System

Abstract

A system is provided.SOLUTION: A system comprising: means for analyzing symptoms of patients using generated AI; means for collecting symptoms information through chats between patients and the generated AI; means for guiding to an appointment system when a medical examination is necessary based on the analysis result; means for storing the collected symptoms information and medical examination appointment information in a database; and means for providing appointment information and symptoms information in a form that can be referred to by medical practitioners.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] When patients feel unwell, they typically look up their symptoms online, but this leaves them feeling uneasy and it takes time to choose the right hospital. It also makes it difficult for hospitals to retain patients and turn them into loyal customers. Furthermore, the long preparation and waiting times before an appointment can be inconvenient for patients. The purpose of this invention is to solve these problems. [Means for solving the problem]

[0005] The system includes a means for analyzing a patient's symptoms using generative AI, a means for collecting symptom information through chat between the patient and generative AI, a means for directing the patient to a reservation system if an examination is necessary based on the analysis results, a means for saving the collected symptom information and examination reservation information in a database, and a means for providing the reservation information and symptom information in a form that can be referenced by medical professionals. This system allows patients to efficiently check their symptoms and make reservations before going to the hospital, and also allows hospitals to efficiently prepare for examinations.

[0006] "Generative AI" is artificial intelligence that uses natural language processing technology to analyze a patient's symptoms and provide appropriate diagnostic results.

[0007] A "patient" is an individual who consults or receives medical treatment at a medical institution for illness or injury.

[0008] "Symptom information" is data that describes the specific symptoms or conditions that a patient is experiencing.

[0009] "Chat" is a means of communication in which information is exchanged in a conversational format in real time using text messages.

[0010] The "reservation system" is an online system that allows patients to specify the date and time of their consultation and make a reservation with the hospital.

[0011] A "database" is a system for efficiently storing and managing collected symptom information and appointment information.

[0012] "Healthcare professionals" are professionals who provide medical services, such as doctors, nurses, and pharmacists.

[0013] An "examination" is the process by which a medical professional checks a patient's symptoms and provides the necessary diagnosis and treatment. [Brief explanation of the drawings]

[0014] [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

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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."

[0035] MODE FOR CARRYING OUT THE INVENTION

[0036] This invention is a system that uses generative AI to analyze patients' symptoms and make appropriate diagnoses and appointments. This system allows patients to make hospital appointments efficiently and hospitals to carry out examinations efficiently.

[0037] Login and session management

[0038] The user must first log in to the system. They enter their user ID and password into the login form displayed on the terminal and send it to the server. The server receives this and authenticates the user ID and password. If authentication is successful, it generates a session ID and sends it to the terminal as a cookie. This completes the user's login.

[0039] Display symptom entry form

[0040] After a successful login, the server displays a symptom entry form on the terminal, which contains a text entry field for the user to enter their symptoms and a submit button.

[0041] Enter and submit symptoms

[0042] The user enters their symptoms in the symptom input form and presses the send button. The device then sends the entered symptom data to the server. The server receives this and requests the generation AI to analyze it.

[0043] Symptom analysis and answer provision

[0044] The server sends the received symptom information to the generation AI, which analyzes the input symptom information and generates a suspected disease name and advice. The response from the generation AI is displayed on the device by the server.

[0045] Encouraging consultations and directing people to the reservation system

[0046] If a consultation is required based on the analysis results of the generated AI, the server displays a link to the reservation system on the terminal. When the user clicks this link, a reservation form will be displayed.

[0047] Enter and submit reservation information

[0048] When the user enters the necessary information into the reservation form (desired date and time of consultation, hospital selection, etc.) and presses the send button, the terminal sends this to the server, which then stores the received reservation information in a database.

[0049] Save chat records

[0050] The server stores the chat contents with the generated AI, as well as the patient's symptoms and appointment information, in a database as a consistent medical record that can be referenced by medical professionals during subsequent consultations.

[0051] Review by a healthcare professional

[0052] The day before the appointment, the hospital terminal retrieves the patient's medical record and appointment information from the server, and medical staff checks it in advance, allowing them to make the necessary preparations.

[0053] On the day of the examination

[0054] When a user visits the hospital at the scheduled time, the consultation begins promptly without filling out a medical questionnaire. Medical professionals can efficiently conduct consultations based on the medical record information provided in advance.

[0055] Specific examples

[0056] For example, suppose patient Suzuki inputs and submits his symptoms as "I have a persistent headache and slight fever." The server sends this to the generation AI, which returns an analysis result saying, "Based on the headache and slight fever, I suspect a cold or flu." The server displays this result to Suzuki, and if it is determined that an examination is necessary, it also displays a link to make an appointment. When Suzuki clicks the appointment link, selects a date and time, and submits the appointment information, the server saves this in a database so that medical professionals can check the information later. On the day of the appointment, Suzuki can be seen promptly without filling out a medical questionnaire.

[0057] In this way, the present invention can realize efficient medical examinations for both patients and hospitals, reduce patient anxiety, and improve the efficiency of medical treatment at hospitals.

[0058] The processing flow will be explained below.

[0059] Step 1:

[0060] The user accesses the system and the login screen is displayed. The user enters the user ID and password and presses the submit button.

[0061] Step 2:

[0062] The terminal sends the entered user ID and password to the server. The server receives this and performs authentication by referencing the database. If authentication is successful, the server generates a session ID and sends it to the terminal as a cookie. This completes the user's login.

[0063] Step 3:

[0064] After the user logs in, the server sends a symptom entry form to the terminal, which displays the form and allows the user to enter symptoms.

[0065] Step 4:

[0066] The user inputs their symptoms and presses the send button. The terminal then sends the input symptom information to the server.

[0067] Step 5:

[0068] The server sends the received symptom information to the AI ​​generator, which analyzes the information and generates a suspected disease name and advice. The AI ​​generator's response is then returned to the server.

[0069] Step 6:

[0070] The server sends the generated AI's answer to the terminal and displays it to the user. The generated AI's answer also includes whether or not a medical examination is necessary.

[0071] Step 7:

[0072] If the user wishes to see a doctor, he or she clicks on the reservation link displayed on the terminal, which then displays a reservation form.

[0073] Step 8:

[0074] The user enters the necessary information, such as the desired date and time and the hospital, into the reservation form and presses the send button. The terminal then sends the entered reservation information to the server.

[0075] Step 9:

[0076] The server stores the received reservation information in a database, which can then be referenced by medical professionals.

[0077] Step 10:

[0078] The server stores the symptom information and appointment information exchanged through chat in a database as a consistent medical record, which can then be referenced during subsequent consultations.

[0079] Step 11:

[0080] The day before the appointment, the hospital terminal retrieves the patient's medical record and appointment information from the server and makes it available for medical staff to check in advance, allowing them to prepare for the appointment.

[0081] Step 12:

[0082] On the day of the appointment, when the user visits the hospital at the scheduled time, the consultation begins immediately without filling out a medical questionnaire. Medical professionals can efficiently conduct the consultation based on the medical record information provided in advance.

[0083] Through this series of steps, the present invention realizes efficient medical examinations for both patients and hospitals, reduces patient anxiety, and improves the efficiency of medical treatment at hospitals.

[0084] Example 1

[0085] 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."

[0086] Conventional medical systems have the problem that it is difficult for patients to easily and accurately communicate their symptoms, and it takes time for medical professionals to accurately understand the patient's symptoms. In addition, the efficiency of medical treatment often declines because appointment scheduling and symptom information management on the system are not carried out efficiently. Furthermore, because symptom information and appointment information are not managed in an integrated manner, there is a risk that advance preparation before the appointment will be insufficient.

[0087] 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.

[0088] In this invention, the server includes a means for analyzing a patient's symptoms using a generating AI, a means for collecting symptom information through chat between the patient and the generating AI, a means for logging in and managing sessions while collecting symptom information, a means for directing the patient to a reservation system if a medical examination is necessary based on the analysis results, a means for storing the collected symptom information and appointment information in a database, and a means for providing appointment information and symptom information in a form that can be referenced by medical professionals. This allows patients to efficiently communicate their symptoms to the medical system, significantly improving the efficiency of medical treatment. Furthermore, the integrated management of symptom information and appointment information ensures advance preparation before an examination, reducing the burden on medical professionals.

[0089] "Generative AI" refers to artificial intelligence that analyzes input data and generates responses or analysis results in natural language.

[0090] "Symptom information" refers to information about the physical and mental condition that a patient reports.

[0091] "Chat" is a text-based means of exchanging information in real time.

[0092] "Logging in" refers to the act of a user authenticating to access a system.

[0093] "Session management" refers to the means of maintaining a user's logged-in state and managing access.

[0094] "Analysis results" refer to the diagnostic information and advice generated by the generation AI based on the symptom information entered.

[0095] A "reservation system" is a system that allows patients to reserve a time and place for an appointment.

[0096] A "database" is a system for organizing and storing symptom information, reservation information, etc.

[0097] A "healthcare professional" is someone who is qualified to examine and treat patients.

[0098] "Accessible form" means a form in which medical professionals can appropriately view the necessary information.

[0099] MODE FOR CARRYING OUT THE INVENTION

[0100] This invention is a system that uses generative AI to analyze patients' symptoms and make appropriate diagnoses and appointments. This system is designed to enable patients to make hospital appointments efficiently and for hospitals to provide efficient consultations.

[0101] Hardware and software used

[0102] Device: A computer, tablet, smartphone, etc. that patients access.

[0103] Server: A central computer that receives and analyzes symptom information and manages the reservation system. The database and generation AI are also located here.

[0104] Specific processing of the system

[0105] A user must first log in to the system. They enter their user ID and password into the login form displayed on the terminal and press the submit button. The terminal then sends the entered user ID and password to the server. The server checks the user ID and password against a database, and if authentication is successful, it generates a session ID and sends it to the terminal as a cookie. This completes the user's login.

[0106] If login is successful, the server displays a symptom input form on the device. The user describes their symptoms in the symptom input form and presses the send button. The device then sends the entered symptom data to the server. The server receives this and requests the generation AI to analyze it.

[0107] Specific working example:

[0108] The user enters the symptoms as "I have a persistent headache and slight fever" and presses the send button.

[0109] The server sends the information "I am suffering from a persistent headache and slight fever" to the generation AI.

[0110] The generating AI generates an analysis result saying, "A cold or influenza is suspected."

[0111] The server sends the analysis results from the generated AI to the terminal and displays them to the user.

[0112] If, based on the analysis results of the generation AI, an examination is necessary, the server displays a link to the reservation system on the device. When the user clicks on this link, a reservation form is displayed. When the user enters the necessary information (desired date and time of examination, choice of hospital, etc.) and presses the send button, the device sends this to the server. The server saves the received reservation information in a database.

[0113] Specific examples

[0114] For example, suppose a patient enters and submits their symptoms as "I have a persistent headache and slight fever." The server sends this to the generation AI, which then returns an analysis result saying, "Based on your headache and slight fever, I suspect you have a cold or the flu." The server displays this result to the patient, and if it is determined that an appointment is necessary, it also displays a link to make an appointment. When the patient clicks the appointment link, selects a date and time, and submits the appointment information, the server saves it in a database, allowing medical professionals to review the information later. On the day of the appointment, the patient can be seen promptly without having to fill out a medical questionnaire.

[0115] Prompt Sentence Examples

[0116] "The patient complains of a persistent headache and slight fever. Please generate possible illnesses and advice based on these symptoms."

[0117] In this way, the present invention can realize efficient medical examinations for both patients and hospitals, reduce patient anxiety, and improve medical efficiency at hospitals.

[0118] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0119] Step 1:

[0120] To log in to the system, the user enters their user ID and password into the login form displayed on the terminal and presses the submit button.

[0121] Input: User ID, Password

[0122] Output: Session ID

[0123] Specific operation: The terminal sends the entered user ID and password to the server. The server compares the received user ID and password with a database, and if authentication is successful, it generates a session ID and sends it to the terminal as a cookie.

[0124] Step 2:

[0125] If the login is successful, the server displays a symptom entry form on the terminal.

[0126] Input: None

[0127] Output: Symptom input form

[0128] Specific operation: The server sends the HTML of the symptom input form to the terminal. The terminal reads the form and displays it in a window.

[0129] Step 3:

[0130] The user describes their symptoms in the symptom input form and presses the submit button.

[0131] Input: Symptom information

[0132] Output: Symptom information data

[0133] Specific operation: The terminal sends the entered symptom data to the server, which receives it and records it in a log.

[0134] Step 4:

[0135] The server sends the received symptom information to the generation AI, which analyzes the input symptom information and generates a suspected disease name and advice.

[0136] Input: Symptom information data

[0137] Output: Analysis result data (suspected disease name, advice)

[0138] Specific operation: The server sends symptom information data to the generation AI. The generation AI analyzes it and sends the analysis result data back to the server. The server receives the analysis result data.

[0139] Step 5:

[0140] The server displays the analysis results received from the generation AI on the terminal.

[0141] Input: Analysis result data

[0142] Output: Display of analysis results

[0143] Specific operation: The server sends the analysis result data to the terminal, which displays the analysis result to the user.

[0144] Step 6:

[0145] If a consultation is required based on the analysis results of the generated AI, the server displays a link to the reservation system on the terminal. When the user clicks this link, a reservation form will be displayed.

[0146] Input: Click

[0147] Output: Reservation form

[0148] Specific operations: The server sends a reservation link to the device. The device displays the link and the user clicks it. The device displays the reservation form.

[0149] Step 7:

[0150] When the user enters the necessary information into the reservation form (desired date and time of consultation, hospital selection, etc.) and presses the send button, the terminal sends this to the server, which then stores the received reservation information in a database.

[0151] Input: Reservation information (desired consultation date and time, hospital selection, etc.)

[0152] Output: Saved reservation information

[0153] Specific operation: The user enters information into the reservation form and presses the submit button. The terminal sends the reservation information to the server. The server saves the reservation information in the database.

[0154] Step 8:

[0155] The server stores the contents of the chat with the generating AI, the patient's symptoms, and appointment information in a database as a consistent medical record.

[0156] Input: Chat content, symptom information, reservation information

[0157] Output: Medical record data

[0158] Specific operation: The server saves the chat contents, symptom information, and reservation information as a medical record in a database.

[0159] Step 9:

[0160] The day before the appointment, the hospital terminal retrieves the patient's medical record and appointment information from the server, and medical staff checks it in advance.

[0161] Input: Request

[0162] Output: Medical record information, reservation information

[0163] Specific operation: The terminal requests medical record information from the server the day before the appointment. The server sends the medical record information to the terminal. The terminal displays the medical record information to the medical staff.

[0164] Step 10:

[0165] When a user visits the hospital at the scheduled time, the consultation begins promptly without filling out a medical questionnaire. Medical professionals can efficiently conduct consultations based on the medical record information provided in advance.

[0166] Input: None

[0167] Output: Smooth progress of consultation

[0168] Specific operation: The user visits the hospital at the scheduled time and gives their name to the receptionist. The medical staff will refer to the patient's medical record information in advance and begin the consultation. The consultation will proceed smoothly.

[0169] (Application example 1)

[0170] 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."

[0171] Conventional symptom analysis systems can delay diagnosis and treatment due to inefficient processes leading to patients visiting medical institutions. This makes it difficult for customers to find products and services suited to their health condition, especially in brick-and-mortar stores. Furthermore, many stores lack reservation systems for in-store consultations. This increases customer inconvenience and creates problems for stores, resulting in time-consuming customer service.

[0172] 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.

[0173] In this invention, the server includes means for analyzing a user's symptoms using a generation AI, means for collecting symptom information through chat between the user and the generation AI, means for proposing appropriate products and services based on the analysis results, means for guiding the user to a reservation system based on the proposed products and services, means for storing the collected symptom information, proposal information, and reservation information in a database, and means for providing the stored information in a form that can be referenced by relevant parties. This allows customers to efficiently receive proposals for appropriate products and services, and, if necessary, makes it easy to make reservations for professional consultations.

[0174] "Generative AI" is a system that uses artificial intelligence technology to analyze information entered by users and automatically generate appropriate suggestions and answers.

[0175] "Symptom information" refers to data entered by the user regarding their own health condition and symptoms.

[0176] "Chat" is a means of communication in which users and the generating AI exchange messages in real time.

[0177] The "analysis results" are the results of the analysis performed by the generating AI based on the symptom information entered.

[0178] "Product and service suggestions" means that the AI ​​will present products and services that are suitable for the user's symptoms based on the analysis results.

[0179] A "reservation system" is a system that allows users to make reservations based on proposed products and services.

[0180] A "database" is an information system that efficiently manages collected and stored information and allows it to be searched and referenced as needed.

[0181] "Relevant parties" refers to staff and experts who can refer to the stored information and take appropriate action.

[0182] The system that realizes this invention allows users to input their health status and symptoms through a smartphone app, and the AI ​​analyzes the information to suggest appropriate products and services. Furthermore, users can make appointments for professional consultations if necessary.

[0183] System configuration and operation

[0184] 1. Hardware: Smartphone

[0185] 2. Software: Flask web framework, Transformers library, database (e.g., SQLite)

[0186] Processing flow

[0187] Log in

[0188] The user enters their user ID and password into the login form displayed on their smartphone and sends it to the server. The server performs authentication, and if successful, generates a session ID and sends it to the user's smartphone as a cookie. This completes the user's login.

[0189] Symptom input

[0190] If login is successful, the server displays a symptom entry form on the smartphone. The user enters their symptoms in the text field and presses the submit button. The smartphone then sends the entered symptom data to the server.

[0191] Symptom analysis

[0192] The server sends the received symptom information to a generative AI model, which uses Natural Language Processing (NLP) technology to analyze the symptoms and generate a suspected illness, advice, and recommended products and services. This generated information is then displayed on the smartphone from the server.

[0193] Product and service suggestions

[0194] Based on the analysis results provided by the AI, suggested products and services are displayed to the user, and if necessary, a link to a reservation system is provided if a professional consultation is deemed necessary.

[0195] Reservation System

[0196] When a user clicks on the reservation link, a reservation form will appear. The user selects the desired date and time and the restaurant, and submits the reservation information. The server saves this information in a database and provides it later in a form that can be viewed by the relevant parties.

[0197] Process Description

[0198] The server runs on the Flask web framework and receives and processes user input data. It uses the Transformers library and generative AI models for symptom analysis. It uses a lightweight database such as SQLite.

[0199] Specific examples

[0200] For example, if a user inputs their symptoms as "I have a persistent cough and a sore throat," the server sends this to the generative AI model, which then suggests "cough syrup or throat spray." It also offers the option of scheduling a consultation with a pharmacist, if necessary.

[0201] Prompt Sentence Examples

[0202] The prompt for the symptom "persistent cough and sore throat" is:

[0203] Symptoms entered:

[0204] I have a persistent cough and a sore throat.

[0205] Prompt to spawn AI:

[0206] Please generate appropriate suggestions for users who have this symptom.

[0207] In this way, the present invention enables quick and accurate response to the needs of users at physical stores, pharmacies, etc.

[0208] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0209] Step 1:

[0210] Log in

[0211] The user enters their user ID and password into the login form displayed on their smartphone and sends it from the device to the server. The server receives the sent user ID and password and performs authentication by referencing its internal database. If authentication is successful, the server generates a session ID and sends it to the user's device as a cookie. This completes the user's login.

[0212] Input: User ID, Password

[0213] Output: Session ID

[0214] Step 2:

[0215] Display symptom entry form

[0216] After the user successfully logs in, the server displays a symptom entry form on the smartphone screen. The form contains a text entry field for the user to enter their symptoms and a submit button.

[0217] Input: Session information for successful login

[0218] Output: Symptom input form

[0219] Step 3:

[0220] Enter and submit symptoms

[0221] The user enters their symptoms into the text field of the symptom input form and presses the send button. The device then sends the entered symptom data to the server.

[0222] Input: Symptom information (e.g., persistent cough and sore throat)

[0223] Output: Symptom data sent to the server

[0224] Step 4:

[0225] Symptom analysis

[0226] The server sends the received symptom data to a generative AI model, which uses Natural Language Processing (NLP) technology to analyze the symptoms and generate a suspected illness, advice, and recommended products and services. The server receives the generated information and displays it on the user's smartphone.

[0227] Input: Submitted symptom data

[0228] Output: Generated analysis results and recommendations

[0229] Step 5:

[0230] Product and service suggestions

[0231] Based on the analysis results, the server sends and displays appropriate product and service recommendations to the user's device, and if necessary, provides a link to a reservation system for professional consultation.

[0232] Input: Analysis results, proposals

[0233] Output: Product and service recommendations, link to reservation system

[0234] Step 6:

[0235] View and complete the reservation form

[0236] When a user clicks on the link to the reservation system, the server displays a reservation form on their smartphone. The user selects the desired date and time and the store, enters the reservation information, and presses the send button. The device then sends the entered reservation information to the server.

[0237] Input: Click on reservation link, reservation information (date, time, store, etc.)

[0238] Output: Booking form, submitted booking information

[0239] Step 7:

[0240] Save reservation information

[0241] The server stores the received reservation information in a database and provides the information in a form that can be later checked by the parties involved.

[0242] Input: Submitted reservation information

[0243] Output: Reservation information stored in the database

[0244] Step 8:

[0245] Reference information

[0246] The relevant parties (staff and specialists) can access the server and refer to the saved symptom information, recommendations, and reservation information, which allows them to prepare the necessary response in advance.

[0247] Input: Symptom information from the database, recommendations, and reservation information

[0248] Output: Referenceable information

[0249] 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.

[0250] MODE FOR CARRYING OUT THE INVENTION

[0251] This invention is a system that uses generative AI to analyze a patient's symptoms and combines it with an emotion engine to make an appropriate diagnosis and appointment that also takes into account the patient's emotional information. This system allows patients to make hospital appointments efficiently and hospitals to carry out examinations efficiently.

[0252] Login and session management

[0253] The user must first log in to the system. They enter their user ID and password into the login form displayed on the terminal and send it to the server. The server receives this and authenticates the user ID and password. If authentication is successful, it generates a session ID and sends it to the terminal as a cookie. This completes the user's login.

[0254] Display symptom entry form

[0255] After a successful login, the server displays a symptom entry form on the terminal, which contains a text entry field for the user to enter their symptoms and a submit button.

[0256] Enter and submit symptoms

[0257] The user enters their symptoms in the symptom input form and presses the send button. The device then sends the entered symptom data to the server. The server receives this and requests the generation AI and emotion engine to analyze it.

[0258] Symptom analysis and emotion recognition

[0259] The server sends the received symptom information to the generation AI and emotion engine. The generation AI analyzes the symptom information and generates a suspected disease name and advice. Meanwhile, the emotion engine analyzes the user's emotions from the symptom information. These results are returned to the server.

[0260] Emotion-based regulation

[0261] The generative AI's diagnosis results and advice are adjusted based on the emotional information analyzed by the emotion engine. For example, if the patient is feeling anxious, supplemental information and advice to alleviate that anxiety will be added. The server then sends these adjusted results to the device and displays them to the user.

[0262] Encouraging consultations and directing people to the reservation system

[0263] If a consultation is required based on the analysis results of the generated AI, the server displays a link to the reservation system on the terminal. When the user clicks this link, a reservation form will be displayed.

[0264] Enter and submit reservation information

[0265] When the user enters the necessary information into the reservation form (desired date and time of consultation, hospital selection, etc.) and presses the send button, the terminal sends this to the server, which then stores the received reservation information in a database.

[0266] Chat content and emotional information records

[0267] The server stores the chat contents with the generative AI, the patient's symptom information, appointment information, and the analysis results of the emotion engine in a database as a consistent medical record, which medical professionals can refer to during subsequent consultations.

[0268] Review by a healthcare professional

[0269] The day before the appointment, the hospital terminal retrieves the patient's medical record and appointment information from the server, which medical staff can then check in advance, allowing them to make the necessary preparations.

[0270] On the day of the examination

[0271] When a user visits the hospital at the scheduled time, the consultation begins promptly without filling out a medical questionnaire. Medical professionals can conduct the consultation efficiently based on the medical record information and emotional information provided in advance.

[0272] Specific examples

[0273] For example, suppose a patient, Mr. Suzuki, enters and submits his symptoms as "I have a persistent headache and slight fever." The server sends this to the generation AI and emotion engine, and the generation AI returns an analysis result saying, "I suspect a cold or flu." Meanwhile, the emotion engine analyzes that Mr. Suzuki is feeling "anxiety." Based on this result, the server adds advice such as "To ease your anxiety, drink plenty of fluids and get plenty of rest" to the analysis result of the generation AI and displays it. After Mr. Suzuki clicks the appointment link, enters the desired date and time of the appointment, and submits the appointment information, the server saves it in the database. On the day of the appointment, Mr. Suzuki can begin the consultation without filling out a medical questionnaire.

[0274] In this way, by combining an emotion engine, the present invention makes it possible to prepare and conduct consultations while taking into consideration the patient's emotional state, providing an efficient and reassuring experience for both the patient and the hospital.

[0275] The processing flow will be explained below.

[0276] Step 1:

[0277] The user accesses the system and the login screen is displayed. The user enters the user ID and password and presses the submit button.

[0278] Step 2:

[0279] The terminal sends the entered user ID and password to the server. The server receives this and performs authentication by referencing the database. If authentication is successful, the server generates a session ID and sends it to the terminal as a cookie. This completes the user login.

[0280] Step 3:

[0281] After successful login, the server sends a symptom input form to the terminal, which displays the form and allows the user to input symptoms.

[0282] Step 4:

[0283] The user enters their symptoms in the symptom input form and presses the send button. The terminal then sends the entered symptom data to the server.

[0284] Step 5:

[0285] The server sends the received symptom data to the generation AI, which analyzes the data and generates a suspected illness and advice. At the same time, the symptom data is sent to the emotion engine, which analyzes the user's emotions.

[0286] Step 6:

[0287] The generation AI returns diagnosis results and advice to the server, and the emotion engine returns the analyzed emotional information to the server.

[0288] Step 7:

[0289] The server adjusts the generative AI's diagnosis and advice based on the emotion engine's analysis results. For example, if the user is feeling anxious, the server may include additional advice to alleviate the anxiety.

[0290] Step 8:

[0291] The server sends the adjusted diagnostic results and advice to the terminal, which displays them to the user.

[0292] Step 9:

[0293] If the user wishes to see a doctor, he or she clicks on the reservation link displayed on the terminal, which then displays a reservation form.

[0294] Step 10:

[0295] The user enters the necessary information, such as the desired date and time and the hospital, into the reservation form and presses the send button. The terminal then sends the entered reservation information to the server.

[0296] Step 11:

[0297] The server stores the received reservation information in a database, which allows the reservation information to be managed.

[0298] Step 12:

[0299] The server stores symptom information and appointment information, including interactions between the generative AI and the emotion engine, in a database as a medical record, which can then be referenced during future consultations.

[0300] Step 13:

[0301] The day before the appointment, the hospital terminal retrieves the patient's medical record and appointment information from the server, which medical staff then checks in advance, allowing the necessary preparations to be made.

[0302] Step 14:

[0303] On the day of the appointment, when the user visits the hospital at the scheduled time, the consultation begins promptly without filling out a medical questionnaire. Medical professionals can conduct the consultation efficiently based on the medical record information and emotional information provided in advance.

[0304] By combining this series of steps with an emotion engine, it is possible to prepare and conduct consultations while taking into account the patient's emotional state, creating a system that provides an efficient and reassuring experience for both patients and hospitals.

[0305] Example 2

[0306] 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."

[0307] Conventional appointment scheduling systems make it difficult to consider patients' emotional state, which often leads to anxiety before an appointment. Furthermore, when preparing for an appointment, medical professionals have limited means of understanding detailed information about the patient's symptoms and emotional state in advance, making it difficult to provide an efficient consultation.

[0308] 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.

[0309] In this invention, the server includes means for analyzing the user's symptoms using a generating AI, means for collecting symptom information through chat between the user and the generating AI, emotion analysis means for analyzing the user's emotions from the symptom information, means for adjusting the analysis results and advice based on the emotion analysis results, means for directing the user to a reservation system if a medical examination is necessary based on the analysis results, means for saving the collected symptom information, emotion analysis results, and medical examination reservation information in a database, and means for providing the saved information in a form that medical professionals can refer to before the examination. This enables medical examination preparation and implementation that takes the patient's emotions into consideration, and enables efficient examinations by medical professionals.

[0310] "Generative AI" is an artificial intelligence technology that analyzes text data entered by users and generates appropriate diagnostic results and advice.

[0311] "Emotion analysis means" is a technology for evaluating and analyzing a user's emotions from their symptom information and text data.

[0312] "Means for collecting symptom information via chat" refers to a means for collecting information about symptoms in an interactive format between the user and the generating AI.

[0313] The "means for adjusting the analysis results and advice" refers to a means for adjusting the diagnostic results and advice of the generating AI according to the user's emotions, based on the emotional information obtained from the emotion analysis means.

[0314] The "means for guiding the user to a reservation system" refers to a means for guiding the user to an appropriate reservation system for receiving the necessary medical examination based on the diagnosis results.

[0315] "Means for storing in a database" refers to a means for centrally managing and storing collected symptom information, emotion analysis results, appointment information, etc.

[0316] "Means to provide information in a form that can be referenced by medical professionals" refers to means to display and provide stored information so that medical professionals can easily refer to it before examining a patient.

[0317] This invention is a system that uses generative AI to analyze the user's symptoms and emotion analysis to provide a diagnosis that takes into account the user's emotional information. Furthermore, based on this diagnosis, the system guides the user to a reservation system and provides the collected information in a form that medical professionals can refer to.

[0318] Login and session management

[0319] The user first authenticates by entering their user ID and password into the login form displayed on the terminal. The server compares the received authentication information with the database, and if authentication is successful, generates a session ID and sends it to the terminal as a cookie. This is how the user's session is managed.

[0320] Display symptom entry form

[0321] After the user logs in, the server displays a symptom entry form on the terminal. This form consists of a text entry field for the user to describe their symptoms and a submit button.

[0322] Enter and submit symptoms

[0323] The user presses the send button to send the symptoms they have entered in the symptom input form to the server, which then sends this data to the generation AI and emotion analysis means to request an analysis.

[0324] Symptom analysis and emotion recognition

[0325] The server sends the received symptom information to the generation AI and emotion analysis means. The generation AI analyzes the symptom information and generates a suspected disease name and advice. Meanwhile, the emotion analysis means analyzes the user's emotions from the symptom information and returns the respective results to the server.

[0326] Emotion-based regulation

[0327] The server adjusts the diagnostic results and advice from the generative AI based on the results of the emotion analysis. For example, if the user is feeling anxious, it adds supplementary information to alleviate that anxiety. The server then displays the adjusted results on the device and provides them to the user.

[0328] Encouraging consultations and directing people to the reservation system

[0329] If the AI ​​determines that a consultation is necessary, the server displays a link to the reservation system on the device. When the user clicks on this link, a reservation form will be displayed.

[0330] Enter and submit reservation information

[0331] The user enters the necessary information into the reservation form, such as the desired date and time of the appointment and the hospital of choice, and sends this to the server, which then stores the received reservation information in a database.

[0332] Chat content and emotional information records

[0333] The server stores the chat content with the AI ​​generator, the user's symptom information, reservation information, and the results of the emotion analysis method in a database as consistent medical record information that can be later referenced by medical professionals.

[0334] Review by a healthcare professional

[0335] The day before the appointment, the hospital terminal retrieves the patient's medical record and appointment information from the server, which medical staff can then check in advance, allowing them to make the necessary preparations.

[0336] On the day of the examination

[0337] When a user visits the hospital at the scheduled time, the consultation can begin promptly without filling out a medical questionnaire. Medical professionals can efficiently conduct consultations based on the medical record information and emotional information provided in advance.

[0338] Specific examples

[0339] For example, consider the case where a user inputs and submits symptoms such as "I have a persistent headache and slight fever." This symptom information is sent to the generation AI and emotion analysis means via the server. The generation AI returns the analysis result as "This is likely a cold or flu," and the emotion analysis means determines that the user is feeling "anxious." Based on this result, the server displays more advice, such as "To ease your anxiety, drink plenty of fluids and get plenty of rest." When the user clicks the appointment link, inputs the desired date and time for the consultation, and submits it, the server saves this information in a database, enabling the consultation to begin promptly on the day of the appointment.

[0340] Prompt Sentence Examples

[0341] User: "I've had a persistent headache and slight fever."

[0342] Generative AI: "This sounds like a cold or flu."

[0343] Sentiment analysis method: User feels "anxiety"

[0344] Server: "To help ease anxiety, drink plenty of fluids and get plenty of rest."

[0345] In this way, by integrating generative AI with emotion analysis means, the present invention makes it possible to prepare for and conduct examinations while taking into account the user's emotional state, thereby supporting efficient and effective examinations for medical professionals.

[0346] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0347] Step 1:

[0348] The user enters their user ID and password into the login form displayed on their terminal and presses the send button. The entered user ID and password are sent from the terminal to the server. The server compares the received user ID and password with the database and performs authentication. If authentication is successful, the server generates a session ID and sends it to the terminal as a cookie. This starts the user's session.

[0349] Input: User ID, Password

[0350] Output: Session ID (cookie)

[0351] Step 2:

[0352] After the user logs in, the server displays a symptom entry form on the terminal. The symptom entry form contains a text entry field for the user to describe their symptoms and a submit button. The user opens the form and can enter their symptoms.

[0353] Input: None

[0354] Output: Display of symptom input form

[0355] Step 3:

[0356] The user enters their symptoms in the symptom input form and presses the send button. The entered symptom data is sent from the device to the server. The server then sends the received symptom data to the generation AI and emotion analysis means.

[0357] Input: Symptom information

[0358] Output: sent to the generative AI and sentiment analysis method

[0359] Step 4:

[0360] The server requests the generation AI to analyze the symptom information. The generation AI analyzes the symptom information and generates a suspected disease name and advice. Meanwhile, the emotion analysis means analyzes the user's emotions from the symptom information. The analysis results are returned to the server.

[0361] Input: Symptom information

[0362] Output: Analysis results (disease name, advice, emotional information)

[0363] Step 5:

[0364] The server adjusts the diagnostic results and advice from the generative AI based on the results of the emotion analysis. For example, if the user is feeling anxious, it adds supplementary information to alleviate the anxiety. The server then displays the adjusted results on the device and provides them to the user.

[0365] Input: Generative AI diagnosis results, emotional information

[0366] Output: Diagnostic results and advice after adjustment

[0367] Step 6:

[0368] If the server determines that a consultation is necessary based on the analysis results of the generated AI, it displays a link to the reservation system on the terminal. When the user clicks this link, a reservation form will be displayed.

[0369] Input: Analysis results

[0370] Output: Link to booking system

[0371] Step 7:

[0372] The user enters the necessary information into the reservation form (desired appointment date and time, hospital selection, etc.) and presses the send button. This sends the entered reservation information from the terminal to the server. The server saves the received reservation information in a database.

[0373] Input: Reservation information

[0374] Output: Saved reservation information

[0375] Step 8:

[0376] The server stores the chat content with the AI ​​generator, the user's symptom information, reservation information, and the results of the emotion analysis method in a database as consistent medical record information that can be referenced by medical professionals during subsequent consultations.

[0377] Input: Chat content, symptom information, reservation information, emotional information

[0378] Output: Saved medical record information

[0379] Step 9:

[0380] The day before the appointment, the hospital terminal retrieves the patient's medical record and appointment information from the server, and medical staff checks it in advance. This confirmation allows the necessary preparations to be made.

[0381] Input: Medical record information, reservation information

[0382] Output: Proactive notification to healthcare professionals

[0383] Step 10:

[0384] When a user visits the hospital at the scheduled time, they can receive a prompt examination without having to fill out a medical questionnaire. Medical staff can conduct examinations efficiently by making decisions based on the medical record information and emotional information provided in advance.

[0385] Input: User visit and medical record information

[0386] Output: Efficient consultation

[0387] (Application example 2)

[0388] 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."

[0389] In conventional online medical consultation systems, symptom analysis is mechanical and lacks consideration of the user's emotional state when providing diagnosis and advice. This can lead to anxiety or discomfort in the diagnosis results, leading to inappropriate medical services. Furthermore, the system redirects users to a medical appointment system without considering their emotions, resulting in lower satisfaction and a worsening efficiency.

[0390] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing the user's symptoms using a generation AI, means for collecting symptom information through chat between the user and the generation AI, means for analyzing the user's emotional information using an emotion engine when analyzing the symptom information, means for adjusting the analysis results and advice of the generation AI based on the emotional information, means for directing the user to an appointment system if a medical examination is necessary based on the analysis results, means for saving the collected symptom information and appointment information in a database, and means for providing the appointment information and symptom information in a form that can be referenced by medical professionals. As a result, an appropriate diagnosis and advice that takes the user's emotions into consideration is provided, reducing anxiety and discomfort and enabling the user to receive efficient, high-quality medical services.

[0391] "Generative AI" is an artificial intelligence technology that generates appropriate diagnoses and advice based on symptom information entered by the user.

[0392] "Symptom information" refers to text data about a medical condition or symptom reported by a user.

[0393] "Chat" is an interface for text-based communication between the user and the generative AI.

[0394] An "emotion engine" is an artificial intelligence technology for analyzing the emotional state of a user from text data entered by the user.

[0395] "Analysis results" refers to the diagnosis and advice generated after the generating AI analyzes the user's symptom information.

[0396] The "reservation system" is an online system for making appointments at medical institutions.

[0397] A "database" is a digital storage system for organizing and storing collected symptom information, appointment information, and other information.

[0398] "Medical professionals" refers to professionals such as doctors and nurses who provide medical examinations and treatment.

[0399] "Logging in" is the process by which a user enters and is recognized as providing authentication information to access a system.

[0400] A "session ID" is an identifier generated by the server to track a user's activities while they are logged into the system.

[0401] This invention is an online medical consultation system that combines generative AI and an emotion engine to analyze users' symptoms and emotions, and then provides appropriate diagnoses and appointment procedures. Specific implementation methods are described below.

[0402] A server contains multiple modules, each responsible for a specific function. The following hardware and software are mainly used: A server uses a server computer with high processing power (e.g., an EC2 instance from Amazon Web Services) and a database system (e.g., Amazon RDS) to store data.

[0403] The server must first process the user's login. The user uses a smartphone application to enter authentication information (user ID and password) and send it to the server. The server receives the authentication information using Flask (a Python framework) and performs user authentication. If authentication is successful, a session ID is generated and sent to the smartphone device as a cookie.

[0404] Next, if login is successful, the server displays a symptom input form to the user. The user enters their symptoms in text format and presses the submit button. At this time, the user-entered data is sent to the server. The transmitted symptom information is analyzed by a generative AI engine (e.g., OpenAI GPT-3). The generative AI engine uses natural language processing technology to analyze the symptom information and generate a suspected disease name and advice.

[0405] At the same time, an emotion engine (e.g., Hume AI) analyzes emotions from the symptom information entered by the user. The emotion engine processes the text data and reveals the user's emotional state (e.g., anxiety, sadness, happiness, etc.).

[0406] The analysis results of the generative AI engine and the emotion engine are returned to the server, which then integrates these results and tailors appropriate diagnoses and advice based on the emotion information. For example, if the user is feeling "anxious," it adds supplementary information and advice to alleviate that anxiety.

[0407] If a consultation is deemed necessary, the server displays a link to the user to make an appointment. When the user clicks on the link, a reservation form appears. The user can enter reservation information, such as the desired date and time of the consultation and the hospital of choice, and submit the form. The submitted reservation information is saved in a database by the server, so that it can be referenced by medical professionals later.

[0408] The interactions between the user and the generated AI, as well as emotional information, are saved in a database as a medical record. Medical professionals can refer to this information in advance, allowing them to make the necessary preparations before the consultation. This allows users to begin their consultation promptly without having to fill out a medical questionnaire.

[0409] For example, if a user reports a headache, the generation AI determines that it is likely a migraine, and the emotion engine analyzes that the user is feeling anxious. As a result, the server displays additional advice such as, "We recommend that you drink plenty of fluids and get adequate rest. Also, take three deep breaths to feel more at ease." If the user wishes to make an appointment, they can be directed to a reservation form with just one click.

[0410] Example prompt sentence:

[0411] "I've been having headaches lately."

[0412] "Please use the Emotion Engine to analyze the emotion of the following symptom: I've been having headaches lately."

[0413] "Please use generative AI to analyze and diagnose the following symptoms: I've been having constant headaches lately."

[0414] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0415] Step 1:

[0416] A user accesses the system through a smartphone application. The user enters their user ID and password into a login form and sends it from their device to the server. The server receives the user ID and password and performs authentication processing. If authentication is successful, the server generates a session ID and sends it to the device as a cookie. This completes the user login and starts a session. The input data is the user's authentication information, and the output data is the session ID.

[0417] Step 2:

[0418] If login is successful, the server displays a symptom input form on the terminal. The user enters their symptoms in text format and presses the send button. The entered symptom information is sent from the terminal to the server. The input data is the user's symptom information, and the output data is the sent symptom information.

[0419] Step 3:

[0420] The server sends the received symptom information to a generative AI engine (e.g., OpenAI GPT-3). The generative AI engine analyzes the symptom information using natural language processing technology and generates a suspected disease name and advice. The input data is the symptom information, and the output data is the diagnosis and advice generated by the generative AI.

[0421] Step 4:

[0422] At the same time, the server sends the symptom information to an emotion engine (e.g., Hume AI), which analyzes the user's emotional state from the text data. The input data is the symptom information, and the output data is the user's emotional state.

[0423] Step 5:

[0424] The analysis results of the generative AI engine and the emotion engine are returned to the server. The server integrates these results and tailors the appropriate diagnosis and advice based on the emotion information. For example, if the user is feeling "anxious," it adds supplementary information and advice to alleviate that anxiety. The input data are the analysis results and emotion information, and the output data are the tailored diagnosis and advice.

[0425] Step 6:

[0426] If it is determined that a consultation is necessary, the server displays a link to make an appointment on the terminal. The user clicks the link, and an appointment form appears. The user selects the desired date and time of the consultation and the medical institution, enters the appointment information, and submits it. The input data is the appointment information, and the output data is the submitted appointment information.

[0427] Step 7:

[0428] The server stores the received appointment information in a database. The stored symptom information and appointment information are organized in the database so that medical professionals can refer to them later. The input data is the appointment information, and the output data is the organized database information.

[0429] Step 8:

[0430] The interactions between the user and the generated AI, as well as emotional information, are saved in a database as a medical record. Medical professionals can refer to this information in advance and prepare for the examination. This allows the user to begin the examination promptly without having to fill out a medical questionnaire. The input data is the history of interactions and emotional information, and the output data is the medical record data.

[0431] 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.

[0432] 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.

[0433] 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.

[0434] [Second embodiment]

[0435] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0436] 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.

[0437] 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).

[0438] 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.

[0439] 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.

[0440] 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).

[0441] 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.

[0442] 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.

[0443] 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.

[0444] 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.

[0445] 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.

[0446] 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."

[0447] MODE FOR CARRYING OUT THE INVENTION

[0448] This invention is a system that uses generative AI to analyze patients' symptoms and make appropriate diagnoses and appointments. This system allows patients to make hospital appointments efficiently and hospitals to carry out examinations efficiently.

[0449] Login and session management

[0450] The user must first log in to the system. They enter their user ID and password into the login form displayed on the terminal and send it to the server. The server receives this and authenticates the user ID and password. If authentication is successful, it generates a session ID and sends it to the terminal as a cookie. This completes the user's login.

[0451] Display symptom entry form

[0452] After a successful login, the server displays a symptom entry form on the terminal, which contains a text entry field for the user to enter their symptoms and a submit button.

[0453] Enter and submit symptoms

[0454] The user enters their symptoms in the symptom input form and presses the send button. The device then sends the entered symptom data to the server. The server receives this and requests the generation AI to analyze it.

[0455] Symptom analysis and answer provision

[0456] The server sends the received symptom information to the generation AI, which analyzes the input symptom information and generates a suspected disease name and advice. The response from the generation AI is displayed on the device by the server.

[0457] Encouraging consultations and directing people to the reservation system

[0458] If a consultation is required based on the analysis results of the generated AI, the server displays a link to the reservation system on the terminal. When the user clicks this link, a reservation form will be displayed.

[0459] Enter and submit reservation information

[0460] When the user enters the necessary information into the reservation form (desired date and time of consultation, hospital selection, etc.) and presses the send button, the terminal sends this to the server, which then stores the received reservation information in a database.

[0461] Save chat records

[0462] The server stores the chat contents with the generated AI, as well as the patient's symptoms and appointment information, in a database as a consistent medical record that can be referenced by medical professionals during subsequent consultations.

[0463] Review by a healthcare professional

[0464] The day before the appointment, the hospital terminal retrieves the patient's medical record and appointment information from the server, and medical staff checks it in advance, allowing them to make the necessary preparations.

[0465] On the day of the examination

[0466] When a user visits the hospital at the scheduled time, the consultation begins promptly without filling out a medical questionnaire. Medical professionals can efficiently conduct consultations based on the medical record information provided in advance.

[0467] Specific examples

[0468] For example, suppose patient Suzuki inputs and submits his symptoms as "I have a persistent headache and slight fever." The server sends this to the generation AI, which returns an analysis result saying, "Based on the headache and slight fever, I suspect a cold or flu." The server displays this result to Suzuki, and if it is determined that an examination is necessary, it also displays a link to make an appointment. When Suzuki clicks the appointment link, selects a date and time, and submits the appointment information, the server saves this in a database so that medical professionals can check the information later. On the day of the appointment, Suzuki can be seen promptly without filling out a medical questionnaire.

[0469] In this way, the present invention can realize efficient medical examinations for both patients and hospitals, reduce patient anxiety, and improve the efficiency of medical treatment at hospitals.

[0470] The processing flow will be explained below.

[0471] Step 1:

[0472] The user accesses the system and the login screen is displayed. The user enters the user ID and password and presses the submit button.

[0473] Step 2:

[0474] The terminal sends the entered user ID and password to the server. The server receives this and performs authentication by referencing the database. If authentication is successful, the server generates a session ID and sends it to the terminal as a cookie. This completes the user's login.

[0475] Step 3:

[0476] After the user logs in, the server sends a symptom entry form to the terminal, which displays the form and allows the user to enter symptoms.

[0477] Step 4:

[0478] The user inputs their symptoms and presses the send button. The terminal then sends the input symptom information to the server.

[0479] Step 5:

[0480] The server sends the received symptom information to the AI ​​generator, which analyzes the information and generates a suspected disease name and advice. The AI ​​generator's response is then returned to the server.

[0481] Step 6:

[0482] The server sends the generated AI's answer to the terminal and displays it to the user. The generated AI's answer also includes whether or not a medical examination is necessary.

[0483] Step 7:

[0484] If the user wishes to see a doctor, he or she clicks on the reservation link displayed on the terminal, which then displays a reservation form.

[0485] Step 8:

[0486] The user enters the necessary information, such as the desired date and time and the hospital, into the reservation form and presses the send button. The terminal then sends the entered reservation information to the server.

[0487] Step 9:

[0488] The server stores the received reservation information in a database, which can then be referenced by medical professionals.

[0489] Step 10:

[0490] The server stores the symptom information and appointment information exchanged through chat in a database as a consistent medical record, which can then be referenced during subsequent consultations.

[0491] Step 11:

[0492] The day before the appointment, the hospital terminal retrieves the patient's medical record and appointment information from the server and makes it available for medical staff to check in advance, allowing them to prepare for the appointment.

[0493] Step 12:

[0494] On the day of the appointment, when the user visits the hospital at the scheduled time, the consultation begins immediately without filling out a medical questionnaire. Medical professionals can efficiently conduct the consultation based on the medical record information provided in advance.

[0495] Through this series of steps, the present invention realizes efficient medical examinations for both patients and hospitals, reduces patient anxiety, and improves the efficiency of medical treatment at hospitals.

[0496] Example 1

[0497] 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."

[0498] Conventional medical systems have the problem that it is difficult for patients to easily and accurately communicate their symptoms, and it takes time for medical professionals to accurately understand the patient's symptoms. In addition, the efficiency of medical treatment often declines because appointment scheduling and symptom information management on the system are not carried out efficiently. Furthermore, because symptom information and appointment information are not managed in an integrated manner, there is a risk that advance preparation before the appointment will be insufficient.

[0499] 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.

[0500] In this invention, the server includes a means for analyzing a patient's symptoms using a generating AI, a means for collecting symptom information through chat between the patient and the generating AI, a means for logging in and managing sessions while collecting symptom information, a means for directing the patient to a reservation system if a medical examination is necessary based on the analysis results, a means for storing the collected symptom information and appointment information in a database, and a means for providing appointment information and symptom information in a form that can be referenced by medical professionals. This allows patients to efficiently communicate their symptoms to the medical system, significantly improving the efficiency of medical treatment. Furthermore, the integrated management of symptom information and appointment information ensures advance preparation before an examination, reducing the burden on medical professionals.

[0501] "Generative AI" refers to artificial intelligence that analyzes input data and generates responses or analysis results in natural language.

[0502] "Symptom information" refers to information about the physical and mental condition that a patient reports.

[0503] "Chat" is a text-based means of exchanging information in real time.

[0504] "Logging in" refers to the act of a user authenticating to access a system.

[0505] "Session management" refers to the means of maintaining a user's logged-in state and managing access.

[0506] "Analysis results" refer to the diagnostic information and advice generated by the generation AI based on the symptom information entered.

[0507] A "reservation system" is a system that allows patients to reserve a time and place for an appointment.

[0508] A "database" is a system for organizing and storing symptom information, reservation information, etc.

[0509] A "healthcare professional" is someone who is qualified to examine and treat patients.

[0510] "Accessible form" means a form in which medical professionals can appropriately view the necessary information.

[0511] MODE FOR CARRYING OUT THE INVENTION

[0512] This invention is a system that uses generative AI to analyze patients' symptoms and make appropriate diagnoses and appointments. This system is designed to enable patients to make hospital appointments efficiently and for hospitals to provide efficient consultations.

[0513] Hardware and software used

[0514] Device: A computer, tablet, smartphone, etc. that patients access.

[0515] Server: A central computer that receives and analyzes symptom information and manages the reservation system. The database and generation AI are also located here.

[0516] Specific processing of the system

[0517] A user must first log in to the system. They enter their user ID and password into the login form displayed on the terminal and press the submit button. The terminal then sends the entered user ID and password to the server. The server checks the user ID and password against a database, and if authentication is successful, it generates a session ID and sends it to the terminal as a cookie. This completes the user's login.

[0518] If login is successful, the server displays a symptom input form on the device. The user describes their symptoms in the symptom input form and presses the send button. The device then sends the entered symptom data to the server. The server receives this and requests the generation AI to analyze it.

[0519] Specific working example:

[0520] The user enters the symptoms as "I have a persistent headache and slight fever" and presses the send button.

[0521] The server sends the information "I am suffering from a persistent headache and slight fever" to the generation AI.

[0522] The generating AI generates an analysis result saying, "A cold or influenza is suspected."

[0523] The server sends the analysis results from the generated AI to the terminal and displays them to the user.

[0524] If, based on the analysis results of the generation AI, an examination is necessary, the server displays a link to the reservation system on the device. When the user clicks on this link, a reservation form is displayed. When the user enters the necessary information (desired date and time of examination, choice of hospital, etc.) and presses the send button, the device sends this to the server. The server saves the received reservation information in a database.

[0525] Specific examples

[0526] For example, suppose a patient enters and submits their symptoms as "I have a persistent headache and slight fever." The server sends this to the generation AI, which then returns an analysis result saying, "Based on your headache and slight fever, I suspect you have a cold or the flu." The server displays this result to the patient, and if it is determined that an appointment is necessary, it also displays a link to make an appointment. When the patient clicks the appointment link, selects a date and time, and submits the appointment information, the server saves it in a database, allowing medical professionals to review the information later. On the day of the appointment, the patient can be seen promptly without having to fill out a medical questionnaire.

[0527] Prompt Sentence Examples

[0528] "The patient complains of a persistent headache and slight fever. Please generate possible illnesses and advice based on these symptoms."

[0529] In this way, the present invention can realize efficient medical examinations for both patients and hospitals, reduce patient anxiety, and improve medical efficiency at hospitals.

[0530] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0531] Step 1:

[0532] To log in to the system, the user enters their user ID and password into the login form displayed on the terminal and presses the submit button.

[0533] Input: User ID, Password

[0534] Output: Session ID

[0535] Specific operation: The terminal sends the entered user ID and password to the server. The server compares the received user ID and password with a database, and if authentication is successful, it generates a session ID and sends it to the terminal as a cookie.

[0536] Step 2:

[0537] If the login is successful, the server displays a symptom entry form on the terminal.

[0538] Input: None

[0539] Output: Symptom input form

[0540] Specific operation: The server sends the HTML of the symptom input form to the terminal. The terminal reads the form and displays it in a window.

[0541] Step 3:

[0542] The user describes their symptoms in the symptom input form and presses the submit button.

[0543] Input: Symptom information

[0544] Output: Symptom information data

[0545] Specific operation: The terminal sends the entered symptom data to the server, which receives it and records it in a log.

[0546] Step 4:

[0547] The server sends the received symptom information to the generation AI, which analyzes the input symptom information and generates a suspected disease name and advice.

[0548] Input: Symptom information data

[0549] Output: Analysis result data (suspected disease name, advice)

[0550] Specific operation: The server sends symptom information data to the generation AI. The generation AI analyzes it and sends the analysis result data back to the server. The server receives the analysis result data.

[0551] Step 5:

[0552] The server displays the analysis results received from the generation AI on the terminal.

[0553] Input: Analysis result data

[0554] Output: Display of analysis results

[0555] Specific operation: The server sends the analysis result data to the terminal, which displays the analysis result to the user.

[0556] Step 6:

[0557] If a consultation is required based on the analysis results of the generated AI, the server displays a link to the reservation system on the terminal. When the user clicks this link, a reservation form will be displayed.

[0558] Input: Click

[0559] Output: Reservation form

[0560] Specific operations: The server sends a reservation link to the device. The device displays the link and the user clicks it. The device displays the reservation form.

[0561] Step 7:

[0562] When the user enters the necessary information into the reservation form (desired date and time of consultation, hospital selection, etc.) and presses the send button, the terminal sends this to the server, which then stores the received reservation information in a database.

[0563] Input: Reservation information (desired consultation date and time, hospital selection, etc.)

[0564] Output: Saved reservation information

[0565] Specific operation: The user enters information into the reservation form and presses the submit button. The terminal sends the reservation information to the server. The server saves the reservation information in the database.

[0566] Step 8:

[0567] The server stores the contents of the chat with the generating AI, the patient's symptoms, and appointment information in a database as a consistent medical record.

[0568] Input: Chat content, symptom information, reservation information

[0569] Output: Medical record data

[0570] Specific operation: The server saves the chat contents, symptom information, and reservation information as a medical record in a database.

[0571] Step 9:

[0572] The day before the appointment, the hospital terminal retrieves the patient's medical record and appointment information from the server, and medical staff checks it in advance.

[0573] Input: Request

[0574] Output: Medical record information, reservation information

[0575] Specific operation: The terminal requests medical record information from the server the day before the appointment. The server sends the medical record information to the terminal. The terminal displays the medical record information to the medical staff.

[0576] Step 10:

[0577] When a user visits the hospital at the scheduled time, the consultation begins promptly without filling out a medical questionnaire. Medical professionals can efficiently conduct consultations based on the medical record information provided in advance.

[0578] Input: None

[0579] Output: Smooth progress of consultation

[0580] Specific operation: The user visits the hospital at the scheduled time and gives their name to the receptionist. The medical staff will refer to the patient's medical record information in advance and begin the consultation. The consultation will proceed smoothly.

[0581] (Application example 1)

[0582] 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."

[0583] Conventional symptom analysis systems can delay diagnosis and treatment due to inefficient processes leading to patients visiting medical institutions. This makes it difficult for customers to find products and services suited to their health condition, especially in brick-and-mortar stores. Furthermore, many stores lack reservation systems for in-store consultations. This increases customer inconvenience and creates problems for stores, resulting in time-consuming customer service.

[0584] 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.

[0585] In this invention, the server includes means for analyzing a user's symptoms using a generation AI, means for collecting symptom information through chat between the user and the generation AI, means for proposing appropriate products and services based on the analysis results, means for guiding the user to a reservation system based on the proposed products and services, means for storing the collected symptom information, proposal information, and reservation information in a database, and means for providing the stored information in a form that can be referenced by relevant parties. This allows customers to efficiently receive proposals for appropriate products and services, and, if necessary, makes it easy to make reservations for professional consultations.

[0586] "Generative AI" is a system that uses artificial intelligence technology to analyze information entered by users and automatically generate appropriate suggestions and answers.

[0587] "Symptom information" refers to data entered by the user regarding their own health condition and symptoms.

[0588] "Chat" is a means of communication in which users and the generating AI exchange messages in real time.

[0589] The "analysis results" are the results of the analysis performed by the generating AI based on the symptom information entered.

[0590] "Product and service suggestions" means that the AI ​​will present products and services that are suitable for the user's symptoms based on the analysis results.

[0591] A "reservation system" is a system that allows users to make reservations based on proposed products and services.

[0592] A "database" is an information system that efficiently manages collected and stored information and allows it to be searched and referenced as needed.

[0593] "Relevant parties" refers to staff and experts who can refer to the stored information and take appropriate action.

[0594] The system that realizes this invention allows users to input their health status and symptoms through a smartphone app, and the AI ​​analyzes the information to suggest appropriate products and services. Furthermore, users can make appointments for professional consultations if necessary.

[0595] System configuration and operation

[0596] 1. Hardware: Smartphone

[0597] 2. Software: Flask web framework, Transformers library, database (e.g., SQLite)

[0598] Processing flow

[0599] Log in

[0600] The user enters their user ID and password into the login form displayed on their smartphone and sends it to the server. The server performs authentication, and if successful, generates a session ID and sends it to the user's smartphone as a cookie. This completes the user's login.

[0601] Symptom input

[0602] If login is successful, the server displays a symptom entry form on the smartphone. The user enters their symptoms in the text field and presses the submit button. The smartphone then sends the entered symptom data to the server.

[0603] Symptom analysis

[0604] The server sends the received symptom information to a generative AI model, which uses Natural Language Processing (NLP) technology to analyze the symptoms and generate a suspected illness, advice, and recommended products and services. This generated information is then displayed on the smartphone from the server.

[0605] Product and service suggestions

[0606] Based on the analysis results provided by the AI, suggested products and services are displayed to the user, and if necessary, a link to a reservation system is provided if a professional consultation is deemed necessary.

[0607] Reservation System

[0608] When a user clicks on the reservation link, a reservation form will appear. The user selects the desired date and time and the restaurant, and submits the reservation information. The server saves this information in a database and provides it later in a form that can be viewed by the relevant parties.

[0609] Process Description

[0610] The server runs on the Flask web framework and receives and processes user input data. It uses the Transformers library and generative AI models for symptom analysis. It uses a lightweight database such as SQLite.

[0611] Specific examples

[0612] For example, if a user inputs their symptoms as "I have a persistent cough and a sore throat," the server sends this to the generative AI model, which then suggests "cough syrup or throat spray." It also offers the option of scheduling a consultation with a pharmacist, if necessary.

[0613] Prompt Sentence Examples

[0614] The prompt for the symptom "persistent cough and sore throat" is:

[0615] Symptoms entered:

[0616] I have a persistent cough and a sore throat.

[0617] Prompt to spawn AI:

[0618] Please generate appropriate suggestions for users who have this symptom.

[0619] In this way, the present invention enables quick and accurate response to the needs of users at physical stores, pharmacies, etc.

[0620] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0621] Step 1:

[0622] Log in

[0623] The user enters their user ID and password into the login form displayed on their smartphone and sends it from the device to the server. The server receives the sent user ID and password and performs authentication by referencing its internal database. If authentication is successful, the server generates a session ID and sends it to the user's device as a cookie. This completes the user's login.

[0624] Input: User ID, Password

[0625] Output: Session ID

[0626] Step 2:

[0627] Display symptom entry form

[0628] After the user successfully logs in, the server displays a symptom entry form on the smartphone screen. The form contains a text entry field for the user to enter their symptoms and a submit button.

[0629] Input: Session information for successful login

[0630] Output: Symptom input form

[0631] Step 3:

[0632] Enter and submit symptoms

[0633] The user enters their symptoms into the text field of the symptom input form and presses the send button. The device then sends the entered symptom data to the server.

[0634] Input: Symptom information (e.g., persistent cough and sore throat)

[0635] Output: Symptom data sent to the server

[0636] Step 4:

[0637] Symptom analysis

[0638] The server sends the received symptom data to a generative AI model, which uses Natural Language Processing (NLP) technology to analyze the symptoms and generate a suspected illness, advice, and recommended products and services. The server receives the generated information and displays it on the user's smartphone.

[0639] Input: Submitted symptom data

[0640] Output: Generated analysis results and recommendations

[0641] Step 5:

[0642] Product and service suggestions

[0643] Based on the analysis results, the server sends and displays appropriate product and service recommendations to the user's device, and if necessary, provides a link to a reservation system for professional consultation.

[0644] Input: Analysis results, proposals

[0645] Output: Product and service recommendations, link to reservation system

[0646] Step 6:

[0647] View and complete the reservation form

[0648] When a user clicks on the link to the reservation system, the server displays a reservation form on their smartphone. The user selects the desired date and time and the store, enters the reservation information, and presses the send button. The device then sends the entered reservation information to the server.

[0649] Input: Click on reservation link, reservation information (date, time, store, etc.)

[0650] Output: Booking form, submitted booking information

[0651] Step 7:

[0652] Save reservation information

[0653] The server stores the received reservation information in a database and provides the information in a form that can be later checked by the parties involved.

[0654] Input: Submitted reservation information

[0655] Output: Reservation information stored in the database

[0656] Step 8:

[0657] Reference information

[0658] The relevant parties (staff and specialists) can access the server and refer to the saved symptom information, recommendations, and reservation information, which allows them to prepare the necessary response in advance.

[0659] Input: Symptom information from the database, recommendations, and reservation information

[0660] Output: Referenceable information

[0661] 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.

[0662] MODE FOR CARRYING OUT THE INVENTION

[0663] This invention is a system that uses generative AI to analyze a patient's symptoms and combines it with an emotion engine to make an appropriate diagnosis and appointment that also takes into account the patient's emotional information. This system allows patients to make hospital appointments efficiently and hospitals to carry out examinations efficiently.

[0664] Login and session management

[0665] The user must first log in to the system. They enter their user ID and password into the login form displayed on the terminal and send it to the server. The server receives this and authenticates the user ID and password. If authentication is successful, it generates a session ID and sends it to the terminal as a cookie. This completes the user's login.

[0666] Display symptom entry form

[0667] After a successful login, the server displays a symptom entry form on the terminal, which contains a text entry field for the user to enter their symptoms and a submit button.

[0668] Enter and submit symptoms

[0669] The user enters their symptoms in the symptom input form and presses the send button. The device then sends the entered symptom data to the server. The server receives this and requests the generation AI and emotion engine to analyze it.

[0670] Symptom analysis and emotion recognition

[0671] The server sends the received symptom information to the generation AI and emotion engine. The generation AI analyzes the symptom information and generates a suspected disease name and advice. Meanwhile, the emotion engine analyzes the user's emotions from the symptom information. These results are returned to the server.

[0672] Emotion-based regulation

[0673] The generative AI's diagnosis results and advice are adjusted based on the emotional information analyzed by the emotion engine. For example, if the patient is feeling anxious, supplemental information and advice to alleviate that anxiety will be added. The server then sends these adjusted results to the device and displays them to the user.

[0674] Encouraging consultations and directing people to the reservation system

[0675] If a consultation is required based on the analysis results of the generated AI, the server displays a link to the reservation system on the terminal. When the user clicks this link, a reservation form will be displayed.

[0676] Enter and submit reservation information

[0677] When the user enters the necessary information into the reservation form (desired date and time of consultation, hospital selection, etc.) and presses the send button, the terminal sends this to the server, which then stores the received reservation information in a database.

[0678] Chat content and emotional information records

[0679] The server stores the chat contents with the generative AI, the patient's symptom information, appointment information, and the analysis results of the emotion engine in a database as a consistent medical record, which medical professionals can refer to during subsequent consultations.

[0680] Review by a healthcare professional

[0681] The day before the appointment, the hospital terminal retrieves the patient's medical record and appointment information from the server, which medical staff can then check in advance, allowing them to make the necessary preparations.

[0682] On the day of the examination

[0683] When a user visits the hospital at the scheduled time, the consultation begins promptly without filling out a medical questionnaire. Medical professionals can conduct the consultation efficiently based on the medical record information and emotional information provided in advance.

[0684] Specific examples

[0685] For example, suppose a patient, Mr. Suzuki, enters and submits his symptoms as "I have a persistent headache and slight fever." The server sends this to the generation AI and emotion engine, and the generation AI returns an analysis result saying, "I suspect a cold or flu." Meanwhile, the emotion engine analyzes that Mr. Suzuki is feeling "anxiety." Based on this result, the server adds advice such as "To ease your anxiety, drink plenty of fluids and get plenty of rest" to the analysis result of the generation AI and displays it. After Mr. Suzuki clicks the appointment link, enters the desired date and time of the appointment, and submits the appointment information, the server saves it in the database. On the day of the appointment, Mr. Suzuki can begin the consultation without filling out a medical questionnaire.

[0686] In this way, by combining an emotion engine, the present invention makes it possible to prepare and conduct consultations while taking into consideration the patient's emotional state, providing an efficient and reassuring experience for both the patient and the hospital.

[0687] The processing flow will be explained below.

[0688] Step 1:

[0689] The user accesses the system and the login screen is displayed. The user enters the user ID and password and presses the submit button.

[0690] Step 2:

[0691] The terminal sends the entered user ID and password to the server. The server receives this and performs authentication by referencing the database. If authentication is successful, the server generates a session ID and sends it to the terminal as a cookie. This completes the user login.

[0692] Step 3:

[0693] After successful login, the server sends a symptom input form to the terminal, which displays the form and allows the user to input symptoms.

[0694] Step 4:

[0695] The user enters their symptoms in the symptom input form and presses the send button. The terminal then sends the entered symptom data to the server.

[0696] Step 5:

[0697] The server sends the received symptom data to the generation AI, which analyzes the data and generates a suspected illness and advice. At the same time, the symptom data is sent to the emotion engine, which analyzes the user's emotions.

[0698] Step 6:

[0699] The generation AI returns diagnosis results and advice to the server, and the emotion engine returns the analyzed emotional information to the server.

[0700] Step 7:

[0701] The server adjusts the generative AI's diagnosis and advice based on the emotion engine's analysis results. For example, if the user is feeling anxious, the server may include additional advice to alleviate the anxiety.

[0702] Step 8:

[0703] The server sends the adjusted diagnostic results and advice to the terminal, which displays them to the user.

[0704] Step 9:

[0705] If the user wishes to see a doctor, he or she clicks on the reservation link displayed on the terminal, which then displays a reservation form.

[0706] Step 10:

[0707] The user enters the necessary information, such as the desired date and time and the hospital, into the reservation form and presses the send button. The terminal then sends the entered reservation information to the server.

[0708] Step 11:

[0709] The server stores the received reservation information in a database, which allows the reservation information to be managed.

[0710] Step 12:

[0711] The server stores symptom information and appointment information, including interactions between the generative AI and the emotion engine, in a database as a medical record, which can then be referenced during future consultations.

[0712] Step 13:

[0713] The day before the appointment, the hospital terminal retrieves the patient's medical record and appointment information from the server, which medical staff then checks in advance, allowing the necessary preparations to be made.

[0714] Step 14:

[0715] On the day of the appointment, when the user visits the hospital at the scheduled time, the consultation begins promptly without filling out a medical questionnaire. Medical professionals can conduct the consultation efficiently based on the medical record information and emotional information provided in advance.

[0716] By combining this series of steps with an emotion engine, it is possible to prepare and conduct consultations while taking into account the patient's emotional state, creating a system that provides an efficient and reassuring experience for both patients and hospitals.

[0717] Example 2

[0718] 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."

[0719] Conventional appointment scheduling systems make it difficult to consider patients' emotional state, which often leads to anxiety before an appointment. Furthermore, when preparing for an appointment, medical professionals have limited means of understanding detailed information about the patient's symptoms and emotional state in advance, making it difficult to provide an efficient consultation.

[0720] 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.

[0721] In this invention, the server includes means for analyzing the user's symptoms using a generating AI, means for collecting symptom information through chat between the user and the generating AI, emotion analysis means for analyzing the user's emotions from the symptom information, means for adjusting the analysis results and advice based on the emotion analysis results, means for directing the user to a reservation system if a medical examination is necessary based on the analysis results, means for saving the collected symptom information, emotion analysis results, and medical examination reservation information in a database, and means for providing the saved information in a form that medical professionals can refer to before the examination. This enables medical examination preparation and implementation that takes the patient's emotions into consideration, and enables efficient examinations by medical professionals.

[0722] "Generative AI" is an artificial intelligence technology that analyzes text data entered by users and generates appropriate diagnostic results and advice.

[0723] "Emotion analysis means" is a technology for evaluating and analyzing a user's emotions from their symptom information and text data.

[0724] "Means for collecting symptom information via chat" refers to a means for collecting information about symptoms in an interactive format between the user and the generating AI.

[0725] The "means for adjusting the analysis results and advice" refers to a means for adjusting the diagnostic results and advice of the generating AI according to the user's emotions, based on the emotional information obtained from the emotion analysis means.

[0726] The "means for guiding the user to a reservation system" refers to a means for guiding the user to an appropriate reservation system for receiving the necessary medical examination based on the diagnosis results.

[0727] "Means for storing in a database" refers to a means for centrally managing and storing collected symptom information, emotion analysis results, appointment information, etc.

[0728] "Means to provide information in a form that can be referenced by medical professionals" refers to means to display and provide stored information so that medical professionals can easily refer to it before examining a patient.

[0729] This invention is a system that uses generative AI to analyze the user's symptoms and emotion analysis to provide a diagnosis that takes into account the user's emotional information. Furthermore, based on this diagnosis, the system guides the user to a reservation system and provides the collected information in a form that medical professionals can refer to.

[0730] Login and session management

[0731] The user first authenticates by entering their user ID and password into the login form displayed on the terminal. The server compares the received authentication information with the database, and if authentication is successful, generates a session ID and sends it to the terminal as a cookie. This is how the user's session is managed.

[0732] Display symptom entry form

[0733] After the user logs in, the server displays a symptom entry form on the terminal. This form consists of a text entry field for the user to describe their symptoms and a submit button.

[0734] Enter and submit symptoms

[0735] The user presses the send button to send the symptoms they have entered in the symptom input form to the server, which then sends this data to the generation AI and emotion analysis means to request an analysis.

[0736] Symptom analysis and emotion recognition

[0737] The server sends the received symptom information to the generation AI and emotion analysis means. The generation AI analyzes the symptom information and generates a suspected disease name and advice. Meanwhile, the emotion analysis means analyzes the user's emotions from the symptom information and returns the respective results to the server.

[0738] Emotion-based regulation

[0739] The server adjusts the diagnostic results and advice from the generative AI based on the results of the emotion analysis. For example, if the user is feeling anxious, it adds supplementary information to alleviate that anxiety. The server then displays the adjusted results on the device and provides them to the user.

[0740] Encouraging consultations and directing people to the reservation system

[0741] If the AI ​​determines that a consultation is necessary, the server displays a link to the reservation system on the device. When the user clicks on this link, a reservation form will be displayed.

[0742] Enter and submit reservation information

[0743] The user enters the necessary information into the reservation form, such as the desired date and time of the appointment and the hospital of choice, and sends this to the server, which then stores the received reservation information in a database.

[0744] Chat content and emotional information records

[0745] The server stores the chat content with the AI ​​generator, the user's symptom information, reservation information, and the results of the emotion analysis method in a database as consistent medical record information that can be later referenced by medical professionals.

[0746] Review by a healthcare professional

[0747] The day before the appointment, the hospital terminal retrieves the patient's medical record and appointment information from the server, which medical staff can then check in advance, allowing them to make the necessary preparations.

[0748] On the day of the examination

[0749] When a user visits the hospital at the scheduled time, the consultation can begin promptly without filling out a medical questionnaire. Medical professionals can efficiently conduct consultations based on the medical record information and emotional information provided in advance.

[0750] Specific examples

[0751] For example, consider the case where a user inputs and submits symptoms such as "I have a persistent headache and slight fever." This symptom information is sent to the generation AI and emotion analysis means via the server. The generation AI returns the analysis result as "This is likely a cold or flu," and the emotion analysis means determines that the user is feeling "anxious." Based on this result, the server displays more advice, such as "To ease your anxiety, drink plenty of fluids and get plenty of rest." When the user clicks the appointment link, inputs the desired date and time for the consultation, and submits it, the server saves this information in a database, enabling the consultation to begin promptly on the day of the appointment.

[0752] Prompt Sentence Examples

[0753] User: "I've had a persistent headache and slight fever."

[0754] Generative AI: "This sounds like a cold or flu."

[0755] Sentiment analysis method: User feels "anxiety"

[0756] Server: "To help ease anxiety, drink plenty of fluids and get plenty of rest."

[0757] In this way, by integrating generative AI with emotion analysis means, the present invention makes it possible to prepare for and conduct examinations while taking into account the user's emotional state, thereby supporting efficient and effective examinations for medical professionals.

[0758] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0759] Step 1:

[0760] The user enters their user ID and password into the login form displayed on their terminal and presses the send button. The entered user ID and password are sent from the terminal to the server. The server compares the received user ID and password with the database and performs authentication. If authentication is successful, the server generates a session ID and sends it to the terminal as a cookie. This starts the user's session.

[0761] Input: User ID, Password

[0762] Output: Session ID (cookie)

[0763] Step 2:

[0764] After the user logs in, the server displays a symptom entry form on the terminal. The symptom entry form contains a text entry field for the user to describe their symptoms and a submit button. The user opens the form and can enter their symptoms.

[0765] Input: None

[0766] Output: Display of symptom input form

[0767] Step 3:

[0768] The user enters their symptoms in the symptom input form and presses the send button. The entered symptom data is sent from the device to the server. The server then sends the received symptom data to the generation AI and emotion analysis means.

[0769] Input: Symptom information

[0770] Output: sent to the generative AI and sentiment analysis method

[0771] Step 4:

[0772] The server requests the generation AI to analyze the symptom information. The generation AI analyzes the symptom information and generates a suspected disease name and advice. Meanwhile, the emotion analysis means analyzes the user's emotions from the symptom information. The analysis results are returned to the server.

[0773] Input: Symptom information

[0774] Output: Analysis results (disease name, advice, emotional information)

[0775] Step 5:

[0776] The server adjusts the diagnostic results and advice from the generative AI based on the results of the emotion analysis. For example, if the user is feeling anxious, it adds supplementary information to alleviate the anxiety. The server then displays the adjusted results on the device and provides them to the user.

[0777] Input: Generative AI diagnosis results, emotional information

[0778] Output: Diagnostic results and advice after adjustment

[0779] Step 6:

[0780] If the server determines that a consultation is necessary based on the analysis results of the generated AI, it displays a link to the reservation system on the terminal. When the user clicks this link, a reservation form will be displayed.

[0781] Input: Analysis results

[0782] Output: Link to booking system

[0783] Step 7:

[0784] The user enters the necessary information into the reservation form (desired appointment date and time, hospital selection, etc.) and presses the send button. This sends the entered reservation information from the terminal to the server. The server saves the received reservation information in a database.

[0785] Input: Reservation information

[0786] Output: Saved reservation information

[0787] Step 8:

[0788] The server stores the chat content with the AI ​​generator, the user's symptom information, reservation information, and the results of the emotion analysis method in a database as consistent medical record information that can be referenced by medical professionals during subsequent consultations.

[0789] Input: Chat content, symptom information, reservation information, emotional information

[0790] Output: Saved medical record information

[0791] Step 9:

[0792] The day before the appointment, the hospital terminal retrieves the patient's medical record and appointment information from the server, and medical staff checks it in advance. This confirmation allows the necessary preparations to be made.

[0793] Input: Medical record information, reservation information

[0794] Output: Proactive notification to healthcare professionals

[0795] Step 10:

[0796] When a user visits the hospital at the scheduled time, they can receive a prompt examination without having to fill out a medical questionnaire. Medical staff can conduct examinations efficiently by making decisions based on the medical record information and emotional information provided in advance.

[0797] Input: User visit and medical record information

[0798] Output: Efficient consultation

[0799] (Application example 2)

[0800] 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."

[0801] In conventional online medical consultation systems, symptom analysis is mechanical and lacks consideration of the user's emotional state when providing diagnosis and advice. This can lead to anxiety or discomfort in the diagnosis results, leading to inappropriate medical services. Furthermore, the system redirects users to a medical appointment system without considering their emotions, resulting in lower satisfaction and a worsening efficiency.

[0802] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing the user's symptoms using a generation AI, means for collecting symptom information through chat between the user and the generation AI, means for analyzing the user's emotional information using an emotion engine when analyzing the symptom information, means for adjusting the analysis results and advice of the generation AI based on the emotional information, means for directing the user to an appointment system if a medical examination is necessary based on the analysis results, means for saving the collected symptom information and appointment information in a database, and means for providing the appointment information and symptom information in a form that can be referenced by medical professionals. As a result, an appropriate diagnosis and advice that takes the user's emotions into consideration is provided, reducing anxiety and discomfort and enabling the user to receive efficient, high-quality medical services.

[0803] "Generative AI" is an artificial intelligence technology that generates appropriate diagnoses and advice based on symptom information entered by the user.

[0804] "Symptom information" refers to text data about a medical condition or symptom reported by a user.

[0805] "Chat" is an interface for text-based communication between the user and the generative AI.

[0806] An "emotion engine" is an artificial intelligence technology for analyzing the emotional state of a user from text data entered by the user.

[0807] "Analysis results" refers to the diagnosis and advice generated after the generating AI analyzes the user's symptom information.

[0808] The "reservation system" is an online system for making appointments at medical institutions.

[0809] A "database" is a digital storage system for organizing and storing collected symptom information, appointment information, and other information.

[0810] "Medical professionals" refers to professionals such as doctors and nurses who provide medical examinations and treatment.

[0811] "Logging in" is the process by which a user enters and is recognized as providing authentication information to access a system.

[0812] A "session ID" is an identifier generated by the server to track a user's activities while they are logged into the system.

[0813] This invention is an online medical consultation system that combines generative AI and an emotion engine to analyze users' symptoms and emotions, and then provides appropriate diagnoses and appointment procedures. Specific implementation methods are described below.

[0814] A server contains multiple modules, each responsible for a specific function. The following hardware and software are mainly used: A server uses a server computer with high processing power (e.g., an EC2 instance from Amazon Web Services) and a database system (e.g., Amazon RDS) to store data.

[0815] The server must first process the user's login. The user uses a smartphone application to enter authentication information (user ID and password) and send it to the server. The server receives the authentication information using Flask (a Python framework) and performs user authentication. If authentication is successful, a session ID is generated and sent to the smartphone device as a cookie.

[0816] Next, if login is successful, the server displays a symptom input form to the user. The user enters their symptoms in text format and presses the submit button. At this time, the user-entered data is sent to the server. The transmitted symptom information is analyzed by a generative AI engine (e.g., OpenAI GPT-3). The generative AI engine uses natural language processing technology to analyze the symptom information and generate a suspected disease name and advice.

[0817] At the same time, an emotion engine (e.g., Hume AI) analyzes emotions from the symptom information entered by the user. The emotion engine processes the text data and reveals the user's emotional state (e.g., anxiety, sadness, happiness, etc.).

[0818] The analysis results of the generative AI engine and the emotion engine are returned to the server, which then integrates these results and tailors appropriate diagnoses and advice based on the emotion information. For example, if the user is feeling "anxious," it adds supplementary information and advice to alleviate that anxiety.

[0819] If a consultation is deemed necessary, the server displays a link to the user to make an appointment. When the user clicks on the link, a reservation form appears. The user can enter reservation information, such as the desired date and time of the consultation and the hospital of choice, and submit the form. The submitted reservation information is saved in a database by the server, so that it can be referenced by medical professionals later.

[0820] The interactions between the user and the generated AI, as well as emotional information, are saved in a database as a medical record. Medical professionals can refer to this information in advance, allowing them to make the necessary preparations before the consultation. This allows users to begin their consultation promptly without having to fill out a medical questionnaire.

[0821] For example, if a user reports a headache, the generation AI determines that it is likely a migraine, and the emotion engine analyzes that the user is feeling anxious. As a result, the server displays additional advice such as, "We recommend that you drink plenty of fluids and get adequate rest. Also, take three deep breaths to feel more at ease." If the user wishes to make an appointment, they can be directed to a reservation form with just one click.

[0822] Example prompt sentence:

[0823] "I've been having headaches lately."

[0824] "Please use the Emotion Engine to analyze the emotion of the following symptom: I've been having headaches lately."

[0825] "Please use generative AI to analyze and diagnose the following symptoms: I've been having constant headaches lately."

[0826] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0827] Step 1:

[0828] A user accesses the system through a smartphone application. The user enters their user ID and password into a login form and sends it from their device to the server. The server receives the user ID and password and performs authentication processing. If authentication is successful, the server generates a session ID and sends it to the device as a cookie. This completes the user login and starts a session. The input data is the user's authentication information, and the output data is the session ID.

[0829] Step 2:

[0830] If login is successful, the server displays a symptom input form on the terminal. The user enters their symptoms in text format and presses the send button. The entered symptom information is sent from the terminal to the server. The input data is the user's symptom information, and the output data is the sent symptom information.

[0831] Step 3:

[0832] The server sends the received symptom information to a generative AI engine (e.g., OpenAI GPT-3). The generative AI engine analyzes the symptom information using natural language processing technology and generates a suspected disease name and advice. The input data is the symptom information, and the output data is the diagnosis and advice generated by the generative AI.

[0833] Step 4:

[0834] At the same time, the server sends the symptom information to an emotion engine (e.g., Hume AI), which analyzes the user's emotional state from the text data. The input data is the symptom information, and the output data is the user's emotional state.

[0835] Step 5:

[0836] The analysis results of the generative AI engine and the emotion engine are returned to the server. The server integrates these results and tailors the appropriate diagnosis and advice based on the emotion information. For example, if the user is feeling "anxious," it adds supplementary information and advice to alleviate that anxiety. The input data are the analysis results and emotion information, and the output data are the tailored diagnosis and advice.

[0837] Step 6:

[0838] If it is determined that a consultation is necessary, the server displays a link to make an appointment on the terminal. The user clicks the link, and an appointment form appears. The user selects the desired date and time of the consultation and the medical institution, enters the appointment information, and submits it. The input data is the appointment information, and the output data is the submitted appointment information.

[0839] Step 7:

[0840] The server stores the received appointment information in a database. The stored symptom information and appointment information are organized in the database so that medical professionals can refer to them later. The input data is the appointment information, and the output data is the organized database information.

[0841] Step 8:

[0842] The interactions between the user and the generated AI, as well as emotional information, are saved in a database as a medical record. Medical professionals can refer to this information in advance and prepare for the examination. This allows the user to begin the examination promptly without having to fill out a medical questionnaire. The input data is the history of interactions and emotional information, and the output data is the medical record data.

[0843] 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.

[0844] 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.

[0845] 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.

[0846] [Third embodiment]

[0847] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0848] 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.

[0849] 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).

[0850] 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.

[0851] 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.

[0852] 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).

[0853] 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.

[0854] 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.

[0855] 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.

[0856] 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.

[0857] 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.

[0858] 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."

[0859] MODE FOR CARRYING OUT THE INVENTION

[0860] This invention is a system that uses generative AI to analyze patients' symptoms and make appropriate diagnoses and appointments. This system allows patients to make hospital appointments efficiently and hospitals to carry out examinations efficiently.

[0861] Login and session management

[0862] The user must first log in to the system. They enter their user ID and password into the login form displayed on the terminal and send it to the server. The server receives this and authenticates the user ID and password. If authentication is successful, it generates a session ID and sends it to the terminal as a cookie. This completes the user's login.

[0863] Display symptom entry form

[0864] After a successful login, the server displays a symptom entry form on the terminal, which contains a text entry field for the user to enter their symptoms and a submit button.

[0865] Enter and submit symptoms

[0866] The user enters their symptoms in the symptom input form and presses the send button. The device then sends the entered symptom data to the server. The server receives this and requests the generation AI to analyze it.

[0867] Symptom analysis and answer provision

[0868] The server sends the received symptom information to the generation AI, which analyzes the input symptom information and generates a suspected disease name and advice. The response from the generation AI is displayed on the device by the server.

[0869] Encouraging consultations and directing people to the reservation system

[0870] If a consultation is required based on the analysis results of the generated AI, the server displays a link to the reservation system on the terminal. When the user clicks this link, a reservation form will be displayed.

[0871] Enter and submit reservation information

[0872] When the user enters the necessary information into the reservation form (desired date and time of consultation, hospital selection, etc.) and presses the send button, the terminal sends this to the server, which then stores the received reservation information in a database.

[0873] Save chat records

[0874] The server stores the chat contents with the generated AI, as well as the patient's symptoms and appointment information, in a database as a consistent medical record that can be referenced by medical professionals during subsequent consultations.

[0875] Review by a healthcare professional

[0876] The day before the appointment, the hospital terminal retrieves the patient's medical record and appointment information from the server, and medical staff checks it in advance, allowing them to make the necessary preparations.

[0877] On the day of the examination

[0878] When a user visits the hospital at the scheduled time, the consultation begins promptly without filling out a medical questionnaire. Medical professionals can efficiently conduct consultations based on the medical record information provided in advance.

[0879] Specific examples

[0880] For example, suppose patient Suzuki inputs and submits his symptoms as "I have a persistent headache and slight fever." The server sends this to the generation AI, which returns an analysis result saying, "Based on the headache and slight fever, I suspect a cold or flu." The server displays this result to Suzuki, and if it is determined that an examination is necessary, it also displays a link to make an appointment. When Suzuki clicks the appointment link, selects a date and time, and submits the appointment information, the server saves this in a database so that medical professionals can check the information later. On the day of the appointment, Suzuki can be seen promptly without filling out a medical questionnaire.

[0881] In this way, the present invention can realize efficient medical examinations for both patients and hospitals, reduce patient anxiety, and improve the efficiency of medical treatment at hospitals.

[0882] The processing flow will be explained below.

[0883] Step 1:

[0884] The user accesses the system and the login screen is displayed. The user enters the user ID and password and presses the submit button.

[0885] Step 2:

[0886] The terminal sends the entered user ID and password to the server. The server receives this and performs authentication by referencing the database. If authentication is successful, the server generates a session ID and sends it to the terminal as a cookie. This completes the user's login.

[0887] Step 3:

[0888] After the user logs in, the server sends a symptom entry form to the terminal, which displays the form and allows the user to enter symptoms.

[0889] Step 4:

[0890] The user inputs their symptoms and presses the send button. The terminal then sends the input symptom information to the server.

[0891] Step 5:

[0892] The server sends the received symptom information to the AI ​​generator, which analyzes the information and generates a suspected disease name and advice. The AI ​​generator's response is then returned to the server.

[0893] Step 6:

[0894] The server sends the generated AI's answer to the terminal and displays it to the user. The generated AI's answer also includes whether or not a medical examination is necessary.

[0895] Step 7:

[0896] If the user wishes to see a doctor, he or she clicks on the reservation link displayed on the terminal, which then displays a reservation form.

[0897] Step 8:

[0898] The user enters the necessary information, such as the desired date and time and the hospital, into the reservation form and presses the send button. The terminal then sends the entered reservation information to the server.

[0899] Step 9:

[0900] The server stores the received reservation information in a database, which can then be referenced by medical professionals.

[0901] Step 10:

[0902] The server stores the symptom information and appointment information exchanged through chat in a database as a consistent medical record, which can then be referenced during subsequent consultations.

[0903] Step 11:

[0904] The day before the appointment, the hospital terminal retrieves the patient's medical record and appointment information from the server and makes it available for medical staff to check in advance, allowing them to prepare for the appointment.

[0905] Step 12:

[0906] On the day of the appointment, when the user visits the hospital at the scheduled time, the consultation begins immediately without filling out a medical questionnaire. Medical professionals can efficiently conduct the consultation based on the medical record information provided in advance.

[0907] Through this series of steps, the present invention realizes efficient medical examinations for both patients and hospitals, reduces patient anxiety, and improves the efficiency of medical treatment at hospitals.

[0908] Example 1

[0909] 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."

[0910] Conventional medical systems have the problem that it is difficult for patients to easily and accurately communicate their symptoms, and it takes time for medical professionals to accurately understand the patient's symptoms. In addition, the efficiency of medical treatment often declines because appointment scheduling and symptom information management on the system are not carried out efficiently. Furthermore, because symptom information and appointment information are not managed in an integrated manner, there is a risk that advance preparation before the appointment will be insufficient.

[0911] 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.

[0912] In this invention, the server includes a means for analyzing a patient's symptoms using a generating AI, a means for collecting symptom information through chat between the patient and the generating AI, a means for logging in and managing sessions while collecting symptom information, a means for directing the patient to a reservation system if a medical examination is necessary based on the analysis results, a means for storing the collected symptom information and appointment information in a database, and a means for providing appointment information and symptom information in a form that can be referenced by medical professionals. This allows patients to efficiently communicate their symptoms to the medical system, significantly improving the efficiency of medical treatment. Furthermore, the integrated management of symptom information and appointment information ensures advance preparation before an examination, reducing the burden on medical professionals.

[0913] "Generative AI" refers to artificial intelligence that analyzes input data and generates responses or analysis results in natural language.

[0914] "Symptom information" refers to information about the physical and mental condition that a patient reports.

[0915] "Chat" is a text-based means of exchanging information in real time.

[0916] "Logging in" refers to the act of a user authenticating to access a system.

[0917] "Session management" refers to the means of maintaining a user's logged-in state and managing access.

[0918] "Analysis results" refer to the diagnostic information and advice generated by the generation AI based on the symptom information entered.

[0919] A "reservation system" is a system that allows patients to reserve a time and place for an appointment.

[0920] A "database" is a system for organizing and storing symptom information, reservation information, etc.

[0921] A "healthcare professional" is someone who is qualified to examine and treat patients.

[0922] "Accessible form" means a form in which medical professionals can appropriately view the necessary information.

[0923] MODE FOR CARRYING OUT THE INVENTION

[0924] This invention is a system that uses generative AI to analyze patients' symptoms and make appropriate diagnoses and appointments. This system is designed to enable patients to make hospital appointments efficiently and for hospitals to provide efficient consultations.

[0925] Hardware and software used

[0926] Device: A computer, tablet, smartphone, etc. that patients access.

[0927] Server: A central computer that receives and analyzes symptom information and manages the reservation system. The database and generation AI are also located here.

[0928] Specific processing of the system

[0929] A user must first log in to the system. They enter their user ID and password into the login form displayed on the terminal and press the submit button. The terminal then sends the entered user ID and password to the server. The server checks the user ID and password against a database, and if authentication is successful, it generates a session ID and sends it to the terminal as a cookie. This completes the user's login.

[0930] If login is successful, the server displays a symptom input form on the device. The user describes their symptoms in the symptom input form and presses the send button. The device then sends the entered symptom data to the server. The server receives this and requests the generation AI to analyze it.

[0931] Specific working example:

[0932] The user enters the symptoms as "I have a persistent headache and slight fever" and presses the send button.

[0933] The server sends the information "I am suffering from a persistent headache and slight fever" to the generation AI.

[0934] The generating AI generates an analysis result saying, "A cold or influenza is suspected."

[0935] The server sends the analysis results from the generated AI to the terminal and displays them to the user.

[0936] If, based on the analysis results of the generation AI, an examination is necessary, the server displays a link to the reservation system on the device. When the user clicks on this link, a reservation form is displayed. When the user enters the necessary information (desired date and time of examination, choice of hospital, etc.) and presses the send button, the device sends this to the server. The server saves the received reservation information in a database.

[0937] Specific examples

[0938] For example, suppose a patient enters and submits their symptoms as "I have a persistent headache and slight fever." The server sends this to the generation AI, which then returns an analysis result saying, "Based on your headache and slight fever, I suspect you have a cold or the flu." The server displays this result to the patient, and if it is determined that an appointment is necessary, it also displays a link to make an appointment. When the patient clicks the appointment link, selects a date and time, and submits the appointment information, the server saves it in a database, allowing medical professionals to review the information later. On the day of the appointment, the patient can be seen promptly without having to fill out a medical questionnaire.

[0939] Prompt Sentence Examples

[0940] "The patient complains of a persistent headache and slight fever. Please generate possible illnesses and advice based on these symptoms."

[0941] In this way, the present invention can realize efficient medical examinations for both patients and hospitals, reduce patient anxiety, and improve medical efficiency at hospitals.

[0942] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0943] Step 1:

[0944] To log in to the system, the user enters their user ID and password into the login form displayed on the terminal and presses the submit button.

[0945] Input: User ID, Password

[0946] Output: Session ID

[0947] Specific operation: The terminal sends the entered user ID and password to the server. The server compares the received user ID and password with a database, and if authentication is successful, it generates a session ID and sends it to the terminal as a cookie.

[0948] Step 2:

[0949] If the login is successful, the server displays a symptom entry form on the terminal.

[0950] Input: None

[0951] Output: Symptom input form

[0952] Specific operation: The server sends the HTML of the symptom input form to the terminal. The terminal reads the form and displays it in a window.

[0953] Step 3:

[0954] The user describes their symptoms in the symptom input form and presses the submit button.

[0955] Input: Symptom information

[0956] Output: Symptom information data

[0957] Specific operation: The terminal sends the entered symptom data to the server, which receives it and records it in a log.

[0958] Step 4:

[0959] The server sends the received symptom information to the generation AI, which analyzes the input symptom information and generates a suspected disease name and advice.

[0960] Input: Symptom information data

[0961] Output: Analysis result data (suspected disease name, advice)

[0962] Specific operation: The server sends symptom information data to the generation AI. The generation AI analyzes it and sends the analysis result data back to the server. The server receives the analysis result data.

[0963] Step 5:

[0964] The server displays the analysis results received from the generation AI on the terminal.

[0965] Input: Analysis result data

[0966] Output: Display of analysis results

[0967] Specific operation: The server sends the analysis result data to the terminal, which displays the analysis result to the user.

[0968] Step 6:

[0969] If a consultation is required based on the analysis results of the generated AI, the server displays a link to the reservation system on the terminal. When the user clicks this link, a reservation form will be displayed.

[0970] Input: Click

[0971] Output: Reservation form

[0972] Specific operations: The server sends a reservation link to the device. The device displays the link and the user clicks it. The device displays the reservation form.

[0973] Step 7:

[0974] When the user enters the necessary information into the reservation form (desired date and time of consultation, hospital selection, etc.) and presses the send button, the terminal sends this to the server, which then stores the received reservation information in a database.

[0975] Input: Reservation information (desired consultation date and time, hospital selection, etc.)

[0976] Output: Saved reservation information

[0977] Specific operation: The user enters information into the reservation form and presses the submit button. The terminal sends the reservation information to the server. The server saves the reservation information in the database.

[0978] Step 8:

[0979] The server stores the contents of the chat with the generating AI, the patient's symptoms, and appointment information in a database as a consistent medical record.

[0980] Input: Chat content, symptom information, reservation information

[0981] Output: Medical record data

[0982] Specific operation: The server saves the chat contents, symptom information, and reservation information as a medical record in a database.

[0983] Step 9:

[0984] The day before the appointment, the hospital terminal retrieves the patient's medical record and appointment information from the server, and medical staff checks it in advance.

[0985] Input: Request

[0986] Output: Medical record information, reservation information

[0987] Specific operation: The terminal requests medical record information from the server the day before the appointment. The server sends the medical record information to the terminal. The terminal displays the medical record information to the medical staff.

[0988] Step 10:

[0989] When a user visits the hospital at the scheduled time, the consultation begins promptly without filling out a medical questionnaire. Medical professionals can efficiently conduct consultations based on the medical record information provided in advance.

[0990] Input: None

[0991] Output: Smooth progress of consultation

[0992] Specific operation: The user visits the hospital at the scheduled time and gives their name to the receptionist. The medical staff will refer to the patient's medical record information in advance and begin the consultation. The consultation will proceed smoothly.

[0993] (Application example 1)

[0994] 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."

[0995] Conventional symptom analysis systems can delay diagnosis and treatment due to inefficient processes leading to patients visiting medical institutions. This makes it difficult for customers to find products and services suited to their health condition, especially in brick-and-mortar stores. Furthermore, many stores lack reservation systems for in-store consultations. This increases customer inconvenience and creates problems for stores, resulting in time-consuming customer service.

[0996] 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.

[0997] In this invention, the server includes means for analyzing a user's symptoms using a generation AI, means for collecting symptom information through chat between the user and the generation AI, means for proposing appropriate products and services based on the analysis results, means for guiding the user to a reservation system based on the proposed products and services, means for storing the collected symptom information, proposal information, and reservation information in a database, and means for providing the stored information in a form that can be referenced by relevant parties. This allows customers to efficiently receive proposals for appropriate products and services, and, if necessary, makes it easy to make reservations for professional consultations.

[0998] "Generative AI" is a system that uses artificial intelligence technology to analyze information entered by users and automatically generate appropriate suggestions and answers.

[0999] "Symptom information" refers to data entered by the user regarding their own health condition and symptoms.

[1000] "Chat" is a means of communication in which users and the generating AI exchange messages in real time.

[1001] The "analysis results" are the results of the analysis performed by the generating AI based on the symptom information entered.

[1002] "Product and service suggestions" means that the AI ​​will present products and services that are suitable for the user's symptoms based on the analysis results.

[1003] A "reservation system" is a system that allows users to make reservations based on proposed products and services.

[1004] A "database" is an information system that efficiently manages collected and stored information and allows it to be searched and referenced as needed.

[1005] "Relevant parties" refers to staff and experts who can refer to the stored information and take appropriate action.

[1006] The system that realizes this invention allows users to input their health status and symptoms through a smartphone app, and the AI ​​analyzes the information to suggest appropriate products and services. Furthermore, users can make appointments for professional consultations if necessary.

[1007] System configuration and operation

[1008] 1. Hardware: Smartphone

[1009] 2. Software: Flask web framework, Transformers library, database (e.g., SQLite)

[1010] Processing flow

[1011] Log in

[1012] The user enters their user ID and password into the login form displayed on their smartphone and sends it to the server. The server performs authentication, and if successful, generates a session ID and sends it to the user's smartphone as a cookie. This completes the user's login.

[1013] Symptom input

[1014] If login is successful, the server displays a symptom entry form on the smartphone. The user enters their symptoms in the text field and presses the submit button. The smartphone then sends the entered symptom data to the server.

[1015] Symptom analysis

[1016] The server sends the received symptom information to a generative AI model, which uses Natural Language Processing (NLP) technology to analyze the symptoms and generate a suspected illness, advice, and recommended products and services. This generated information is then displayed on the smartphone from the server.

[1017] Product and service suggestions

[1018] Based on the analysis results provided by the AI, suggested products and services are displayed to the user, and if necessary, a link to a reservation system is provided if a professional consultation is deemed necessary.

[1019] Reservation System

[1020] When a user clicks on the reservation link, a reservation form will appear. The user selects the desired date and time and the restaurant, and submits the reservation information. The server saves this information in a database and provides it later in a form that can be viewed by the relevant parties.

[1021] Process Description

[1022] The server runs on the Flask web framework and receives and processes user input data. It uses the Transformers library and generative AI models for symptom analysis. It uses a lightweight database such as SQLite.

[1023] Specific examples

[1024] For example, if a user inputs their symptoms as "I have a persistent cough and a sore throat," the server sends this to the generative AI model, which then suggests "cough syrup or throat spray." It also offers the option of scheduling a consultation with a pharmacist, if necessary.

[1025] Prompt Sentence Examples

[1026] The prompt for the symptom "persistent cough and sore throat" is:

[1027] Symptoms entered:

[1028] I have a persistent cough and a sore throat.

[1029] Prompt to spawn AI:

[1030] Please generate appropriate suggestions for users who have this symptom.

[1031] In this way, the present invention enables quick and accurate response to the needs of users at physical stores, pharmacies, etc.

[1032] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1033] Step 1:

[1034] Log in

[1035] The user enters their user ID and password into the login form displayed on their smartphone and sends it from the device to the server. The server receives the sent user ID and password and performs authentication by referencing its internal database. If authentication is successful, the server generates a session ID and sends it to the user's device as a cookie. This completes the user's login.

[1036] Input: User ID, Password

[1037] Output: Session ID

[1038] Step 2:

[1039] Display symptom entry form

[1040] After the user successfully logs in, the server displays a symptom entry form on the smartphone screen. The form contains a text entry field for the user to enter their symptoms and a submit button.

[1041] Input: Session information for successful login

[1042] Output: Symptom input form

[1043] Step 3:

[1044] Enter and submit symptoms

[1045] The user enters their symptoms into the text field of the symptom input form and presses the send button. The device then sends the entered symptom data to the server.

[1046] Input: Symptom information (e.g., persistent cough and sore throat)

[1047] Output: Symptom data sent to the server

[1048] Step 4:

[1049] Symptom analysis

[1050] The server sends the received symptom data to a generative AI model, which uses Natural Language Processing (NLP) technology to analyze the symptoms and generate a suspected illness, advice, and recommended products and services. The server receives the generated information and displays it on the user's smartphone.

[1051] Input: Submitted symptom data

[1052] Output: Generated analysis results and recommendations

[1053] Step 5:

[1054] Product and service suggestions

[1055] Based on the analysis results, the server sends and displays appropriate product and service recommendations to the user's device, and if necessary, provides a link to a reservation system for professional consultation.

[1056] Input: Analysis results, proposals

[1057] Output: Product and service recommendations, link to reservation system

[1058] Step 6:

[1059] View and complete the reservation form

[1060] When a user clicks on the link to the reservation system, the server displays a reservation form on their smartphone. The user selects the desired date and time and the store, enters the reservation information, and presses the send button. The device then sends the entered reservation information to the server.

[1061] Input: Click on reservation link, reservation information (date, time, store, etc.)

[1062] Output: Booking form, submitted booking information

[1063] Step 7:

[1064] Save reservation information

[1065] The server stores the received reservation information in a database and provides the information in a form that can be later checked by the parties involved.

[1066] Input: Submitted reservation information

[1067] Output: Reservation information stored in the database

[1068] Step 8:

[1069] Reference information

[1070] The relevant parties (staff and specialists) can access the server and refer to the saved symptom information, recommendations, and reservation information, which allows them to prepare the necessary response in advance.

[1071] Input: Symptom information from the database, recommendations, and reservation information

[1072] Output: Referenceable information

[1073] 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.

[1074] MODE FOR CARRYING OUT THE INVENTION

[1075] This invention is a system that uses generative AI to analyze a patient's symptoms and combines it with an emotion engine to make an appropriate diagnosis and appointment that also takes into account the patient's emotional information. This system allows patients to make hospital appointments efficiently and hospitals to carry out examinations efficiently.

[1076] Login and session management

[1077] The user must first log in to the system. They enter their user ID and password into the login form displayed on the terminal and send it to the server. The server receives this and authenticates the user ID and password. If authentication is successful, it generates a session ID and sends it to the terminal as a cookie. This completes the user's login.

[1078] Display symptom entry form

[1079] After a successful login, the server displays a symptom entry form on the terminal, which contains a text entry field for the user to enter their symptoms and a submit button.

[1080] Enter and submit symptoms

[1081] The user enters their symptoms in the symptom input form and presses the send button. The device then sends the entered symptom data to the server. The server receives this and requests the generation AI and emotion engine to analyze it.

[1082] Symptom analysis and emotion recognition

[1083] The server sends the received symptom information to the generation AI and emotion engine. The generation AI analyzes the symptom information and generates a suspected disease name and advice. Meanwhile, the emotion engine analyzes the user's emotions from the symptom information. These results are returned to the server.

[1084] Emotion-based regulation

[1085] The generative AI's diagnosis results and advice are adjusted based on the emotional information analyzed by the emotion engine. For example, if the patient is feeling anxious, supplemental information and advice to alleviate that anxiety will be added. The server then sends these adjusted results to the device and displays them to the user.

[1086] Encouraging consultations and directing people to the reservation system

[1087] If a consultation is required based on the analysis results of the generated AI, the server displays a link to the reservation system on the terminal. When the user clicks this link, a reservation form will be displayed.

[1088] Enter and submit reservation information

[1089] When the user enters the necessary information into the reservation form (desired date and time of consultation, hospital selection, etc.) and presses the send button, the terminal sends this to the server, which then stores the received reservation information in a database.

[1090] Chat content and emotional information records

[1091] The server stores the chat contents with the generative AI, the patient's symptom information, appointment information, and the analysis results of the emotion engine in a database as a consistent medical record, which medical professionals can refer to during subsequent consultations.

[1092] Review by a healthcare professional

[1093] The day before the appointment, the hospital terminal retrieves the patient's medical record and appointment information from the server, which medical staff can then check in advance, allowing them to make the necessary preparations.

[1094] On the day of the examination

[1095] When a user visits the hospital at the scheduled time, the consultation begins promptly without filling out a medical questionnaire. Medical professionals can conduct the consultation efficiently based on the medical record information and emotional information provided in advance.

[1096] Specific examples

[1097] For example, suppose a patient, Mr. Suzuki, enters and submits his symptoms as "I have a persistent headache and slight fever." The server sends this to the generation AI and emotion engine, and the generation AI returns an analysis result saying, "I suspect a cold or flu." Meanwhile, the emotion engine analyzes that Mr. Suzuki is feeling "anxiety." Based on this result, the server adds advice such as "To ease your anxiety, drink plenty of fluids and get plenty of rest" to the analysis result of the generation AI and displays it. After Mr. Suzuki clicks the appointment link, enters the desired date and time of the appointment, and submits the appointment information, the server saves it in the database. On the day of the appointment, Mr. Suzuki can begin the consultation without filling out a medical questionnaire.

[1098] In this way, by combining an emotion engine, the present invention makes it possible to prepare and conduct consultations while taking into consideration the patient's emotional state, providing an efficient and reassuring experience for both the patient and the hospital.

[1099] The processing flow will be explained below.

[1100] Step 1:

[1101] The user accesses the system and the login screen is displayed. The user enters the user ID and password and presses the submit button.

[1102] Step 2:

[1103] The terminal sends the entered user ID and password to the server. The server receives this and performs authentication by referencing the database. If authentication is successful, the server generates a session ID and sends it to the terminal as a cookie. This completes the user login.

[1104] Step 3:

[1105] After successful login, the server sends a symptom input form to the terminal, which displays the form and allows the user to input symptoms.

[1106] Step 4:

[1107] The user enters their symptoms in the symptom input form and presses the send button. The terminal then sends the entered symptom data to the server.

[1108] Step 5:

[1109] The server sends the received symptom data to the generation AI, which analyzes the data and generates a suspected illness and advice. At the same time, the symptom data is sent to the emotion engine, which analyzes the user's emotions.

[1110] Step 6:

[1111] The generation AI returns diagnosis results and advice to the server, and the emotion engine returns the analyzed emotional information to the server.

[1112] Step 7:

[1113] The server adjusts the generative AI's diagnosis and advice based on the emotion engine's analysis results. For example, if the user is feeling anxious, the server may include additional advice to alleviate the anxiety.

[1114] Step 8:

[1115] The server sends the adjusted diagnostic results and advice to the terminal, which displays them to the user.

[1116] Step 9:

[1117] If the user wishes to see a doctor, he or she clicks on the reservation link displayed on the terminal, which then displays a reservation form.

[1118] Step 10:

[1119] The user enters the necessary information, such as the desired date and time and the hospital, into the reservation form and presses the send button. The terminal then sends the entered reservation information to the server.

[1120] Step 11:

[1121] The server stores the received reservation information in a database, which allows the reservation information to be managed.

[1122] Step 12:

[1123] The server stores symptom information and appointment information, including interactions between the generative AI and the emotion engine, in a database as a medical record, which can then be referenced during future consultations.

[1124] Step 13:

[1125] The day before the appointment, the hospital terminal retrieves the patient's medical record and appointment information from the server, which medical staff then checks in advance, allowing the necessary preparations to be made.

[1126] Step 14:

[1127] On the day of the appointment, when the user visits the hospital at the scheduled time, the consultation begins promptly without filling out a medical questionnaire. Medical professionals can conduct the consultation efficiently based on the medical record information and emotional information provided in advance.

[1128] By combining this series of steps with an emotion engine, it is possible to prepare and conduct consultations while taking into account the patient's emotional state, creating a system that provides an efficient and reassuring experience for both patients and hospitals.

[1129] Example 2

[1130] 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."

[1131] Conventional appointment scheduling systems make it difficult to consider patients' emotional state, which often leads to anxiety before an appointment. Furthermore, when preparing for an appointment, medical professionals have limited means of understanding detailed information about the patient's symptoms and emotional state in advance, making it difficult to provide an efficient consultation.

[1132] 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.

[1133] In this invention, the server includes means for analyzing the user's symptoms using a generating AI, means for collecting symptom information through chat between the user and the generating AI, emotion analysis means for analyzing the user's emotions from the symptom information, means for adjusting the analysis results and advice based on the emotion analysis results, means for directing the user to a reservation system if a medical examination is necessary based on the analysis results, means for saving the collected symptom information, emotion analysis results, and medical examination reservation information in a database, and means for providing the saved information in a form that medical professionals can refer to before the examination. This enables medical examination preparation and implementation that takes the patient's emotions into consideration, and enables efficient examinations by medical professionals.

[1134] "Generative AI" is an artificial intelligence technology that analyzes text data entered by users and generates appropriate diagnostic results and advice.

[1135] "Emotion analysis means" is a technology for evaluating and analyzing a user's emotions from their symptom information and text data.

[1136] "Means for collecting symptom information via chat" refers to a means for collecting information about symptoms in an interactive format between the user and the generating AI.

[1137] The "means for adjusting the analysis results and advice" refers to a means for adjusting the diagnostic results and advice of the generating AI according to the user's emotions, based on the emotional information obtained from the emotion analysis means.

[1138] The "means for guiding the user to a reservation system" refers to a means for guiding the user to an appropriate reservation system for receiving the necessary medical examination based on the diagnosis results.

[1139] "Means for storing in a database" refers to a means for centrally managing and storing collected symptom information, emotion analysis results, appointment information, etc.

[1140] "Means to provide information in a form that can be referenced by medical professionals" refers to means to display and provide stored information so that medical professionals can easily refer to it before examining a patient.

[1141] This invention is a system that uses generative AI to analyze the user's symptoms and emotion analysis to provide a diagnosis that takes into account the user's emotional information. Furthermore, based on this diagnosis, the system guides the user to a reservation system and provides the collected information in a form that medical professionals can refer to.

[1142] Login and session management

[1143] The user first authenticates by entering their user ID and password into the login form displayed on the terminal. The server compares the received authentication information with the database, and if authentication is successful, generates a session ID and sends it to the terminal as a cookie. This is how the user's session is managed.

[1144] Display symptom entry form

[1145] After the user logs in, the server displays a symptom entry form on the terminal. This form consists of a text entry field for the user to describe their symptoms and a submit button.

[1146] Enter and submit symptoms

[1147] The user presses the send button to send the symptoms they have entered in the symptom input form to the server, which then sends this data to the generation AI and emotion analysis means to request an analysis.

[1148] Symptom analysis and emotion recognition

[1149] The server sends the received symptom information to the generation AI and emotion analysis means. The generation AI analyzes the symptom information and generates a suspected disease name and advice. Meanwhile, the emotion analysis means analyzes the user's emotions from the symptom information and returns the respective results to the server.

[1150] Emotion-based regulation

[1151] The server adjusts the diagnostic results and advice from the generative AI based on the results of the emotion analysis. For example, if the user is feeling anxious, it adds supplementary information to alleviate that anxiety. The server then displays the adjusted results on the device and provides them to the user.

[1152] Encouraging consultations and directing people to the reservation system

[1153] If the AI ​​determines that a consultation is necessary, the server displays a link to the reservation system on the device. When the user clicks on this link, a reservation form will be displayed.

[1154] Enter and submit reservation information

[1155] The user enters the necessary information into the reservation form, such as the desired date and time of the appointment and the hospital of choice, and sends this to the server, which then stores the received reservation information in a database.

[1156] Chat content and emotional information records

[1157] The server stores the chat content with the AI ​​generator, the user's symptom information, reservation information, and the results of the emotion analysis method in a database as consistent medical record information that can be later referenced by medical professionals.

[1158] Review by a healthcare professional

[1159] The day before the appointment, the hospital terminal retrieves the patient's medical record and appointment information from the server, which medical staff can then check in advance, allowing them to make the necessary preparations.

[1160] On the day of the examination

[1161] When a user visits the hospital at the scheduled time, the consultation can begin promptly without filling out a medical questionnaire. Medical professionals can efficiently conduct consultations based on the medical record information and emotional information provided in advance.

[1162] Specific examples

[1163] For example, consider the case where a user inputs and submits symptoms such as "I have a persistent headache and slight fever." This symptom information is sent to the generation AI and emotion analysis means via the server. The generation AI returns the analysis result as "This is likely a cold or flu," and the emotion analysis means determines that the user is feeling "anxious." Based on this result, the server displays more advice, such as "To ease your anxiety, drink plenty of fluids and get plenty of rest." When the user clicks the appointment link, inputs the desired date and time for the consultation, and submits it, the server saves this information in a database, enabling the consultation to begin promptly on the day of the appointment.

[1164] Prompt Sentence Examples

[1165] User: "I've had a persistent headache and slight fever."

[1166] Generative AI: "This sounds like a cold or flu."

[1167] Sentiment analysis method: User feels "anxiety"

[1168] Server: "To help ease anxiety, drink plenty of fluids and get plenty of rest."

[1169] In this way, by integrating generative AI with emotion analysis means, the present invention makes it possible to prepare for and conduct examinations while taking into account the user's emotional state, thereby supporting efficient and effective examinations for medical professionals.

[1170] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1171] Step 1:

[1172] The user enters their user ID and password into the login form displayed on their terminal and presses the send button. The entered user ID and password are sent from the terminal to the server. The server compares the received user ID and password with the database and performs authentication. If authentication is successful, the server generates a session ID and sends it to the terminal as a cookie. This starts the user's session.

[1173] Input: User ID, Password

[1174] Output: Session ID (cookie)

[1175] Step 2:

[1176] After the user logs in, the server displays a symptom entry form on the terminal. The symptom entry form contains a text entry field for the user to describe their symptoms and a submit button. The user opens the form and can enter their symptoms.

[1177] Input: None

[1178] Output: Display of symptom input form

[1179] Step 3:

[1180] The user enters their symptoms in the symptom input form and presses the send button. The entered symptom data is sent from the device to the server. The server then sends the received symptom data to the generation AI and emotion analysis means.

[1181] Input: Symptom information

[1182] Output: sent to the generative AI and sentiment analysis method

[1183] Step 4:

[1184] The server requests the generation AI to analyze the symptom information. The generation AI analyzes the symptom information and generates a suspected disease name and advice. Meanwhile, the emotion analysis means analyzes the user's emotions from the symptom information. The analysis results are returned to the server.

[1185] Input: Symptom information

[1186] Output: Analysis results (disease name, advice, emotional information)

[1187] Step 5:

[1188] The server adjusts the diagnostic results and advice from the generative AI based on the results of the emotion analysis. For example, if the user is feeling anxious, it adds supplementary information to alleviate the anxiety. The server then displays the adjusted results on the device and provides them to the user.

[1189] Input: Generative AI diagnosis results, emotional information

[1190] Output: Diagnostic results and advice after adjustment

[1191] Step 6:

[1192] If the server determines that a consultation is necessary based on the analysis results of the generated AI, it displays a link to the reservation system on the terminal. When the user clicks this link, a reservation form will be displayed.

[1193] Input: Analysis results

[1194] Output: Link to booking system

[1195] Step 7:

[1196] The user enters the necessary information into the reservation form (desired appointment date and time, hospital selection, etc.) and presses the send button. This sends the entered reservation information from the terminal to the server. The server saves the received reservation information in a database.

[1197] Input: Reservation information

[1198] Output: Saved reservation information

[1199] Step 8:

[1200] The server stores the chat content with the AI ​​generator, the user's symptom information, reservation information, and the results of the emotion analysis method in a database as consistent medical record information that can be referenced by medical professionals during subsequent consultations.

[1201] Input: Chat content, symptom information, reservation information, emotional information

[1202] Output: Saved medical record information

[1203] Step 9:

[1204] The day before the appointment, the hospital terminal retrieves the patient's medical record and appointment information from the server, and medical staff checks it in advance. This confirmation allows the necessary preparations to be made.

[1205] Input: Medical record information, reservation information

[1206] Output: Proactive notification to healthcare professionals

[1207] Step 10:

[1208] When a user visits the hospital at the scheduled time, they can receive a prompt examination without having to fill out a medical questionnaire. Medical staff can conduct examinations efficiently by making decisions based on the medical record information and emotional information provided in advance.

[1209] Input: User visit and medical record information

[1210] Output: Efficient consultation

[1211] (Application example 2)

[1212] 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."

[1213] In conventional online medical consultation systems, symptom analysis is mechanical and lacks consideration of the user's emotional state when providing diagnosis and advice. This can lead to anxiety or discomfort in the diagnosis results, leading to inappropriate medical services. Furthermore, the system redirects users to a medical appointment system without considering their emotions, resulting in lower satisfaction and a worsening efficiency.

[1214] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing the user's symptoms using a generation AI, means for collecting symptom information through chat between the user and the generation AI, means for analyzing the user's emotional information using an emotion engine when analyzing the symptom information, means for adjusting the analysis results and advice of the generation AI based on the emotional information, means for directing the user to an appointment system if a medical examination is necessary based on the analysis results, means for saving the collected symptom information and appointment information in a database, and means for providing the appointment information and symptom information in a form that can be referenced by medical professionals. As a result, an appropriate diagnosis and advice that takes the user's emotions into consideration is provided, reducing anxiety and discomfort and enabling the user to receive efficient, high-quality medical services.

[1215] "Generative AI" is an artificial intelligence technology that generates appropriate diagnoses and advice based on symptom information entered by the user.

[1216] "Symptom information" refers to text data about a medical condition or symptom reported by a user.

[1217] "Chat" is an interface for text-based communication between the user and the generative AI.

[1218] An "emotion engine" is an artificial intelligence technology for analyzing the emotional state of a user from text data entered by the user.

[1219] "Analysis results" refers to the diagnosis and advice generated after the generating AI analyzes the user's symptom information.

[1220] The "reservation system" is an online system for making appointments at medical institutions.

[1221] A "database" is a digital storage system for organizing and storing collected symptom information, appointment information, and other information.

[1222] "Medical professionals" refers to professionals such as doctors and nurses who provide medical examinations and treatment.

[1223] "Logging in" is the process by which a user enters and is recognized as providing authentication information to access a system.

[1224] A "session ID" is an identifier generated by the server to track a user's activities while they are logged into the system.

[1225] This invention is an online medical consultation system that combines generative AI and an emotion engine to analyze users' symptoms and emotions, and then provides appropriate diagnoses and appointment procedures. Specific implementation methods are described below.

[1226] A server contains multiple modules, each responsible for a specific function. The following hardware and software are mainly used: A server uses a server computer with high processing power (e.g., an EC2 instance from Amazon Web Services) and a database system (e.g., Amazon RDS) to store data.

[1227] The server must first process the user's login. The user uses a smartphone application to enter authentication information (user ID and password) and send it to the server. The server receives the authentication information using Flask (a Python framework) and performs user authentication. If authentication is successful, a session ID is generated and sent to the smartphone device as a cookie.

[1228] Next, if login is successful, the server displays a symptom input form to the user. The user enters their symptoms in text format and presses the submit button. At this time, the user-entered data is sent to the server. The transmitted symptom information is analyzed by a generative AI engine (e.g., OpenAI GPT-3). The generative AI engine uses natural language processing technology to analyze the symptom information and generate a suspected disease name and advice.

[1229] At the same time, an emotion engine (e.g., Hume AI) analyzes emotions from the symptom information entered by the user. The emotion engine processes the text data and reveals the user's emotional state (e.g., anxiety, sadness, happiness, etc.).

[1230] The analysis results of the generative AI engine and the emotion engine are returned to the server, which then integrates these results and tailors appropriate diagnoses and advice based on the emotion information. For example, if the user is feeling "anxious," it adds supplementary information and advice to alleviate that anxiety.

[1231] If a consultation is deemed necessary, the server displays a link to the user to make an appointment. When the user clicks on the link, a reservation form appears. The user can enter reservation information, such as the desired date and time of the consultation and the hospital of choice, and submit the form. The submitted reservation information is saved in a database by the server, so that it can be referenced by medical professionals later.

[1232] The interactions between the user and the generated AI, as well as emotional information, are saved in a database as a medical record. Medical professionals can refer to this information in advance, allowing them to make the necessary preparations before the consultation. This allows users to begin their consultation promptly without having to fill out a medical questionnaire.

[1233] For example, if a user reports a headache, the generation AI determines that it is likely a migraine, and the emotion engine analyzes that the user is feeling anxious. As a result, the server displays additional advice such as, "We recommend that you drink plenty of fluids and get adequate rest. Also, take three deep breaths to feel more at ease." If the user wishes to make an appointment, they can be directed to a reservation form with just one click.

[1234] Example prompt sentence:

[1235] "I've been having headaches lately."

[1236] "Please use the Emotion Engine to analyze the emotion of the following symptom: I've been having headaches lately."

[1237] "Please use generative AI to analyze and diagnose the following symptoms: I've been having constant headaches lately."

[1238] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1239] Step 1:

[1240] A user accesses the system through a smartphone application. The user enters their user ID and password into a login form and sends it from their device to the server. The server receives the user ID and password and performs authentication processing. If authentication is successful, the server generates a session ID and sends it to the device as a cookie. This completes the user login and starts a session. The input data is the user's authentication information, and the output data is the session ID.

[1241] Step 2:

[1242] If login is successful, the server displays a symptom input form on the terminal. The user enters their symptoms in text format and presses the send button. The entered symptom information is sent from the terminal to the server. The input data is the user's symptom information, and the output data is the sent symptom information.

[1243] Step 3:

[1244] The server sends the received symptom information to a generative AI engine (e.g., OpenAI GPT-3). The generative AI engine analyzes the symptom information using natural language processing technology and generates a suspected disease name and advice. The input data is the symptom information, and the output data is the diagnosis and advice generated by the generative AI.

[1245] Step 4:

[1246] At the same time, the server sends the symptom information to an emotion engine (e.g., Hume AI), which analyzes the user's emotional state from the text data. The input data is the symptom information, and the output data is the user's emotional state.

[1247] Step 5:

[1248] The analysis results of the generative AI engine and the emotion engine are returned to the server. The server integrates these results and tailors the appropriate diagnosis and advice based on the emotion information. For example, if the user is feeling "anxious," it adds supplementary information and advice to alleviate that anxiety. The input data are the analysis results and emotion information, and the output data are the tailored diagnosis and advice.

[1249] Step 6:

[1250] If it is determined that a consultation is necessary, the server displays a link to make an appointment on the terminal. The user clicks the link, and an appointment form appears. The user selects the desired date and time of the consultation and the medical institution, enters the appointment information, and submits it. The input data is the appointment information, and the output data is the submitted appointment information.

[1251] Step 7:

[1252] The server stores the received appointment information in a database. The stored symptom information and appointment information are organized in the database so that medical professionals can refer to them later. The input data is the appointment information, and the output data is the organized database information.

[1253] Step 8:

[1254] The interactions between the user and the generated AI, as well as emotional information, are saved in a database as a medical record. Medical professionals can refer to this information in advance and prepare for the examination. This allows the user to begin the examination promptly without having to fill out a medical questionnaire. The input data is the history of interactions and emotional information, and the output data is the medical record data.

[1255] 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.

[1256] 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.

[1257] 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.

[1258] [Fourth embodiment]

[1259] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1260] 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.

[1261] 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).

[1262] 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.

[1263] 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.

[1264] 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).

[1265] 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.

[1266] 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.

[1267] 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.

[1268] 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.

[1269] 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.

[1270] 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.

[1271] 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."

[1272] MODE FOR CARRYING OUT THE INVENTION

[1273] This invention is a system that uses generative AI to analyze patients' symptoms and make appropriate diagnoses and appointments. This system allows patients to make hospital appointments efficiently and hospitals to carry out examinations efficiently.

[1274] Login and session management

[1275] The user must first log in to the system. They enter their user ID and password into the login form displayed on the terminal and send it to the server. The server receives this and authenticates the user ID and password. If authentication is successful, it generates a session ID and sends it to the terminal as a cookie. This completes the user's login.

[1276] Display symptom entry form

[1277] After a successful login, the server displays a symptom entry form on the terminal, which contains a text entry field for the user to enter their symptoms and a submit button.

[1278] Enter and submit symptoms

[1279] The user enters their symptoms in the symptom input form and presses the send button. The device then sends the entered symptom data to the server. The server receives this and requests the generation AI to analyze it.

[1280] Symptom analysis and answer provision

[1281] The server sends the received symptom information to the generation AI, which analyzes the input symptom information and generates a suspected disease name and advice. The response from the generation AI is displayed on the device by the server.

[1282] Encouraging consultations and directing people to the reservation system

[1283] If a consultation is required based on the analysis results of the generated AI, the server displays a link to the reservation system on the terminal. When the user clicks this link, a reservation form will be displayed.

[1284] Enter and submit reservation information

[1285] When the user enters the necessary information into the reservation form (desired date and time of consultation, hospital selection, etc.) and presses the send button, the terminal sends this to the server, which then stores the received reservation information in a database.

[1286] Save chat records

[1287] The server stores the chat contents with the generated AI, as well as the patient's symptoms and appointment information, in a database as a consistent medical record that can be referenced by medical professionals during subsequent consultations.

[1288] Review by a healthcare professional

[1289] The day before the appointment, the hospital terminal retrieves the patient's medical record and appointment information from the server, and medical staff checks it in advance, allowing them to make the necessary preparations.

[1290] On the day of the examination

[1291] When a user visits the hospital at the scheduled time, the consultation begins promptly without filling out a medical questionnaire. Medical professionals can efficiently conduct consultations based on the medical record information provided in advance.

[1292] Specific examples

[1293] For example, suppose patient Suzuki inputs and submits his symptoms as "I have a persistent headache and slight fever." The server sends this to the generation AI, which returns an analysis result saying, "Based on the headache and slight fever, I suspect a cold or flu." The server displays this result to Suzuki, and if it is determined that an examination is necessary, it also displays a link to make an appointment. When Suzuki clicks the appointment link, selects a date and time, and submits the appointment information, the server saves this in a database so that medical professionals can check the information later. On the day of the appointment, Suzuki can be seen promptly without filling out a medical questionnaire.

[1294] In this way, the present invention can realize efficient medical examinations for both patients and hospitals, reduce patient anxiety, and improve the efficiency of medical treatment at hospitals.

[1295] The processing flow will be explained below.

[1296] Step 1:

[1297] The user accesses the system and the login screen is displayed. The user enters the user ID and password and presses the submit button.

[1298] Step 2:

[1299] The terminal sends the entered user ID and password to the server. The server receives this and performs authentication by referencing the database. If authentication is successful, the server generates a session ID and sends it to the terminal as a cookie. This completes the user login.

[1300] Step 3:

[1301] After the user logs in, the server sends a symptom entry form to the terminal, which displays the form and allows the user to enter symptoms.

[1302] Step 4:

[1303] The user inputs their symptoms and presses the send button. The terminal then sends the input symptom information to the server.

[1304] Step 5:

[1305] The server sends the received symptom information to the AI ​​generator, which analyzes the information and generates a suspected disease name and advice. The AI ​​generator's response is then returned to the server.

[1306] Step 6:

[1307] The server sends the generated AI's answer to the terminal and displays it to the user. The generated AI's answer also includes whether or not a medical examination is necessary.

[1308] Step 7:

[1309] If the user wishes to see a doctor, he or she clicks on the reservation link displayed on the terminal, which then displays a reservation form.

[1310] Step 8:

[1311] The user enters the necessary information, such as the desired date and time and the hospital, into the reservation form and presses the send button. The terminal then sends the entered reservation information to the server.

[1312] Step 9:

[1313] The server stores the received reservation information in a database, which can then be referenced by medical professionals.

[1314] Step 10:

[1315] The server stores the symptom information and appointment information exchanged through chat in a database as a consistent medical record, which can then be referenced during subsequent consultations.

[1316] Step 11:

[1317] The day before the appointment, the hospital terminal retrieves the patient's medical record and appointment information from the server and makes it available for medical staff to check in advance, allowing them to prepare for the appointment.

[1318] Step 12:

[1319] On the day of the appointment, when the user visits the hospital at the scheduled time, the consultation begins immediately without filling out a medical questionnaire. Medical professionals can efficiently conduct the consultation based on the medical record information provided in advance.

[1320] Through this series of steps, the present invention realizes efficient medical examinations for both patients and hospitals, reduces patient anxiety, and improves the efficiency of medical treatment at hospitals.

[1321] Example 1

[1322] 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."

[1323] Conventional medical systems have the problem that it is difficult for patients to easily and accurately communicate their symptoms, and it takes time for medical professionals to accurately understand the patient's symptoms. In addition, the efficiency of medical treatment often declines because appointment scheduling and symptom information management on the system are not carried out efficiently. Furthermore, because symptom information and appointment information are not managed in an integrated manner, there is a risk that advance preparation before the appointment will be insufficient.

[1324] 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.

[1325] In this invention, the server includes a means for analyzing a patient's symptoms using a generating AI, a means for collecting symptom information through chat between the patient and the generating AI, a means for logging in and managing sessions while collecting symptom information, a means for directing the patient to a reservation system if a medical examination is necessary based on the analysis results, a means for storing the collected symptom information and appointment information in a database, and a means for providing appointment information and symptom information in a form that can be referenced by medical professionals. This allows patients to efficiently communicate their symptoms to the medical system, significantly improving the efficiency of medical treatment. Furthermore, the integrated management of symptom information and appointment information ensures advance preparation before an examination, reducing the burden on medical professionals.

[1326] "Generative AI" refers to artificial intelligence that analyzes input data and generates responses or analysis results in natural language.

[1327] "Symptom information" refers to information about the physical or mental condition that a patient reports.

[1328] "Chat" is a text-based means of exchanging information in real time.

[1329] "Logging in" refers to the act of a user authenticating to access a system.

[1330] "Session management" refers to the means of maintaining a user's logged-in state and managing access.

[1331] "Analysis results" refer to the diagnostic information and advice generated by the generation AI based on the symptom information entered.

[1332] A "reservation system" is a system that allows patients to reserve a time and place for an appointment.

[1333] A "database" is a system for organizing and storing symptom information, reservation information, etc.

[1334] A "healthcare professional" is someone who is qualified to examine and treat patients.

[1335] "Accessible form" means a form in which medical professionals can appropriately view the necessary information.

[1336] MODE FOR CARRYING OUT THE INVENTION

[1337] This invention is a system that uses generative AI to analyze patients' symptoms and make appropriate diagnoses and appointments. This system is designed to enable patients to make hospital appointments efficiently and for hospitals to provide efficient consultations.

[1338] Hardware and software used

[1339] Device: A computer, tablet, smartphone, etc. that patients access.

[1340] Server: A central computer that receives and analyzes symptom information and manages the reservation system. The database and generation AI are also located here.

[1341] Specific processing of the system

[1342] The user must first log in to the system. They enter their user ID and password into the login form displayed on the terminal and press the submit button. The terminal then sends the entered user ID and password to the server. The server checks the user ID and password against a database, and if authentication is successful, it generates a session ID and sends it to the terminal as a cookie. This completes the user's login.

[1343] If login is successful, the server displays a symptom input form on the device. The user describes their symptoms in the symptom input form and presses the send button. The device then sends the entered symptom data to the server. The server receives this and requests the generation AI to analyze it.

[1344] Specific working example:

[1345] The user enters the symptoms as "I have a persistent headache and slight fever" and presses the send button.

[1346] The server sends the information "I am suffering from a persistent headache and slight fever" to the generation AI.

[1347] The generating AI generates an analysis result saying, "A cold or influenza is suspected."

[1348] The server sends the analysis results from the generated AI to the terminal and displays them to the user.

[1349] If, based on the analysis results of the generation AI, an examination is necessary, the server displays a link to the reservation system on the device. When the user clicks on this link, a reservation form is displayed. When the user enters the necessary information (desired date and time of examination, choice of hospital, etc.) and presses the send button, the device sends this to the server. The server saves the received reservation information in a database.

[1350] Specific examples

[1351] For example, suppose a patient enters and submits their symptoms as "I have a persistent headache and slight fever." The server sends this to the generation AI, which then returns an analysis result saying, "Based on your headache and slight fever, I suspect you have a cold or the flu." The server displays this result to the patient, and if it is determined that an appointment is necessary, it also displays a link to make an appointment. When the patient clicks the appointment link, selects a date and time, and submits the appointment information, the server saves it in a database, allowing medical professionals to review the information later. On the day of the appointment, the patient can be seen promptly without having to fill out a medical questionnaire.

[1352] Prompt Sentence Examples

[1353] "The patient complains of a persistent headache and slight fever. Please generate possible illnesses and advice based on these symptoms."

[1354] In this way, the present invention can realize efficient medical examinations for both patients and hospitals, reduce patient anxiety, and improve medical efficiency at hospitals.

[1355] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1356] Step 1:

[1357] To log in to the system, the user enters their user ID and password into the login form displayed on the terminal and presses the submit button.

[1358] Input: User ID, Password

[1359] Output: Session ID

[1360] Specific operation: The terminal sends the entered user ID and password to the server. The server compares the received user ID and password with a database, and if authentication is successful, it generates a session ID and sends it to the terminal as a cookie.

[1361] Step 2:

[1362] If the login is successful, the server displays a symptom entry form on the terminal.

[1363] Input: None

[1364] Output: Symptom input form

[1365] Specific operation: The server sends the HTML of the symptom input form to the terminal. The terminal reads the form and displays it in a window.

[1366] Step 3:

[1367] The user describes their symptoms in the symptom input form and presses the submit button.

[1368] Input: Symptom information

[1369] Output: Symptom information data

[1370] Specific operation: The terminal sends the entered symptom data to the server, which receives it and records it in a log.

[1371] Step 4:

[1372] The server sends the received symptom information to the generation AI, which analyzes the input symptom information and generates a suspected disease name and advice.

[1373] Input: Symptom information data

[1374] Output: Analysis result data (suspected disease name, advice)

[1375] Specific operation: The server sends symptom information data to the generation AI. The generation AI analyzes it and sends the analysis result data back to the server. The server receives the analysis result data.

[1376] Step 5:

[1377] The server displays the analysis results received from the generation AI on the terminal.

[1378] Input: Analysis result data

[1379] Output: Display of analysis results

[1380] Specific operation: The server sends the analysis result data to the terminal, which displays the analysis result to the user.

[1381] Step 6:

[1382] If a consultation is required based on the analysis results of the generated AI, the server displays a link to the reservation system on the terminal. When the user clicks this link, a reservation form will be displayed.

[1383] Input: Click

[1384] Output: Reservation form

[1385] Specific operations: The server sends a reservation link to the device. The device displays the link and the user clicks it. The device displays the reservation form.

[1386] Step 7:

[1387] When the user enters the necessary information into the reservation form (desired date and time of consultation, hospital selection, etc.) and presses the send button, the terminal sends this to the server, which then stores the received reservation information in a database.

[1388] Input: Reservation information (desired consultation date and time, hospital selection, etc.)

[1389] Output: Saved reservation information

[1390] Specific operation: The user enters information into the reservation form and presses the submit button. The terminal sends the reservation information to the server. The server saves the reservation information in the database.

[1391] Step 8:

[1392] The server stores the contents of the chat with the generating AI, the patient's symptoms, and appointment information in a database as a consistent medical record.

[1393] Input: Chat content, symptom information, reservation information

[1394] Output: Medical record data

[1395] Specific operation: The server saves the chat contents, symptom information, and reservation information as a medical record in a database.

[1396] Step 9:

[1397] The day before the appointment, the hospital terminal retrieves the patient's medical record and appointment information from the server, and medical staff checks it in advance.

[1398] Input: Request

[1399] Output: Medical record information, reservation information

[1400] Specific operation: The terminal requests medical record information from the server the day before the appointment. The server sends the medical record information to the terminal. The terminal displays the medical record information to the medical staff.

[1401] Step 10:

[1402] When a user visits the hospital at the scheduled time, the consultation begins promptly without filling out a medical questionnaire. Medical professionals can efficiently conduct consultations based on the medical record information provided in advance.

[1403] Input: None

[1404] Output: Smooth progress of consultation

[1405] Specific operation: The user visits the hospital at the scheduled time and gives their name to the receptionist. The medical staff will refer to the patient's medical record information in advance and begin the consultation. The consultation will proceed smoothly.

[1406] (Application example 1)

[1407] 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."

[1408] Conventional symptom analysis systems can delay diagnosis and treatment due to inefficient processes leading to patients visiting medical institutions. This makes it difficult for customers to find products and services suited to their health condition, especially in brick-and-mortar stores. Furthermore, many stores lack reservation systems for in-store consultations. This increases customer inconvenience and creates problems for stores, resulting in time-consuming customer service.

[1409] 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.

[1410] In this invention, the server includes means for analyzing a user's symptoms using a generation AI, means for collecting symptom information through chat between the user and the generation AI, means for proposing appropriate products and services based on the analysis results, means for guiding the user to a reservation system based on the proposed products and services, means for storing the collected symptom information, proposal information, and reservation information in a database, and means for providing the stored information in a form that can be referenced by relevant parties. This allows customers to efficiently receive proposals for appropriate products and services, and, if necessary, makes it easy to make reservations for professional consultations.

[1411] "Generative AI" is a system that uses artificial intelligence technology to analyze information entered by users and automatically generate appropriate suggestions and answers.

[1412] "Symptom information" refers to data entered by the user regarding their own health condition and symptoms.

[1413] "Chat" is a means of communication in which users and the generating AI exchange messages in real time.

[1414] The "analysis results" are the results of the analysis performed by the generating AI based on the symptom information entered.

[1415] "Product and service suggestions" means that the AI ​​will present products and services that are suitable for the user's symptoms based on the analysis results.

[1416] A "reservation system" is a system that allows users to make reservations based on proposed products and services.

[1417] A "database" is an information system that efficiently manages collected and stored information and allows it to be searched and referenced as needed.

[1418] "Relevant parties" refers to staff and experts who can refer to the stored information and take appropriate action.

[1419] The system that realizes this invention allows users to input their health status and symptoms through a smartphone app, and the AI ​​analyzes the information to suggest appropriate products and services. Furthermore, users can make appointments for professional consultations if necessary.

[1420] System configuration and operation

[1421] 1. Hardware: Smartphone

[1422] 2. Software: Flask web framework, Transformers library, database (e.g., SQLite)

[1423] Processing flow

[1424] Log in

[1425] The user enters their user ID and password into the login form displayed on their smartphone and sends it to the server. The server performs authentication, and if successful, generates a session ID and sends it to the user's smartphone as a cookie. This completes the user's login.

[1426] Symptom input

[1427] If login is successful, the server displays a symptom entry form on the smartphone. The user enters their symptoms in the text field and presses the submit button. The smartphone then sends the entered symptom data to the server.

[1428] Symptom analysis

[1429] The server sends the received symptom information to a generative AI model, which uses Natural Language Processing (NLP) technology to analyze the symptoms and generate a suspected illness, advice, and recommended products and services. This generated information is then displayed on the smartphone from the server.

[1430] Product and service suggestions

[1431] Based on the analysis results provided by the AI, suggested products and services are displayed to the user, and if necessary, a link to a reservation system is provided if a professional consultation is deemed necessary.

[1432] Reservation System

[1433] When a user clicks on the reservation link, a reservation form will appear. The user selects the desired date and time and the restaurant, and submits the reservation information. The server saves this information in a database and provides it later in a form that can be viewed by the relevant parties.

[1434] Process Description

[1435] The server runs on the Flask web framework and receives and processes user input data. It uses the Transformers library and generative AI models for symptom analysis. It uses a lightweight database such as SQLite.

[1436] Specific examples

[1437] For example, if a user inputs their symptoms as "I have a persistent cough and a sore throat," the server sends this to the generative AI model, which then suggests "cough syrup or throat spray." It also offers the option of scheduling a consultation with a pharmacist, if necessary.

[1438] Prompt Sentence Examples

[1439] The prompt for the symptom "persistent cough and sore throat" is:

[1440] Symptoms entered:

[1441] I have a persistent cough and a sore throat.

[1442] Prompt to spawn AI:

[1443] Please generate appropriate suggestions for users who have this symptom.

[1444] In this way, the present invention enables quick and accurate response to the needs of users at physical stores, pharmacies, etc.

[1445] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1446] Step 1:

[1447] Log in

[1448] The user enters their user ID and password into the login form displayed on their smartphone and sends it from the device to the server. The server receives the sent user ID and password and performs authentication by referencing its internal database. If authentication is successful, the server generates a session ID and sends it to the user's device as a cookie. This completes the user's login.

[1449] Input: User ID, Password

[1450] Output: Session ID

[1451] Step 2:

[1452] Display symptom entry form

[1453] After the user successfully logs in, the server displays a symptom entry form on the smartphone screen. The form contains a text entry field for the user to enter their symptoms and a submit button.

[1454] Input: Session information for successful login

[1455] Output: Symptom input form

[1456] Step 3:

[1457] Enter and submit symptoms

[1458] The user enters their symptoms into the text field of the symptom input form and presses the send button. The device then sends the entered symptom data to the server.

[1459] Input: Symptom information (e.g., persistent cough and sore throat)

[1460] Output: Symptom data sent to the server

[1461] Step 4:

[1462] Symptom analysis

[1463] The server sends the received symptom data to a generative AI model, which uses Natural Language Processing (NLP) technology to analyze the symptoms and generate a suspected illness, advice, and recommended products and services. The server receives the generated information and displays it on the user's smartphone.

[1464] Input: Submitted symptom data

[1465] Output: Generated analysis results and recommendations

[1466] Step 5:

[1467] Product and service suggestions

[1468] Based on the analysis results, the server sends and displays appropriate product and service recommendations to the user's device, and if necessary, provides a link to a reservation system for professional consultation.

[1469] Input: Analysis results, proposals

[1470] Output: Product and service recommendations, link to reservation system

[1471] Step 6:

[1472] View and complete the reservation form

[1473] When a user clicks on the link to the reservation system, the server displays a reservation form on their smartphone. The user selects the desired date and time and the store, enters the reservation information, and presses the send button. The device then sends the entered reservation information to the server.

[1474] Input: Click on reservation link, reservation information (date, time, store, etc.)

[1475] Output: Booking form, submitted booking information

[1476] Step 7:

[1477] Save reservation information

[1478] The server stores the received reservation information in a database and provides the information in a form that can be later checked by the parties involved.

[1479] Input: Submitted reservation information

[1480] Output: Reservation information stored in the database

[1481] Step 8:

[1482] Reference information

[1483] The relevant parties (staff and specialists) can access the server and refer to the saved symptom information, recommendations, and reservation information, which allows them to prepare the necessary response in advance.

[1484] Input: Symptom information from the database, recommendations, and reservation information

[1485] Output: Referenceable information

[1486] 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.

[1487] MODE FOR CARRYING OUT THE INVENTION

[1488] This invention is a system that uses generative AI to analyze a patient's symptoms and combines it with an emotion engine to make an appropriate diagnosis and appointment that also takes into account the patient's emotional information. This system allows patients to make hospital appointments efficiently and hospitals to carry out examinations efficiently.

[1489] Login and session management

[1490] The user must first log in to the system. They enter their user ID and password into the login form displayed on the terminal and send it to the server. The server receives this and authenticates the user ID and password. If authentication is successful, it generates a session ID and sends it to the terminal as a cookie. This completes the user's login.

[1491] Display symptom entry form

[1492] After a successful login, the server displays a symptom entry form on the terminal, which contains a text entry field for the user to enter their symptoms and a submit button.

[1493] Enter and submit symptoms

[1494] The user enters their symptoms in the symptom input form and presses the send button. The device then sends the entered symptom data to the server. The server receives this and requests the generation AI and emotion engine to analyze it.

[1495] Symptom analysis and emotion recognition

[1496] The server sends the received symptom information to the generation AI and emotion engine. The generation AI analyzes the symptom information and generates a suspected disease name and advice. Meanwhile, the emotion engine analyzes the user's emotions from the symptom information. These results are returned to the server.

[1497] Emotion-based regulation

[1498] The generative AI's diagnosis results and advice are adjusted based on the emotional information analyzed by the emotion engine. For example, if the patient is feeling anxious, supplemental information and advice to alleviate that anxiety will be added. The server then sends these adjusted results to the device and displays them to the user.

[1499] Encouraging consultations and directing people to the reservation system

[1500] If a consultation is required based on the analysis results of the generated AI, the server displays a link to the reservation system on the terminal. When the user clicks this link, a reservation form will be displayed.

[1501] Enter and submit reservation information

[1502] When the user enters the necessary information into the reservation form (desired date and time of consultation, hospital selection, etc.) and presses the send button, the terminal sends this to the server, which then stores the received reservation information in a database.

[1503] Chat content and emotional information records

[1504] The server stores the chat contents with the generative AI, the patient's symptom information, appointment information, and the analysis results of the emotion engine in a database as a consistent medical record, which medical professionals can refer to during subsequent consultations.

[1505] Review by a healthcare professional

[1506] The day before the appointment, the hospital terminal retrieves the patient's medical record and appointment information from the server, which medical staff can then check in advance, allowing them to make the necessary preparations.

[1507] On the day of the examination

[1508] When a user visits the hospital at the scheduled time, the consultation begins promptly without filling out a medical questionnaire. Medical professionals can conduct the consultation efficiently based on the medical record information and emotional information provided in advance.

[1509] Specific examples

[1510] For example, suppose a patient, Mr. Suzuki, enters and submits his symptoms as "I have a persistent headache and slight fever." The server sends this to the generation AI and emotion engine, and the generation AI returns an analysis result saying, "I suspect a cold or flu." Meanwhile, the emotion engine analyzes that Mr. Suzuki is feeling "anxiety." Based on this result, the server adds advice such as "To ease your anxiety, drink plenty of fluids and get plenty of rest" to the analysis result of the generation AI and displays it. After Mr. Suzuki clicks the appointment link, enters the desired date and time of the appointment, and submits the appointment information, the server saves it in the database. On the day of the appointment, Mr. Suzuki can begin the consultation without filling out a medical questionnaire.

[1511] In this way, by combining an emotion engine, the present invention makes it possible to prepare and conduct consultations while taking into consideration the patient's emotional state, providing an efficient and reassuring experience for both the patient and the hospital.

[1512] The processing flow will be explained below.

[1513] Step 1:

[1514] The user accesses the system and the login screen is displayed. The user enters the user ID and password and presses the submit button.

[1515] Step 2:

[1516] The terminal sends the entered user ID and password to the server. The server receives this and performs authentication by referencing the database. If authentication is successful, the server generates a session ID and sends it to the terminal as a cookie. This completes the user login.

[1517] Step 3:

[1518] After successful login, the server sends a symptom input form to the terminal, which displays the form and allows the user to input symptoms.

[1519] Step 4:

[1520] The user enters their symptoms in the symptom input form and presses the send button. The terminal then sends the entered symptom data to the server.

[1521] Step 5:

[1522] The server sends the received symptom data to the generation AI, which analyzes the data and generates a suspected illness and advice. At the same time, the symptom data is sent to the emotion engine, which analyzes the user's emotions.

[1523] Step 6:

[1524] The generation AI returns diagnosis results and advice to the server, and the emotion engine returns the analyzed emotional information to the server.

[1525] Step 7:

[1526] The server adjusts the generative AI's diagnosis and advice based on the emotion engine's analysis results. For example, if the user is feeling anxious, the server may include additional advice to alleviate the anxiety.

[1527] Step 8:

[1528] The server sends the adjusted diagnostic results and advice to the terminal, which displays them to the user.

[1529] Step 9:

[1530] If the user wishes to see a doctor, he or she clicks on the reservation link displayed on the terminal, which then displays a reservation form.

[1531] Step 10:

[1532] The user enters the necessary information, such as the desired date and time and the hospital, into the reservation form and presses the send button. The terminal then sends the entered reservation information to the server.

[1533] Step 11:

[1534] The server stores the received reservation information in a database, which allows the reservation information to be managed.

[1535] Step 12:

[1536] The server stores symptom information and appointment information, including interactions between the generative AI and the emotion engine, in a database as a medical record, which can then be referenced during future consultations.

[1537] Step 13:

[1538] The day before the appointment, the hospital terminal retrieves the patient's medical record and appointment information from the server, which medical staff then checks in advance, allowing the necessary preparations to be made.

[1539] Step 14:

[1540] On the day of the appointment, when the user visits the hospital at the scheduled time, the consultation begins promptly without filling out a medical questionnaire. Medical professionals can conduct the consultation efficiently based on the medical record information and emotional information provided in advance.

[1541] By combining this series of steps with an emotion engine, it is possible to prepare and conduct consultations while taking into account the patient's emotional state, creating a system that provides an efficient and reassuring experience for both patients and hospitals.

[1542] Example 2

[1543] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1544] Conventional appointment scheduling systems make it difficult to consider patients' emotional state, which often leads to anxiety before an appointment. Furthermore, when preparing for an appointment, medical professionals have limited means of understanding detailed information about the patient's symptoms and emotional state in advance, making it difficult to provide an efficient consultation.

[1545] 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.

[1546] In this invention, the server includes means for analyzing the user's symptoms using a generating AI, means for collecting symptom information through chat between the user and the generating AI, emotion analysis means for analyzing the user's emotions from the symptom information, means for adjusting the analysis results and advice based on the emotion analysis results, means for directing the user to a reservation system if a medical examination is necessary based on the analysis results, means for saving the collected symptom information, emotion analysis results, and medical examination reservation information in a database, and means for providing the saved information in a form that medical professionals can refer to before the examination. This enables medical examination preparation and implementation that takes the patient's emotions into consideration, and enables efficient examinations by medical professionals.

[1547] "Generative AI" is an artificial intelligence technology that analyzes text data entered by users and generates appropriate diagnostic results and advice.

[1548] "Emotion analysis means" is a technology for evaluating and analyzing a user's emotions from their symptom information and text data.

[1549] "Means for collecting symptom information via chat" refers to a means for collecting information about symptoms in an interactive format between the user and the generating AI.

[1550] The "means for adjusting the analysis results and advice" refers to a means for adjusting the diagnostic results and advice of the generating AI according to the user's emotions, based on the emotional information obtained from the emotion analysis means.

[1551] The "means for guiding the user to a reservation system" refers to a means for guiding the user to an appropriate reservation system for receiving the necessary medical examination based on the diagnosis results.

[1552] "Means for storing in a database" refers to a means for centrally managing and storing collected symptom information, emotion analysis results, appointment information, etc.

[1553] "Means to provide information in a form that can be referenced by medical professionals" refers to means to display and provide stored information so that medical professionals can easily refer to it before examining a patient.

[1554] This invention is a system that uses generative AI to analyze the user's symptoms and emotion analysis to provide a diagnosis that takes into account the user's emotional information. Furthermore, based on this diagnosis, the system guides the user to a reservation system and provides the collected information in a form that medical professionals can refer to.

[1555] Login and session management

[1556] The user first authenticates by entering their user ID and password into the login form displayed on the terminal. The server compares the received authentication information with the database, and if authentication is successful, generates a session ID and sends it to the terminal as a cookie. This is how the user's session is managed.

[1557] Display symptom entry form

[1558] After the user logs in, the server displays a symptom entry form on the terminal. This form consists of a text entry field for the user to describe their symptoms and a submit button.

[1559] Enter and submit symptoms

[1560] The user presses the send button to send the symptoms they have entered in the symptom input form to the server, which then sends this data to the generation AI and emotion analysis means to request an analysis.

[1561] Symptom analysis and emotion recognition

[1562] The server sends the received symptom information to the generation AI and emotion analysis means. The generation AI analyzes the symptom information and generates a suspected disease name and advice. Meanwhile, the emotion analysis means analyzes the user's emotions from the symptom information and returns the respective results to the server.

[1563] Emotion-based regulation

[1564] The server adjusts the diagnostic results and advice from the generative AI based on the results of the emotion analysis. For example, if the user is feeling anxious, it adds supplementary information to alleviate that anxiety. The server then displays the adjusted results on the device and provides them to the user.

[1565] Encouraging consultations and directing people to the reservation system

[1566] If the AI ​​determines that a consultation is necessary, the server displays a link to the reservation system on the device. When the user clicks on this link, a reservation form will be displayed.

[1567] Enter and submit reservation information

[1568] The user enters the necessary information into the reservation form, such as the desired date and time of the appointment and the hospital of choice, and sends this to the server, which then stores the received reservation information in a database.

[1569] Chat content and emotional information records

[1570] The server stores the chat content with the AI ​​generator, the user's symptom information, reservation information, and the results of the emotion analysis method in a database as consistent medical record information that can be later referenced by medical professionals.

[1571] Review by a healthcare professional

[1572] The day before the appointment, the hospital terminal retrieves the patient's medical record and appointment information from the server, which medical staff can then check in advance, allowing them to make the necessary preparations.

[1573] On the day of the examination

[1574] When a user visits the hospital at the scheduled time, the consultation can begin promptly without filling out a medical questionnaire. Medical professionals can conduct the consultation efficiently based on the medical record information and emotional information provided in advance.

[1575] Specific examples

[1576] For example, consider the case where a user inputs and submits their symptoms as "I have a persistent headache and slight fever." This symptom information is sent to the generation AI and emotion analysis means via the server. The generation AI returns the analysis result as "This is likely a cold or flu," and the emotion analysis means determines that the user is feeling "anxious." Based on this result, the server displays more advice, such as "To ease your anxiety, drink plenty of fluids and get plenty of rest." When the user clicks the appointment link, inputs the desired date and time for the consultation, and submits it, the server saves this information in a database, enabling the consultation to begin promptly on the day of the appointment.

[1577] Prompt Sentence Examples

[1578] User: "I've had a persistent headache and slight fever."

[1579] Generative AI: "This sounds like a cold or flu."

[1580] Sentiment analysis method: User feels "anxiety"

[1581] Server: "To help ease anxiety, drink plenty of fluids and get plenty of rest."

[1582] In this way, by integrating generative AI with emotion analysis means, the present invention makes it possible to prepare for and conduct examinations while taking into account the user's emotional state, thereby supporting efficient and effective examinations for medical professionals.

[1583] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1584] Step 1:

[1585] The user enters their user ID and password into the login form displayed on their terminal and presses the send button. The entered user ID and password are sent from the terminal to the server. The server compares the received user ID and password with the database and performs authentication. If authentication is successful, the server generates a session ID and sends it to the terminal as a cookie. This starts the user's session.

[1586] Input: User ID, Password

[1587] Output: Session ID (cookie)

[1588] Step 2:

[1589] After the user logs in, the server displays a symptom entry form on the terminal. The symptom entry form contains a text entry field for the user to describe their symptoms and a submit button. The user opens the form and can enter their symptoms.

[1590] Input: None

[1591] Output: Display of symptom input form

[1592] Step 3:

[1593] The user enters their symptoms in the symptom input form and presses the send button. The entered symptom data is sent from the device to the server. The server then sends the received symptom data to the generation AI and emotion analysis means.

[1594] Input: Symptom information

[1595] Output: sent to the generative AI and sentiment analysis method

[1596] Step 4:

[1597] The server requests the generation AI to analyze the symptom information. The generation AI analyzes the symptom information and generates a suspected disease name and advice. Meanwhile, the emotion analysis means analyzes the user's emotions from the symptom information. The analysis results are returned to the server.

[1598] Input: Symptom information

[1599] Output: Analysis results (disease name, advice, emotional information)

[1600] Step 5:

[1601] The server adjusts the diagnostic results and advice from the generative AI based on the results of the emotion analysis. For example, if the user is feeling anxious, it adds supplementary information to alleviate the anxiety. The server then displays the adjusted results on the device and provides them to the user.

[1602] Input: Generative AI diagnosis results, emotional information

[1603] Output: Diagnostic results and advice after adjustment

[1604] Step 6:

[1605] If the server determines that a consultation is necessary based on the analysis results of the generated AI, it displays a link to the reservation system on the terminal. When the user clicks this link, a reservation form will be displayed.

[1606] Input: Analysis results

[1607] Output: Link to booking system

[1608] Step 7:

[1609] The user enters the necessary information into the reservation form (desired appointment date and time, hospital selection, etc.) and presses the send button. This sends the entered reservation information from the terminal to the server. The server saves the received reservation information in a database.

[1610] Input: Reservation information

[1611] Output: Saved reservation information

[1612] Step 8:

[1613] The server stores the chat content with the AI ​​generator, the user's symptom information, reservation information, and the results of the emotion analysis method in a database as consistent medical record information that can be referenced by medical professionals during subsequent consultations.

[1614] Input: Chat content, symptom information, reservation information, emotional information

[1615] Output: Saved medical record information

[1616] Step 9:

[1617] The day before the appointment, the hospital terminal retrieves the patient's medical record and appointment information from the server, and medical staff checks it in advance. This confirmation allows the necessary preparations to be made.

[1618] Input: Medical record information, reservation information

[1619] Output: Proactive notification to healthcare professionals

[1620] Step 10:

[1621] When a user visits the hospital at the scheduled time, they can receive a prompt examination without having to fill out a medical questionnaire. Medical staff can conduct examinations efficiently by making judgments based on the medical record information and emotional information provided in advance.

[1622] Input: User visit and medical record information

[1623] Output: Efficient consultation

[1624] (Application example 2)

[1625] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1626] In conventional online medical consultation systems, symptom analysis is mechanical and lacks consideration of the user's emotional state when providing diagnosis and advice. This can lead to anxiety or discomfort in the diagnosis results, leading to inappropriate medical services. Furthermore, the system redirects users to a medical appointment system without considering their emotions, resulting in lower satisfaction and a worsening efficiency.

[1627] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for analyzing the user's symptoms using a generation AI, means for collecting symptom information through chat between the user and the generation AI, means for analyzing the user's emotional information using an emotion engine when analyzing the symptom information, means for adjusting the analysis results and advice of the generation AI based on the emotional information, means for directing the user to an appointment system if a medical examination is necessary based on the analysis results, means for saving the collected symptom information and appointment information in a database, and means for providing the appointment information and symptom information in a form that can be referenced by medical professionals. As a result, an appropriate diagnosis and advice that takes the user's emotions into consideration is provided, reducing anxiety and discomfort and enabling the user to receive efficient, high-quality medical services.

[1628] "Generative AI" is an artificial intelligence technology that generates appropriate diagnoses and advice based on symptom information entered by the user.

[1629] "Symptom information" refers to text data about a medical condition or symptom reported by a user.

[1630] "Chat" is an interface for text-based communication between the user and the generative AI.

[1631] An "emotion engine" is an artificial intelligence technology for analyzing the emotional state of a user from text data entered by the user.

[1632] "Analysis results" refers to the diagnosis and advice generated after the generating AI analyzes the user's symptom information.

[1633] The "reservation system" is an online system for making appointments at medical institutions.

[1634] A "database" is a digital storage system for organizing and storing collected symptom information, appointment information, and other information.

[1635] "Medical professionals" refers to professionals such as doctors and nurses who provide medical examinations and treatment.

[1636] "Logging in" is the process by which a user enters and is recognized as providing authentication information to access a system.

[1637] A "session ID" is an identifier generated by the server to track a user's activities while they are logged into the system.

[1638] This invention is an online medical consultation system that combines generative AI and an emotion engine to analyze users' symptoms and emotions, and then provides appropriate diagnoses and appointment procedures. Specific implementation methods are described below.

[1639] A server contains multiple modules, each responsible for a specific function. The following hardware and software are mainly used: A server uses a server computer with high processing power (e.g., an EC2 instance from Amazon Web Services) and a database system (e.g., Amazon RDS) to store data.

[1640] The server must first process the user's login. The user uses a smartphone application to enter authentication information (user ID and password) and send it to the server. The server receives the authentication information using Flask (a Python framework) and performs user authentication. If authentication is successful, a session ID is generated and sent to the smartphone device as a cookie.

[1641] Next, if login is successful, the server displays a symptom input form to the user. The user enters their symptoms in text format and presses the submit button. At this time, the user-entered data is sent to the server. The transmitted symptom information is analyzed by a generative AI engine (e.g., OpenAI GPT-3). The generative AI engine uses natural language processing technology to analyze the symptom information and generate a suspected disease name and advice.

[1642] At the same time, an emotion engine (e.g., Hume AI) analyzes emotions from the symptom information entered by the user. The emotion engine processes the text data and reveals the user's emotional state (e.g., anxiety, sadness, happiness, etc.).

[1643] The analysis results of the generative AI engine and the emotion engine are returned to the server, which then integrates these results and tailors appropriate diagnoses and advice based on the emotion information. For example, if the user is feeling "anxious," it adds supplementary information and advice to alleviate that anxiety.

[1644] If a consultation is deemed necessary, the server displays a link to the user to make an appointment. When the user clicks on the link, a reservation form appears. The user can enter reservation information, such as the desired date and time of the consultation and the hospital of choice, and submit the form. The submitted reservation information is saved in a database by the server, so that it can be referenced by medical professionals later.

[1645] The interactions between the user and the generated AI, as well as emotional information, are saved in a database as a medical record. Medical professionals can refer to this information in advance, allowing them to make the necessary preparations before the consultation. This allows users to begin their consultation promptly without having to fill out a medical questionnaire.

[1646] For example, if a user reports a headache, the generation AI determines that it is likely a migraine, and the emotion engine analyzes that the user is feeling anxious. As a result, the server displays additional advice such as, "We recommend that you drink plenty of fluids and get adequate rest. Also, take three deep breaths to feel more at ease." If the user wishes to make an appointment, they can be directed to a reservation form with just one click.

[1647] Example prompt sentence:

[1648] "I've been having headaches lately."

[1649] "Please use the Emotion Engine to analyze the emotion of the following symptom: I've been having headaches lately."

[1650] "Please use generative AI to analyze and diagnose the following symptoms: I've been having constant headaches lately."

[1651] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1652] Step 1:

[1653] A user accesses the system through a smartphone application. The user enters their user ID and password into a login form and sends it from their device to the server. The server receives the user ID and password and performs authentication processing. If authentication is successful, the server generates a session ID and sends it to the device as a cookie. This completes the user login and starts a session. The input data is the user's authentication information, and the output data is the session ID.

[1654] Step 2:

[1655] If login is successful, the server displays a symptom input form on the terminal. The user enters their symptoms in text format and presses the send button. The entered symptom information is sent from the terminal to the server. The input data is the user's symptom information, and the output data is the sent symptom information.

[1656] Step 3:

[1657] The server sends the received symptom information to a generative AI engine (e.g., OpenAI GPT-3). The generative AI engine analyzes the symptom information using natural language processing technology and generates a suspected disease name and advice. The input data is the symptom information, and the output data is the diagnosis and advice generated by the generative AI.

[1658] Step 4:

[1659] At the same time, the server sends the symptom information to an emotion engine (e.g., Hume AI), which analyzes the user's emotional state from the text data. The input data is the symptom information, and the output data is the user's emotional state.

[1660] Step 5:

[1661] The analysis results of the generative AI engine and the emotion engine are returned to the server. The server integrates these results and tailors the appropriate diagnosis and advice based on the emotion information. For example, if the user is feeling "anxious," it adds supplementary information and advice to alleviate that anxiety. The input data are the analysis results and emotion information, and the output data are the tailored diagnosis and advice.

[1662] Step 6:

[1663] If it is determined that a consultation is necessary, the server displays a link to make an appointment on the terminal. The user clicks the link, and an appointment form appears. The user selects the desired date and time of the consultation and the medical institution, enters the appointment information, and submits it. The input data is the appointment information, and the output data is the submitted appointment information.

[1664] Step 7:

[1665] The server stores the received appointment information in a database. The stored symptom information and appointment information are organized in the database so that medical professionals can refer to them later. The input data is the appointment information, and the output data is the organized database information.

[1666] Step 8:

[1667] The interactions between the user and the generated AI, as well as emotional information, are saved in a database as a medical record. Medical professionals can refer to this information in advance and prepare for the examination. This allows the user to begin the examination promptly without having to fill out a medical questionnaire. The input data is the history of interactions and emotional information, and the output data is the medical record data.

[1668] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1669] 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.

[1670] 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 robot 414.

[1671] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1672] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1673] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1674] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1675] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1676] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1677] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1678] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1679] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1680] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1681] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1682] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1683] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1684] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1685] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1686] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1687] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1688] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1689] The following is further disclosed regarding the above embodiment.

[1690] (Claim 1)

[1691] A means of analyzing patient symptoms using generative AI;

[1692] A means of collecting symptom information through chat between the patient and the generating AI;

[1693] A means of directing the patient to a reservation system if a consultation is required based on the analysis results;

[1694] A means for storing the collected symptom information and appointment information in a database;

[1695] A means for providing reservation information and symptom information in a form that can be referenced by medical professionals;

[1696] A system including:

[1697] (Claim 2)

[1698] 2. The system according to claim 1, wherein natural language processing technology is used as a symptom information analysis means.

[1699] (Claim 3)

[1700] The system according to claim 1, wherein chat interactions with patients are recorded in real time and made available to medical professionals for reference before consultation.

[1701] "Example 1"

[1702] (Claim 1)

[1703] A means of analyzing patient symptoms using generative AI;

[1704] A means of collecting symptom information through chat between the patient and the generating AI;

[1705] means for login and session management along with symptom information collection;

[1706] A means of directing the patient to a reservation system if a consultation is required based on the analysis results;

[1707] A means for storing the collected symptom information and appointment information in a database;

[1708] A means for providing reservation information and symptom information in a form that can be referenced by medical professionals;

[1709] A system including:

[1710] (Claim 2)

[1711] 2. The system according to claim 1, wherein natural language processing technology is used as a symptom information analysis means.

[1712] (Claim 3)

[1713] The system according to claim 1, wherein chat interactions with patients are recorded in real time and made available to medical professionals for reference before consultation.

[1714] "Application Example 1"

[1715] (Claim 1)

[1716] A means of analyzing the user's symptoms using generative AI;

[1717] A means of collecting symptom information through chat between the user and the generating AI;

[1718] A means of proposing appropriate products and services based on the analysis results,

[1719] a means of directing the user to a reservation system based on the proposed products and services;

[1720] A means for storing the collected symptom information, suggestion information, and reservation information in a database;

[1721] A means of providing the stored information in a form that can be referenced by interested parties;

[1722] A system including:

[1723] (Claim 2)

[1724] 2. The system according to claim 1, wherein natural language processing technology is used as a symptom information analysis means.

[1725] (Claim 3)

[1726] The system according to claim 1, wherein chat interactions with users are recorded in real time and made available to relevant parties for reference before proposing products.

[1727] "Example 2: Combining Emotion Engines"

[1728] (Claim 1)

[1729] A means of analyzing the user's symptoms using generative AI;

[1730] A means of collecting symptom information through chat between the user and the generating AI;

[1731] emotion analysis means for analyzing the emotion of the user from the symptom information;

[1732] a means for adjusting analysis results and advice based on the sentiment analysis results;

[1733] A means of directing the patient to a reservation system if a consultation is required based on the analysis results;

[1734] A means for storing the collected symptom information, emotion analysis results, and medical appointment information in a database;

[1735] A means for providing the stored information in a form that can be referenced by medical professionals before consultation;

[1736] A system including:

[1737] (Claim 2)

[1738] 2. The system according to claim 1, wherein natural language processing technology is used as a symptom information analysis means.

[1739] (Claim 3)

[1740] The system according to claim 1, wherein chat interactions with users are recorded in real time and made available to medical professionals for reference before consultation.

[1741] "Application example 2 when combining emotion engines"

[1742] (Claim 1)

[1743] A means of analyzing the user's symptoms using generative AI;

[1744] A means for collecting symptom information through chat between the user and the generating AI;

[1745] means for analyzing the user's emotional information using an emotion engine when analyzing the symptom information;

[1746] A means to adjust the analysis results and advice of the generative AI based on emotional information, and

[1747] A means of directing the patient to a reservation system if a consultation is required based on the analysis results;

[1748] A means for storing the collected symptom information and appointment information in a database;

[1749] A means for providing reservation information and symptom information in a form that can be referenced by medical professionals;

[1750] A system including:

[1751] (Claim 2)

[1752] 2. The system according to claim 1, wherein natural language processing technology is used as a symptom information analysis means.

[1753] (Claim 3)

[1754] 2. The system according to claim 1, wherein chat interactions with the user are recorded in real time and made available to medical professionals for reference before consultation. [Explanation of symbols]

[1755] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of analyzing patient symptoms using generative AI; A means of collecting symptom information through chat between the patient and the generating AI; A means of directing the patient to a reservation system if a consultation is required based on the analysis results; A means for storing the collected symptom information and appointment information in a database; A means for providing reservation information and symptom information in a form that can be referenced by medical professionals; A system including:

2. 2. The system according to claim 1, wherein the symptom information analysis means uses natural language processing technology.

3. The system according to claim 1, wherein chat interactions with patients are recorded in real time and made available to medical professionals for reference before consultation.

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