued. Can you diagnose my illness based on these symptoms and tell me where to find a nearby medical facility? Also, can you recommend any over-the-counter medicines?"

[0195] In this way, this system analyzes patient interview information, quickly introduces appropriate medical institutions and over-the-counter medications, simplifies appointment procedures, and also provides follow-up after consultation, thereby providing consistent medical support to patients.

[0196] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0197] Step 1:

[0198] Enter medical interview information

[0199] The user uses the application to enter their symptoms into a questionnaire. In the input field, they write specific symptoms such as "fever, sore throat, cough, general fatigue." The device stores this information in a local database. The input data format is as follows:

[0200] input:

[0201] Symptoms: fever, sore throat, cough, general fatigue

[0202] output:

[0203] Medical interview information stored in a local database

[0204] Step 2:

[0205] Sending medical interview information

[0206] The terminal serializes the medical interview information stored in the local database into JSON format and sends it to the server via the communication module.

[0207] input:

[0208] Medical interview information stored in a local database

[0209] Data processing:

[0210] Serialize to JSON format

[0211] output:

[0212] Send serialized data to the server

[0213] Specific working example:

[0214] json

[0215] {

[0216] "temperature": "38 degrees",

[0217] "symptoms": ["sore throat", "cough", "general fatigue"]

[0218] }

[0219] Step 3:

[0220] Analysis of medical interview information

[0221] The server makes an API request to send the received medical interview information to the AI ​​engine, which analyzes the information and outputs recommended actions and possible diagnoses based on the symptoms.

[0222] input:

[0223] Interview information sent to the server

[0224] Data processing:

[0225] Analysis by artificial intelligence engine

[0226] output:

[0227] A list of symptoms, suggested actions, and possible illnesses

[0228] Specific working example:

[0229] json

[0230] {

[0231] "possible_conditions": ["acute pharyngitis", "influenza"],

[0232] "recommendations": ["Seek medical help", "Use over-the-counter pain relievers"]

[0233] }

[0234] Step 4:

[0235] Medical institution introduction

[0236] Based on the analysis results, the server accesses a medical institution database and lists appropriate nearby medical institutions.

[0237] input:

[0238] Analysis results by artificial intelligence engine

[0239] Data processing:

[0240] Use SQL queries to retrieve information from medical institution databases

[0241] output:

[0242] A list of suitable medical institutions

[0243] Specific working example:

[0244] json

[0245] [

[0246] {"name": "Internal Medicine Medical Institution A", "address": "Address A", "contact": "Telephone A"},

[0247] {"name": "Internal Medicine Medical Institution B", "address": "Address B", "contact": "Telephone B"}

[0248] ]

[0249] The server sends this information to the terminal and presents it to the user.

[0250] Step 5:

[0251] Over-the-counter medication suggestions

[0252] The server accesses the over-the-counter drug database and retrieves appropriate over-the-counter drug information based on the analysis results.

[0253] input:

[0254] Analysis results by artificial intelligence engine

[0255] Data processing:

[0256] Use SQL queries to retrieve information from a database of over-the-counter drugs

[0257] output:

[0258] List of appropriate over-the-counter medications

[0259] Specific working example:

[0260] json

[0261] [

[0262] {"name": "Acetaminophen", "type": "Antipyretic analgesic", "usage": "One tablet, three times a day"}

[0263] ]

[0264] The server sends this information to the terminal and provides it to the user.

[0265] Step 6:

[0266] Medical appointment

[0267] The user selects a medical institution from the provided list and enters reservation information. The terminal then sends a reservation request to the server.

[0268] input:

[0269] Booking information entered by the user

[0270] Data processing:

[0271] Serialize to JSON format

[0272] output:

[0273] Send reservation information to the server

[0274] Specific working example:

[0275] json

[0276] {

[0277] "clinic_id": "1",

[0278] "user_id": "1001",

[0279] "appointment_date": "2023-10-15",

[0280] "appointment_time": "10:00"

[0281] }

[0282] The server connects this information to the medical institution's reservation system to confirm the reservation, after which a notification of reservation confirmation is sent to the terminal.

[0283] Step 7:

[0284] Follow-up after the examination

[0285] After the consultation, the user enters progress information into the application, and the terminal sends this information to the server.

[0286] input:

[0287] Progress information entered by the user

[0288] Data processing:

[0289] Serialize to JSON format

[0290] output:

[0291] Send progress information to the server

[0292] Specific working example:

[0293] json

[0294] {

[0295] "user_id": "1001",

[0296] "symptom_update": "Sore throat relieved"

[0297] }

[0298] The server then uses its AI engine to analyze the data again and determine whether a follow-up visit is necessary. If necessary, the server recommends a follow-up visit to the user and assists with the referral and appointment procedures.

[0299] (Application example 1)

[0300] 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."

[0301] Currently, when a patient feels unwell, it is difficult to immediately find an appropriate medical institution or over-the-counter medication and make an appointment. Furthermore, if a patient's condition suddenly worsens at work or in a public place, it is difficult to respond quickly and to access an appropriate medical institution. This creates a risk of worsening the patient's condition, so there is a need for a method to monitor the user's health in real time and respond quickly when an abnormality is detected.

[0302] 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.

[0303] In this invention, the server includes: a means for inputting medical interview information; a means for transmitting the input medical interview information to the server; a means for transmitting the received medical interview information to an artificial intelligence engine for analysis; a means for the artificial intelligence engine to analyze the medical interview information and output recommended actions corresponding to the symptoms and possible disease names; a means for the server to receive the analysis results, access a medical institution database to acquire and transmit appropriate medical institution information; a means for presenting the acquired medical institution information and recommended actions to the user; and a means for monitoring the user's health status in real time and automatically notifying a medical institution if an abnormality is detected. This allows users to immediately find appropriate medical institutions and over-the-counter medications and make appointments when they feel unwell. It also enables users to respond quickly to sudden changes in their health at work or in public places, preventing the condition from worsening.

[0304] "Medical interview information" is data entered by a patient about their health condition and symptoms.

[0305] "Input means" refers to a method or device for inputting medical interview information into a terminal or the like.

[0306] The "transmission means" refers to a method or device for transmitting the input medical interview information to the server.

[0307] An "artificial intelligence engine" is an algorithm or program that analyzes medical interview information and outputs recommended actions for symptoms and possible disease names.

[0308] "Analysis results" are information regarding diagnoses and recommended actions derived by the artificial intelligence engine based on the medical interview information.

[0309] The "medical institution database" is a database that stores information on various medical institutions.

[0310] "Medical institution information" refers to basic information such as the name, address, and contact information of the medical institution.

[0311] "Real-time monitoring" refers to the act of continuously monitoring a user's health status and immediately processing that information.

[0312] "Anomaly detection" is the act of recognizing abnormal health conditions from monitored data.

[0313] "Over-the-counter" drugs are medicines that can be purchased without a doctor's prescription.

[0314] A "reservation means" is a method or device for reserving a date and time for visiting a medical institution in advance.

[0315] To implement the invention, a system is constructed that uses the following components:

[0316] Hardware used

[0317] Smartphones (e.g., iPhone (registered trademark), ANDROID (registered trademark) devices)

[0318] Smart glasses (e.g., Google® Glass®, Microsoft® HoloLens®)

[0319] Software used

[0320] Python Program

[0321] Python libraries: requests, json, datetime

[0322] System configuration

[0323] 1. Input method for medical interview information: Users use a smartphone or smart glasses to input their medical interview information, such as their symptoms and temperature, through the application interface. This information is then stored on the device by the application.

[0324] 2. Method for transmitting medical interview information: The terminal converts the entered medical interview information into JSON format and sends it to the server via a secure HTTP POST request. The transmitted data is structured in JSON format.

[0325] 3. Analysis of medical interview information: The server sends the received medical interview information to the AI ​​engine for analysis. The AI ​​engine analyzes the input information and outputs recommended actions for the symptoms and possible illnesses.

[0326] 4. Medical institution database access means: Based on the analysis results, the server accesses the medical institution database and obtains information on appropriate nearby medical institutions.

[0327] 5. Means for accessing the over-the-counter drug database: Based on the analysis results, the server accesses the over-the-counter drug database and obtains the appropriate over-the-counter drug information.

[0328] 6. Medical institution reservation means: The user selects a medical institution from the list of medical institutions presented and enters reservation information. The terminal then sends a reservation request to the server and makes a reservation at the medical institution.

[0329] 7. Post-consultation follow-up: After the consultation, the user enters progress information through the application, and the terminal sends this information to the server. The server then requests the AI ​​engine to analyze it and provide recommendations for follow-up visits or additional actions.

[0330] 8. Real-time monitoring: The device monitors the user's health status in real time and automatically notifies nearby medical institutions if an abnormality is detected. This function allows for quick response to sudden illness.

[0331] Specific examples

[0332] For example, if a user inputs symptoms such as "fever," "headache," and "cough" into their smartphone, the AI ​​engine will analyze this and determine the possibility of "influenza" and recommend the use of antipyretics and analgesics. The server will obtain information on nearby medical institutions and present a list to the user. The user can then select a medical institution from the list, make an appointment, and confirm the information. Furthermore, if the user's health condition suddenly changes during treatment, the device will automatically notify the medical institution, enabling a prompt response.

[0333] Prompt Sentence Examples

[0334] "Please tell me the best medical institution and over-the-counter medication for a patient with a temperature of 38 degrees and symptoms of sore throat, headache, and cough."

[0335] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0336] Step 1:

[0337] The user inputs the medical interview information.

[0338] input:

[0339] Symptoms (e.g., fever, headache, cough)

[0340] Body temperature (e.g. 38 degrees)

[0341] Operation:

[0342] Using a smartphone or smart glasses interface, users input their symptoms and temperature.

[0343] The entered information is saved by an application in the device.

[0344] output:

[0345] Saved medical interview information (JSON format)

[0346] Step 2:

[0347] The terminal transmits the medical interview information to the server.

[0348] input:

[0349] JSON format medical interview information

[0350] Operation:

[0351] The device converts the medical interview information into JSON format and sends it to the server using a secure HTTP POST request.

[0352] output:

[0353] Interview information sent to the server

[0354] Step 3:

[0355] The server sends the medical interview information to an artificial intelligence engine for analysis.

[0356] input:

[0357] Medical interview information arrived at the server

[0358] Operation:

[0359] The server sends the received medical interview information to the artificial intelligence engine.

[0360] An artificial intelligence engine analyzes the medical history information and generates recommended actions and possible diagnoses.

[0361] output:

[0362] Analysis results (recommended actions and possible illnesses)

[0363] Step 4:

[0364] The server receives the analysis results and accesses the medical institution database to obtain the appropriate medical institution information.

[0365] input:

[0366] Analysis results

[0367] Operation:

[0368] The server receives the analysis results and accesses the medical institution database based on that data.

[0369] Obtain information on appropriate medical facilities in the vicinity.

[0370] output:

[0371] List of medical institution information (JSON format)

[0372] Step 5:

[0373] The server presents the acquired medical institution information and recommended actions to the user.

[0374] input:

[0375] List of medical institution information

[0376] Recommended Actions

[0377] Operation:

[0378] The server sends the acquired medical institution information and recommended actions to the user's terminal.

[0379] output:

[0380] Medical institution information and recommended actions displayed on the user's screen

[0381] Step 6:

[0382] The user selects from a list of medical institutions and enters reservation information. The terminal sends a reservation request to the server and makes a reservation at the medical institution.

[0383] input:

[0384] Medical institution selected by the user

[0385] Reservation information (date and time, user ID)

[0386] Operation:

[0387] The user selects a medical institution from the list of medical institutions presented.

[0388] The user inputs reservation information, and the terminal transmits the information to the server.

[0389] The server connects with the medical institution's reservation system to confirm the reservation.

[0390] output:

[0391] Reservation confirmation notification

[0392] Step 7:

[0393] The device monitors the user's health status in real time and automatically notifies a medical institution if any abnormalities are detected.

[0394] input:

[0395] Health data collected in real time

[0396] Operation:

[0397] The device continuously monitors the user's health status.

[0398] If abnormal data is detected, the terminal sends the information to a server, which automatically notifies nearby medical institutions.

[0399] output:

[0400] Notifying medical institutions if abnormalities are detected

[0401] Step 8:

[0402] After the consultation, the user uses the follow-up tool to input progress information, which is then analyzed by the server again, and a follow-up consultation or additional recommended actions are made as needed.

[0403] input:

[0404] Progress information after examination

[0405] Operation:

[0406] The user enters progress information into the application.

[0407] The terminal sends this information to the server.

[0408] The server then uses an artificial intelligence engine to analyze the data again and determine recommended actions and follow-up appointments.

[0409] output:

[0410] Notification of follow-up visits and additional recommended actions

[0411] 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.

[0412] This invention is a system in which patients input their symptoms, and based on that information, an AI engine and an emotion engine are linked to perform analysis and recommend appropriate medical institutions and treatment methods. This system comprehensively covers everything from inputting medical history information, analysis, referring to medical institutions, confirming appointments, suggesting over-the-counter medications, customizing using emotion recognition, and follow-up.

[0413] System configuration

[0414] The system consists of the following main components:

[0415] 1. Means of inputting medical interview information

[0416] 2. Means of sending medical interview information

[0417] 3. Analysis of medical interview information

[0418] 4. Emotion recognition means

[0419] 5. Means of accessing medical institution databases

[0420] 6. Access to the over-the-counter drug database

[0421] 7. Medical institution reservation methods

[0422] 8. Follow-up measures after consultation

[0423] Explanation of program processing

[0424] 1. Enter medical interview information

[0425] Using the application, users enter their symptoms and basic information into a questionnaire. For example, a user might enter symptoms such as "fever, sore throat, cough, and general fatigue." Input data also includes current body temperature and the time of onset of symptoms. Once input is complete, the user presses the "Submit" button.

[0426] 2. Sending medical interview information and emotional information

[0427] The terminal collects the questionnaire information and emotional information entered by the user. Examples of emotional information include keywords entered in text and facial expression data detected by a face recognition system.

[0428] 3. Send to the server

[0429] The device sends the interview information and emotion information to the server, where the data is structured in JSON format and prepared for storage and analysis.

[0430] 4. Analysis of interview information and emotional information

[0431] The server sends the received medical interview information to the AI ​​engine and the emotional information to the emotion engine. The AI ​​engine analyzes the medical interview information and outputs possible illnesses and recommended actions. The emotion engine analyzes the emotional information and estimates the emotional state. The estimation results affect the analysis results of the AI ​​engine.

[0432] 5. Obtaining diagnostic results

[0433] The server receives the analysis results from the AI ​​engine and emotion engine. The diagnosis results include the following information:

[0434] json

[0435] {

[0436] "possible_conditions": ["acute pharyngitis", "influenza"],

[0437] "recommendations": ["Seek medical attention", "Use over-the-counter pain relievers"],

[0438] "emotional_state": "anxiety"

[0439] }

[0440] 6. Referral to medical institutions

[0441] The server accesses a medical institution database based on the analysis results. It searches for medical institutions that match the disease name and emotional state of the analysis results and obtains information on nearby medical institutions. For example, if a patient is in an anxious state, it can prioritize referrals to medical institutions that can provide psychological care.

[0442] 7. Generating and sending a list of medical institutions

[0443] The server structures the medical institution information it has acquired in list format and sends it to the terminal. For example, the following list is presented to the user.

[0444] json

[0445] [

[0446] {"name": "Internal Medicine Clinic, Chuo Ward, Tokyo", "address": "Chuo Ward, Tokyo ~", "contact": "0123-456-789"},

[0447] {"name": "Tokyo Medical Center", "address": "Minato-ku, Tokyo", "contact": "0987-654-321"}

[0448] ]

[0449] 8. Over-the-counter medication suggestions

[0450] The server accesses the over-the-counter drug database and retrieves appropriate over-the-counter drug information based on the analysis results. The retrieved over-the-counter drug information is sent to the terminal in JSON format. For example, the following over-the-counter drug information can be obtained:

[0451] json

[0452] [

[0453] {"name": "Acetaminophen", "type": "Antipyretic analgesic", "usage": "One tablet, three times a day"}

[0454] ]

[0455] The terminal displays over-the-counter drug information to the user.

[0456] 9. Medical appointments

[0457] The user selects a medical institution from the provided list of medical institutions and enters reservation information, including the desired date and time of the examination. After completing the input, the terminal sends a reservation request to the server.

[0458] 10. Booking confirmation and notification

[0459] The server receives the reservation request and confirms the reservation in cooperation with the medical institution's reservation system. The reservation information is sent to the medical institution, and the confirmed reservation information is notified to the user's terminal.

[0460] 11. Follow-up after consultation

[0461] After the consultation, the user enters progress information into the app. The entered information is sent from the device to the server. The server then uses an artificial intelligence engine and emotion engine to reanalyze the progress information and recommend a follow-up visit if necessary. If a follow-up visit is necessary, the user is referred to a medical institution and the reservation procedure is carried out again.

[0462] In this way, this system analyzes the patient's medical history and emotional information, promptly introduces appropriate medical institutions and over-the-counter medications, and comprehensively handles a series of processes, including appointment procedures and post-examination follow-up, allowing patients to receive prompt and accurate medical services.

[0463] The processing flow will be explained below.

[0464] Step 1:

[0465] The user opens the application and enters their symptoms and basic information into a questionnaire. For example, they might enter "fever, sore throat, cough, and general fatigue." In addition, the application obtains the user's emotions through text analysis (e.g., keywords that indicate emotions) and a facial recognition system. Once the input is complete, the user presses the "Submit" button.

[0466] Step 2:

[0467] The device collects medical interview information and emotional information. Medical interview information includes data such as body temperature and details of symptoms, and emotional information includes the user's emotional state obtained from facial expressions and text analysis. This information is structured in JSON format.

[0468] Step 3:

[0469] The terminal sends the medical interview information and emotion information to the server. The data sent is in the following format:

[0470] json

[0471] {

[0472] "temperature": "38 degrees",

[0473] "symptoms": ["sore throat", "cough", "general fatigue"],

[0474] "emotions": {"text": "anxiety", "face": "sad"}

[0475] }

[0476] Step 4:

[0477] The server receives the medical interview information and emotion information sent from the device, checks the integrity of the received data, and stores it in a database.

[0478] Step 5:

[0479] The server sends the saved medical interview information to the AI ​​engine and the emotion information to the emotion engine, which calls the API and creates a request to analyze the medical interview information and estimate the emotional state.

[0480] Step 6:

[0481] The AI ​​engine analyzes the medical interview information and outputs possible illnesses and recommended actions. For example, the following analysis results can be obtained:

[0482] json

[0483] {

[0484] "possible_conditions": ["acute pharyngitis", "influenza"],

[0485] "recommendations": ["Seek medical help", "Use over-the-counter pain relievers"]

[0486] }

[0487] Step 7:

[0488] The emotion engine analyzes the emotion information and estimates the user's emotional state. For example, the emotional state "anxiety" is output.

[0489] Step 8:

[0490] The server receives the analysis results from the AI ​​engine and the emotion engine, integrates the analysis results, and generates a comprehensive diagnosis.

[0491] json

[0492] {

[0493] "possible_conditions": ["acute pharyngitis", "influenza"],

[0494] "recommendations": ["Seek medical attention", "Use over-the-counter pain relievers"],

[0495] "emotional_state": "anxiety"

[0496] }

[0497] Step 9:

[0498] The server accesses a medical institution database based on the analysis results. It searches for medical institutions that match the disease name and emotional state of the analysis results and obtains information on nearby medical institutions. For example, if a user is in an anxious state, it will preferentially introduce medical institutions that offer counseling and mental care.

[0499] Step 10:

[0500] The server structures the acquired medical institution information in list format and sends it to the terminal. For example, the following list is generated:

[0501] json

[0502] [

[0503] {"name": "Chuo Ward Internal Medicine Clinic", "address": "Chuo Ward ~", "contact": "0123-456-789"},

[0504] {"name": "Tokyo Medical Center", "address": "Minato Ward ~", "contact": "0987-654-321"},

[0505] {"name": "Mental Health Care Clinic", "address": "Shinjuku-ku~", "contact": "0245-678-910"}

[0506] ]

[0507] Step 11:

[0508] The terminal presents the user with a list of medical institutions. The user selects a medical institution from the provided list and enters the desired date and time for an appointment. The user then presses the "Make an appointment" button.

[0509] Step 12:

[0510] The terminal sends the reservation information to the server. An example of the data sent is as follows:

[0511] json

[0512] {

[0513] "clinic_id": "1",

[0514] "user_id": "1001",

[0515] "appointment_date": "2023-10-15",

[0516] "appointment_time": "10:00"

[0517] }

[0518] Step 13:

[0519] The server receives the reservation request and confirms the reservation in cooperation with the medical institution's reservation system. The reservation information is sent to the medical institution, and the confirmed reservation information is notified to the user's terminal.

[0520] Step 14:

[0521] After the examination, the user enters progress information into the app. For example, they enter information such as "the fever continues" or "the general feeling of fatigue is increasing." Once the information is entered, the device sends the progress information to the server.

[0522] Step 15:

[0523] Based on the progress information and emotion information received by the server, the AI ​​engine and emotion engine are again requested to perform analysis. The necessity of a follow-up examination is evaluated and, if necessary, a follow-up examination is recommended. If a follow-up examination is necessary, the patient is referred to a medical institution and the appointment procedure is carried out again.

[0524] This series of processes enables the prompt provision of optimal medical services according to the user's symptoms and emotions.

[0525] Example 2

[0526] 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."

[0527] Conventional medical support systems often only handle patient interview information, and do not adequately consider the patient's emotional and psychological state when making diagnoses or referrals to medical institutions. Furthermore, the procedures for users to select a medical institution and make an appointment are complicated, making it difficult to respond quickly. Furthermore, over-the-counter drug recommendations are not centrally managed, making it difficult for users to select appropriate over-the-counter drugs as needed. A system that can solve this problem is needed.

[0528] 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.

[0529] In this invention, the server includes: means for inputting medical history information and emotional information; means for the terminal to transmit the input medical history information and emotional information to the server; means for transmitting the medical history information and emotional information received by the server to an artificial intelligence engine and an emotion engine, respectively, for analysis; means for the artificial intelligence engine to analyze the medical history information and output recommended actions corresponding to the symptoms and possible disease names; means for the emotion engine to analyze the emotional information, estimate the user's emotional state, and reflect the result in the analysis of the artificial intelligence engine; means for the server to receive the analysis result, access the medical institution database to acquire appropriate medical institution information, structuring it, and transmitting it to the terminal; means for the terminal to present the acquired medical institution information and recommended actions to the user; means for the server to access the over-the-counter drug database, acquire appropriate over-the-counter drug information based on the analysis result, structuring it, and transmitting it to the terminal, which provides it to the user; and means for the server to cooperate with the medical institution's reservation system, receive a reservation request, confirm a medical institution reservation, and notify the terminal of the information. This enables a comprehensive diagnosis that takes into account not only the analysis of the medical history information but also the emotional information, and to quickly and reliably recommend appropriate medical institutions and over-the-counter drugs and complete reservation procedures.

[0530] "Medical interview information" refers to data entered by the patient about their symptoms and basic information.

[0531] "Emotional information" refers to data that indicates a patient's emotional state or psychological condition.

[0532] "Terminal" means an electronic device used by a user to input and transmit information.

[0533] A "server" is a computer system that analyzes received data and provides necessary information in cooperation with various databases.

[0534] An "artificial intelligence engine" is a program that analyzes medical interview information and outputs recommended actions and possible disease names corresponding to symptoms.

[0535] The "emotion engine" is a program that analyzes emotional information and estimates the patient's emotional state.

[0536] A "database" is a collection of data that stores various types of information in an organized manner and allows it to be searched and retrieved as needed.

[0537] A "medical institution database" is a database that contains information about medical institutions.

[0538] "Over-the-counter drug database" means a database containing information about over-the-counter drugs.

[0539] A "reservation system" is a system that manages reservations at medical institutions.

[0540] This invention is a system in which patients input their symptoms, and based on that information, an AI engine and an emotion engine are linked to perform analysis and recommend appropriate medical institutions and treatment methods. This system comprehensively covers everything from inputting medical history information, analysis, referring to medical institutions, confirming appointments, suggesting over-the-counter medications, customizing using emotion recognition, and follow-up.

[0541] System configuration

[0542] The system consists of the following main components:

[0543] 1. Means of inputting medical interview information

[0544] 2. Means of sending medical interview information

[0545] 3. Analysis of interview information and emotional information

[0546] 4. Emotion recognition means

[0547] 5. Means of accessing medical institution databases

[0548] 6. Access to the over-the-counter drug database

[0549] 7. Medical institution reservation methods

[0550] 8. Follow-up measures after consultation

[0551] The user opens the application and enters their symptoms and basic information into a questionnaire. At this stage, the user enters specific symptoms, such as "fever, sore throat, cough, and general fatigue." They also enter detailed information such as body temperature and the time of onset, and press the submit button.

[0552] The device collects emotional information at the same time as collecting the medical interview information received from the user. Examples of emotional information include keywords entered during text input and facial expression data obtained by a facial recognition system. For example, if the user enters an expression or keyword indicating "anxiety," the device will detect it.

[0553] The device structures the medical interview information and emotion information in JSON format and sends it to the server. The server receives this data and stores it in a database. For example, the following JSON format data is sent:

[0554] {

[0555] "symptoms": ["fever", "sore throat", "cough", "general fatigue"],

[0556] "temperature": "38.5",

[0557] "onset_date": "2023-10-01",

[0558] "emotional_state": "anxiety"

[0559] }

[0560] The server sends the stored medical interview information to an artificial intelligence engine. Specifically, it analyzes symptoms based on the interview information and outputs a possible illness and recommended actions. At the same time, it sends emotional information to the emotion engine and analyzes the emotional state. For example, it analyzes "fever, sore throat, cough, and general fatigue" and suspects "acute pharyngitis" or "influenza," recommending that the patient "visit a medical institution."

[0561] The server receives the analysis results from the AI ​​engine and emotion engine. The results include disease candidates, recommended actions, and emotional state. The JSON result is as follows:

[0562] {

[0563] "possible_conditions": ["acute pharyngitis", "influenza"],

[0564] "recommendations": ["Seek medical attention", "Use over-the-counter pain relievers"],

[0565] "emotional_state": "anxiety"

[0566] }

[0567] The server accesses a medical institution database based on the analysis results. It searches for the medical institution that best suits the patient's symptoms and emotional state, and retrieves information on nearby medical institutions on the server. For example, taking into account the patient's "anxiety" state, it prioritizes searches for medical institutions that offer counseling.

[0568] The server structures the medical institution information it has acquired in list format and sends it to the terminal. For example, the following list is presented to the user.

[0569] List of medical institutions:

[0570] Internal Medicine Clinic (Address: Chuo Ward, Contact: 0123-456-789)

[0571] Medical Center (Address: Minato-ku, Contact: 0987-654-321)

[0572] The user selects the desired medical institution from the list.

[0573] The server accesses the over-the-counter drug database and retrieves appropriate over-the-counter drug information based on the medical interview information and analysis results. The retrieved over-the-counter drug information is structured in JSON format and sent to the terminal. For example, information on antipyretic analgesics is retrieved as follows:

[0574] Over-the-counter drug information:

[0575] Acetaminophen (type: antipyretic analgesic, usage: 1 tablet 3 times a day)

[0576] The terminal displays this information to the user.

[0577] The user selects the desired medical institution from the provided list of medical institutions and enters the reservation information. For example, if the user selects "Internal Medicine Clinic" and "October 2nd, 3:00 PM," the corresponding reservation request is sent to the server.

[0578] The server receives the reservation request and confirms the reservation in cooperation with the medical institution's reservation system. For example, the server uses the clinic's reservation management API to receive the confirmed information, and then notifies the user's device.

[0579] After the consultation, the user enters progress information into the app. For example, they can enter information such as "symptoms have not improved" or "new symptoms have appeared." The device then sends this information to the server, which then analyzes it again using its artificial intelligence engine and emotion engine. If necessary, the app will recommend a follow-up visit and will refer the patient to a medical institution and make a reservation again.

[0580] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0581] Step 1:

[0582] The user opens the application and enters their symptoms and basic information into a questionnaire. The user enters symptoms such as "fever," "sore throat," "cough," and "general fatigue," and also enters detailed information such as their current body temperature and when symptoms first appeared. Then, the user presses the submit button.

[0583] Input: Medical interview information such as symptoms, temperature, and onset date

[0584] Output: The medical interview information entered by the user is saved on the terminal.

[0585] Step 2:

[0586] The device collects emotional information at the same time as collecting the medical interview information received from the user. Emotional information includes keywords entered in the text and facial expression data detected by the facial recognition system. For example, if the text contains the word "anxiety" or if an anxious facial expression is detected, that information is collected.

[0587] Input: medical interview information, text keywords, facial expression data

[0588] Output: Interview information and emotion information are saved on the device.

[0589] Step 3:

[0590] The device structures the medical interview information and emotion information in JSON format and sends it to the server.

[0591] Input: Interview information and emotion information structured in JSON format

[0592] Output: The server receives the data and stores it in a database.

[0593] Step 4:

[0594] The server sends the received medical interview information to an AI engine, and sends the emotional information to the emotion engine. Based on the medical interview information, the AI ​​engine analyzes symptoms and outputs possible illnesses and recommended actions. At the same time, the emotion engine analyzes the emotional information and estimates the user's emotional state.

[0595] Input: medical interview information (to AI engine), emotion information (to emotion engine)

[0596] Output: Disease candidates and recommended actions from the AI ​​engine, and emotional state analysis results from the emotion engine

[0597] Step 5:

[0598] The server receives the analysis results from the AI ​​engine and the emotion engine. For example, the analysis results include the following information:

[0599] {

[0600] "possible_conditions": ["acute pharyngitis", "influenza"],

[0601] "recommendations": ["Seek medical attention", "Use over-the-counter pain relievers"],

[0602] "emotional_state": "anxiety"

[0603] }

[0604] Input: Analysis results from the AI ​​engine and emotion engine

[0605] Output: Analysis results saved on the server

[0606] Step 6:

[0607] The server accesses a medical institution database based on the analysis results. It searches for the medical institution that best suits the patient's symptoms and emotional state, and retrieves information on nearby medical institutions on the server. For example, taking into account the patient's "anxiety" state, it prioritizes searches for medical institutions that offer counseling.

[0608] Input: Analysis results

[0609] Output: Appropriate medical institution information

[0610] Step 7:

[0611] The server structures the medical institution information it has acquired in list format and sends it to the terminal. For example, the following list is presented to the user.

[0612] List of medical institutions:

[0613] Internal Medicine Clinic (Address: Chuo Ward, Contact: 0123-456-789)

[0614] Medical Center (Address: Minato-ku, Contact: 0987-654-321)

[0615] Input: Acquired medical institution information

[0616] Output: Medical institution information in a structured list format on the user's terminal

[0617] Step 8:

[0618] The server accesses the over-the-counter drug database and retrieves appropriate over-the-counter drug information based on the analysis results. For example, the following over-the-counter drug information can be obtained:

[0619] Over-the-counter drug information:

[0620] Acetaminophen (type: antipyretic analgesic, usage: 1 tablet 3 times a day)

[0621] The terminal displays this information to the user.

[0622] Input: Analysis results

[0623] Output: Pertinent over-the-counter drug information and its use

[0624] Step 9:

[0625] The user selects the desired medical institution from the provided list of medical institutions and enters the reservation information. For example, if the user selects "Internal Medicine Clinic" and "October 2nd, 3:00 PM," the corresponding reservation request is sent to the server.

[0626] Input: Reservation information (name of medical institution, desired consultation date and time)

[0627] Output: A reservation request is sent to the server

[0628] Step 10:

[0629] The server receives the reservation request and confirms the reservation in cooperation with the medical institution's reservation system. For example, the server uses a clinic's reservation management API to receive information that the reservation has been confirmed, and then notifies the user's device.

[0630] Input: Reservation Request

[0631] Output: The confirmed reservation information is sent to the user's device.

[0632] Step 11:

[0633] After the consultation, the user enters progress information into the app. For example, they can enter information such as "symptoms have not improved" or "new symptoms have appeared." The device then sends this information to the server, which then analyzes it again using its artificial intelligence engine and emotion engine. If necessary, the app will recommend a follow-up visit and will refer the patient to a medical institution and make a reservation again.

[0634] Input: Progress information

[0635] Output: Recommended follow-up visit information and referral to a medical institution and reservation procedures

[0636] The above are the specific processing steps of the program in the system of the present invention.

[0637] (Application example 2)

[0638] 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."

[0639] Current autonomous vehicle systems lack the functionality to respond quickly and appropriately to the health conditions of passengers. Furthermore, they do not come equipped with a standard system to guide passengers to the most appropriate medical facility in an emergency, which risks delaying response in the event of an emergency. Furthermore, they lack sufficient functionality to reassure passengers who may be in an unstable emotional state during an emergency. To solve these issues, a unified system is needed that can handle everything from inputting health information to navigating to medical facilities and stabilizing emotions.

[0640] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0641] In this invention, the server includes: means for inputting medical interview information; means for transmitting the input medical interview information to the server; means for transmitting the received medical interview information to an artificial intelligence engine for analysis by the server; means for the artificial intelligence engine to analyze the medical interview information and output recommended actions corresponding to the symptoms and possible disease names; means for the server to receive the analysis results and access a medical institution database to obtain and transmit appropriate medical institution information; means for presenting the obtained medical institution information and recommended actions to the user; a vehicle interface having means for the occupant to input their health condition; means for the vehicle to automatically guide the occupant to the most appropriate medical institution based on the analyzed information; and means for adjusting the in-vehicle environment (music, audio guidance, lighting, etc.) to stabilize the occupant's emotions. This enables prompt and appropriate response to the occupant's health condition and makes it possible to guide the occupant to the most appropriate medical institution while reassuring them even in an emergency.

[0642] "Medical interview information" is data entered by passengers or patients about their own health conditions and symptoms.

[0643] A "server" is a computer system that stores, processes, and provides data over a network.

[0644] "Artificial intelligence engine" refers to the algorithms and computational models used to analyze medical interview information and output possible disease diagnoses and recommended actions.

[0645] A "medical institution database" is a database that stores and makes searchable information about medical facilities and medical professionals.

[0646] A "vehicle interface" is a device that includes an operating means for an occupant of an autonomous vehicle to input health information.

[0647] "Autonomous driving" is a technology that allows vehicles to move and change direction autonomously without human operation.

[0648] The "emotion engine" is a system that analyzes the emotional state of the occupants and takes appropriate action based on the results.

[0649] "Analysis results" are output information obtained when the artificial intelligence engine and emotion engine process the input data.

[0650] "Recommended actions" are specific actions that should be taken by the user or passengers, which are proposed based on the analysis results.

[0651] A "medical institution appointment system" is software and services for making appointments for medical appointments at medical facilities.

[0652] This invention is a system in which the driver inputs their health condition, and based on that information, a server analyzes it by linking an artificial intelligence engine and an emotion engine, and then guides the driver in the self-driving vehicle to the appropriate medical institution and treatment method. This system comprehensively covers the input and analysis of medical history information, medical institution referral, appointment confirmation, over-the-counter drug suggestions, customization using emotion recognition, and follow-up.

[0653] The server includes a means for inputting medical interview information, a means for transmitting the input medical interview information, a means for transmitting the received medical interview information to an artificial intelligence engine, a means for the artificial intelligence engine to analyze the medical interview information and output recommended actions and possible disease names, a means for receiving the analysis results and accessing a medical institution database to obtain and transmit information on appropriate medical institutions, a means for presenting the obtained medical institution information and recommended actions to the user, a vehicle interface for the occupant to input their health condition, a means for the vehicle to automatically guide the occupant to the most appropriate medical institution based on the analysis information, and a means for adjusting the in-vehicle environment to stabilize the emotions of the occupant.

[0654] Program processing

[0655] First, the occupant uses an interface installed in the vehicle to input their symptoms. For example, a touchscreen display or a voice recognition system is used. When the occupant inputs symptoms such as "chest pain" or "difficulty breathing," the data is collected by the vehicle's on-board computer. The collected data is then sent to a cloud server via the network. The ONVIF protocol is used for data transmission.

[0656] The data is structured and stored in JSON format on a cloud server. The server then sends the data to an artificial intelligence engine (e.g., a deep learning model using TENSORFLOW®) and an emotion engine (e.g., Microsoft's Azure® Cognitive Services). The artificial intelligence engine analyzes the data and outputs a possible diagnosis and recommended actions. The emotion engine estimates the passenger's emotional state (e.g., "anxiety") and influences the analysis results.

[0657] The analysis results and estimated emotional state are sent to the server. For example, if the analysis results indicate a high possibility of "acute myocardial infarction," the recommended action would be to "go directly to an emergency medical facility." If the emotional state is estimated to be "anxiety," the vehicle's environment (music playback, voice guidance tone change, lighting adjustment, etc.) will be automatically adjusted to reassure the occupants.

[0658] Based on the analysis results, the server searches a database of medical institutions to obtain information on the most suitable medical institution. For example, the nearest emergency hospital or cardiologist may be selected as a candidate. The obtained medical institution information is sent to the vehicle, and the autonomous driving system (such as Waymo's autonomous driving system) navigates the occupant to the medical institution.

[0659] Specific examples

[0660] Below are examples of specific prompt sentences to input into the generative AI model.

[0661] User dictation: "My chest hurts and I'm having trouble breathing... I'm so scared..."

[0662] System Response:

[0663] 1. Analyze symptoms: "chest pain," "difficulty breathing," "fear"

[0664] 2. Search for emergency medical facilities: "Nearby emergency hospital" "Cardiologist"

[0665] 3. Emotional response: "Please stay calm. I will get you to the nearest emergency room right away."

[0666] The above processing enables prompt and appropriate responses to the health conditions of passengers, and in the event of an emergency, navigation to the most appropriate medical institution is possible. In addition, the use of an emotion engine can increase passenger peace of mind.

[0667] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0668] Step 1:

[0669] The user uses the vehicle's in-vehicle interface (touchscreen display or voice recognition system) to input their symptoms. The user inputs specific symptoms (e.g., "chest pain," "difficulty breathing," "headache," etc.), and the information is sent to the vehicle's on-board computer. The input data is formatted and collected. Examples of input data:

[0670] "My chest hurts. I'm having trouble breathing."

[0671] Step 2:

[0672] The terminal (vehicle's on-board computer) sends the collected data to a cloud server via the network. This data is usually structured in JSON format, and the on-board computer uses the ONVIF protocol for transmission. Example of transmitted data:

[0673] json

[0674] {

[0675] "symptoms": ["chest pain", "difficulty breathing"]

[0676] }

[0677] Step 3:

[0678] The server sends the received medical interview information to an artificial intelligence engine for analysis. The AI ​​model used is, for example, a deep learning model using TensorFlow. The server inputs the received data into the AI ​​engine for analysis. Input data:

[0679] json

[0680] {

[0681] "symptoms": ["chest pain", "difficulty breathing"]

[0682] }

[0683] Example of output data:

[0684] json

[0685] {

[0686] "possible_conditions": ["acute myocardial infarction"],

[0687] "recommendations": ["Go straight to emergency medical care"]

[0688] }

[0689] Step 4:

[0690] The server sends the interview information to the emotion engine, which analyzes the user's emotional state. The emotion recognition system used is, for example, Azure Cognitive Services. The emotion engine infers the user's emotional state based on voice and text input. Input data:

[0691] json

[0692] {

[0693] "text": "My chest hurts. I'm having trouble breathing. I'm so scared."

[0694] }

[0695] Example of output data:

[0696] json

[0697] {

[0698] "emotional_state": "anxiety"

[0699] }

[0700] Step 5:

[0701] The server receives the analysis results from the AI ​​and emotion engine and selects the most suitable medical institution based on that. The server accesses the medical institution database to obtain information on appropriate medical institutions. For example, emergency hospitals and medical institutions specializing in cardiac care are prioritized. Input data:

[0702] json

[0703] {

[0704] "possible_conditions": ["acute myocardial infarction"],

[0705] "emotional_state": "anxiety"

[0706] }

[0707] Example of output data:

[0708] json

[0709] {

[0710] "medical_facilities": [{"name": "Regional Emergency Hospital", "address": "XX City △△ Town", "contact": "012-3456-7890"}]

[0711] }

[0712] Step 6:

[0713] The server sends the acquired medical institution information to the vehicle, and the autonomous driving system navigates the vehicle. The vehicle's autonomous driving system (such as Waymo's system) calculates the shortest route based on the received data and begins navigating to the medical institution. Input data:

[0714] json

[0715] {

[0716] "medical_facilities": [{"name": "Regional Emergency Hospital", "address": "XX City △△ Town", "contact": "012-3456-7890"}]

[0717] }

[0718] Specific operation: The autonomous vehicle departs for the designated medical facility.

[0719] Step 7:

[0720] The server adjusts the environment inside the vehicle based on the emotion analysis results, such as playing music, changing the tone of the voice guidance, and adjusting the lighting. For example, if the emotion "anxiety" is detected, it will play relaxing music to give the passengers a sense of security. Input data:

[0721] json

[0722] {

[0723] "emotional_state": "anxiety"

[0724] }

[0725] Specific actions: Playing music, warm voice guidance from the speaker, changing the lighting to a warmer color, etc.

[0726] The above is a series of processing steps for the inference and operation of the "Smart Emergency Navigation" system. This system not only quickly and accurately assesses the health condition of the occupants and navigates them to the appropriate medical institution, but also adjusts the environment to reduce the anxiety of the occupants in an emergency, allowing them to arrive at the medical institution with peace of mind.

[0727] 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.

[0728] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0729] 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.

[0730] [Second embodiment]

[0731] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0732] 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.

[0733] 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).

[0734] 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.

[0735] 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.

[0736] 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).

[0737] 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.

[0738] 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.

[0739] 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.

[0740] 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.

[0741] 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.

[0742] 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."

[0743] This system allows patients to input their symptoms anywhere, and an AI engine analyzes the information to recommend appropriate medical institutions and treatment methods. This system covers the entire process, from inputting medical history information to analysis, referring appropriate medical institutions, confirming appointments, suggesting over-the-counter medications, and providing follow-up care.

[0744] System configuration

[0745] The system consists of the following main components:

[0746] 1. Means of inputting medical interview information

[0747] 2. Means of sending medical interview information

[0748] 3. Analysis of medical interview information

[0749] 4. Means of accessing medical institution databases

[0750] 5. Access to the over-the-counter drug database

[0751] 6. Medical institution reservation methods

[0752] 7. Follow-up measures after consultation

[0753] Explanation of program processing

[0754] 1. Enter medical interview information

[0755] Through the application, users enter their symptoms into a questionnaire, for example, "fever, sore throat, cough, general fatigue," and this information is stored on the device.

[0756] 2. Sending medical interview information

[0757] The terminal sends the entered medical interview information to the server. The data sent is structured in JSON format, for example, as shown below.

[0758] json

[0759] {

[0760] "temperature": "38 degrees",

[0761] "symptoms": ["sore throat", "cough", "general fatigue"]

[0762] }

[0763] 3. Analysis of medical interview information

[0764] The server sends the received medical interview information to the AI ​​engine, which analyzes the information and obtains the following diagnosis results:

[0765] json

[0766] {

[0767] "possible_conditions": ["acute pharyngitis", "influenza"],

[0768] "recommendations": ["Seek medical help", "Use over-the-counter pain relievers"]

[0769] }

[0770] The server receives the analysis results and uses them for the next process.

[0771] 4. Referral to medical institutions

[0772] Based on the analysis results, the server accesses the medical institution database and creates a list of nearby appropriate medical institutions. For example, the following list is generated:

[0773] json

[0774] [

[0775] {"name": "Internal Medicine Clinic, Chuo Ward, Tokyo", "address": "Chuo Ward, Tokyo ~", "contact": "0123-456-789"},

[0776] {"name": "Tokyo Medical Center", "address": "Minato-ku, Tokyo", "contact": "0987-654-321"}

[0777] ]

[0778] The server sends this information to the terminal and presents it to the user.

[0779] 5. Over-the-counter medication suggestions

[0780] The server accesses the over-the-counter drug database and retrieves appropriate over-the-counter drug information based on the analysis results. For example, the following over-the-counter drug information can be obtained:

[0781] json

[0782] [

[0783] {"name": "Acetaminophen", "type": "Antipyretic analgesic", "usage": "One tablet, three times a day"}

[0784] ]

[0785] This information is also provided to the user.

[0786] 6. Medical appointments

[0787] The user selects a medical institution from the provided list of medical institutions and enters reservation information. The terminal sends a reservation request to the server. For example, the format is as follows:

[0788] json

[0789] {

[0790] "clinic_id": "1",

[0791] "user_id": "1001",

[0792] "appointment_date": "2023-10-15",

[0793] "appointment_time": "10:00"

[0794] }

[0795] The server passes this information to the medical institution's system to confirm the appointment, after which a notification of the appointment is sent to the terminal.

[0796] 7. Follow-up after consultation

[0797] After the consultation, the user enters progress information into the app. The device then sends this information to the server. The server then uses the AI ​​engine to analyze the progress information again and recommends a follow-up visit if necessary. If a follow-up visit is necessary, the user is referred to a medical institution and an appointment is made.

[0798] In this way, this system analyzes patient interview information and quickly introduces appropriate medical institutions and over-the-counter medications, simplifies the appointment process, and provides follow-up after the consultation, thereby providing consistent medical support to patients.

[0799] The processing flow will be explained below.

[0800] Step 1:

[0801] The user opens the application. The user enters their symptom information into the input screen. For example, they enter "fever, sore throat, cough, general fatigue." Once they have completed the input, they press the "Submit" button.

[0802] Step 2:

[0803] The device collects the medical interview information entered by the user, structures the information in JSON format, and prepares it for transmission.

[0804] Step 3:

[0805] The terminal sends the formatted and structured medical interview information to the server. The following JSON format is used as an example of the data to be sent:

[0806] json

[0807] {

[0808] "temperature": "38 degrees",

[0809] "symptoms": ["sore throat", "cough", "general fatigue"]

[0810] }

[0811] Step 4:

[0812] The server receives the medical interview information sent from the terminal, checks the integrity of the received data, and stores it in a database.

[0813] Step 5:

[0814] The server sends the stored medical interview information to the AI ​​engine, which then calls the AI ​​engine's API and creates a request to analyze the medical interview information.

[0815] Step 6:

[0816] The AI ​​engine analyzes the medical interview information. The analysis results include recommended actions to take based on the symptoms and possible illnesses. For example, the analysis results shown below are output in JSON format.

[0817] json

[0818] {

[0819] "possible_conditions": ["acute pharyngitis", "influenza"],

[0820] "recommendations": ["Seek medical help", "Use over-the-counter pain relievers"]

[0821] }

[0822] Step 7:

[0823] The server receives the analysis results from the AI ​​engine and proceeds to the next step based on the received analysis results.

[0824] Step 8:

[0825] The server accesses a medical institution database based on the analysis results, searches for medical institutions that match the disease name in the analysis results, and obtains information on nearby medical institutions.

[0826] Step 9:

[0827] The server generates the medical institution information it has acquired in list format. For example, the following list is generated in JSON format:

[0828] json

[0829] [

[0830] {"name": "Internal Medicine Clinic, Chuo Ward, Tokyo", "address": "Chuo Ward, Tokyo ~", "contact": "0123-456-789"},

[0831] {"name": "Tokyo Medical Center", "address": "Minato-ku, Tokyo", "contact": "0987-654-321"}

[0832] ]

[0833] This medical institution information is sent to the terminal and presented to the user.

[0834] Step 10:

[0835] The server accesses the over-the-counter drug database and retrieves appropriate over-the-counter drug information based on the analysis results. The retrieved over-the-counter drug information is structured in JSON format and sent to the terminal as follows:

[0836] json

[0837] [

[0838] {"name": "Acetaminophen", "type": "Antipyretic analgesic", "usage": "One tablet, three times a day"}

[0839] ]

[0840] The terminal presents over-the-counter drug information to the user.

[0841] Step 11:

[0842] The user selects a medical institution from the provided list of medical institutions. Based on the information about the selected medical institution, the user inputs reservation information, for example, specifying the desired date and time for the examination.

[0843] Step 12:

[0844] The device sends the user's reservation request to the server. The following JSON format is used as an example of the data sent:

[0845] json

[0846] {

[0847] "clinic_id": "1",

[0848] "user_id": "1001",

[0849] "appointment_date": "2023-10-15",

[0850] "appointment_time": "10:00"

[0851] }

[0852] Step 13:

[0853] The server receives the reservation request and confirms the reservation in cooperation with the medical institution's reservation system. The reservation information is sent to the medical institution, which confirms the reservation. The server then sends the reservation confirmation information to the terminal and notifies the user.

[0854] Step 14:

[0855] After the examination, the user enters progress information into the app. For example, they enter information such as "the fever continues" or "the general feeling of fatigue is increasing." Once the information is entered, the device sends the progress information to the server.

[0856] Step 15:

[0857] Based on the progress information received by the server, the AI ​​engine is requested to perform another analysis. The AI ​​engine analyzes the progress information and evaluates the need for a follow-up visit. If a follow-up visit is recommended, the server searches again for a medical institution that requires a follow-up visit and introduces it to the user. It also completes the follow-up appointment procedure.

[0858] These are the specific processing steps of the program for this system. Through this series of processes, users can quickly and accurately find a medical institution and receive appropriate examinations and follow-up care.

[0859] Example 1

[0860] 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."

[0861] Conventional medical consultation systems are often limited to allowing patients to input their symptoms and introducing appropriate medical institutions and treatment methods, and lack sufficient functionality to assess the need for follow-up or re-examination after the consultation. Furthermore, they are unable to consistently suggest over-the-counter medications or schedule appointments at medical institutions, making it difficult to provide consistent medical support to patients.

[0862] 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.

[0863] In this invention, the server includes: means for inputting medical interview information; means for transmitting the input medical interview information to the server; means for the server to transmit the received medical interview information to an artificial intelligence engine for analysis; means for the artificial intelligence engine to analyze the medical interview information and output recommended actions corresponding to the symptoms and possible disease names; means for the server to receive the analysis results and access a medical institution database to obtain and transmit appropriate medical institution information; means for presenting the obtained medical institution information and recommended actions to the user; means for the server to transmit appointment information to a medical institution selected by the user and confirm the appointment in cooperation with the medical institution's system; and means for the user to input progress information after the consultation, and for the server to have the artificial intelligence engine analyze it again and recommend a re-examination. This makes it possible to provide consistent medical support to patients and evaluate the need for follow-up and re-examination after the consultation.

[0864] "Medical interview information" is information that a user inputs through an application regarding their own health condition and symptoms.

[0865] "Server" refers to the central system that receives, analyzes, and processes the medical interview information.

[0866] The "artificial intelligence engine" is a software module that analyzes the input medical interview information and outputs recommended actions and possible disease names corresponding to the symptoms.

[0867] The "medical institution database" is a database that stores information about medical institutions, and is used by the server to access and acquire appropriate medical institution information.

[0868] The "over-the-counter drug database" is a database that stores information about various over-the-counter drugs, and is used by the server to access and obtain appropriate over-the-counter drug information.

[0869] The "reservation system" is an online system that works in conjunction with medical institutions to process reservations.

[0870] "Progress information" is information about the health condition and progress of symptoms that the user inputs through the application after the medical examination.

[0871] "Recommended actions" refer to appropriate actions suggested to the user as a result of the AI ​​engine analyzing the medical interview information.

[0872] Defining these terms will help you understand the system better.

[0873] The present invention is a system in which a patient inputs their symptoms anywhere, an artificial intelligence engine analyzes the information, and introduces appropriate medical institutions and treatment methods. Specific embodiments for carrying out the present invention are described in detail below.

[0874] First, users enter their symptoms using a dedicated application installed on their smartphone, tablet, or other device. During this process, users enter specific symptoms such as fever, sore throat, cough, and general fatigue using text boxes and drop-down menus. For example, if a user enters "fever, sore throat, cough, and general fatigue," each piece of information is stored in a local database on the device.

[0875] Next, the device serializes the saved medical interview information into JSON format and sends it to the server via the communication module. At this time, the following format is used as an example of the data sent.

[0876] json

[0877] {

[0878] "temperature": "38 degrees",

[0879] "symptoms": ["sore throat", "cough", "general fatigue"]

[0880] }

[0881] The server makes an API request to send the received medical interview information to the AI ​​engine. The AI ​​engine analyzes the medical interview information and outputs recommended actions and possible illnesses corresponding to the symptoms. For example, the following diagnosis results may be obtained:

[0882] json

[0883] {

[0884] "possible_conditions": ["acute pharyngitis", "influenza"],

[0885] "recommendations": ["Seek medical help", "Use over-the-counter pain relievers"]

[0886] }

[0887] The server receives the analysis results and accesses the medical institution database to create a list of appropriate nearby medical institutions. This list is converted into JSON format and sent to the device. The device then presents the appropriate medical institution information to the user based on the received information. The user can view the list on the application and select as needed.

[0888] In addition, the server also accesses the over-the-counter drug database and obtains appropriate over-the-counter drug information based on the analysis results. For example, the following over-the-counter drug information can be obtained:

[0889] json

[0890] {

[0891] "name": "Acetaminophen",

[0892] "type": "Antipyretic analgesic",

[0893] "usage": "One tablet, three times a day"

[0894] }

[0895] The acquired over-the-counter drug information is also provided to the user, who can check the over-the-counter drug information on the application.

[0896] When the user selects an appropriate medical institution from the list of medical institutions and enters reservation information, the terminal sends a reservation request to the server. The server connects this information to the reservation system and confirms the reservation. A reservation confirmation notification is then sent to the terminal, allowing the user to confirm the reservation details.

[0897] After the consultation, the user enters progress information into the application. This progress information is sent from the device to the server, where it is analyzed again using an artificial intelligence engine to determine whether a follow-up visit is necessary. If necessary, the server will recommend a follow-up visit and assist with the referral and appointment procedures at a medical institution.

[0898] Specific examples

[0899] For example, a user opens the application and enters their symptoms as "fever, sore throat, cough, and general fatigue." The system then collects this information, and an artificial intelligence engine analyzes it. The system suggests the possibility of "acute pharyngitis" or "influenza," and recommends nearby medical institutions such as an "internal medicine clinic" or "medical center." It also suggests "acetaminophen" as an appropriate over-the-counter medication. If the user selects "internal medicine clinic" and sets the appointment date to "2023-10-15," the system confirms the appointment and notifies the user.

[0900] Example prompts for generative AI models

[0901] "I have a fever, a sore throat, and a severe cough. I also feel fatigued. Can you diagnose my illness based on these symptoms and tell me where to find a nearby medical facility? Also, can you recommend any over-the-counter medicines?"

[0902] In this way, this system analyzes patient interview information, quickly introduces appropriate medical institutions and over-the-counter medications, simplifies appointment procedures, and also provides follow-up after consultation, thereby providing consistent medical support to patients.

[0903] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0904] Step 1:

[0905] Enter medical interview information

[0906] The user uses the application to enter their symptoms into a questionnaire. In the input field, they write specific symptoms such as "fever, sore throat, cough, general fatigue." The device stores this information in a local database. The input data format is as follows:

[0907] input:

[0908] Symptoms: fever, sore throat, cough, general fatigue

[0909] output:

[0910] Medical interview information stored in a local database

[0911] Step 2:

[0912] Sending medical interview information

[0913] The terminal serializes the medical interview information stored in the local database into JSON format and sends it to the server via the communication module.

[0914] input:

[0915] Medical interview information stored in a local database

[0916] Data processing:

[0917] Serialize to JSON format

[0918] output:

[0919] Send serialized data to the server

[0920] Specific working example:

[0921] json

[0922] {

[0923] "temperature": "38 degrees",

[0924] "symptoms": ["sore throat", "cough", "general fatigue"]

[0925] }

[0926] Step 3:

[0927] Analysis of medical interview information

[0928] The server makes an API request to send the received medical interview information to the AI ​​engine, which analyzes the information and outputs recommended actions and possible diagnoses based on the symptoms.

[0929] input:

[0930] Interview information sent to the server

[0931] Data processing:

[0932] Analysis by artificial intelligence engine

[0933] output:

[0934] A list of symptoms, suggested actions, and possible illnesses

[0935] Specific working example:

[0936] json

[0937] {

[0938] "possible_conditions": ["acute pharyngitis", "influenza"],

[0939] "recommendations": ["Seek medical help", "Use over-the-counter pain relievers"]

[0940] }

[0941] Step 4:

[0942] Medical institution introduction

[0943] Based on the analysis results, the server accesses a medical institution database and lists appropriate nearby medical institutions.

[0944] input:

[0945] Analysis results by artificial intelligence engine

[0946] Data processing:

[0947] Use SQL queries to retrieve information from medical institution databases

[0948] output:

[0949] A list of suitable medical institutions

[0950] Specific working example:

[0951] json

[0952] [

[0953] {"name": "Internal Medicine Medical Institution A", "address": "Address A", "contact": "Telephone A"},

[0954] {"name": "Internal Medicine Medical Institution B", "address": "Address B", "contact": "Telephone B"}

[0955] ]

[0956] The server sends this information to the terminal and presents it to the user.

[0957] Step 5:

[0958] Over-the-counter medication suggestions

[0959] The server accesses the over-the-counter drug database and retrieves appropriate over-the-counter drug information based on the analysis results.

[0960] input:

[0961] Analysis results by artificial intelligence engine

[0962] Data processing:

[0963] Use SQL queries to retrieve information from a database of over-the-counter drugs

[0964] output:

[0965] List of appropriate over-the-counter medications

[0966] Specific working example:

[0967] json

[0968] [

[0969] {"name": "Acetaminophen", "type": "Antipyretic analgesic", "usage": "One tablet, three times a day"}

[0970] ]

[0971] The server sends this information to the terminal and provides it to the user.

[0972] Step 6:

[0973] Medical appointment

[0974] The user selects a medical institution from the provided list and enters reservation information. The terminal then sends a reservation request to the server.

[0975] input:

[0976] Booking information entered by the user

[0977] Data processing:

[0978] Serialize to JSON format

[0979] output:

[0980] Send reservation information to the server

[0981] Specific working example:

[0982] json

[0983] {

[0984] "clinic_id": "1",

[0985] "user_id": "1001",

[0986] "appointment_date": "2023-10-15",

[0987] "appointment_time": "10:00"

[0988] }

[0989] The server connects this information to the medical institution's reservation system to confirm the reservation, after which a notification of reservation confirmation is sent to the terminal.

[0990] Step 7:

[0991] Follow-up after the examination

[0992] After the consultation, the user enters progress information into the application, and the terminal sends this information to the server.

[0993] input:

[0994] Progress information entered by the user

[0995] Data processing:

[0996] Serialize to JSON format

[0997] output:

[0998] Send progress information to the server

[0999] Specific working example:

[1000] json

[1001] {

[1002] "user_id": "1001",

[1003] "symptom_update": "Sore throat relieved"

[1004] }

[1005] The server then uses its AI engine to analyze the data again and determine whether a follow-up visit is necessary. If necessary, the server recommends a follow-up visit to the user and assists with the referral and appointment procedures.

[1006] (Application example 1)

[1007] 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."

[1008] Currently, when a patient feels unwell, it is difficult to immediately find an appropriate medical institution or over-the-counter medication and make an appointment. Furthermore, if a patient's condition suddenly worsens at work or in a public place, it is difficult to respond quickly and to access an appropriate medical institution. This creates a risk of worsening the patient's condition, so there is a need for a method to monitor the user's health in real time and respond quickly when an abnormality is detected.

[1009] 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.

[1010] In this invention, the server includes: a means for inputting medical interview information; a means for transmitting the input medical interview information to the server; a means for transmitting the received medical interview information to an artificial intelligence engine for analysis; a means for the artificial intelligence engine to analyze the medical interview information and output recommended actions corresponding to the symptoms and possible disease names; a means for the server to receive the analysis results, access a medical institution database to acquire and transmit appropriate medical institution information; a means for presenting the acquired medical institution information and recommended actions to the user; and a means for monitoring the user's health status in real time and automatically notifying a medical institution if an abnormality is detected. This allows users to immediately find appropriate medical institutions and over-the-counter medications and make appointments when they feel unwell. It also enables users to respond quickly to sudden changes in their health at work or in public places, preventing the condition from worsening.

[1011] "Medical interview information" is data entered by a patient about their health condition and symptoms.

[1012] "Input means" refers to a method or device for inputting medical interview information into a terminal or the like.

[1013] The "transmission means" refers to a method or device for transmitting the input medical interview information to the server.

[1014] An "artificial intelligence engine" is an algorithm or program that analyzes medical interview information and outputs recommended actions for symptoms and possible disease names.

[1015] "Analysis results" are information regarding diagnoses and recommended actions derived by the artificial intelligence engine based on the medical interview information.

[1016] The "medical institution database" is a database that stores information on various medical institutions.

[1017] "Medical institution information" refers to basic information such as the name, address, and contact information of the medical institution.

[1018] "Real-time monitoring" refers to the act of continuously monitoring a user's health status and immediately processing that information.

[1019] "Anomaly detection" is the act of recognizing abnormal health conditions from monitored data.

[1020] "Over-the-counter" drugs are medicines that can be purchased without a doctor's prescription.

[1021] A "reservation means" is a method or device for reserving a date and time for visiting a medical institution in advance.

[1022] To implement the invention, a system is constructed that uses the following components:

[1023] Hardware used

[1024] Smartphones (e.g. iPhone, Android devices)

[1025] Smart glasses (e.g., Google Glass, Microsoft HoloLens)

[1026] Software used

[1027] Python Program

[1028] Python libraries: requests, json, datetime

[1029] System configuration

[1030] 1. Input method for medical interview information: Users use a smartphone or smart glasses to input their medical interview information, such as their symptoms and temperature, through the application interface. This information is then stored on the device by the application.

[1031] 2. Method for transmitting medical interview information: The terminal converts the entered medical interview information into JSON format and sends it to the server via a secure HTTP POST request. The transmitted data is structured in JSON format.

[1032] 3. Analysis of medical interview information: The server sends the received medical interview information to the AI ​​engine for analysis. The AI ​​engine analyzes the input information and outputs recommended actions for the symptoms and possible illnesses.

[1033] 4. Medical institution database access means: Based on the analysis results, the server accesses the medical institution database and obtains information on appropriate nearby medical institutions.

[1034] 5. Means for accessing the over-the-counter drug database: Based on the analysis results, the server accesses the over-the-counter drug database and obtains the appropriate over-the-counter drug information.

[1035] 6. Medical institution reservation means: The user selects a medical institution from the list of medical institutions presented and enters reservation information. The terminal then sends a reservation request to the server and makes a reservation at the medical institution.

[1036] 7. Post-consultation follow-up: After the consultation, the user enters progress information through the application, and the terminal sends this information to the server. The server then requests the AI ​​engine to analyze it and provide recommendations for follow-up visits or additional actions.

[1037] 8. Real-time monitoring: The device monitors the user's health status in real time and automatically notifies nearby medical institutions if an abnormality is detected. This function allows for quick response to sudden illness.

[1038] Specific examples

[1039] For example, if a user inputs symptoms such as "fever," "headache," and "cough" into their smartphone, the AI ​​engine will analyze this and determine the possibility of "influenza" and recommend the use of antipyretics and analgesics. The server will obtain information on nearby medical institutions and present a list to the user. The user can then select a medical institution from the list, make an appointment, and confirm the information. Furthermore, if the user's health condition suddenly changes during treatment, the device will automatically notify the medical institution, enabling a prompt response.

[1040] Prompt Sentence Examples

[1041] "Please tell me the best medical institution and over-the-counter medication for a patient with a temperature of 38 degrees and symptoms of sore throat, headache, and cough."

[1042] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1043] Step 1:

[1044] The user inputs the medical interview information.

[1045] input:

[1046] Symptoms (e.g., fever, headache, cough)

[1047] Body temperature (e.g. 38 degrees)

[1048] Operation:

[1049] Using a smartphone or smart glasses interface, users input their symptoms and temperature.

[1050] The entered information is saved by an application in the device.

[1051] output:

[1052] Saved medical interview information (JSON format)

[1053] Step 2:

[1054] The terminal transmits the medical interview information to the server.

[1055] input:

[1056] JSON format medical interview information

[1057] Operation:

[1058] The device converts the medical interview information into JSON format and sends it to the server using a secure HTTP POST request.

[1059] output:

[1060] Interview information sent to the server

[1061] Step 3:

[1062] The server sends the medical interview information to an artificial intelligence engine for analysis.

[1063] input:

[1064] Medical interview information arrived at the server

[1065] Operation:

[1066] The server sends the received medical interview information to the artificial intelligence engine.

[1067] An artificial intelligence engine analyzes the medical history information and generates recommended actions and possible diagnoses.

[1068] output:

[1069] Analysis results (recommended actions and possible illnesses)

[1070] Step 4:

[1071] The server receives the analysis results and accesses the medical institution database to obtain the appropriate medical institution information.

[1072] input:

[1073] Analysis results

[1074] Operation:

[1075] The server receives the analysis results and accesses the medical institution database based on that data.

[1076] Obtain information on appropriate medical facilities in the vicinity.

[1077] output:

[1078] List of medical institution information (JSON format)

[1079] Step 5:

[1080] The server presents the acquired medical institution information and recommended actions to the user.

[1081] input:

[1082] List of medical institution information

[1083] Recommended Actions

[1084] Operation:

[1085] The server sends the acquired medical institution information and recommended actions to the user's terminal.

[1086] output:

[1087] Medical institution information and recommended actions displayed on the user's screen

[1088] Step 6:

[1089] The user selects from a list of medical institutions and enters reservation information. The terminal sends a reservation request to the server and makes a reservation at the medical institution.

[1090] input:

[1091] Medical institution selected by the user

[1092] Reservation information (date and time, user ID)

[1093] Operation:

[1094] The user selects a medical institution from the list of medical institutions presented.

[1095] The user inputs reservation information, and the terminal transmits the information to the server.

[1096] The server connects with the medical institution's reservation system to confirm the reservation.

[1097] output:

[1098] Reservation confirmation notification

[1099] Step 7:

[1100] The device monitors the user's health status in real time and automatically notifies a medical institution if any abnormalities are detected.

[1101] input:

[1102] Health data collected in real time

[1103] Operation:

[1104] The device continuously monitors the user's health status.

[1105] If abnormal data is detected, the terminal sends the information to a server, which automatically notifies nearby medical institutions.

[1106] output:

[1107] Notifying medical institutions if abnormalities are detected

[1108] Step 8:

[1109] After the consultation, the user uses the follow-up tool to input progress information, which is then analyzed by the server again, and a follow-up consultation or additional recommended actions are made as needed.

[1110] input:

[1111] Progress information after examination

[1112] Operation:

[1113] The user enters progress information into the application.

[1114] The terminal sends this information to the server.

[1115] The server then uses an artificial intelligence engine to analyze the data again and determine recommended actions and follow-up appointments.

[1116] output:

[1117] Notification of follow-up visits and additional recommended actions

[1118] 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.

[1119] This invention is a system in which patients input their symptoms, and based on that information, an AI engine and an emotion engine are linked to perform analysis and recommend appropriate medical institutions and treatment methods. This system comprehensively covers everything from inputting medical history information, analysis, referring to medical institutions, confirming appointments, suggesting over-the-counter medications, customizing using emotion recognition, and follow-up.

[1120] System configuration

[1121] The system consists of the following main components:

[1122] 1. Means of inputting medical interview information

[1123] 2. Means of sending medical interview information

[1124] 3. Analysis of medical interview information

[1125] 4. Emotion recognition means

[1126] 5. Means of accessing medical institution databases

[1127] 6. Access to the over-the-counter drug database

[1128] 7. Medical institution reservation methods

[1129] 8. Follow-up measures after consultation

[1130] Explanation of program processing

[1131] 1. Enter medical interview information

[1132] Using the application, users enter their symptoms and basic information into a questionnaire. For example, a user might enter symptoms such as "fever, sore throat, cough, and general fatigue." Input data also includes current body temperature and the time of onset of symptoms. Once input is complete, the user presses the "Submit" button.

[1133] 2. Sending medical interview information and emotional information

[1134] The terminal collects the questionnaire information and emotional information entered by the user. Examples of emotional information include keywords entered in text and facial expression data detected by a face recognition system.

[1135] 3. Send to the server

[1136] The device sends the interview information and emotion information to the server, where the data is structured in JSON format and prepared for storage and analysis.

[1137] 4. Analysis of interview information and emotional information

[1138] The server sends the received medical interview information to the AI ​​engine and the emotional information to the emotion engine. The AI ​​engine analyzes the medical interview information and outputs possible illnesses and recommended actions. The emotion engine analyzes the emotional information and estimates the emotional state. The estimation results affect the analysis results of the AI ​​engine.

[1139] 5. Obtaining diagnostic results

[1140] The server receives the analysis results from the AI ​​engine and emotion engine. The diagnosis results include the following information:

[1141] json

[1142] {

[1143] "possible_conditions": ["acute pharyngitis", "influenza"],

[1144] "recommendations": ["Seek medical attention", "Use over-the-counter pain relievers"],

[1145] "emotional_state": "anxiety"

[1146] }

[1147] 6. Referral to medical institutions

[1148] The server accesses a medical institution database based on the analysis results. It searches for medical institutions that match the disease name and emotional state of the analysis results and obtains information on nearby medical institutions. For example, if a patient is in an anxious state, it can prioritize referrals to medical institutions that can provide psychological care.

[1149] 7. Generating and sending a list of medical institutions

[1150] The server structures the medical institution information it has acquired in list format and sends it to the terminal. For example, the following list is presented to the user.

[1151] json

[1152] [

[1153] {"name": "Internal Medicine Clinic, Chuo Ward, Tokyo", "address": "Chuo Ward, Tokyo ~", "contact": "0123-456-789"},

[1154] {"name": "Tokyo Medical Center", "address": "Minato-ku, Tokyo", "contact": "0987-654-321"}

[1155] ]

[1156] 8. Over-the-counter medication suggestions

[1157] The server accesses the over-the-counter drug database and retrieves appropriate over-the-counter drug information based on the analysis results. The retrieved over-the-counter drug information is sent to the terminal in JSON format. For example, the following over-the-counter drug information can be obtained:

[1158] json

[1159] [

[1160] {"name": "Acetaminophen", "type": "Antipyretic analgesic", "usage": "One tablet, three times a day"}

[1161] ]

[1162] The terminal displays over-the-counter drug information to the user.

[1163] 9. Medical appointments

[1164] The user selects a medical institution from the provided list of medical institutions and enters reservation information, including the desired date and time of the examination. After completing the input, the terminal sends a reservation request to the server.

[1165] 10. Booking confirmation and notification

[1166] The server receives the reservation request and confirms the reservation in cooperation with the medical institution's reservation system. The reservation information is sent to the medical institution, and the confirmed reservation information is notified to the user's terminal.

[1167] 11. Follow-up after consultation

[1168] After the consultation, the user enters progress information into the app. The entered information is sent from the device to the server. The server then uses an artificial intelligence engine and emotion engine to reanalyze the progress information and recommend a follow-up visit if necessary. If a follow-up visit is necessary, the user is referred to a medical institution and the reservation procedure is carried out again.

[1169] In this way, this system analyzes the patient's medical history and emotional information, promptly introduces appropriate medical institutions and over-the-counter medications, and comprehensively handles a series of processes, including appointment procedures and post-examination follow-up, allowing patients to receive prompt and accurate medical services.

[1170] The processing flow will be explained below.

[1171] Step 1:

[1172] The user opens the application and enters their symptoms and basic information into a questionnaire. For example, they might enter "fever, sore throat, cough, and general fatigue." In addition, the application obtains the user's emotions through text analysis (e.g., keywords that indicate emotions) and a facial recognition system. Once the input is complete, the user presses the "Submit" button.

[1173] Step 2:

[1174] The device collects medical interview information and emotional information. Medical interview information includes data such as body temperature and details of symptoms, and emotional information includes the user's emotional state obtained from facial expressions and text analysis. This information is structured in JSON format.

[1175] Step 3:

[1176] The terminal sends the medical interview information and emotion information to the server. The data sent is in the following format:

[1177] json

[1178] {

[1179] "temperature": "38 degrees",

[1180] "symptoms": ["sore throat", "cough", "general fatigue"],

[1181] "emotions": {"text": "anxiety", "face": "sad"}

[1182] }

[1183] Step 4:

[1184] The server receives the medical interview information and emotion information sent from the device, checks the integrity of the received data, and stores it in a database.

[1185] Step 5:

[1186] The server sends the saved medical interview information to the AI ​​engine and the emotion information to the emotion engine, which calls the API and creates a request to analyze the medical interview information and estimate the emotional state.

[1187] Step 6:

[1188] The AI ​​engine analyzes the medical interview information and outputs possible illnesses and recommended actions. For example, the following analysis results can be obtained:

[1189] json

[1190] {

[1191] "possible_conditions": ["acute pharyngitis", "influenza"],

[1192] "recommendations": ["Seek medical help", "Use over-the-counter pain relievers"]

[1193] }

[1194] Step 7:

[1195] The emotion engine analyzes the emotion information and estimates the user's emotional state. For example, the emotional state "anxiety" is output.

[1196] Step 8:

[1197] The server receives the analysis results from the AI ​​engine and the emotion engine, integrates the analysis results, and generates a comprehensive diagnosis.

[1198] json

[1199] {

[1200] "possible_conditions": ["acute pharyngitis", "influenza"],

[1201] "recommendations": ["Seek medical attention", "Use over-the-counter pain relievers"],

[1202] "emotional_state": "anxiety"

[1203] }

[1204] Step 9:

[1205] The server accesses a medical institution database based on the analysis results. It searches for medical institutions that match the disease name and emotional state of the analysis results and obtains information on nearby medical institutions. For example, if a user is in an anxious state, it will preferentially introduce medical institutions that offer counseling and mental care.

[1206] Step 10:

[1207] The server structures the acquired medical institution information in list format and sends it to the terminal. For example, the following list is generated:

[1208] json

[1209] [

[1210] {"name": "Chuo Ward Internal Medicine Clinic", "address": "Chuo Ward ~", "contact": "0123-456-789"},

[1211] {"name": "Tokyo Medical Center", "address": "Minato Ward ~", "contact": "0987-654-321"},

[1212] {"name": "Mental Health Care Clinic", "address": "Shinjuku-ku~", "contact": "0245-678-910"}

[1213] ]

[1214] Step 11:

[1215] The terminal presents the user with a list of medical institutions. The user selects a medical institution from the provided list and enters the desired date and time for an appointment. The user then presses the "Make an appointment" button.

[1216] Step 12:

[1217] The terminal sends the reservation information to the server. An example of the data sent is as follows:

[1218] json

[1219] {

[1220] "clinic_id": "1",

[1221] "user_id": "1001",

[1222] "appointment_date": "2023-10-15",

[1223] "appointment_time": "10:00"

[1224] }

[1225] Step 13:

[1226] The server receives the reservation request and confirms the reservation in cooperation with the medical institution's reservation system. The reservation information is sent to the medical institution, and the confirmed reservation information is notified to the user's terminal.

[1227] Step 14:

[1228] After the examination, the user enters progress information into the app. For example, they enter information such as "the fever continues" or "the general feeling of fatigue is increasing." Once the information is entered, the device sends the progress information to the server.

[1229] Step 15:

[1230] Based on the progress information and emotion information received by the server, the AI ​​engine and emotion engine are again requested to perform analysis. The necessity of a follow-up examination is evaluated and, if necessary, a follow-up examination is recommended. If a follow-up examination is necessary, the patient is referred to a medical institution and the appointment procedure is carried out again.

[1231] This series of processes enables the prompt provision of optimal medical services according to the user's symptoms and emotions.

[1232] Example 2

[1233] 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."

[1234] Conventional medical support systems often only handle patient interview information, and do not adequately consider the patient's emotional and psychological state when making diagnoses or referrals to medical institutions. Furthermore, the procedures for users to select a medical institution and make an appointment are complicated, making it difficult to respond quickly. Furthermore, over-the-counter drug recommendations are not centrally managed, making it difficult for users to select appropriate over-the-counter drugs as needed. A system that can solve this problem is needed.

[1235] 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.

[1236] In this invention, the server includes: means for inputting medical history information and emotional information; means for the terminal to transmit the input medical history information and emotional information to the server; means for transmitting the medical history information and emotional information received by the server to an artificial intelligence engine and an emotion engine, respectively, for analysis; means for the artificial intelligence engine to analyze the medical history information and output recommended actions corresponding to the symptoms and possible disease names; means for the emotion engine to analyze the emotional information, estimate the user's emotional state, and reflect the result in the analysis of the artificial intelligence engine; means for the server to receive the analysis result, access the medical institution database to acquire appropriate medical institution information, structuring it, and transmitting it to the terminal; means for the terminal to present the acquired medical institution information and recommended actions to the user; means for the server to access the over-the-counter drug database, acquire appropriate over-the-counter drug information based on the analysis result, structuring it, and transmitting it to the terminal, which provides it to the user; and means for the server to cooperate with the medical institution's reservation system, receive a reservation request, confirm a medical institution reservation, and notify the terminal of the information. This enables a comprehensive diagnosis that takes into account not only the analysis of the medical history information but also the emotional information, and to quickly and reliably recommend appropriate medical institutions and over-the-counter drugs and complete reservation procedures.

[1237] "Medical interview information" refers to data entered by the patient about their symptoms and basic information.

[1238] "Emotional information" refers to data that indicates a patient's emotional state or psychological condition.

[1239] "Terminal" means an electronic device used by a user to input and transmit information.

[1240] A "server" is a computer system that analyzes received data and provides necessary information in cooperation with various databases.

[1241] An "artificial intelligence engine" is a program that analyzes medical interview information and outputs recommended actions and possible disease names corresponding to symptoms.

[1242] The "emotion engine" is a program that analyzes emotional information and estimates the patient's emotional state.

[1243] A "database" is a collection of data that stores various types of information in an organized manner and allows it to be searched and retrieved as needed.

[1244] A "medical institution database" is a database that contains information about medical institutions.

[1245] "Over-the-counter drug database" means a database containing information about over-the-counter drugs.

[1246] A "reservation system" is a system that manages reservations at medical institutions.

[1247] This invention is a system in which patients input their symptoms, and based on that information, an AI engine and an emotion engine are linked to perform analysis and recommend appropriate medical institutions and treatment methods. This system comprehensively covers everything from inputting medical history information, analysis, referring to medical institutions, confirming appointments, suggesting over-the-counter medications, customizing using emotion recognition, and follow-up.

[1248] System configuration

[1249] The system consists of the following main components:

[1250] 1. Means of inputting medical interview information

[1251] 2. Means of sending medical interview information

[1252] 3. Analysis of interview information and emotional information

[1253] 4. Emotion recognition means

[1254] 5. Means of accessing medical institution databases

[1255] 6. Access to the over-the-counter drug database

[1256] 7. Medical institution reservation methods

[1257] 8. Follow-up measures after consultation

[1258] The user opens the application and enters their symptoms and basic information into a questionnaire. At this stage, the user enters specific symptoms, such as "fever, sore throat, cough, and general fatigue." They also enter detailed information such as body temperature and the time of onset, and press the submit button.

[1259] The device collects emotional information at the same time as collecting the medical interview information received from the user. Examples of emotional information include keywords entered during text input and facial expression data obtained by a facial recognition system. For example, if the user enters an expression or keyword indicating "anxiety," the device will detect it.

[1260] The device structures the medical interview information and emotion information in JSON format and sends it to the server. The server receives this data and stores it in a database. For example, the following JSON format data is sent:

[1261] {

[1262] "symptoms": ["fever", "sore throat", "cough", "general fatigue"],

[1263] "temperature": "38.5",

[1264] "onset_date": "2023-10-01",

[1265] "emotional_state": "anxiety"

[1266] }

[1267] The server sends the stored medical interview information to an artificial intelligence engine. Specifically, it analyzes symptoms based on the interview information and outputs a possible illness and recommended actions. At the same time, it sends emotional information to the emotion engine and analyzes the emotional state. For example, it analyzes "fever, sore throat, cough, and general fatigue" and suspects "acute pharyngitis" or "influenza," recommending that the patient "visit a medical institution."

[1268] The server receives the analysis results from the AI ​​engine and emotion engine. The results include disease candidates, recommended actions, and emotional state. The JSON result is as follows:

[1269] {

[1270] "possible_conditions": ["acute pharyngitis", "influenza"],

[1271] "recommendations": ["Seek medical attention", "Use over-the-counter pain relievers"],

[1272] "emotional_state": "anxiety"

[1273] }

[1274] The server accesses a medical institution database based on the analysis results. It searches for the medical institution that best suits the patient's symptoms and emotional state, and retrieves information on nearby medical institutions on the server. For example, taking into account the patient's "anxiety" state, it prioritizes searches for medical institutions that offer counseling.

[1275] The server structures the medical institution information it has acquired in list format and sends it to the terminal. For example, the following list is presented to the user.

[1276] List of medical institutions:

[1277] Internal Medicine Clinic (Address: Chuo Ward, Contact: 0123-456-789)

[1278] Medical Center (Address: Minato-ku, Contact: 0987-654-321)

[1279] The user selects the desired medical institution from the list.

[1280] The server accesses the over-the-counter drug database and retrieves appropriate over-the-counter drug information based on the medical interview information and analysis results. The retrieved over-the-counter drug information is structured in JSON format and sent to the terminal. For example, information on antipyretic analgesics is retrieved as follows:

[1281] Over-the-counter drug information:

[1282] Acetaminophen (type: antipyretic analgesic, usage: 1 tablet 3 times a day)

[1283] The terminal displays this information to the user.

[1284] The user selects the desired medical institution from the provided list of medical institutions and enters the reservation information. For example, if the user selects "Internal Medicine Clinic" and "October 2nd, 3:00 PM," the corresponding reservation request is sent to the server.

[1285] The server receives the reservation request and confirms the reservation in cooperation with the medical institution's reservation system. For example, the server uses the clinic's reservation management API to receive the confirmed information, and then notifies the user's device.

[1286] After the consultation, the user enters progress information into the app. For example, they can enter information such as "symptoms have not improved" or "new symptoms have appeared." The device then sends this information to the server, which then analyzes it again using its artificial intelligence engine and emotion engine. If necessary, the app will recommend a follow-up visit and will refer the patient to a medical institution and make a reservation again.

[1287] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1288] Step 1:

[1289] The user opens the application and enters their symptoms and basic information into a questionnaire. The user enters symptoms such as "fever," "sore throat," "cough," and "general fatigue," and also enters detailed information such as their current body temperature and when symptoms first appeared. Then, the user presses the submit button.

[1290] Input: Medical interview information such as symptoms, temperature, and onset date

[1291] Output: The medical interview information entered by the user is saved on the terminal.

[1292] Step 2:

[1293] The device collects emotional information at the same time as collecting the medical interview information received from the user. Emotional information includes keywords entered in the text and facial expression data detected by the facial recognition system. For example, if the text contains the word "anxiety" or if an anxious facial expression is detected, that information is collected.

[1294] Input: medical interview information, text keywords, facial expression data

[1295] Output: Interview information and emotion information are saved on the device.

[1296] Step 3:

[1297] The device structures the medical interview information and emotion information in JSON format and sends it to the server.

[1298] Input: Interview information and emotion information structured in JSON format

[1299] Output: The server receives the data and stores it in a database.

[1300] Step 4:

[1301] The server sends the received medical interview information to an AI engine, and sends the emotional information to the emotion engine. Based on the medical interview information, the AI ​​engine analyzes symptoms and outputs possible illnesses and recommended actions. At the same time, the emotion engine analyzes the emotional information and estimates the user's emotional state.

[1302] Input: medical interview information (to AI engine), emotion information (to emotion engine)

[1303] Output: Disease candidates and recommended actions from the AI ​​engine, and emotional state analysis results from the emotion engine

[1304] Step 5:

[1305] The server receives the analysis results from the AI ​​engine and the emotion engine. For example, the analysis results include the following information:

[1306] {

[1307] "possible_conditions": ["acute pharyngitis", "influenza"],

[1308] "recommendations": ["Seek medical attention", "Use over-the-counter pain relievers"],

[1309] "emotional_state": "anxiety"

[1310] }

[1311] Input: Analysis results from the AI ​​engine and emotion engine

[1312] Output: Analysis results saved on the server

[1313] Step 6:

[1314] The server accesses a medical institution database based on the analysis results. It searches for the medical institution that best suits the patient's symptoms and emotional state, and retrieves information on nearby medical institutions on the server. For example, taking into account the patient's "anxiety" state, it prioritizes searches for medical institutions that offer counseling.

[1315] Input: Analysis results

[1316] Output: Appropriate medical institution information

[1317] Step 7:

[1318] The server structures the medical institution information it has acquired in list format and sends it to the terminal. For example, the following list is presented to the user.

[1319] List of medical institutions:

[1320] Internal Medicine Clinic (Address: Chuo Ward, Contact: 0123-456-789)

[1321] Medical Center (Address: Minato-ku, Contact: 0987-654-321)

[1322] Input: Acquired medical institution information

[1323] Output: Medical institution information in a structured list format on the user's terminal

[1324] Step 8:

[1325] The server accesses the over-the-counter drug database and retrieves appropriate over-the-counter drug information based on the analysis results. For example, the following over-the-counter drug information can be obtained:

[1326] Over-the-counter drug information:

[1327] Acetaminophen (type: antipyretic analgesic, usage: 1 tablet 3 times a day)

[1328] The terminal displays this information to the user.

[1329] Input: Analysis results

[1330] Output: Pertinent over-the-counter drug information and its use

[1331] Step 9:

[1332] The user selects the desired medical institution from the provided list of medical institutions and enters the reservation information. For example, if the user selects "Internal Medicine Clinic" and "October 2nd, 3:00 PM," the corresponding reservation request is sent to the server.

[1333] Input: Reservation information (name of medical institution, desired consultation date and time)

[1334] Output: A reservation request is sent to the server

[1335] Step 10:

[1336] The server receives the reservation request and confirms the reservation in cooperation with the medical institution's reservation system. For example, the server uses a clinic's reservation management API to receive information that the reservation has been confirmed, and then notifies the user's device.

[1337] Input: Reservation Request

[1338] Output: The confirmed reservation information is sent to the user's device.

[1339] Step 11:

[1340] After the consultation, the user enters progress information into the app. For example, they can enter information such as "symptoms have not improved" or "new symptoms have appeared." The device then sends this information to the server, which then analyzes it again using its artificial intelligence engine and emotion engine. If necessary, the app will recommend a follow-up visit and will refer the patient to a medical institution and make a reservation again.

[1341] Input: Progress information

[1342] Output: Recommended follow-up visit information and referral to a medical institution and reservation procedures

[1343] The above are the specific processing steps of the program in the system of the present invention.

[1344] (Application example 2)

[1345] 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."

[1346] Current autonomous vehicle systems lack the functionality to respond quickly and appropriately to the health conditions of passengers. Furthermore, they do not come equipped with a standard system to guide passengers to the most appropriate medical facility in an emergency, which risks delaying response in the event of an emergency. Furthermore, they lack sufficient functionality to reassure passengers who may be in an unstable emotional state during an emergency. To solve these issues, a unified system is needed that can handle everything from inputting health information to navigating to medical facilities and stabilizing emotions.

[1347] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1348] In this invention, the server includes: means for inputting medical interview information; means for transmitting the input medical interview information to the server; means for transmitting the received medical interview information to an artificial intelligence engine for analysis by the server; means for the artificial intelligence engine to analyze the medical interview information and output recommended actions corresponding to the symptoms and possible disease names; means for the server to receive the analysis results and access a medical institution database to obtain and transmit appropriate medical institution information; means for presenting the obtained medical institution information and recommended actions to the user; a vehicle interface having means for the occupant to input their health condition; means for the vehicle to automatically guide the occupant to the most appropriate medical institution based on the analyzed information; and means for adjusting the in-vehicle environment (music, audio guidance, lighting, etc.) to stabilize the occupant's emotions. This enables prompt and appropriate response to the occupant's health condition and makes it possible to guide the occupant to the most appropriate medical institution while reassuring them even in an emergency.

[1349] "Medical interview information" is data entered by passengers or patients about their own health conditions and symptoms.

[1350] A "server" is a computer system that stores, processes, and provides data over a network.

[1351] "Artificial intelligence engine" refers to the algorithms and computational models used to analyze medical interview information and output possible disease diagnoses and recommended actions.

[1352] A "medical institution database" is a database that stores and makes searchable information about medical facilities and medical professionals.

[1353] A "vehicle interface" is a device that includes an operating means for an occupant of an autonomous vehicle to input health information.

[1354] "Autonomous driving" is a technology that allows vehicles to move and change direction autonomously without human operation.

[1355] The "emotion engine" is a system that analyzes the emotional state of the occupants and takes appropriate action based on the results.

[1356] "Analysis results" are output information obtained when the artificial intelligence engine and emotion engine process the input data.

[1357] "Recommended actions" are specific actions that should be taken by the user or passengers, which are proposed based on the analysis results.

[1358] A "medical institution appointment system" is software and services for making appointments for medical appointments at medical facilities.

[1359] This invention is a system in which the driver inputs their health condition, and based on that information, a server analyzes it by linking an artificial intelligence engine and an emotion engine, and then guides the driver in the self-driving vehicle to the appropriate medical institution and treatment method. This system comprehensively covers the input and analysis of medical history information, medical institution referral, appointment confirmation, over-the-counter drug suggestions, customization using emotion recognition, and follow-up.

[1360] The server includes a means for inputting medical interview information, a means for transmitting the input medical interview information, a means for transmitting the received medical interview information to an artificial intelligence engine, a means for the artificial intelligence engine to analyze the medical interview information and output recommended actions and possible disease names, a means for receiving the analysis results and accessing a medical institution database to obtain and transmit information on appropriate medical institutions, a means for presenting the obtained medical institution information and recommended actions to the user, a vehicle interface for the occupant to input their health condition, a means for the vehicle to automatically guide the occupant to the most appropriate medical institution based on the analysis information, and a means for adjusting the in-vehicle environment to stabilize the emotions of the occupant.

[1361] Program processing

[1362] First, the occupant uses an interface installed in the vehicle to input their symptoms. For example, a touchscreen display or a voice recognition system is used. When the occupant inputs symptoms such as "chest pain" or "difficulty breathing," the data is collected by the vehicle's on-board computer. The collected data is then sent to a cloud server via the network. The ONVIF protocol is used for data transmission.

[1363] The data is structured and stored in JSON format on a cloud server. The server then sends the data to an artificial intelligence engine (e.g., a deep learning model using TensorFlow) and an emotion engine (e.g., Microsoft's Azure Cognitive Services). The artificial intelligence engine analyzes the data and outputs a possible diagnosis and recommended actions. The emotion engine estimates the passenger's emotional state (e.g., "anxiety") and influences the analysis results.

[1364] The analysis results and estimated emotional state are sent to the server. For example, if the analysis results indicate a high possibility of "acute myocardial infarction," the recommended action would be to "go directly to an emergency medical facility." If the emotional state is estimated to be "anxiety," the vehicle's environment (music playback, voice guidance tone change, lighting adjustment, etc.) will be automatically adjusted to reassure the occupants.

[1365] Based on the analysis results, the server searches a database of medical institutions to obtain information on the most suitable medical institution. For example, the nearest emergency hospital or cardiologist may be selected as a candidate. The obtained medical institution information is sent to the vehicle, and the autonomous driving system (such as Waymo's autonomous driving system) navigates the occupant to the medical institution.

[1366] Specific examples

[1367] Below are examples of specific prompt sentences to input into the generative AI model.

[1368] User dictation: "My chest hurts and I'm having trouble breathing... I'm so scared..."

[1369] System Response:

[1370] 1. Analyze symptoms: "chest pain," "difficulty breathing," "fear"

[1371] 2. Search for emergency medical facilities: "Nearby emergency hospital" "Cardiologist"

[1372] 3. Emotional response: "Please stay calm. I will get you to the nearest emergency room right away."

[1373] The above processing enables prompt and appropriate responses to the health conditions of passengers, and in the event of an emergency, navigation to the most appropriate medical institution is possible. In addition, the use of an emotion engine can increase passenger peace of mind.

[1374] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1375] Step 1:

[1376] The user uses the vehicle's in-vehicle interface (touchscreen display or voice recognition system) to input their symptoms. The user inputs specific symptoms (e.g., "chest pain," "difficulty breathing," "headache," etc.), and the information is sent to the vehicle's on-board computer. The input data is formatted and collected. Examples of input data:

[1377] "My chest hurts. I'm having trouble breathing."

[1378] Step 2:

[1379] The terminal (vehicle's on-board computer) sends the collected data to a cloud server via the network. This data is usually structured in JSON format, and the on-board computer uses the ONVIF protocol for transmission. Example of transmitted data:

[1380] json

[1381] {

[1382] "symptoms": ["chest pain", "difficulty breathing"]

[1383] }

[1384] Step 3:

[1385] The server sends the received medical interview information to an artificial intelligence engine for analysis. The AI ​​model used is, for example, a deep learning model using TensorFlow. The server inputs the received data into the AI ​​engine for analysis. Input data:

[1386] json

[1387] {

[1388] "symptoms": ["chest pain", "difficulty breathing"]

[1389] }

[1390] Example of output data:

[1391] json

[1392] {

[1393] "possible_conditions": ["acute myocardial infarction"],

[1394] "recommendations": ["Go straight to emergency medical care"]

[1395] }

[1396] Step 4:

[1397] The server sends the interview information to the emotion engine, which analyzes the user's emotional state. The emotion recognition system used is, for example, Azure Cognitive Services. The emotion engine infers the user's emotional state based on voice and text input. Input data:

[1398] json

[1399] {

[1400] "text": "My chest hurts. I'm having trouble breathing. I'm so scared."

[1401] }

[1402] Example of output data:

[1403] json

[1404] {

[1405] "emotional_state": "anxiety"

[1406] }

[1407] Step 5:

[1408] The server receives the analysis results from the AI ​​and emotion engine and selects the most suitable medical institution based on that. The server accesses the medical institution database to obtain information on appropriate medical institutions. For example, emergency hospitals and medical institutions specializing in cardiac care are prioritized. Input data:

[1409] json

[1410] {

[1411] "possible_conditions": ["acute myocardial infarction"],

[1412] "emotional_state": "anxiety"

[1413] }

[1414] Example of output data:

[1415] json

[1416] {

[1417] "medical_facilities": [{"name": "Regional Emergency Hospital", "address": "XX City △△ Town", "contact": "012-3456-7890"}]

[1418] }

[1419] Step 6:

[1420] The server sends the acquired medical institution information to the vehicle, and the autonomous driving system navigates the vehicle. The vehicle's autonomous driving system (such as Waymo's system) calculates the shortest route based on the received data and begins navigating to the medical institution. Input data:

[1421] json

[1422] {

[1423] "medical_facilities": [{"name": "Regional Emergency Hospital", "address": "XX City △△ Town", "contact": "012-3456-7890"}]

[1424] }

[1425] Specific operation: The autonomous vehicle departs for the designated medical facility.

[1426] Step 7:

[1427] The server adjusts the environment inside the vehicle based on the emotion analysis results, such as playing music, changing the tone of the voice guidance, and adjusting the lighting. For example, if the emotion "anxiety" is detected, it will play relaxing music to give the passengers a sense of security. Input data:

[1428] json

[1429] {

[1430] "emotional_state": "anxiety"

[1431] }

[1432] Specific actions: Playing music, warm voice guidance from the speaker, changing the lighting to a warmer color, etc.

[1433] The above is a series of processing steps for the inference and operation of the "Smart Emergency Navigation" system. This system not only quickly and accurately assesses the health condition of the occupants and navigates them to the appropriate medical institution, but also adjusts the environment to reduce the anxiety of the occupants in an emergency, allowing them to arrive at the medical institution with peace of mind.

[1434] 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.

[1435] 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.

[1436] 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.

[1437] [Third embodiment]

[1438] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1439] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1440] 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).

[1441] 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.

[1442] 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.

[1443] 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).

[1444] 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.

[1445] 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.

[1446] 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.

[1447] 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.

[1448] 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.

[1449] 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."

[1450] This system allows patients to input their symptoms anywhere, and an AI engine analyzes the information to recommend appropriate medical institutions and treatment methods. This system covers the entire process, from inputting medical history information to analysis, referring appropriate medical institutions, confirming appointments, suggesting over-the-counter medications, and providing follow-up care.

[1451] System configuration

[1452] The system consists of the following main components:

[1453] 1. Means of inputting medical interview information

[1454] 2. Means of sending medical interview information

[1455] 3. Analysis of medical interview information

[1456] 4. Means of accessing medical institution databases

[1457] 5. Access to the over-the-counter drug database

[1458] 6. Medical institution reservation methods

[1459] 7. Follow-up measures after consultation

[1460] Explanation of program processing

[1461] 1. Enter medical interview information

[1462] Through the application, users enter their symptoms into a questionnaire, for example, "fever, sore throat, cough, general fatigue," and this information is stored on the device.

[1463] 2. Sending medical interview information

[1464] The terminal sends the entered medical interview information to the server. The data sent is structured in JSON format, for example, as shown below.

[1465] json

[1466] {

[1467] "temperature": "38 degrees",

[1468] "symptoms": ["sore throat", "cough", "general fatigue"]

[1469] }

[1470] 3. Analysis of medical interview information

[1471] The server sends the received medical interview information to the AI ​​engine, which analyzes the information and obtains the following diagnosis results:

[1472] json

[1473] {

[1474] "possible_conditions": ["acute pharyngitis", "influenza"],

[1475] "recommendations": ["Seek medical help", "Use over-the-counter pain relievers"]

[1476] }

[1477] The server receives the analysis results and uses them for the next process.

[1478] 4. Referral to medical institutions

[1479] Based on the analysis results, the server accesses the medical institution database and creates a list of nearby appropriate medical institutions. For example, the following list is generated:

[1480] json

[1481] [

[1482] {"name": "Internal Medicine Clinic, Chuo Ward, Tokyo", "address": "Chuo Ward, Tokyo ~", "contact": "0123-456-789"},

[1483] {"name": "Tokyo Medical Center", "address": "Minato-ku, Tokyo", "contact": "0987-654-321"}

[1484] ]

[1485] The server sends this information to the terminal and presents it to the user.

[1486] 5. Over-the-counter medication suggestions

[1487] The server accesses the over-the-counter drug database and retrieves appropriate over-the-counter drug information based on the analysis results. For example, the following over-the-counter drug information can be obtained:

[1488] json

[1489] [

[1490] {"name": "Acetaminophen", "type": "Antipyretic analgesic", "usage": "One tablet, three times a day"}

[1491] ]

[1492] This information is also provided to the user.

[1493] 6. Medical appointments

[1494] The user selects a medical institution from the provided list of medical institutions and enters reservation information. The terminal sends a reservation request to the server. For example, the format is as follows:

[1495] json

[1496] {

[1497] "clinic_id": "1",

[1498] "user_id": "1001",

[1499] "appointment_date": "2023-10-15",

[1500] "appointment_time": "10:00"

[1501] }

[1502] The server passes this information to the medical institution's system to confirm the appointment, after which a notification of the appointment is sent to the terminal.

[1503] 7. Follow-up after consultation

[1504] After the consultation, the user enters progress information into the app. The device then sends this information to the server. The server then uses the AI ​​engine to analyze the progress information again and recommends a follow-up visit if necessary. If a follow-up visit is necessary, the user is referred to a medical institution and an appointment is made.

[1505] In this way, this system analyzes patient interview information and quickly introduces appropriate medical institutions and over-the-counter medications, simplifies the appointment process, and provides follow-up after the consultation, thereby providing consistent medical support to patients.

[1506] The processing flow will be explained below.

[1507] Step 1:

[1508] The user opens the application. The user enters their symptom information into the input screen. For example, they enter "fever, sore throat, cough, general fatigue." Once they have completed the input, they press the "Submit" button.

[1509] Step 2:

[1510] The device collects the medical interview information entered by the user, structures the information in JSON format, and prepares it for transmission.

[1511] Step 3:

[1512] The terminal sends the formatted and structured medical interview information to the server. The following JSON format is used as an example of the data to be sent:

[1513] json

[1514] {

[1515] "temperature": "38 degrees",

[1516] "symptoms": ["sore throat", "cough", "general fatigue"]

[1517] }

[1518] Step 4:

[1519] The server receives the medical interview information sent from the terminal, checks the integrity of the received data, and stores it in a database.

[1520] Step 5:

[1521] The server sends the stored medical interview information to the AI ​​engine, which then calls the AI ​​engine's API and creates a request to analyze the medical interview information.

[1522] Step 6:

[1523] The AI ​​engine analyzes the medical interview information. The analysis results include recommended actions to take based on the symptoms and possible illnesses. For example, the analysis results shown below are output in JSON format.

[1524] json

[1525] {

[1526] "possible_conditions": ["acute pharyngitis", "influenza"],

[1527] "recommendations": ["Seek medical help", "Use over-the-counter pain relievers"]

[1528] }

[1529] Step 7:

[1530] The server receives the analysis results from the AI ​​engine and proceeds to the next step based on the received analysis results.

[1531] Step 8:

[1532] The server accesses a medical institution database based on the analysis results, searches for medical institutions that match the disease name in the analysis results, and obtains information on nearby medical institutions.

[1533] Step 9:

[1534] The server generates the medical institution information it has acquired in list format. For example, the following list is generated in JSON format:

[1535] json

[1536] [

[1537] {"name": "Internal Medicine Clinic, Chuo Ward, Tokyo", "address": "Chuo Ward, Tokyo ~", "contact": "0123-456-789"},

[1538] {"name": "Tokyo Medical Center", "address": "Minato-ku, Tokyo", "contact": "0987-654-321"}

[1539] ]

[1540] This medical institution information is sent to the terminal and presented to the user.

[1541] Step 10:

[1542] The server accesses the over-the-counter drug database and retrieves appropriate over-the-counter drug information based on the analysis results. The retrieved over-the-counter drug information is structured in JSON format and sent to the terminal as follows:

[1543] json

[1544] [

[1545] {"name": "Acetaminophen", "type": "Antipyretic analgesic", "usage": "One tablet, three times a day"}

[1546] ]

[1547] The terminal presents over-the-counter drug information to the user.

[1548] Step 11:

[1549] The user selects a medical institution from the provided list of medical institutions. Based on the information about the selected medical institution, the user inputs reservation information, for example, specifying the desired date and time for the examination.

[1550] Step 12:

[1551] The device sends the user's reservation request to the server. The following JSON format is used as an example of the data sent:

[1552] json

[1553] {

[1554] "clinic_id": "1",

[1555] "user_id": "1001",

[1556] "appointment_date": "2023-10-15",

[1557] "appointment_time": "10:00"

[1558] }

[1559] Step 13:

[1560] The server receives the reservation request and confirms the reservation in cooperation with the medical institution's reservation system. The reservation information is sent to the medical institution, which confirms the reservation. The server then sends the reservation confirmation information to the terminal and notifies the user.

[1561] Step 14:

[1562] After the examination, the user enters progress information into the app. For example, they enter information such as "the fever continues" or "the general feeling of fatigue is increasing." Once the information is entered, the device sends the progress information to the server.

[1563] Step 15:

[1564] Based on the progress information received by the server, the AI ​​engine is requested to perform another analysis. The AI ​​engine analyzes the progress information and evaluates the need for a follow-up visit. If a follow-up visit is recommended, the server searches again for a medical institution that requires a follow-up visit and introduces it to the user. It also completes the follow-up appointment procedure.

[1565] These are the specific processing steps of the program for this system. Through this series of processes, users can quickly and accurately find a medical institution and receive appropriate examinations and follow-up care.

[1566] Example 1

[1567] 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."

[1568] Conventional medical consultation systems are often limited to allowing patients to input their symptoms and introducing appropriate medical institutions and treatment methods, and lack sufficient functionality to assess the need for follow-up or re-examination after the consultation. Furthermore, they are unable to consistently suggest over-the-counter medications or schedule appointments at medical institutions, making it difficult to provide consistent medical support to patients.

[1569] 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.

[1570] In this invention, the server includes: means for inputting medical interview information; means for transmitting the input medical interview information to the server; means for the server to transmit the received medical interview information to an artificial intelligence engine for analysis; means for the artificial intelligence engine to analyze the medical interview information and output recommended actions corresponding to the symptoms and possible disease names; means for the server to receive the analysis results and access a medical institution database to obtain and transmit appropriate medical institution information; means for presenting the obtained medical institution information and recommended actions to the user; means for the server to transmit appointment information to a medical institution selected by the user and confirm the appointment in cooperation with the medical institution's system; and means for the user to input progress information after the consultation, and for the server to have the artificial intelligence engine analyze it again and recommend a re-examination. This makes it possible to provide consistent medical support to patients and evaluate the need for follow-up and re-examination after the consultation.

[1571] "Medical interview information" is information that a user inputs through an application regarding their own health condition and symptoms.

[1572] "Server" refers to the central system that receives, analyzes, and processes the medical interview information.

[1573] The "artificial intelligence engine" is a software module that analyzes the input medical interview information and outputs recommended actions and possible disease names corresponding to the symptoms.

[1574] The "medical institution database" is a database that stores information about medical institutions, and is used by the server to access and acquire appropriate medical institution information.

[1575] The "over-the-counter drug database" is a database that stores information about various over-the-counter drugs, and is used by the server to access and obtain appropriate over-the-counter drug information.

[1576] The "reservation system" is an online system that works in conjunction with medical institutions to process reservations.

[1577] "Progress information" is information about the health condition and progress of symptoms that the user inputs through the application after the medical examination.

[1578] "Recommended actions" refer to appropriate actions suggested to the user as a result of the AI ​​engine analyzing the medical interview information.

[1579] Defining these terms will help you understand the system better.

[1580] The present invention is a system in which a patient inputs their symptoms anywhere, an artificial intelligence engine analyzes the information, and introduces appropriate medical institutions and treatment methods. Specific embodiments for carrying out the present invention are described in detail below.

[1581] First, users enter their symptoms using a dedicated application installed on their smartphone, tablet, or other device. During this process, users enter specific symptoms such as fever, sore throat, cough, and general fatigue using text boxes and drop-down menus. For example, if a user enters "fever, sore throat, cough, and general fatigue," each piece of information is stored in a local database on the device.

[1582] Next, the device serializes the saved medical interview information into JSON format and sends it to the server via the communication module. At this time, the following format is used as an example of the data sent.

[1583] json

[1584] {

[1585] "temperature": "38 degrees",

[1586] "symptoms": ["sore throat", "cough", "general fatigue"]

[1587] }

[1588] The server makes an API request to send the received medical interview information to the AI ​​engine. The AI ​​engine analyzes the medical interview information and outputs recommended actions and possible illnesses corresponding to the symptoms. For example, the following diagnosis results may be obtained:

[1589] json

[1590] {

[1591] "possible_conditions": ["acute pharyngitis", "influenza"],

[1592] "recommendations": ["Seek medical help", "Use over-the-counter pain relievers"]

[1593] }

[1594] The server receives the analysis results and accesses the medical institution database to create a list of appropriate nearby medical institutions. This list is converted into JSON format and sent to the device. The device then presents the appropriate medical institution information to the user based on the received information. The user can view the list on the application and select as needed.

[1595] In addition, the server also accesses the over-the-counter drug database and obtains appropriate over-the-counter drug information based on the analysis results. For example, the following over-the-counter drug information can be obtained:

[1596] json

[1597] {

[1598] "name": "Acetaminophen",

[1599] "type": "Antipyretic analgesic",

[1600] "usage": "One tablet, three times a day"

[1601] }

[1602] The acquired over-the-counter drug information is also provided to the user, who can check the over-the-counter drug information on the application.

[1603] When the user selects an appropriate medical institution from the list of medical institutions and enters reservation information, the terminal sends a reservation request to the server. The server connects this information to the reservation system and confirms the reservation. A reservation confirmation notification is then sent to the terminal, allowing the user to confirm the reservation details.

[1604] After the consultation, the user enters progress information into the application. This progress information is sent from the device to the server, where it is analyzed again using an artificial intelligence engine to determine whether a follow-up visit is necessary. If necessary, the server will recommend a follow-up visit and assist with the referral and appointment procedures at a medical institution.

[1605] Specific examples

[1606] For example, a user opens the application and enters their symptoms as "fever, sore throat, cough, and general fatigue." The system then collects this information, and an artificial intelligence engine analyzes it. The system suggests the possibility of "acute pharyngitis" or "influenza," and recommends nearby medical institutions such as an "internal medicine clinic" or "medical center." It also suggests "acetaminophen" as an appropriate over-the-counter medication. If the user selects "internal medicine clinic" and sets the appointment date to "2023-10-15," the system confirms the appointment and notifies the user.

[1607] Example prompts for generative AI models

[1608] "I have a fever, a sore throat, and a severe cough. I also feel fatigued. Can you diagnose my illness based on these symptoms and tell me where to find a nearby medical facility? Also, can you recommend any over-the-counter medicines?"

[1609] In this way, this system analyzes patient interview information, quickly introduces appropriate medical institutions and over-the-counter medications, simplifies appointment procedures, and also provides follow-up after consultation, thereby providing consistent medical support to patients.

[1610] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1611] Step 1:

[1612] Enter medical interview information

[1613] The user uses the application to enter their symptoms into a questionnaire. In the input field, they write specific symptoms such as "fever, sore throat, cough, general fatigue." The device stores this information in a local database. The input data format is as follows:

[1614] input:

[1615] Symptoms: fever, sore throat, cough, general fatigue

[1616] output:

[1617] Medical interview information stored in a local database

[1618] Step 2:

[1619] Sending medical interview information

[1620] The terminal serializes the medical interview information stored in the local database into JSON format and sends it to the server via the communication module.

[1621] input:

[1622] Medical interview information stored in a local database

[1623] Data processing:

[1624] Serialize to JSON format

[1625] output:

[1626] Send serialized data to the server

[1627] Specific working example:

[1628] json

[1629] {

[1630] "temperature": "38 degrees",

[1631] "symptoms": ["sore throat", "cough", "general fatigue"]

[1632] }

[1633] Step 3:

[1634] Analysis of medical interview information

[1635] The server makes an API request to send the received medical interview information to the AI ​​engine, which analyzes the information and outputs recommended actions and possible diagnoses based on the symptoms.

[1636] input:

[1637] Interview information sent to the server

[1638] Data processing:

[1639] Analysis by artificial intelligence engine

[1640] output:

[1641] A list of symptoms, suggested actions, and possible illnesses

[1642] Specific working example:

[1643] json

[1644] {

[1645] "possible_conditions": ["acute pharyngitis", "influenza"],

[1646] "recommendations": ["Seek medical help", "Use over-the-counter pain relievers"]

[1647] }

[1648] Step 4:

[1649] Medical institution introduction

[1650] Based on the analysis results, the server accesses a medical institution database and lists appropriate nearby medical institutions.

[1651] input:

[1652] Analysis results by artificial intelligence engine

[1653] Data processing:

[1654] Use SQL queries to retrieve information from medical institution databases

[1655] output:

[1656] A list of suitable medical institutions

[1657] Specific working example:

[1658] json

[1659] [

[1660] {"name": "Internal Medicine Medical Institution A", "address": "Address A", "contact": "Telephone A"},

[1661] {"name": "Internal Medicine Medical Institution B", "address": "Address B", "contact": "Telephone B"}

[1662] ]

[1663] The server sends this information to the terminal and presents it to the user.

[1664] Step 5:

[1665] Over-the-counter medication suggestions

[1666] The server accesses the over-the-counter drug database and retrieves appropriate over-the-counter drug information based on the analysis results.

[1667] input:

[1668] Analysis results by artificial intelligence engine

[1669] Data processing:

[1670] Use SQL queries to retrieve information from a database of over-the-counter drugs

[1671] output:

[1672] List of appropriate over-the-counter medications

[1673] Specific working example:

[1674] json

[1675] [

[1676] {"name": "Acetaminophen", "type": "Antipyretic analgesic", "usage": "One tablet, three times a day"}

[1677] ]

[1678] The server sends this information to the terminal and provides it to the user.

[1679] Step 6:

[1680] Medical appointment

[1681] The user selects a medical institution from the provided list and enters reservation information. The terminal then sends a reservation request to the server.

[1682] input:

[1683] Booking information entered by the user

[1684] Data processing:

[1685] Serialize to JSON format

[1686] output:

[1687] Send reservation information to the server

[1688] Specific working example:

[1689] json

[1690] {

[1691] "clinic_id": "1",

[1692] "user_id": "1001",

[1693] "appointment_date": "2023-10-15",

[1694] "appointment_time": "10:00"

[1695] }

[1696] The server connects this information to the medical institution's reservation system to confirm the reservation, after which a notification of reservation confirmation is sent to the terminal.

[1697] Step 7:

[1698] Follow-up after the examination

[1699] After the consultation, the user enters progress information into the application, and the terminal sends this information to the server.

[1700] input:

[1701] Progress information entered by the user

[1702] Data processing:

[1703] Serialize to JSON format

[1704] output:

[1705] Send progress information to the server

[1706] Specific working example:

[1707] json

[1708] {

[1709] "user_id": "1001",

[1710] "symptom_update": "Sore throat relieved"

[1711] }

[1712] The server then uses its AI engine to analyze the data again and determine whether a follow-up visit is necessary. If necessary, the server recommends a follow-up visit to the user and assists with the referral and appointment procedures.

[1713] (Application example 1)

[1714] 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."

[1715] Currently, when a patient feels unwell, it is difficult to immediately find an appropriate medical institution or over-the-counter medication and make an appointment. Furthermore, if a patient's condition suddenly worsens at work or in a public place, it is difficult to respond quickly and to access an appropriate medical institution. This creates a risk of worsening the patient's condition, so there is a need for a method to monitor the user's health in real time and respond quickly when an abnormality is detected.

[1716] 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.

[1717] In this invention, the server includes: a means for inputting medical interview information; a means for transmitting the input medical interview information to the server; a means for transmitting the received medical interview information to an artificial intelligence engine for analysis; a means for the artificial intelligence engine to analyze the medical interview information and output recommended actions corresponding to the symptoms and possible disease names; a means for the server to receive the analysis results, access a medical institution database to acquire and transmit appropriate medical institution information; a means for presenting the acquired medical institution information and recommended actions to the user; and a means for monitoring the user's health status in real time and automatically notifying a medical institution if an abnormality is detected. This allows users to immediately find appropriate medical institutions and over-the-counter medications and make appointments when they feel unwell. It also enables users to respond quickly to sudden changes in their health at work or in public places, preventing the condition from worsening.

[1718] "Medical interview information" is data entered by a patient about their health condition and symptoms.

[1719] "Input means" refers to a method or device for inputting medical interview information into a terminal or the like.

[1720] The "transmission means" refers to a method or device for transmitting the input medical interview information to the server.

[1721] An "artificial intelligence engine" is an algorithm or program that analyzes medical interview information and outputs recommended actions for symptoms and possible disease names.

[1722] "Analysis results" are information regarding diagnoses and recommended actions derived by the artificial intelligence engine based on the medical interview information.

[1723] The "medical institution database" is a database that stores information on various medical institutions.

[1724] "Medical institution information" refers to basic information such as the name, address, and contact information of the medical institution.

[1725] "Real-time monitoring" refers to the act of continuously monitoring a user's health status and immediately processing that information.

[1726] "Anomaly detection" is the act of recognizing abnormal health conditions from monitored data.

[1727] "Over-the-counter" drugs are medicines that can be purchased without a doctor's prescription.

[1728] A "reservation means" is a method or device for reserving a date and time for visiting a medical institution in advance.

[1729] To implement the invention, a system is constructed that uses the following components:

[1730] Hardware used

[1731] Smartphones (e.g. iPhone, Android devices)

[1732] Smart glasses (e.g., Google Glass, Microsoft HoloLens)

[1733] Software used

[1734] Python Program

[1735] Python libraries: requests, json, datetime

[1736] System configuration

[1737] 1. Input method for medical interview information: Users use a smartphone or smart glasses to input their medical interview information, such as their symptoms and temperature, through the application interface. This information is then stored on the device by the application.

[1738] 2. Method for transmitting medical interview information: The terminal converts the entered medical interview information into JSON format and sends it to the server via a secure HTTP POST request. The transmitted data is structured in JSON format.

[1739] 3. Analysis of medical interview information: The server sends the received medical interview information to the AI ​​engine for analysis. The AI ​​engine analyzes the input information and outputs recommended actions for the symptoms and possible illnesses.

[1740] 4. Medical institution database access means: Based on the analysis results, the server accesses the medical institution database and obtains information on appropriate nearby medical institutions.

[1741] 5. Means for accessing the over-the-counter drug database: Based on the analysis results, the server accesses the over-the-counter drug database and obtains the appropriate over-the-counter drug information.

[1742] 6. Medical institution reservation means: The user selects a medical institution from the list of medical institutions presented and enters reservation information. The terminal then sends a reservation request to the server and makes a reservation at the medical institution.

[1743] 7. Post-consultation follow-up: After the consultation, the user enters progress information through the application, and the terminal sends this information to the server. The server then requests the AI ​​engine to analyze it and provide recommendations for follow-up visits or additional actions.

[1744] 8. Real-time monitoring: The device monitors the user's health status in real time and automatically notifies nearby medical institutions if an abnormality is detected. This function allows for quick response to sudden illness.

[1745] Specific examples

[1746] For example, if a user inputs symptoms such as "fever," "headache," and "cough" into their smartphone, the AI ​​engine will analyze this and determine the possibility of "influenza" and recommend the use of antipyretics and analgesics. The server will obtain information on nearby medical institutions and present a list to the user. The user can then select a medical institution from the list, make an appointment, and confirm the information. Furthermore, if the user's health condition suddenly changes during treatment, the device will automatically notify the medical institution, enabling a prompt response.

[1747] Prompt Sentence Examples

[1748] "Please tell me the best medical institution and over-the-counter medication for a patient with a temperature of 38 degrees and symptoms of sore throat, headache, and cough."

[1749] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1750] Step 1:

[1751] The user inputs the medical interview information.

[1752] input:

[1753] Symptoms (e.g., fever, headache, cough)

[1754] Body temperature (e.g. 38 degrees)

[1755] Operation:

[1756] Using a smartphone or smart glasses interface, users input their symptoms and temperature.

[1757] The entered information is saved by an application in the device.

[1758] output:

[1759] Saved medical interview information (JSON format)

[1760] Step 2:

[1761] The terminal transmits the medical interview information to the server.

[1762] input:

[1763] JSON format medical interview information

[1764] Operation:

[1765] The device converts the medical interview information into JSON format and sends it to the server using a secure HTTP POST request.

[1766] output:

[1767] Interview information sent to the server

[1768] Step 3:

[1769] The server sends the medical interview information to an artificial intelligence engine for analysis.

[1770] input:

[1771] Medical interview information arrived at the server

[1772] Operation:

[1773] The server sends the received medical interview information to the artificial intelligence engine.

[1774] An artificial intelligence engine analyzes the medical history information and generates recommended actions and possible diagnoses.

[1775] output:

[1776] Analysis results (recommended actions and possible illnesses)

[1777] Step 4:

[1778] The server receives the analysis results and accesses the medical institution database to obtain the appropriate medical institution information.

[1779] input:

[1780] Analysis results

[1781] Operation:

[1782] The server receives the analysis results and accesses the medical institution database based on that data.

[1783] Obtain information on appropriate medical facilities in the vicinity.

[1784] output:

[1785] List of medical institution information (JSON format)

[1786] Step 5:

[1787] The server presents the acquired medical institution information and recommended actions to the user.

[1788] input:

[1789] List of medical institution information

[1790] Recommended Actions

[1791] Operation:

[1792] The server sends the acquired medical institution information and recommended actions to the user's terminal.

[1793] output:

[1794] Medical institution information and recommended actions displayed on the user's screen

[1795] Step 6:

[1796] The user selects from a list of medical institutions and enters reservation information. The terminal sends a reservation request to the server and makes a reservation at the medical institution.

[1797] input:

[1798] Medical institution selected by the user

[1799] Reservation information (date and time, user ID)

[1800] Operation:

[1801] The user selects a medical institution from the list of medical institutions presented.

[1802] The user inputs reservation information, and the terminal transmits the information to the server.

[1803] The server connects with the medical institution's reservation system to confirm the reservation.

[1804] output:

[1805] Reservation confirmation notification

[1806] Step 7:

[1807] The device monitors the user's health status in real time and automatically notifies a medical institution if any abnormalities are detected.

[1808] input:

[1809] Health data collected in real time

[1810] Operation:

[1811] The device continuously monitors the user's health status.

[1812] If abnormal data is detected, the terminal sends the information to a server, which automatically notifies nearby medical institutions.

[1813] output:

[1814] Notifying medical institutions if abnormalities are detected

[1815] Step 8:

[1816] After the consultation, the user uses the follow-up tool to input progress information, which is then analyzed by the server again, and a follow-up consultation or additional recommended actions are made as needed.

[1817] input:

[1818] Progress information after examination

[1819] Operation:

[1820] The user enters progress information into the application.

[1821] The terminal sends this information to the server.

[1822] The server then uses an artificial intelligence engine to analyze the data again and determine recommended actions and follow-up appointments.

[1823] output:

[1824] Notification of follow-up visits and additional recommended actions

[1825] 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.

[1826] This invention is a system in which patients input their symptoms, and based on that information, an AI engine and an emotion engine are linked to perform analysis and recommend appropriate medical institutions and treatment methods. This system comprehensively covers everything from inputting medical history information, analysis, referring to medical institutions, confirming appointments, suggesting over-the-counter medications, customizing using emotion recognition, and follow-up.

[1827] System configuration

[1828] The system consists of the following main components:

[1829] 1. Means of inputting medical interview information

[1830] 2. Means of sending medical interview information

[1831] 3. Analysis of medical interview information

[1832] 4. Emotion recognition means

[1833] 5. Means of accessing medical institution databases

[1834] 6. Access to the over-the-counter drug database

[1835] 7. Medical institution reservation methods

[1836] 8. Follow-up measures after consultation

[1837] Explanation of program processing

[1838] 1. Enter medical interview information

[1839] Using the application, users enter their symptoms and basic information into a questionnaire. For example, a user might enter symptoms such as "fever, sore throat, cough, and general fatigue." Input data also includes current body temperature and the time of onset of symptoms. Once input is complete, the user presses the "Submit" button.

[1840] 2. Sending medical interview information and emotional information

[1841] The terminal collects the questionnaire information and emotional information entered by the user. Examples of emotional information include keywords entered in text and facial expression data detected by a face recognition system.

[1842] 3. Send to the server

[1843] The device sends the interview information and emotion information to the server, where the data is structured in JSON format and prepared for storage and analysis.

[1844] 4. Analysis of interview information and emotional information

[1845] The server sends the received medical interview information to the AI ​​engine and the emotional information to the emotion engine. The AI ​​engine analyzes the medical interview information and outputs possible illnesses and recommended actions. The emotion engine analyzes the emotional information and estimates the emotional state. The estimation results affect the analysis results of the AI ​​engine.

[1846] 5. Obtaining diagnostic results

[1847] The server receives the analysis results from the AI ​​engine and emotion engine. The diagnosis results include the following information:

[1848] json

[1849] {

[1850] "possible_conditions": ["acute pharyngitis", "influenza"],

[1851] "recommendations": ["Seek medical attention", "Use over-the-counter pain relievers"],

[1852] "emotional_state": "anxiety"

[1853] }

[1854] 6. Referral to medical institutions

[1855] The server accesses a medical institution database based on the analysis results. It searches for medical institutions that match the disease name and emotional state of the analysis results and obtains information on nearby medical institutions. For example, if a patient is in an anxious state, it can prioritize referrals to medical institutions that can provide psychological care.

[1856] 7. Generating and sending a list of medical institutions

[1857] The server structures the medical institution information it has acquired in list format and sends it to the terminal. For example, the following list is presented to the user.

[1858] json

[1859] [

[1860] {"name": "Internal Medicine Clinic, Chuo Ward, Tokyo", "address": "Chuo Ward, Tokyo ~", "contact": "0123-456-789"},

[1861] {"name": "Tokyo Medical Center", "address": "Minato-ku, Tokyo", "contact": "0987-654-321"}

[1862] ]

[1863] 8. Over-the-counter medication suggestions

[1864] The server accesses the over-the-counter drug database and retrieves appropriate over-the-counter drug information based on the analysis results. The retrieved over-the-counter drug information is sent to the terminal in JSON format. For example, the following over-the-counter drug information can be obtained:

[1865] json

[1866] [

[1867] {"name": "Acetaminophen", "type": "Antipyretic analgesic", "usage": "One tablet, three times a day"}

[1868] ]

[1869] The terminal displays over-the-counter drug information to the user.

[1870] 9. Medical appointments

[1871] The user selects a medical institution from the provided list of medical institutions and enters reservation information, including the desired date and time of the examination. After completing the input, the terminal sends a reservation request to the server.

[1872] 10. Booking confirmation and notification

[1873] The server receives the reservation request and confirms the reservation in cooperation with the medical institution's reservation system. The reservation information is sent to the medical institution, and the confirmed reservation information is notified to the user's terminal.

[1874] 11. Follow-up after consultation

[1875] After the consultation, the user enters progress information into the app. The entered information is sent from the device to the server. The server then uses an artificial intelligence engine and emotion engine to reanalyze the progress information and recommend a follow-up visit if necessary. If a follow-up visit is necessary, the user is referred to a medical institution and the reservation procedure is carried out again.

[1876] In this way, this system analyzes the patient's medical history and emotional information, promptly introduces appropriate medical institutions and over-the-counter medications, and comprehensively handles a series of processes, including appointment procedures and post-examination follow-up, allowing patients to receive prompt and accurate medical services.

[1877] The processing flow will be explained below.

[1878] Step 1:

[1879] The user opens the application and enters their symptoms and basic information into a questionnaire. For example, they might enter "fever, sore throat, cough, and general fatigue." In addition, the application obtains the user's emotions through text analysis (e.g., keywords that indicate emotions) and a facial recognition system. Once the input is complete, the user presses the "Submit" button.

[1880] Step 2:

[1881] The device collects medical interview information and emotional information. Medical interview information includes data such as body temperature and details of symptoms, and emotional information includes the user's emotional state obtained from facial expressions and text analysis. This information is structured in JSON format.

[1882] Step 3:

[1883] The terminal sends the medical interview information and emotion information to the server. The data sent is in the following format:

[1884] json

[1885] {

[1886] "temperature": "38 degrees",

[1887] "symptoms": ["sore throat", "cough", "general fatigue"],

[1888] "emotions": {"text": "anxiety", "face": "sad"}

[1889] }

[1890] Step 4:

[1891] The server receives the medical interview information and emotion information sent from the device, checks the integrity of the received data, and stores it in a database.

[1892] Step 5:

[1893] The server sends the saved medical interview information to the AI ​​engine and the emotion information to the emotion engine, which calls the API and creates a request to analyze the medical interview information and estimate the emotional state.

[1894] Step 6:

[1895] The AI ​​engine analyzes the medical interview information and outputs possible illnesses and recommended actions. For example, the following analysis results can be obtained:

[1896] json

[1897] {

[1898] "possible_conditions": ["acute pharyngitis", "influenza"],

[1899] "recommendations": ["Seek medical help", "Use over-the-counter pain relievers"]

[1900] }

[1901] Step 7:

[1902] The emotion engine analyzes the emotion information and estimates the user's emotional state. For example, the emotional state "anxiety" is output.

[1903] Step 8:

[1904] The server receives the analysis results from the AI ​​engine and the emotion engine, integrates the analysis results, and generates a comprehensive diagnosis.

[1905] json

[1906] {

[1907] "possible_conditions": ["acute pharyngitis", "influenza"],

[1908] "recommendations": ["Seek medical attention", "Use over-the-counter pain relievers"],

[1909] "emotional_state": "anxiety"

[1910] }

[1911] Step 9:

[1912] The server accesses a medical institution database based on the analysis results. It searches for medical institutions that match the disease name and emotional state of the analysis results and obtains information on nearby medical institutions. For example, if a user is in an anxious state, it will preferentially introduce medical institutions that offer counseling and mental care.

[1913] Step 10:

[1914] The server structures the acquired medical institution information in list format and sends it to the terminal. For example, the following list is generated:

[1915] json

[1916] [

[1917] {"name": "Chuo Ward Internal Medicine Clinic", "address": "Chuo Ward ~", "contact": "0123-456-789"},

[1918] {"name": "Tokyo Medical Center", "address": "Minato Ward ~", "contact": "0987-654-321"},

[1919] {"name": "Mental Health Care Clinic", "address": "Shinjuku-ku~", "contact": "0245-678-910"}

[1920] ]

[1921] Step 11:

[1922] The terminal presents the user with a list of medical institutions. The user selects a medical institution from the provided list and enters the desired date and time for an appointment. The user then presses the "Make an appointment" button.

[1923] Step 12:

[1924] The terminal sends the reservation information to the server. An example of the data sent is as follows:

[1925] json

[1926] {

[1927] "clinic_id": "1",

[1928] "user_id": "1001",

[1929] "appointment_date": "2023-10-15",

[1930] "appointment_time": "10:00"

[1931] }

[1932] Step 13:

[1933] The server receives the reservation request and confirms the reservation in cooperation with the medical institution's reservation system. The reservation information is sent to the medical institution, and the confirmed reservation information is notified to the user's terminal.

[1934] Step 14:

[1935] After the examination, the user enters progress information into the app. For example, they enter information such as "the fever continues" or "the general feeling of fatigue is increasing." Once the information is entered, the device sends the progress information to the server.

[1936] Step 15:

[1937] Based on the progress information and emotion information received by the server, the AI ​​engine and emotion engine are again requested to perform analysis. The necessity of a follow-up examination is evaluated and, if necessary, a follow-up examination is recommended. If a follow-up examination is necessary, the patient is referred to a medical institution and the appointment procedure is carried out again.

[1938] This series of processes enables the prompt provision of optimal medical services according to the user's symptoms and emotions.

[1939] Example 2

[1940] 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."

[1941] Conventional medical support systems often only handle patient interview information, and do not adequately consider the patient's emotional and psychological state when making diagnoses or referrals to medical institutions. Furthermore, the procedures for users to select a medical institution and make an appointment are complicated, making it difficult to respond quickly. Furthermore, over-the-counter drug recommendations are not centrally managed, making it difficult for users to select appropriate over-the-counter drugs as needed. A system that can solve this problem is needed.

[1942] 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.

[1943] In this invention, the server includes: means for inputting medical history information and emotional information; means for the terminal to transmit the input medical history information and emotional information to the server; means for transmitting the medical history information and emotional information received by the server to an artificial intelligence engine and an emotion engine, respectively, for analysis; means for the artificial intelligence engine to analyze the medical history information and output recommended actions corresponding to the symptoms and possible disease names; means for the emotion engine to analyze the emotional information, estimate the user's emotional state, and reflect the result in the analysis of the artificial intelligence engine; means for the server to receive the analysis result, access the medical institution database to acquire appropriate medical institution information, structuring it, and transmitting it to the terminal; means for the terminal to present the acquired medical institution information and recommended actions to the user; means for the server to access the over-the-counter drug database, acquire appropriate over-the-counter drug information based on the analysis result, structuring it, and transmitting it to the terminal, which provides it to the user; and means for the server to cooperate with the medical institution's reservation system, receive a reservation request, confirm a medical institution reservation, and notify the terminal of the information. This enables a comprehensive diagnosis that takes into account not only the analysis of the medical history information but also the emotional information, and to quickly and reliably recommend appropriate medical institutions and over-the-counter drugs and complete reservation procedures.

[1944] "Medical interview information" refers to data entered by the patient about their symptoms and basic information.

[1945] "Emotional information" refers to data that indicates a patient's emotional state or psychological condition.

[1946] "Terminal" means an electronic device used by a user to input and transmit information.

[1947] A "server" is a computer system that analyzes received data and provides necessary information in cooperation with various databases.

[1948] An "artificial intelligence engine" is a program that analyzes medical interview information and outputs recommended actions and possible disease names corresponding to symptoms.

[1949] The "emotion engine" is a program that analyzes emotional information and estimates the patient's emotional state.

[1950] A "database" is a collection of data that stores various types of information in an organized manner and allows it to be searched and retrieved as needed.

[1951] A "medical institution database" is a database that contains information about medical institutions.

[1952] "Over-the-counter drug database" means a database containing information about over-the-counter drugs.

[1953] A "reservation system" is a system that manages reservations at medical institutions.

[1954] This invention is a system in which patients input their symptoms, and based on that information, an AI engine and an emotion engine are linked to perform analysis and recommend appropriate medical institutions and treatment methods. This system comprehensively covers everything from inputting medical history information, analysis, referring to medical institutions, confirming appointments, suggesting over-the-counter medications, customizing using emotion recognition, and follow-up.

[1955] System configuration

[1956] The system consists of the following main components:

[1957] 1. Means of inputting medical interview information

[1958] 2. Means of sending medical interview information

[1959] 3. Analysis of interview information and emotional information

[1960] 4. Emotion recognition means

[1961] 5. Means of accessing medical institution databases

[1962] 6. Access to the over-the-counter drug database

[1963] 7. Medical institution reservation methods

[1964] 8. Follow-up measures after consultation

[1965] The user opens the application and enters their symptoms and basic information into a questionnaire. At this stage, the user enters specific symptoms, such as "fever, sore throat, cough, and general fatigue." They also enter detailed information such as body temperature and the time of onset, and press the submit button.

[1966] The device collects emotional information at the same time as collecting the medical interview information received from the user. Examples of emotional information include keywords entered during text input and facial expression data obtained by a facial recognition system. For example, if the user enters an expression or keyword indicating "anxiety," the device will detect it.

[1967] The device structures the medical interview information and emotion information in JSON format and sends it to the server. The server receives this data and stores it in a database. For example, the following JSON format data is sent:

[1968] {

[1969] "symptoms": ["fever", "sore throat", "cough", "general fatigue"],

[1970] "temperature": "38.5",

[1971] "onset_date": "2023-10-01",

[1972] "emotional_state": "anxiety"

[1973] }

[1974] The server sends the stored medical interview information to an artificial intelligence engine. Specifically, it analyzes symptoms based on the interview information and outputs a possible illness and recommended actions. At the same time, it sends emotional information to the emotion engine and analyzes the emotional state. For example, it analyzes "fever, sore throat, cough, and general fatigue" and suspects "acute pharyngitis" or "influenza," recommending that the patient "visit a medical institution."

[1975] The server receives the analysis results from the AI ​​engine and emotion engine. The results include disease candidates, recommended actions, and emotional state. The JSON result is as follows:

[1976] {

[1977] "possible_conditions": ["acute pharyngitis", "influenza"],

[1978] "recommendations": ["Seek medical attention", "Use over-the-counter pain relievers"],

[1979] "emotional_state": "anxiety"

[1980] }

[1981] The server accesses a medical institution database based on the analysis results. It searches for the medical institution that best suits the patient's symptoms and emotional state, and retrieves information on nearby medical institutions on the server. For example, taking into account the patient's "anxiety" state, it prioritizes searches for medical institutions that offer counseling.

[1982] The server structures the medical institution information it has acquired in list format and sends it to the terminal. For example, the following list is presented to the user.

[1983] List of medical institutions:

[1984] Internal Medicine Clinic (Address: Chuo Ward, Contact: 0123-456-789)

[1985] Medical Center (Address: Minato-ku, Contact: 0987-654-321)

[1986] The user selects the desired medical institution from the list.

[1987] The server accesses the over-the-counter drug database and retrieves appropriate over-the-counter drug information based on the medical interview information and analysis results. The retrieved over-the-counter drug information is structured in JSON format and sent to the terminal. For example, information on antipyretic analgesics is retrieved as follows:

[1988] Over-the-counter drug information:

[1989] Acetaminophen (type: antipyretic analgesic, usage: 1 tablet 3 times a day)

[1990] The terminal displays this information to the user.

[1991] The user selects the desired medical institution from the provided list of medical institutions and enters the reservation information. For example, if the user selects "Internal Medicine Clinic" and "October 2nd, 3:00 PM," the corresponding reservation request is sent to the server.

[1992] The server receives the reservation request and confirms the reservation in cooperation with the medical institution's reservation system. For example, the server uses the clinic's reservation management API to receive the confirmed information, and then notifies the user's device.

[1993] After the consultation, the user enters progress information into the app. For example, they can enter information such as "symptoms have not improved" or "new symptoms have appeared." The device then sends this information to the server, which then analyzes it again using its artificial intelligence engine and emotion engine. If necessary, the app will recommend a follow-up visit and will refer the patient to a medical institution and make a reservation again.

[1994] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1995] Step 1:

[1996] The user opens the application and enters their symptoms and basic information into a questionnaire. The user enters symptoms such as "fever," "sore throat," "cough," and "general fatigue," and also enters detailed information such as their current body temperature and when symptoms first appeared. Then, the user presses the submit button.

[1997] Input: Medical interview information such as symptoms, temperature, and onset date

[1998] Output: The medical interview information entered by the user is saved on the terminal.

[1999] Step 2:

[2000] The device collects emotional information at the same time as collecting the medical interview information received from the user. Emotional information includes keywords entered in the text and facial expression data detected by the facial recognition system. For example, if the text contains the word "anxiety" or if an anxious facial expression is detected, that information is collected.

[2001] Input: medical interview information, text keywords, facial expression data

[2002] Output: Interview information and emotion information are saved on the device.

[2003] Step 3:

[2004] The device structures the medical interview information and emotion information in JSON format and sends it to the server.

[2005] Input: Interview information and emotion information structured in JSON format

[2006] Output: The server receives the data and stores it in a database.

[2007] Step 4:

[2008] The server sends the received medical interview information to an AI engine, and sends the emotional information to the emotion engine. Based on the medical interview information, the AI ​​engine analyzes symptoms and outputs possible illnesses and recommended actions. At the same time, the emotion engine analyzes the emotional information and estimates the user's emotional state.

[2009] Input: medical interview information (to AI engine), emotion information (to emotion engine)

[2010] Output: Disease candidates and recommended actions from the AI ​​engine, and emotional state analysis results from the emotion engine

[2011] Step 5:

[2012] The server receives the analysis results from the AI ​​engine and the emotion engine. For example, the analysis results include the following information:

[2013] {

[2014] "possible_conditions": ["acute pharyngitis", "influenza"],

[2015] "recommendations": ["Seek medical attention", "Use over-the-counter pain relievers"],

[2016] "emotional_state": "anxiety"

[2017] }

[2018] Input: Analysis results from the AI ​​engine and emotion engine

[2019] Output: Analysis results saved on the server

[2020] Step 6:

[2021] The server accesses a medical institution database based on the analysis results. It searches for the medical institution that best suits the patient's symptoms and emotional state, and retrieves information on nearby medical institutions on the server. For example, taking into account the patient's "anxiety" state, it prioritizes searches for medical institutions that offer counseling.

[2022] Input: Analysis results

[2023] Output: Appropriate medical institution information

[2024] Step 7:

[2025] The server structures the medical institution information it has acquired in list format and sends it to the terminal. For example, the following list is presented to the user.

[2026] List of medical institutions:

[2027] Internal Medicine Clinic (Address: Chuo Ward, Contact: 0123-456-789)

[2028] Medical Center (Address: Minato-ku, Contact: 0987-654-321)

[2029] Input: Acquired medical institution information

[2030] Output: Medical institution information in a structured list format on the user's terminal

[2031] Step 8:

[2032] The server accesses the over-the-counter drug database and retrieves appropriate over-the-counter drug information based on the analysis results. For example, the following over-the-counter drug information can be obtained:

[2033] Over-the-counter drug information:

[2034] Acetaminophen (type: antipyretic analgesic, usage: 1 tablet 3 times a day)

[2035] The terminal displays this information to the user.

[2036] Input: Analysis results

[2037] Output: Pertinent over-the-counter drug information and its use

[2038] Step 9:

[2039] The user selects the desired medical institution from the provided list of medical institutions and enters the reservation information. For example, if the user selects "Internal Medicine Clinic" and "October 2nd, 3:00 PM," the corresponding reservation request is sent to the server.

[2040] Input: Reservation information (name of medical institution, desired consultation date and time)

[2041] Output: A reservation request is sent to the server

[2042] Step 10:

[2043] The server receives the reservation request and confirms the reservation in cooperation with the medical institution's reservation system. For example, the server uses a clinic's reservation management API to receive information that the reservation has been confirmed, and then notifies the user's device.

[2044] Input: Reservation Request

[2045] Output: The confirmed reservation information is sent to the user's device.

[2046] Step 11:

[2047] After the consultation, the user enters progress information into the app. For example, they can enter information such as "symptoms have not improved" or "new symptoms have appeared." The device then sends this information to the server, which then analyzes it again using its artificial intelligence engine and emotion engine. If necessary, the app will recommend a follow-up visit and will refer the patient to a medical institution and make a reservation again.

[2048] Input: Progress information

[2049] Output: Recommended follow-up visit information and referral to a medical institution and reservation procedures

[2050] The above are the specific processing steps of the program in the system of the present invention.

[2051] (Application example 2)

[2052] 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."

[2053] Current autonomous vehicle systems lack the functionality to respond quickly and appropriately to the health conditions of passengers. Furthermore, they do not come equipped with a standard system to guide passengers to the most appropriate medical facility in an emergency, which risks delaying response in the event of an emergency. Furthermore, they lack sufficient functionality to reassure passengers who may be in an unstable emotional state during an emergency. To solve these issues, a unified system is needed that can handle everything from inputting health information to navigating to medical facilities and stabilizing emotions.

[2054] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[2055] In this invention, the server includes: means for inputting medical interview information; means for transmitting the input medical interview information to the server; means for transmitting the received medical interview information to an artificial intelligence engine for analysis by the server; means for the artificial intelligence engine to analyze the medical interview information and output recommended actions corresponding to the symptoms and possible disease names; means for the server to receive the analysis results and access a medical institution database to obtain and transmit appropriate medical institution information; means for presenting the obtained medical institution information and recommended actions to the user; a vehicle interface having means for the occupant to input their health condition; means for the vehicle to automatically guide the occupant to the most appropriate medical institution based on the analyzed information; and means for adjusting the in-vehicle environment (music, audio guidance, lighting, etc.) to stabilize the occupant's emotions. This enables prompt and appropriate response to the occupant's health condition and makes it possible to guide the occupant to the most appropriate medical institution while reassuring them even in an emergency.

[2056] "Medical interview information" is data entered by passengers or patients about their own health conditions and symptoms.

[2057] A "server" is a computer system that stores, processes, and provides data over a network.

[2058] "Artificial intelligence engine" refers to the algorithms and computational models used to analyze medical interview information and output possible disease diagnoses and recommended actions.

[2059] A "medical institution database" is a database that stores and makes searchable information about medical facilities and medical professionals.

[2060] A "vehicle interface" is a device that includes an operating means for an occupant of an autonomous vehicle to input health information.

[2061] "Autonomous driving" is a technology that allows vehicles to move and change direction autonomously without human operation.

[2062] The "emotion engine" is a system that analyzes the emotional state of the occupants and takes appropriate action based on the results.

[2063] "Analysis results" are output information obtained when the artificial intelligence engine and emotion engine process the input data.

[2064] "Recommended actions" are specific actions that should be taken by the user or passengers, which are proposed based on the analysis results.

[2065] A "medical institution appointment system" is software and services for making appointments for medical appointments at medical facilities.

[2066] This invention is a system in which the driver inputs their health condition, and based on that information, a server analyzes it by linking an artificial intelligence engine and an emotion engine, and then guides the driver in the self-driving vehicle to the appropriate medical institution and treatment method. This system comprehensively covers the input and analysis of medical history information, medical institution referral, appointment confirmation, over-the-counter drug suggestions, customization using emotion recognition, and follow-up.

[2067] The server includes a means for inputting medical interview information, a means for transmitting the input medical interview information, a means for transmitting the received medical interview information to an artificial intelligence engine, a means for the artificial intelligence engine to analyze the medical interview information and output recommended actions and possible disease names, a means for receiving the analysis results and accessing a medical institution database to obtain and transmit information on appropriate medical institutions, a means for presenting the obtained medical institution information and recommended actions to the user, a vehicle interface for the occupant to input their health condition, a means for the vehicle to automatically guide the occupant to the most appropriate medical institution based on the analysis information, and a means for adjusting the in-vehicle environment to stabilize the emotions of the occupant.

[2068] Program processing

[2069] First, the occupant uses an interface installed in the vehicle to input their symptoms. For example, a touchscreen display or a voice recognition system is used. When the occupant inputs symptoms such as "chest pain" or "difficulty breathing," the data is collected by the vehicle's on-board computer. The collected data is then sent to a cloud server via the network. The ONVIF protocol is used for data transmission.

[2070] The data is structured and stored in JSON format on a cloud server. The server then sends the data to an artificial intelligence engine (e.g., a deep learning model using TensorFlow) and an emotion engine (e.g., Microsoft's Azure Cognitive Services). The artificial intelligence engine analyzes the data and outputs a possible diagnosis and recommended actions. The emotion engine estimates the passenger's emotional state (e.g., "anxiety") and influences the analysis results.

[2071] The analysis results and estimated emotional state are sent to the server. For example, if the analysis results indicate a high possibility of "acute myocardial infarction," the recommended action would be to "go directly to an emergency medical facility." If the emotional state is estimated to be "anxiety," the vehicle's environment (music playback, voice guidance tone change, lighting adjustment, etc.) will be automatically adjusted to reassure the occupants.

[2072] Based on the analysis results, the server searches a database of medical institutions to obtain information on the most suitable medical institution. For example, the nearest emergency hospital or cardiologist may be selected as a candidate. The obtained medical institution information is sent to the vehicle, and the autonomous driving system (such as Waymo's autonomous driving system) navigates the occupant to the medical institution.

[2073] Specific examples

[2074] Below are examples of specific prompt sentences to input into the generative AI model.

[2075] User dictation: "My chest hurts and I'm having trouble breathing... I'm so scared..."

[2076] System Response:

[2077] 1. Analyze symptoms: "chest pain," "difficulty breathing," "fear"

[2078] 2. Search for emergency medical facilities: "Nearby emergency hospital" "Cardiologist"

[2079] 3. Emotional response: "Please stay calm. I will get you to the nearest emergency room right away."

[2080] The above processing enables prompt and appropriate responses to the health conditions of passengers, and in the event of an emergency, navigation to the most appropriate medical institution is possible. In addition, the use of an emotion engine can increase passenger peace of mind.

[2081] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2082] Step 1:

[2083] The user uses the vehicle's in-vehicle interface (touchscreen display or voice recognition system) to input their symptoms. The user inputs specific symptoms (e.g., "chest pain," "difficulty breathing," "headache," etc.), and the information is sent to the vehicle's on-board computer. The input data is formatted and collected. Examples of input data:

[2084] "My chest hurts. I'm having trouble breathing."

[2085] Step 2:

[2086] The terminal (vehicle's on-board computer) sends the collected data to a cloud server via the network. This data is usually structured in JSON format, and the on-board computer uses the ONVIF protocol for transmission. Example of transmitted data:

[2087] json

[2088] {

[2089] "symptoms": ["chest pain", "difficulty breathing"]

[2090] }

[2091] Step 3:

[2092] The server sends the received medical interview information to an artificial intelligence engine for analysis. The AI ​​model used is, for example, a deep learning model using TensorFlow. The server inputs the received data into the AI ​​engine for analysis. Input data:

[2093] json

[2094] {

[2095] "symptoms": ["chest pain", "difficulty breathing"]

[2096] }

[2097] Example of output data:

[2098] json

[2099] {

[2100] "possible_conditions": ["acute myocardial infarction"],

[2101] "recommendations": ["Go straight to emergency medical care"]

[2102] }

[2103] Step 4:

[2104] The server sends the interview information to the emotion engine, which analyzes the user's emotional state. The emotion recognition system used is, for example, Azure Cognitive Services. The emotion engine infers the user's emotional state based on voice and text input. Input data:

[2105] json

[2106] {

[2107] "text": "My chest hurts. I'm having trouble breathing. I'm so scared."

[2108] }

[2109] Example of output data:

[2110] json

[2111] {

[2112] "emotional_state": "anxiety"

[2113] }

[2114] Step 5:

[2115] The server receives the analysis results from the AI ​​and emotion engine and selects the most suitable medical institution based on that. The server accesses the medical institution database to obtain information on appropriate medical institutions. For example, emergency hospitals and medical institutions specializing in cardiac care are prioritized. Input data:

[2116] json

[2117] {

[2118] "possible_conditions": ["acute myocardial infarction"],

[2119] "emotional_state": "anxiety"

[2120] }

[2121] Example of output data:

[2122] json

[2123] {

[2124] "medical_facilities": [{"name": "Regional Emergency Hospital", "address": "XX City △△ Town", "contact": "012-3456-7890"}]

[2125] }

[2126] Step 6:

[2127] The server sends the acquired medical institution information to the vehicle, and the autonomous driving system navigates the vehicle. The vehicle's autonomous driving system (such as Waymo's system) calculates the shortest route based on the received data and begins navigating to the medical institution. Input data:

[2128] json

[2129] {

[2130] "medical_facilities": [{"name": "Regional Emergency Hospital", "address": "XX City △△ Town", "contact": "012-3456-7890"}]

[2131] }

[2132] Specific operation: The autonomous vehicle departs for the designated medical facility.

[2133] Step 7:

[2134] The server adjusts the environment inside the vehicle based on the emotion analysis results, such as playing music, changing the tone of the voice guidance, and adjusting the lighting. For example, if the emotion "anxiety" is detected, it will play relaxing music to give the passengers a sense of security. Input data:

[2135] json

[2136] {

[2137] "emotional_state": "anxiety"

[2138] }

[2139] Specific actions: Playing music, warm voice guidance from the speaker, changing the lighting to a warmer color, etc.

[2140] The above is a series of processing steps for the inference and operation of the "Smart Emergency Navigation" system. This system not only quickly and accurately assesses the health condition of the occupants and navigates them to the appropriate medical institution, but also adjusts the environment to reduce the anxiety of the occupants in an emergency, allowing them to arrive at the medical institution with peace of mind.

[2141] 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.

[2142] 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.

[2143] 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.

[2144] [Fourth embodiment]

[2145] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[2146] 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.

[2147] 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).

[2148] 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.

[2149] 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.

[2150] 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).

[2151] 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.

[2152] 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.

[2153] 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.

[2154] 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.

[2155] 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.

[2156] 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.

[2157] 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."

[2158] This system allows patients to input their symptoms anywhere, and an AI engine analyzes the information to recommend appropriate medical institutions and treatment methods. This system covers the entire process, from inputting medical history information to analysis, referring appropriate medical institutions, confirming appointments, suggesting over-the-counter medications, and providing follow-up care.

[2159] System configuration

[2160] The system consists of the following main components:

[2161] 1. Means of inputting medical interview information

[2162] 2. Means of sending medical interview information

[2163] 3. Analysis of medical interview information

[2164] 4. Means of accessing medical institution databases

[2165] 5. Access to the over-the-counter drug database

[2166] 6. Medical institution reservation methods

[2167] 7. Follow-up measures after consultation

[2168] Explanation of program processing

[2169] 1. Enter medical interview information

[2170] Through the application, users enter their symptoms into a questionnaire, for example, "fever, sore throat, cough, general fatigue," and this information is stored on the device.

[2171] 2. Sending medical interview information

[2172] The terminal sends the entered medical interview information to the server. The data sent is structured in JSON format, for example, as shown below.

[2173] json

[2174] {

[2175] "temperature": "38 degrees",

[2176] "symptoms": ["sore throat", "cough", "general fatigue"]

[2177] }

[2178] 3. Analysis of medical interview information

[2179] The server sends the received medical interview information to the AI ​​engine, which analyzes the information and obtains the following diagnosis results:

[2180] json

[2181] {

[2182] "possible_conditions": ["acute pharyngitis", "influenza"],

[2183] "recommendations": ["Seek medical help", "Use over-the-counter pain relievers"]

[2184] }

[2185] The server receives the analysis results and uses them for the next process.

[2186] 4. Referral to medical institutions

[2187] Based on the analysis results, the server accesses the medical institution database and creates a list of nearby appropriate medical institutions. For example, the following list is generated:

[2188] json

[2189] [

[2190] {"name": "Internal Medicine Clinic, Chuo Ward, Tokyo", "address": "Chuo Ward, Tokyo ~", "contact": "0123-456-789"},

[2191] {"name": "Tokyo Medical Center", "address": "Minato-ku, Tokyo", "contact": "0987-654-321"}

[2192] ]

[2193] The server sends this information to the terminal and presents it to the user.

[2194] 5. Over-the-counter medication suggestions

[2195] The server accesses the over-the-counter drug database and retrieves appropriate over-the-counter drug information based on the analysis results. For example, the following over-the-counter drug information can be obtained:

[2196] json

[2197] [

[2198] {"name": "Acetaminophen", "type": "Antipyretic analgesic", "usage": "One tablet, three times a day"}

[2199] ]

[2200] This information is also provided to the user.

[2201] 6. Medical appointments

[2202] The user selects a medical institution from the provided list of medical institutions and enters reservation information. The terminal sends a reservation request to the server. For example, the format is as follows:

[2203] json

[2204] {

[2205] "clinic_id": "1",

[2206] "user_id": "1001",

[2207] "appointment_date": "2023-10-15",

[2208] "appointment_time": "10:00"

[2209] }

[2210] The server passes this information to the medical institution's system to confirm the appointment, after which a notification of the appointment is sent to the terminal.

[2211] 7. Follow-up after consultation

[2212] After the consultation, the user enters progress information into the app. The device then sends this information to the server. The server then uses the AI ​​engine to analyze the progress information again and recommends a follow-up visit if necessary. If a follow-up visit is necessary, the user is referred to a medical institution and an appointment is made.

[2213] In this way, this system analyzes patient interview information and quickly introduces appropriate medical institutions and over-the-counter medications, simplifies the appointment process, and provides follow-up after the consultation, thereby providing consistent medical support to patients.

[2214] The processing flow will be explained below.

[2215] Step 1:

[2216] The user opens the application. The user enters their symptom information into the input screen. For example, they enter "fever, sore throat, cough, general fatigue." Once they have completed the input, they press the "Submit" button.

[2217] Step 2:

[2218] The device collects the medical interview information entered by the user, structures the information in JSON format, and prepares it for transmission.

[2219] Step 3:

[2220] The terminal sends the formatted and structured medical interview information to the server. The following JSON format is used as an example of the data to be sent:

[2221] json

[2222] {

[2223] "temperature": "38 degrees",

[2224] "symptoms": ["sore throat", "cough", "general fatigue"]

[2225] }

[2226] Step 4:

[2227] The server receives the medical interview information sent from the terminal, checks the integrity of the received data, and stores it in a database.

[2228] Step 5:

[2229] The server sends the stored medical interview information to the AI ​​engine, which then calls the AI ​​engine's API and creates a request to analyze the medical interview information.

[2230] Step 6:

[2231] The AI ​​engine analyzes the medical interview information. The analysis results include recommended actions to take based on the symptoms and possible illnesses. For example, the analysis results shown below are output in JSON format.

[2232] json

[2233] {

[2234] "possible_conditions": ["acute pharyngitis", "influenza"],

[2235] "recommendations": ["Seek medical help", "Use over-the-counter pain relievers"]

[2236] }

[2237] Step 7:

[2238] The server receives the analysis results from the AI ​​engine and proceeds to the next step based on the received analysis results.

[2239] Step 8:

[2240] The server accesses a medical institution database based on the analysis results, searches for medical institutions that match the disease name in the analysis results, and obtains information on nearby medical institutions.

[2241] Step 9:

[2242] The server generates the medical institution information it has acquired in list format. For example, the following list is generated in JSON format:

[2243] json

[2244] [

[2245] {"name": "Internal Medicine Clinic, Chuo Ward, Tokyo", "address": "Chuo Ward, Tokyo ~", "contact": "0123-456-789"},

[2246] {"name": "Tokyo Medical Center", "address": "Minato-ku, Tokyo", "contact": "0987-654-321"}

[2247] ]

[2248] This medical institution information is sent to the terminal and presented to the user.

[2249] Step 10:

[2250] The server accesses the over-the-counter drug database and retrieves appropriate over-the-counter drug information based on the analysis results. The retrieved over-the-counter drug information is structured in JSON format and sent to the terminal as follows:

[2251] json

[2252] [

[2253] {"name": "Acetaminophen", "type": "Antipyretic analgesic", "usage": "One tablet, three times a day"}

[2254] ]

[2255] The terminal presents over-the-counter drug information to the user.

[2256] Step 11:

[2257] The user selects a medical institution from the provided list of medical institutions. Based on the information about the selected medical institution, the user inputs reservation information, for example, specifying the desired date and time for the examination.

[2258] Step 12:

[2259] The device sends the user's reservation request to the server. The following JSON format is used as an example of the data sent:

[2260] json

[2261] {

[2262] "clinic_id": "1",

[2263] "user_id": "1001",

[2264] "appointment_date": "2023-10-15",

[2265] "appointment_time": "10:00"

[2266] }

[2267] Step 13:

[2268] The server receives the reservation request and confirms the reservation in cooperation with the medical institution's reservation system. The reservation information is sent to the medical institution, which confirms the reservation. The server then sends the reservation confirmation information to the terminal and notifies the user.

[2269] Step 14:

[2270] After the examination, the user enters progress information into the app. For example, they enter information such as "the fever continues" or "the general feeling of fatigue is increasing." Once the information is entered, the device sends the progress information to the server.

[2271] Step 15:

[2272] Based on the progress information received by the server, the AI ​​engine is requested to perform another analysis. The AI ​​engine analyzes the progress information and evaluates the need for a follow-up visit. If a follow-up visit is recommended, the server searches again for a medical institution that requires a follow-up visit and introduces it to the user. It also completes the follow-up appointment procedure.

[2273] These are the specific processing steps of the program for this system. Through this series of processes, users can quickly and accurately find a medical institution and receive appropriate examinations and follow-up care.

[2274] Example 1

[2275] 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."

[2276] Conventional medical consultation systems are often limited to allowing patients to input their symptoms and introducing appropriate medical institutions and treatment methods, and lack sufficient functionality to assess the need for follow-up or re-examination after the consultation. Furthermore, they are unable to consistently suggest over-the-counter medications or schedule appointments at medical institutions, making it difficult to provide consistent medical support to patients.

[2277] 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.

[2278] In this invention, the server includes: means for inputting medical interview information; means for transmitting the input medical interview information to the server; means for the server to transmit the received medical interview information to an artificial intelligence engine for analysis; means for the artificial intelligence engine to analyze the medical interview information and output recommended actions corresponding to the symptoms and possible disease names; means for the server to receive the analysis results and access a medical institution database to obtain and transmit appropriate medical institution information; means for presenting the obtained medical institution information and recommended actions to the user; means for the server to transmit appointment information to a medical institution selected by the user and confirm the appointment in cooperation with the medical institution's system; and means for the user to input progress information after the consultation, and for the server to have the artificial intelligence engine analyze it again and recommend a re-examination. This makes it possible to provide consistent medical support to patients and evaluate the need for follow-up and re-examination after the consultation.

[2279] "Medical interview information" is information that a user inputs through an application regarding their own health condition and symptoms.

[2280] "Server" refers to the central system that receives, analyzes, and processes the medical interview information.

[2281] The "artificial intelligence engine" is a software module that analyzes the input medical interview information and outputs recommended actions and possible disease names corresponding to the symptoms.

[2282] The "medical institution database" is a database that stores information about medical institutions, and is used by the server to access and acquire appropriate medical institution information.

[2283] The "over-the-counter drug database" is a database that stores information about various over-the-counter drugs, and is used by the server to access and obtain appropriate over-the-counter drug information.

[2284] The "reservation system" is an online system that works in conjunction with medical institutions to process reservations.

[2285] "Progress information" is information about the health condition and progress of symptoms that the user inputs through the application after the medical examination.

[2286] "Recommended actions" refer to appropriate actions suggested to the user as a result of the AI ​​engine analyzing the medical interview information.

[2287] Defining these terms will help you understand the system better.

[2288] The present invention is a system in which a patient inputs their symptoms anywhere, an artificial intelligence engine analyzes the information, and introduces appropriate medical institutions and treatment methods. Specific embodiments for carrying out the present invention are described in detail below.

[2289] First, users enter their symptoms using a dedicated application installed on their smartphone, tablet, or other device. During this process, users enter specific symptoms such as fever, sore throat, cough, and general fatigue using text boxes and drop-down menus. For example, if a user enters "fever, sore throat, cough, and general fatigue," each piece of information is stored in a local database on the device.

[2290] Next, the device serializes the saved medical interview information into JSON format and sends it to the server via the communication module. At this time, the following format is used as an example of the data sent.

[2291] json

[2292] {

[2293] "temperature": "38 degrees",

[2294] "symptoms": ["sore throat", "cough", "general fatigue"]

[2295] }

[2296] The server makes an API request to send the received medical interview information to the AI ​​engine. The AI ​​engine analyzes the medical interview information and outputs recommended actions and possible illnesses corresponding to the symptoms. For example, the following diagnosis results may be obtained:

[2297] json

[2298] {

[2299] "possible_conditions": ["acute pharyngitis", "influenza"],

[2300] "recommendations": ["Seek medical help", "Use over-the-counter pain relievers"]

[2301] }

[2302] The server receives the analysis results and accesses the medical institution database to create a list of appropriate nearby medical institutions. This list is converted into JSON format and sent to the device. The device then presents the appropriate medical institution information to the user based on the received information. The user can view the list on the application and select as needed.

[2303] In addition, the server also accesses the over-the-counter drug database and obtains appropriate over-the-counter drug information based on the analysis results. For example, the following over-the-counter drug information can be obtained:

[2304] json

[2305] {

[2306] "name": "Acetaminophen",

[2307] "type": "Antipyretic analgesic",

[2308] "usage": "One tablet, three times a day"

[2309] }

[2310] The acquired over-the-counter drug information is also provided to the user, who can check the over-the-counter drug information on the application.

[2311] When the user selects an appropriate medical institution from the list of medical institutions and enters reservation information, the terminal sends a reservation request to the server. The server connects this information to the reservation system and confirms the reservation. A reservation confirmation notification is then sent to the terminal, allowing the user to confirm the reservation details.

[2312] After the consultation, the user enters progress information into the application. This progress information is sent from the device to the server, where it is analyzed again using an artificial intelligence engine to determine whether a follow-up visit is necessary. If necessary, the server will recommend a follow-up visit and assist with the referral and appointment procedures at a medical institution.

[2313] Specific examples

[2314] For example, a user opens the application and enters their symptoms as "fever, sore throat, cough, and general fatigue." The system then collects this information, and an artificial intelligence engine analyzes it. The system suggests the possibility of "acute pharyngitis" or "influenza," and recommends nearby medical institutions such as an "internal medicine clinic" or "medical center." It also suggests "acetaminophen" as an appropriate over-the-counter medication. If the user selects "internal medicine clinic" and sets the appointment date to "2023-10-15," the system confirms the appointment and notifies the user.

[2315] Example prompts for generative AI models

[2316] "I have a fever, a sore throat, and a severe cough. I also feel fatigued. Can you diagnose my illness based on these symptoms and tell me where to find a nearby medical facility? Also, can you recommend any over-the-counter medicines?"

[2317] In this way, this system analyzes patient interview information, quickly introduces appropriate medical institutions and over-the-counter medications, simplifies appointment procedures, and also provides follow-up after consultation, thereby providing consistent medical support to patients.

[2318] The flow of the identification process in the first embodiment will be described with reference to FIG.

[2319] Step 1:

[2320] Enter medical interview information

[2321] The user uses the application to enter their symptoms into a questionnaire. In the input field, they write specific symptoms such as "fever, sore throat, cough, general fatigue." The device stores this information in a local database. The input data format is as follows:

[2322] input:

[2323] Symptoms: fever, sore throat, cough, general fatigue

[2324] output:

[2325] Medical interview information stored in a local database

[2326] Step 2:

[2327] Sending medical interview information

[2328] The terminal serializes the medical interview information stored in the local database into JSON format and sends it to the server via the communication module.

[2329] input:

[2330] Medical interview information stored in a local database

[2331] Data processing:

[2332] Serialize to JSON format

[2333] output:

[2334] Send serialized data to the server

[2335] Specific working example:

[2336] json

[2337] {

[2338] "temperature": "38 degrees",

[2339] "symptoms": ["sore throat", "cough", "general fatigue"]

[2340] }

[2341] Step 3:

[2342] Analysis of medical interview information

[2343] The server makes an API request to send the received medical interview information to the AI ​​engine, which analyzes the information and outputs recommended actions and possible diagnoses based on the symptoms.

[2344] input:

[2345] Interview information sent to the server

[2346] Data processing:

[2347] Analysis by artificial intelligence engine

[2348] output:

[2349] A list of symptoms, suggested actions, and possible illnesses

[2350] Specific working example:

[2351] json

[2352] {

[2353] "possible_conditions": ["acute pharyngitis", "influenza"],

[2354] "recommendations": ["Seek medical help", "Use over-the-counter pain relievers"]

[2355] }

[2356] Step 4:

[2357] Medical institution introduction

[2358] Based on the analysis results, the server accesses a medical institution database and lists appropriate nearby medical institutions.

[2359] input:

[2360] Analysis results by artificial intelligence engine

[2361] Data processing:

[2362] Use SQL queries to retrieve information from medical institution databases

[2363] output:

[2364] A list of suitable medical institutions

[2365] Specific working example:

[2366] json

[2367] [

[2368] {"name": "Internal Medicine Medical Institution A", "address": "Address A", "contact": "Telephone A"},

[2369] {"name": "Internal Medicine Medical Institution B", "address": "Address B", "contact": "Telephone B"}

[2370] ]

[2371] The server sends this information to the terminal and presents it to the user.

[2372] Step 5:

[2373] Over-the-counter medication suggestions

[2374] The server accesses the over-the-counter drug database and retrieves appropriate over-the-counter drug information based on the analysis results.

[2375] input:

[2376] Analysis results by artificial intelligence engine

[2377] Data processing:

[2378] Use SQL queries to retrieve information from a database of over-the-counter drugs

[2379] output:

[2380] List of appropriate over-the-counter medications

[2381] Specific working example:

[2382] json

[2383] [

[2384] {"name": "Acetaminophen", "type": "Antipyretic analgesic", "usage": "One tablet, three times a day"}

[2385] ]

[2386] The server sends this information to the terminal and provides it to the user.

[2387] Step 6:

[2388] Medical appointment

[2389] The user selects a medical institution from the provided list and enters reservation information. The terminal then sends a reservation request to the server.

[2390] input:

[2391] Booking information entered by the user

[2392] Data processing:

[2393] Serialize to JSON format

[2394] output:

[2395] Send reservation information to the server

[2396] Specific working example:

[2397] json

[2398] {

[2399] "clinic_id": "1",

[2400] "user_id": "1001",

[2401] "appointment_date": "2023-10-15",

[2402] "appointment_time": "10:00"

[2403] }

[2404] The server connects this information to the medical institution's reservation system to confirm the reservation, after which a notification of reservation confirmation is sent to the terminal.

[2405] Step 7:

[2406] Follow-up after the examination

[2407] After the consultation, the user enters progress information into the application, and the terminal sends this information to the server.

[2408] input:

[2409] Progress information entered by the user

[2410] Data processing:

[2411] Serialize to JSON format

[2412] output:

[2413] Send progress information to the server

[2414] Specific working example:

[2415] json

[2416] {

[2417] "user_id": "1001",

[2418] "symptom_update": "Sore throat relieved"

[2419] }

[2420] The server then uses its AI engine to analyze the data again and determine whether a follow-up visit is necessary. If necessary, the server recommends a follow-up visit to the user and assists with the referral and appointment procedures.

[2421] (Application example 1)

[2422] 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."

[2423] Currently, when a patient feels unwell, it is difficult to immediately find an appropriate medical institution or over-the-counter medication and make an appointment. Furthermore, if a patient's condition suddenly worsens at work or in a public place, it is difficult to respond quickly and to access an appropriate medical institution. This creates a risk of worsening the patient's condition, so there is a need for a method to monitor the user's health in real time and respond quickly when an abnormality is detected.

[2424] 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.

[2425] In this invention, the server includes: a means for inputting medical interview information; a means for transmitting the input medical interview information to the server; a means for transmitting the received medical interview information to an artificial intelligence engine for analysis; a means for the artificial intelligence engine to analyze the medical interview information and output recommended actions corresponding to the symptoms and possible disease names; a means for the server to receive the analysis results, access a medical institution database to acquire and transmit appropriate medical institution information; a means for presenting the acquired medical institution information and recommended actions to the user; and a means for monitoring the user's health status in real time and automatically notifying a medical institution if an abnormality is detected. This allows users to immediately find appropriate medical institutions and over-the-counter medications and make appointments when they feel unwell. It also enables users to respond quickly to sudden changes in their health at work or in public places, preventing the condition from worsening.

[2426] "Medical interview information" is data entered by a patient about their health condition and symptoms.

[2427] "Input means" refers to a method or device for inputting medical interview information into a terminal or the like.

[2428] The "transmission means" refers to a method or device for transmitting the input medical interview information to the server.

[2429] An "artificial intelligence engine" is an algorithm or program that analyzes medical interview information and outputs recommended actions for symptoms and possible disease names.

[2430] "Analysis results" are information regarding diagnoses and recommended actions derived by the artificial intelligence engine based on the medical interview information.

[2431] The "medical institution database" is a database that stores information on various medical institutions.

[2432] "Medical institution information" refers to basic information such as the name, address, and contact information of the medical institution.

[2433] "Real-time monitoring" refers to the act of continuously monitoring a user's health status and immediately processing that information.

[2434] "Anomaly detection" is the act of recognizing abnormal health conditions from monitored data.

[2435] "Over-the-counter" drugs are medicines that can be purchased without a doctor's prescription.

[2436] A "reservation means" is a method or device for reserving a date and time for visiting a medical institution in advance.

[2437] To implement the invention, a system is constructed that uses the following components:

[2438] Hardware used

[2439] Smartphones (e.g. iPhone, Android devices)

[2440] Smart glasses (e.g., Google Glass, Microsoft HoloLens)

[2441] Software used

[2442] Python Program

[2443] Python libraries: requests, json, datetime

[2444] System configuration

[2445] 1. Input method for medical interview information: Users use a smartphone or smart glasses to input their medical interview information, such as their symptoms and temperature, through the application interface. This information is then stored on the device by the application.

[2446] 2. Method for transmitting medical interview information: The terminal converts the entered medical interview information into JSON format and sends it to the server via a secure HTTP POST request. The transmitted data is structured in JSON format.

[2447] 3. Analysis of medical interview information: The server sends the received medical interview information to the AI ​​engine for analysis. The AI ​​engine analyzes the input information and outputs recommended actions for the symptoms and possible illnesses.

[2448] 4. Medical institution database access means: Based on the analysis results, the server accesses the medical institution database and obtains information on appropriate nearby medical institutions.

[2449] 5. Means for accessing the over-the-counter drug database: Based on the analysis results, the server accesses the over-the-counter drug database and obtains the appropriate over-the-counter drug information.

[2450] 6. Medical institution reservation means: The user selects a medical institution from the list of medical institutions presented and enters reservation information. The terminal then sends a reservation request to the server and makes a reservation at the medical institution.

[2451] 7. Post-consultation follow-up: After the consultation, the user enters progress information through the application, and the terminal sends this information to the server. The server then requests the AI ​​engine to analyze it and provide recommendations for follow-up visits or additional actions.

[2452] 8. Real-time monitoring: The device monitors the user's health status in real time and automatically notifies nearby medical institutions if an abnormality is detected. This function allows for quick response to sudden illness.

[2453] Specific examples

[2454] For example, if a user inputs symptoms such as "fever," "headache," and "cough" into their smartphone, the AI ​​engine will analyze this and determine the possibility of "influenza" and recommend the use of antipyretics and analgesics. The server will obtain information on nearby medical institutions and present a list to the user. The user can then select a medical institution from the list, make an appointment, and confirm the information. Furthermore, if the user's health condition suddenly changes during treatment, the device will automatically notify the medical institution, enabling a prompt response.

[2455] Prompt Sentence Examples

[2456] "Please tell me the best medical institution and over-the-counter medication for a patient with a temperature of 38 degrees and symptoms of sore throat, headache, and cough."

[2457] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2458] Step 1:

[2459] The user inputs the medical interview information.

[2460] input:

[2461] Symptoms (e.g., fever, headache, cough)

[2462] Body temperature (e.g. 38 degrees)

[2463] Operation:

[2464] Using a smartphone or smart glasses interface, users input their symptoms and temperature.

[2465] The entered information is saved by an application in the device.

[2466] output:

[2467] Saved medical interview information (JSON format)

[2468] Step 2:

[2469] The terminal transmits the medical interview information to the server.

[2470] input:

[2471] JSON format medical interview information

[2472] Operation:

[2473] The device converts the medical interview information into JSON format and sends it to the server using a secure HTTP POST request.

[2474] output:

[2475] Interview information sent to the server

[2476] Step 3:

[2477] The server sends the medical interview information to an artificial intelligence engine for analysis.

[2478] input:

[2479] Medical interview information arrived at the server

[2480] Operation:

[2481] The server sends the received medical interview information to the artificial intelligence engine.

[2482] An artificial intelligence engine analyzes the medical history information and generates recommended actions and possible diagnoses.

[2483] output:

[2484] Analysis results (recommended actions and possible illnesses)

[2485] Step 4:

[2486] The server receives the analysis results and accesses the medical institution database to obtain the appropriate medical institution information.

[2487] input:

[2488] Analysis results

[2489] Operation:

[2490] The server receives the analysis results and accesses the medical institution database based on that data.

[2491] Obtain information on appropriate medical facilities in the vicinity.

[2492] output:

[2493] List of medical institution information (JSON format)

[2494] Step 5:

[2495] The server presents the acquired medical institution information and recommended actions to the user.

[2496] input:

[2497] List of medical institution information

[2498] Recommended Actions

[2499] Operation:

[2500] The server sends the acquired medical institution information and recommended actions to the user's terminal.

[2501] output:

[2502] Medical institution information and recommended actions displayed on the user's screen

[2503] Step 6: 【...

Claims

1. A means for inputting medical interview information; means for transmitting the input medical interview information to a server; A means for transmitting the received medical interview information to an artificial intelligence engine for analysis by the server; A means for an artificial intelligence engine to analyze the medical interview information and output recommended actions and possible disease names corresponding to the symptoms; A server receives the analysis results, accesses the medical institution database, and acquires and transmits appropriate medical institution information; A means for presenting the acquired medical institution information and recommended actions to the user; A system including:

2. 2. The system according to claim 1, further comprising means for the server to access a commercially available drug database, obtain appropriate commercially available drug information based on the analysis results, and provide the information to the user.

3. 2. The system according to claim 1, further comprising means for the server to cooperate with a reservation system of a medical institution and to allow the user to make a reservation at the medical institution.

Images