System

Abstract

A system is provided.SOLUTION: A system comprising: means for inputting symptom information from a patient before a visit; means for analyzing the input symptom information and searching for an appropriate medical institution or medical worker; means for presenting information on a plurality of medical institutions or medical workers based on the analysis result and allowing the patient to make a selection; means for confirming an appointment with the selected medical institution or medical worker; and means for storing the patient's medical data as electronic data and sharing the data among medical institutions.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] In the current medical system, patients face several challenges when visiting a hospital. These challenges include long waiting times due to hospital congestion, not knowing the cost of treatment in advance, difficulty in comparing prices with other hospitals, a lack of information on each hospital's specialty, and the need to repeat the same tests and medical interviews each time a patient visits a different hospital. Ignoring these challenges increases the burden on patients and makes it difficult to provide efficient medical services. Therefore, the purpose of the present invention is to solve these challenges. [Means for solving the problem]

[0005] In order to solve the above problems, the present invention provides a system including a means for having patients input symptom information before visiting a hospital, a means for analyzing the input symptom information and searching for appropriate medical institutions and medical professionals, a means for presenting information on multiple medical institutions and medical professionals based on the analysis results and allowing the patient to select one, a means for confirming an appointment with the selected medical institution or medical professional, and a means for storing the patient's medical data as electronic data and sharing the data between medical institutions.

[0006] Specifically, when a patient launches the application on a device such as a smartphone or PC and enters their symptoms and basic information, the generative AI analyzes the information and suggests several appropriate medical institutions. At the same time, the treatment methods and estimated medical costs at each institution are also presented, allowing the patient to select the institution of their choice and complete the reservation on the spot. In addition, the patient's medical data is stored electronically and shared when the patient visits other medical institutions, reducing unnecessary tests and interviews.

[0007] "Symptom information" is data that describes the patient's physical condition, pain, discomfort, etc.

[0008] "Medical institution" refers to any facility that provides medical services, such as a hospital, medical clinic, or other facility.

[0009] "Healthcare professionals" refers to professionals who work in medical institutions and provide medical services, such as doctors, nurses, and pharmacists.

[0010] "Generative AI" refers to artificial intelligence technology that analyzes information provided by patients and generates appropriate suggestions.

[0011] "Electronic data" refers to patient medical information stored in digital form.

[0012] A "terminal" is a device, such as a smartphone or computer, that a user uses to perform operations such as entering information and making appointments.

[0013] "Means for confirming appointments" refers to the process or system by which a patient schedules an appointment with a medical institution or healthcare professional of their choice and finalizes the appointment.

[0014] "Means of data sharing" refers to the technologies and processes for efficiently linking and sharing stored electronic data between different medical institutions. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

[0023] [First embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0036] The present invention is a system for enabling patients to receive medical services efficiently, and embodiments thereof will be described in detail below.

[0037] 1. System Overview

[0038] This system allows patients to input symptom information from devices such as smartphones or computers, and based on that information, searches for and suggests appropriate medical institutions and medical professionals, and then completes the process of booking an appointment.It also has the ability to store patients' medical data electronically and share it between different medical institutions.

[0039] 2. System Configuration

[0040] The system mainly consists of the following components:

[0041] User terminal: A device where the patient enters information and receives results.

[0042] Server: A central system that analyzes the input information and generates appropriate suggestions.

[0043] Medical institution system: A system that receives medical data and uses it for medical treatment.

[0044] 3. Program Processing

[0045] Processing on the user terminal

[0046] The user launches the dedicated app and enters symptoms and basic information, which is then sent to a server running the AI.

[0047] Processing on the server

[0048] The server analyzes the received information and searches the database for appropriate medical institutions and medical professionals. The search results include information on multiple medical institutions and medical professionals, including each institution's treatment methods and estimated medical costs.

[0049] Displaying the proposal results on the user's device

[0050] The server sends information about the selected medical institutions and medical professionals to the user's device, where the user can check the information and select the medical institution of their choice.

[0051] Confirmation of reservation

[0052] Once the user selects the desired medical institution, the reservation confirmation process begins. The reservation information is sent via the server to the medical institution's reservation system, and the reservation is confirmed. A reservation completion notice is sent to the user's terminal, and confirmation information is displayed.

[0053] Medical data storage and sharing

[0054] The patient's initial consultation information and subsequent medical information are stored as electronic data on a server. When the user visits another medical institution, the relevant medical data is shared with the new institution, making medical treatment more efficient.

[0055] Specific examples

[0056] Example 1: Common cold symptoms

[0057] The user launches the app and types, "I have a bad cough and a sore throat."

[0058] The terminal transmits the input information to the server.

[0059] The server analyzes the information and lists multiple medical institutions that primarily provide internal medicine care.

[0060] The device displays a list of medical institutions and estimated treatment costs.

[0061] The user selects the nearest clinic and confirms the appointment.

[0062] The server confirms the appointment and shares the data with the clinic.

[0063] When a user visits the clinic, they receive prompt medical treatment based on the information shared in advance.

[0064] Example 2: Treating long-term back pain

[0065] Users use the app to enter their past medical history and current symptoms.

[0066] The terminal sends the information to the server.

[0067] The server analyzes the information and suggests multiple medical institutions that specialize in treating lower back pain.

[0068] The device displays a list of medical institutions, treatment costs, and past reviews.

[0069] The user selects a specific orthopedic surgeon and confirms the appointment.

[0070] The server shares past medical data with the orthopedic system.

[0071] The user visits an orthopedic clinic, and the new doctor creates an appropriate treatment plan based on past treatment data.

[0072] The above is an embodiment of the present invention, which constitutes a system that reduces the burden on patients and provides efficient medical services.

[0073] The processing flow will be explained below.

[0074] Step 1:

[0075] The user launches an application on their smartphone or computer.

[0076] Step 2:

[0077] The device uses generative AI to prompt the user with questions such as, "What are your symptoms today?"

[0078] Step 3:

[0079] The user types, "I have a headache."

[0080] Step 4:

[0081] The device then asks, "How long have you been experiencing these symptoms?"

[0082] Step 5:

[0083] The user responds, "From three days ago."

[0084] Step 6:

[0085] The terminal collects and formats the information entered.

[0086] Step 7:

[0087] The device sends the collected information to the server.

[0088] Step 8:

[0089] The server analyzes the received symptom information.

[0090] Step 9:

[0091] The server identifies the corresponding medical department (e.g., internal medicine, neurology, etc.) and searches the database for medical institutions and medical professionals in that department.

[0092] Step 10:

[0093] The server collects information such as each medical institution's areas of expertise, past treatment results, patient reviews, and estimated medical costs.

[0094] Step 11:

[0095] Based on the data collected by the server, several of the most appropriate medical institutions and medical professionals are selected.

[0096] Step 12:

[0097] The server sends information about the selected medical institution and medical personnel to the terminal.

[0098] Step 13:

[0099] The terminal displays the information received from the server to the user.

[0100] Step 14:

[0101] The user reviews a list of suggested medical institutions and practitioners.

[0102] Step 15:

[0103] The user selects the desired medical institution or medical professional. For example, select "Clinic B."

[0104] Step 16:

[0105] The terminal displays a reservation button to the user.

[0106] Step 17:

[0107] The user taps the reservation button.

[0108] Step 18:

[0109] The terminal transmits the reservation information to the server.

[0110] Step 19:

[0111] The server communicates with the medical institution's reservation system and confirms the reservation.

[0112] Step 20:

[0113] The server sends a reservation completion notification to the terminal.

[0114] Step 21:

[0115] The terminal displays the reservation confirmation information to the user.

[0116] Step 22:

[0117] The user's initial consultation information and subsequent medical information are stored as electronic data on the server.

[0118] Step 23:

[0119] The user makes an appointment and visits the medical institution.

[0120] Step 24:

[0121] The medical institution's system retrieves the electronic data from the server.

[0122] Step 25:

[0123] The server sends the user's symptom information and basic information to the medical institution.

[0124] Step 26:

[0125] Medical institutions provide medical treatment based on information shared in advance.

[0126] Step 27:

[0127] After a diagnosis or treatment is performed, new medical data is updated from the medical institution's system to the server.

[0128] Step 28:

[0129] The server updates the user's personal data with the new medical data.

[0130] Step 29:

[0131] When the user visits another medical institution, the server shares the relevant medical data with the new medical institution.

[0132] Step 30:

[0133] The new medical institution receives the data from the server and provides efficient medical care.

[0134] Example 1

[0135] 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."

[0136] In conventional medical systems, when a patient visits multiple medical institutions, they must re-enter their initial consultation information and medical treatment information at each institution, which is time-consuming and can lead to information inconsistencies and delays. It also makes it difficult to select the appropriate medical institution, and confirmation and adjustments when making appointments are cumbersome. These issues reduce the efficiency of medical treatment for patients and make it difficult to provide prompt medical care.

[0137] 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.

[0138] In this invention, the server includes a terminal for patients to input symptom information, a means for transmitting the input symptom information to the server, and a means for the server to analyze the received information and search for appropriate medical institutions and medical professionals using a generative AI model. This allows patients to quickly and accurately input symptom information and select an appropriate medical institution. The server also includes a means for transmitting and displaying information on multiple searched medical institutions and medical professionals to the terminal, a means for confirming an appointment with the medical institution or medical professional selected by the patient, a means for sending a reservation completion notification to the terminal, and a means for storing the patient's medical data as electronic data and sharing it among medical institutions. This allows information entered by a patient once to be shared among multiple medical institutions, eliminating the need for time-consuming information input and reducing the complexity of appointment confirmation and adjustment, thereby significantly improving medical treatment efficiency.

[0139] A "terminal" is a device used by a patient to input symptom information, and includes smartphones, personal computers, etc.

[0140] The "server" is a central system that receives symptom information sent by patients, analyzes it, and searches for appropriate medical institutions and medical professionals.

[0141] A "generative AI model" is an artificial intelligence model used to analyze received information and suggest appropriate medical institutions and medical professionals, including, for example, advanced natural language processing models such as GPT-3.

[0142] "Symptom information" refers to specific information about a patient's health condition or illness that the patient enters into the device. This includes specific symptoms such as "I have a bad cough and a sore throat."

[0143] A "medical institution" is a facility where patients can receive medical treatment, and includes hospitals, clinics, and medical offices.

[0144] "Healthcare professionals" are professionals who are qualified to perform medical procedures on patients at medical institutions, and include doctors, nurses, pharmacists, etc.

[0145] A "reservation" is the act of a patient specifying a date and time to receive medical treatment from a medical institution or medical professional of their choice in advance.

[0146] "Electronic data" refers to data used to store and manage patient symptom information, medical records, and other information in digital format.

[0147] "Sharing" refers to electronic data being transmitted between different medical institutions and being made available to each other.

[0148] "Expected medical expenses" are a guideline for predicted medical expenses based on symptom information and medical treatment details.

[0149] This invention is a system that enables patients to receive medical services efficiently. This system allows patients to input symptom information from devices such as smartphones or computers, and based on that information, the system searches for and suggests appropriate medical institutions and medical professionals, and then completes the process of booking appointments. It also has the function of storing patients' medical data electronically and sharing the data between different medical institutions.

[0150] 1. System Overview

[0151] The user launches the dedicated app and enters symptoms and basic information. This input information is sent to a server running the generative AI model. The server analyzes the received information and searches a database for appropriate medical institutions and medical professionals. The server then sends the search results to the user's device, allowing the user to review the information and select the medical institution of their choice. Finally, the server confirms the reservation and sends a reservation completion notification to the user's device. The patient's medical data is also stored as electronic data and shared between medical institutions.

[0152] 2. Hardware and Software Used

[0153] User device: The device where the user enters information and receives the results (smartphone, PC, etc.)

[0154] Server: A central system that analyzes input information and generates appropriate suggestions. An environment for running Python or Java programs.

[0155] Database management system: A system that stores and manages patient medical data (e.g., MySQL)

[0156] Generative AI model: An artificial intelligence model (such as GPT-3) that analyzes the received information and suggests appropriate medical institutions and medical professionals.

[0157] 3. Specific Examples

[0158] A specific example of use is shown below.

[0159] Example 1: Common cold symptoms

[0160] The user launches the app and types, "I have a bad cough and a sore throat."

[0161] The terminal transmits the input information to the server.

[0162] The server analyzes the information and lists multiple medical institutions that primarily provide internal medicine care.

[0163] The device displays a list of medical institutions and estimated treatment costs.

[0164] The user selects the nearest clinic and confirms the appointment.

[0165] The server confirms the appointment and shares the data with the clinic.

[0166] When a user visits the clinic, they receive prompt medical treatment based on the information shared in advance.

[0167] Example 2: Treating long-term back pain

[0168] Users use the app to enter their past medical history and current symptoms.

[0169] The terminal sends the information to the server.

[0170] The server analyzes the information and suggests multiple medical institutions that specialize in treating lower back pain.

[0171] The device displays a list of medical institutions, treatment costs, and past reviews.

[0172] The user selects a specific orthopedic surgeon and confirms the appointment.

[0173] The server shares past medical data with the orthopedic system.

[0174] The user visits an orthopedic clinic, and the new doctor creates an appropriate treatment plan based on past treatment data.

[0175] Prompt Sentence Examples

[0176] Below is an example of a prompt sentence to input to the generative AI model.

[0177] Symptom information that makes diagnosis easier: "I have a bad cough and a sore throat. I'm looking for an internist."

[0178] Generated medical institution suggestions: "The following internal medicine clinics are nearby: XX Clinic, △△ Clinic, and □□ Hospital. The treatment costs for each are..."

[0179] Appointment confirmation process: "Please make an appointment at XX Clinic." -> "The appointment has been completed."

[0180] The above is an embodiment of the present invention, which constitutes a system that reduces the burden on patients and provides efficient medical services.

[0181] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0182] Step 1: Enter user information

[0183] The user launches the app and inputs their symptoms and basic information, including specific symptoms such as "severe cough and sore throat." This generates data on the user's health condition.

[0184] Input: Symptom information (e.g., "I have a bad cough and a sore throat")

[0185] Output: Symptom information data entered

[0186] Step 2: Sending information to the server

[0187] The device encrypts the input symptom information and sends it to the server. At this time, encryption protocols such as SSL / TLS are used to ensure the security of the communication. The transmitted data is then received by the server.

[0188] Input: Symptom information data entered

[0189] Output: Data sent to the server

[0190] Step 3: Analyze the received information

[0191] The server analyzes the received symptom information using Python or Java scripts. A generative AI model (e.g., GPT-3) is used to calculate the data and suggest appropriate medical institutions and medical professionals. Specifically, a prompt is input into the generative AI model, and the analysis results are obtained.

[0192] Input: Symptom information data received by the server

[0193] Output: Proposed results data from the generative AI model

[0194] Step 4: Search and select a medical institution

[0195] The server searches and selects appropriate medical institutions and medical professionals from the database based on the results of the generative AI model. A list is generated that includes each medical institution's treatment method, estimated medical costs, address, etc. This is done using SQL queries.

[0196] Input: Proposal result data of generative AI model

[0197] Output: List of medical institutions

[0198] Step 5: Submit search results

[0199] The server packages the searched and selected information on medical institutions and medical professionals in JSON format and sends it to the user's device, where the user receives detailed information on selectable medical institutions.

[0200] Input: List of medical institutions

[0201] Output: Data sent to the terminal

[0202] Step 6: Review and select the suggestions

[0203] The user can then view information about medical institutions and medical professionals based on the search results on their device, including the name, address, treatment details, and reviews of each medical institution. The user can then select the medical institution of their choice.

[0204] Input: Medical institution information displayed on the terminal

[0205] Output: User selection data

[0206] Step 7: Submit your reservation information

[0207] The terminal sends the reservation information of the medical institution selected by the user to the server. The information is sent to the server in JSON format. The server receives the information and processes it to confirm the reservation at the selected medical institution.

[0208] Input: User selected data

[0209] Output: Data scheduled to be sent to the server

[0210] Step 8: Send a reservation completion notification

[0211] The server checks the reservation information database to confirm that the reservation has been confirmed, and sends a reservation completion notice to the user's terminal, allowing the user to confirm that their reservation has been confirmed.

[0212] Input: Confirmed reservation data

[0213] Output: Reservation completion notification data to the terminal

[0214] Step 9: Storing and sharing medical data

[0215] The server stores the patient's initial consultation information and subsequent medical information as electronic data. The data is managed using a database management system (e.g., MySQL). When a user receives treatment at another medical institution, the relevant medical data is encrypted and sent to the new medical institution.

[0216] Input: Patient medical information

[0217] Output: Stored electronic data and shared data

[0218] (Application example 1)

[0219] 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."

[0220] Conventional medical systems lack an integrated means for patients to effectively select appropriate medical institutions and medical professionals and quickly confirm appointments. Furthermore, it is difficult to share medical data between different medical institutions, resulting in inefficient medical treatment. Furthermore, security services require real-time information analysis and communication methods to detect suspicious individuals and respond quickly to emergencies. A system that can solve these issues in an integrated manner is needed.

[0221] 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.

[0222] In this invention, the server includes means for having a patient input symptom information before visiting a hospital, means for analyzing the input symptom information and searching for an appropriate medical institution or medical professional, means for presenting information on multiple medical institutions and medical professionals based on the analysis results and having the patient select one, means for confirming an appointment with the selected medical institution or medical professional, means for saving the patient's medical data as electronic data and sharing the data between medical institutions, means for having a user input information on abnormal behavior or suspicious individuals and analyzing the information to generate appropriate suggestions, and means for contacting a security team or the police based on the analysis results. This not only streamlines the patient's medical treatment process and makes it easier to share data between medical institutions, but also enables quick and appropriate responses in security services.

[0223] definition statement

[0224] "Means for patients to input symptom information before visiting the hospital" refers to a function that allows patients to input their symptoms and basic information using their own devices (smartphones, computers, etc.).

[0225] "Means of analyzing input symptom information and searching for appropriate medical institutions and medical professionals" is a function that identifies and suggests the most appropriate medical institutions and medical professionals from a database based on the symptom information input by the patient.

[0226] "Means of presenting information on multiple medical institutions and medical professionals based on the analysis results and allowing the patient to make a selection" is a function that provides the patient with multiple appropriate options for medical institutions and medical professionals based on the analyzed information, allowing the patient to make the selection themselves.

[0227] "Means for confirming appointments with selected medical institutions and healthcare professionals" refers to a function that allows a patient to securely confirm appointments with the medical institutions and healthcare professionals selected by the patient within the system and send a confirmation notification.

[0228] "Means for storing patient medical data as electronic data and sharing the data between medical institutions" refers to a function that allows patients' medical information to be recorded electronically and the data to be shared with different medical institutions as needed.

[0229] "A means for having users input information about abnormal behavior or suspicious individuals, analyzing that information, and generating appropriate suggestions" is a function in security services that allows users to report suspicious activity or abnormal behavior, analyze that information, and suggest optimal countermeasures.

[0230] "Means of contacting security teams or police based on analysis results" is a function that allows for quick contact with specialized security teams or police as necessary based on the analyzed information.

[0231] MODE FOR CARRYING OUT THE INVENTION

[0232] This invention is an integrated system that enables patients to receive medical services efficiently and also enables prompt response in security. This system is designed for use in Japanese medical institutions, but can also be applied in other countries.

[0233] Overall system configuration

[0234] The system mainly consists of the following components:

[0235] User terminal: A device, such as a smartphone or smart glasses, through which the patient or user enters information and receives results.

[0236] Server: A central system that analyzes input information and generates appropriate suggestions, using generative AI models, image recognition, natural language processing, etc.

[0237] Medical institution system: A system that receives medical data and uses it for medical treatment. It mainly works in conjunction with electronic medical record systems and reservation systems.

[0238] Program Generation

[0239] The system program is generated as follows:

[0240] Processing on the user terminal

[0241] The user launches a dedicated application using a smartphone or smart glasses, and inputs information about symptoms and suspicious individuals into the application. This information is then sent to the server.

[0242] Processing on the server

[0243] The server has the following features:

[0244] Symptom analysis: Analyzes the input symptom information and suggests appropriate medical institutions and medical professionals. A generative AI model is used for the analysis.

[0245] Image Recognition: Analyzes images of suspicious individuals and detects abnormalities and suspicious movements. Uses OpenCV.

[0246] Natural Language Processing: Analyzes input text information and generates appropriate suggestions. Uses the transformers library.

[0247] Recommendation generation: Based on the analysis results, appropriate medical institutions and action instructions are suggested to the user.

[0248] Specific examples

[0249] Examples of medical services

[0250] 1. The user launches the app and types, "I have a bad cough and a sore throat."

[0251] 2. The terminal sends the entered information to the server.

[0252] 3. The server analyzes the information and lists multiple medical institutions that primarily provide internal medicine care.

[0253] 4. The device will display a list of medical institutions and estimated treatment costs.

[0254] 5. The user selects the nearest clinic and confirms the appointment.

[0255] 6. The server confirms the appointment and shares the data with the clinic.

[0256] 7. When the user visits the clinic, they receive prompt medical treatment based on the information shared in advance.

[0257] Security Service Examples

[0258] 1. The user uses smart glasses to capture a photo of a suspicious person and sends the data to the server via the app.

[0259] 2. The server analyzes the image and, if it determines that the person is suspicious, it instructs the user to contact the police immediately.

[0260] 3. Based on this, users can take prompt action and implement appropriate security measures.

[0261] Prompt Sentence Examples

[0262] "Enter an image and generate a suspicious person identification and necessary action instructions."

[0263] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0264] Program processing flow

[0265] The process by which users enter their medical information and make appointments

[0266] Step 1:

[0267] User enters symptom information

[0268] Input: The user launches a dedicated application on a smartphone or smart glasses and enters symptom information such as "I have a bad cough and a sore throat."

[0269] Processing: The application temporarily stores the entered information and converts it into the required format.

[0270] Output: Formatted symptom information data.

[0271] Step 2:

[0272] The device sends the information to the server

[0273] Input: Formatted symptom information data.

[0274] Processing: The device sends data to the server over the network using an HTTP POST request.

[0275] Output: The data sent to the server.

[0276] Step 3:

[0277] The server analyzes the symptom information

[0278] Input: Submitted symptom information data.

[0279] Processing: Generative AI models are used to analyze the symptom information provided, using natural language processing libraries to identify symptom patterns.

[0280] Output: A list of appropriate medical institutions and medical professionals.

[0281] Step 4:

[0282] The server sends the proposal results to the user's device.

[0283] Input: A list of appropriate medical institutions and practitioners.

[0284] Processing: The server sends the analysis results to the user's device, again using an HTTP POST request.

[0285] Output: A list of medical institutions and estimated treatment costs displayed on the user's terminal.

[0286] Step 5:

[0287] The user selects a medical institution and confirms the appointment

[0288] Input: A list of medical institutions and estimated treatment costs displayed on the user's terminal.

[0289] Processing: The user selects the desired medical institution and taps the reservation button. The device resends the reservation information to the server.

[0290] Output: Reservation information sent to the server.

[0291] Step 6:

[0292] The server confirms the reservation and shares the data with the medical institution.

[0293] Input: Reservation information submitted by the user.

[0294] Processing: The server accesses the reservation system and confirms the reservation. At the same time, it sends the reservation information and the patient's symptom information to the medical institution's system.

[0295] Output: Appointment confirmation notice and medical information stored in the medical institution's system.

[0296] Flow of how users enter security information and take emergency action

[0297] Step 1:

[0298] The user captures and enters the suspicious person information

[0299] Input: The user uses the smart glasses to capture a photo of the suspicious person and input it into the security app.

[0300] Processing: The app temporarily stores the image data.

[0301] Output: Saved image data.

[0302] Step 2:

[0303] The device sends the image data to the server.

[0304] Input: Saved image data.

[0305] Processing: The device sends the image data to the server over the network, again using an HTTP POST request.

[0306] Output: Image data sent to the server.

[0307] Step 3:

[0308] The server analyzes the image data

[0309] Input: The submitted image data.

[0310] Processing: Image data is analyzed using OpenCV. This analysis identifies suspicious individuals and abnormal behavior.

[0311] Output: Analysis results, including whether suspicious activity was detected and recommended actions.

[0312] Step 4:

[0313] The server sends the proposal results to the user's device.

[0314] Input: Analysis results.

[0315] Processing: The server sends the analysis results and instructions to the user's device using an HTTP POST request.

[0316] Output: Instructions to be displayed on the user's device. In the case of a suspicious person, instructions to contact the police, etc.

[0317] Step 5:

[0318] User takes action based on the suggestion

[0319] Input: Response instructions.

[0320] Action: The user follows the instructions on the smart device and takes appropriate action, such as contacting the police. The device collects a log of the action.

[0321] Output: Action log data and emergency response implementation.

[0322] Prompt Sentence Examples

[0323] "Enter an image and generate a suspicious person identification and necessary action instructions."

[0324] 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.

[0325] The present invention is a system that allows patients to input symptom information before visiting a hospital, analyzes that information to search for appropriate medical institutions and medical professionals, and further combines it with an emotion engine that recognizes the user's emotions to make more appropriate medical recommendations. The following describes in detail an embodiment of the system.

[0326] 1. System Overview

[0327] This system allows patients to input symptom and emotional information from devices such as smartphones or PCs, and based on that information, the system searches for and suggests appropriate medical institutions and medical professionals, and then confirms appointments.It also has the function of storing patients' medical data electronically and sharing it between different medical institutions.

[0328] 2. System Configuration

[0329] The system mainly consists of the following components:

[0330] User terminal: A device where patients input information and receive results. It also collects emotional information.

[0331] Server: A central system that analyzes the input information and generates appropriate suggestions.

[0332] Medical institution system: A system that receives medical data and uses it for medical treatment.

[0333] Emotion engine: A system that analyzes the user's emotions and suggests medical treatment priorities and appropriate medical institutions.

[0334] 3. Program Processing

[0335] Processing on the user terminal

[0336] The user launches the app and inputs symptoms and basic information. Along with this input information, the emotion engine collects emotional information through tone of voice and text analysis. The collected information is sent to a server where the generative AI runs.

[0337] Processing on the server

[0338] The server analyzes the received symptom and emotion information and searches the database for the appropriate medical department, medical institution, and medical professional. The search results include information on multiple medical institutions and medical professionals. This information also reflects each institution's treatment method, estimated medical costs, and the analysis of the user's stress level and urgency using an emotion engine.

[0339] Displaying the proposal results on the user's device

[0340] The server sends information about the selected medical institutions and medical professionals to the user's device. The user can then check this information on the device and select the medical institution of their choice. The analysis results of the emotion engine are also displayed.

[0341] Confirmation of reservation

[0342] Once the user selects the desired medical institution, the reservation confirmation process begins. The reservation information is sent via the server to the medical institution's reservation system, and the reservation is confirmed. A reservation completion notice is sent to the user's terminal, and confirmation information is displayed.

[0343] Medical data storage and sharing

[0344] The patient's initial consultation information and subsequent medical information are stored as electronic data on a server. When the user visits another medical institution, the relevant medical data is shared with the new institution, making medical treatment more efficient.

[0345] Specific examples

[0346] Example 1: Common cold symptoms

[0347] The user launches the app and types, "I have a bad cough and a sore throat."

[0348] The emotion engine analyzes emotions from the user's input text and tone of voice and determines that the anxiety level is high.

[0349] The terminal transmits the input information and emotion information to the server.

[0350] The server analyzes the information and lists multiple medical institutions that primarily provide internal medicine care.

[0351] The device displays a list of medical institutions, estimated treatment costs, and sentiment analysis results.

[0352] The user selects the nearest clinic and confirms the appointment.

[0353] The server confirms the appointment and shares the data with the clinic.

[0354] When a user visits the clinic, they receive prompt medical treatment based on the information shared in advance.

[0355] Example 2: Treating long-term back pain

[0356] Users use the app to enter their past medical history and current symptoms.

[0357] The emotion engine recognizes from the input data that the user's stress level is high.

[0358] The terminal sends the information to the server.

[0359] The server analyzes the information and suggests multiple medical institutions that specialize in treating lower back pain.

[0360] The device displays a list of medical institutions, treatment costs, past reviews, and sentiment analysis results.

[0361] The user selects a specific orthopedic surgeon and confirms the appointment.

[0362] The server shares past medical data and emotional information with the orthopedic system.

[0363] The user visits an orthopedic clinic, and the new doctor creates an appropriate treatment plan based on past treatment data and emotional data.

[0364] The above is an embodiment of the present invention, which constitutes a system that reduces the burden on patients and provides efficient and appropriate medical services that take into account their emotional state.

[0365] The processing flow will be explained below.

[0366] Step 1:

[0367] The user launches an application on their smartphone or computer.

[0368] Step 2:

[0369] The device uses generative AI and an emotion engine to prompt the user with questions such as, "What are your symptoms today?"

[0370] Step 3:

[0371] The user types, "I have a headache."

[0372] Step 4:

[0373] The emotion engine analyzes emotions from the user's input text and tone of voice and determines that the anxiety level is high.

[0374] Step 5:

[0375] The device will ask, "How long have you been experiencing these symptoms?"

[0376] Step 6:

[0377] The user responds, "From three days ago."

[0378] Step 7:

[0379] The device collects and formats the entered symptom information and emotion analysis results.

[0380] Step 8:

[0381] The device sends the collected information to the server.

[0382] Step 9:

[0383] The server analyzes the received symptom information and emotion information.

[0384] Step 10:

[0385] The server identifies the corresponding medical department (e.g., internal medicine, neurology, etc.) and searches the database for medical institutions and medical professionals in that department.

[0386] Step 11:

[0387] The server collects each medical institution's areas of expertise, past treatment records, patient reviews, estimated medical costs, and sentiment analysis results.

[0388] Step 12:

[0389] Based on the data collected by the server, several of the most appropriate medical institutions and medical professionals are selected.

[0390] Step 13:

[0391] The server sends information about the selected medical institution and medical personnel to the terminal.

[0392] Step 14:

[0393] The device displays the information received from the server to the user, including the results of emotion analysis.

[0394] Step 15:

[0395] The user reviews a list of suggested medical institutions and practitioners.

[0396] Step 16:

[0397] The user selects the desired medical institution or medical professional. For example, select "Clinic B."

[0398] Step 17:

[0399] The terminal displays a reservation button to the user.

[0400] Step 18:

[0401] The user taps the reservation button.

[0402] Step 19:

[0403] The terminal transmits the reservation information to the server.

[0404] Step 20:

[0405] The server communicates with the medical institution's reservation system and confirms the reservation.

[0406] Step 21:

[0407] The server sends a reservation completion notification to the terminal.

[0408] Step 22:

[0409] The terminal displays the reservation confirmation information to the user.

[0410] Step 23:

[0411] The user's initial consultation information and subsequent medical information are stored as electronic data on the server.

[0412] Step 24:

[0413] The user visits the medical institution for which they have made an appointment.

[0414] Step 25:

[0415] The medical institution's system retrieves electronic data and emotional information from the server.

[0416] Step 26:

[0417] The server transmits the user's symptom information and emotional information to the medical institution.

[0418] Step 27:

[0419] Medical institutions provide medical treatment based on information shared in advance.

[0420] Step 28:

[0421] After a diagnosis or treatment is performed, new medical data is updated from the medical institution's system to the server.

[0422] Step 29:

[0423] The server updates the user's personal data with the new medical data.

[0424] Step 30:

[0425] When the user visits another medical institution, the server shares relevant medical data and emotional information with the new medical institution.

[0426] Step 31:

[0427] The new medical institution receives the data from the server and provides efficient medical care.

[0428] Example 2

[0429] 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."

[0430] The current medical system faces the problem of not providing patients with enough information to select the appropriate medical institution or medical professional when making a medical appointment. Furthermore, medical treatment recommendations do not take into account the patient's emotional state, which can increase anxiety and stress. Furthermore, medical data is not shared enough, hindering efficient medical care delivery between different medical institutions. To solve these issues, appropriate medical treatment recommendations based on the patient's symptom and emotional information, as well as effective management and sharing of medical data, are needed.

[0431] 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.

[0432] In this invention, the server includes a means for having the patient input symptom information and emotional information before visiting the hospital, a means for analyzing the input symptom information and emotional information to search for appropriate medical institutions and medical professionals, and a means for presenting information on multiple medical institutions and medical professionals based on the analysis results and allowing the patient to select one. This makes it possible to propose appropriate medical care that takes into account not only the patient's symptoms but also their emotional information. In addition, by providing a means for storing patient medical data as electronic data and sharing the data between medical institutions, smooth and efficient medical care can be provided.

[0433] "Symptom information" is data that indicates physical discomfort or pain that a patient is experiencing.

[0434] "Emotional information" is data obtained by analyzing a patient's psychological state and emotions.

[0435] A "medical institution" is an organization or facility that provides medical services.

[0436] "Medical professionals" are medical professionals such as doctors and nurses who provide medical care.

[0437] "Analysis" is the process of processing input data and extracting meaningful information.

[0438] "Searching" is the process of finding information from a database based on specific criteria.

[0439] "Presentation" means displaying the analyzed information in a way that is easy for the user to understand.

[0440] "Confirming an appointment" means officially deciding on a date and time for consultation with the selected medical institution or healthcare professional.

[0441] "Electronic data" means information stored in digital form.

[0442] "Sharing" means exchanging and making available data between different medical institutions.

[0443] This system allows patients to input symptom and emotional information before visiting a hospital, analyzes that information, searches for appropriate medical institutions and medical professionals, and confirms appointments, thereby reducing the burden on patients and providing efficient medical services. The system includes a user terminal that runs a dedicated application, a server that performs data analysis, and an emotion engine that processes emotional information.

[0444] Hardware and software used

[0445] User terminal

[0446] Hardware: Smartphones, PCs

[0447] Software: Dedicated application, emotion engine

[0448] The user launches the dedicated app and inputs symptom and emotional information. The emotion engine analyzes the user's tone of voice and input text to collect emotional information. The device then sends the collected information to the server.

[0449] server

[0450] Hardware: Server system

[0451] Software: Database system, analysis software, emotion engine

[0452] The server analyzes the received symptom and emotion information and searches the database for appropriate medical institutions and medical professionals. Based on the analysis results, it generates a list of search results, including treatment methods, estimated medical costs, and the analysis results of the emotion engine.

[0453] Confirmed reservations and data sharing

[0454] Medical Institution System

[0455] The server sends information about the selected medical institutions and medical professionals to the user's terminal, and once the user selects the desired medical institution, the reservation confirmation process begins. The reservation information is sent via the server to the medical institution's reservation system, and the reservation is confirmed. The confirmed reservation information is shared with the medical institution's system. Furthermore, the patient's medical data is stored as electronic data and shared so that it can be used between different medical institutions.

[0456] Specific operation example

[0457] Example 1: Common cold symptoms

[0458] The user launches the app and types, "I have a bad cough and a sore throat."

[0459] The emotion engine analyzes emotions from the user's input text and tone of voice and determines that the anxiety level is high.

[0460] The terminal transmits the input information and emotion information to the server.

[0461] The server analyzes the symptom information and emotional information and lists multiple medical institutions that primarily provide internal medicine care.

[0462] The device displays a list of medical institutions, estimated treatment costs, and sentiment analysis results.

[0463] The user selects the nearest clinic and confirms the appointment.

[0464] The server confirms the appointment and shares the data with the clinic.

[0465] When a user visits the clinic, they receive prompt medical treatment based on the information shared in advance.

[0466] Prompt Sentence Examples

[0467] "The user enters 'severe cough, sore throat' into a dedicated app, and the emotion engine determines that the anxiety level is high. The server then lists medical institutions that primarily provide internal medicine care, and the user confirms an appointment. The server then shares information to ensure prompt treatment when visiting the clinic."

[0468] As described above, the present invention is a system that realizes the provision of efficient and appropriate medical services based on the symptom information and emotional information of patients.

[0469] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0470] Program processing flow

[0471] Step 1: Enter your user information

[0472] The user launches the dedicated app.

[0473] The user enters symptom information (e.g., "bad cough, sore throat").

[0474] The device receives the input symptom information and prompts the user to input emotional information.

[0475] The emotion engine analyzes the user's tone of voice and input text to generate emotion information (e.g., "high anxiety level").

[0476] The symptom information and emotion information collected by the terminal are transmitted to a server.

[0477] Input: Symptom information (text), emotion information (analysis results).

[0478] Output: Symptom and emotion information sent to the server.

[0479] Step 2: Information analysis

[0480] The server analyzes the received symptom information and emotion information.

[0481] The server determines the predicted medical specialty based on the symptom information (e.g., "internal medicine").

[0482] The server evaluates the user's level of urgency based on emotional information (e.g., "high anxiety level").

[0483] The server searches the database for appropriate medical institutions and medical professionals.

[0484] The server lists the search results and generates information including treatment methods, estimated medical costs, and sentiment analysis results.

[0485] Input: Symptom information, emotion information.

[0486] Output: List of medical institutions, treatment methods, medical costs, and sentiment analysis results.

[0487] Step 3: Viewing the Suggestion Results

[0488] The server sends the generated list of medical institutions to the user terminal.

[0489] The device displays to the user a list of medical institutions, estimated treatment costs, and sentiment analysis results.

[0490] The user selects the desired medical institution from the displayed list.

[0491] Input: List of medical institutions, treatment methods, medical costs, and sentiment analysis results.

[0492] Output: Display to user, user selection.

[0493] Step 4: Confirm your reservation

[0494] The terminal receives the user's selection and transmits information about the selected medical institution to the server.

[0495] The server sends the reservation information to the medical institution's reservation system and confirms the reservation.

[0496] The server receives the reservation confirmation notice and sends it to the user terminal.

[0497] The terminal displays a reservation completion notice to the user.

[0498] Input: User selection information, medical institution reservation system.

[0499] Output: Reservation confirmation notice, user notification.

[0500] Step 5: Managing and sharing medical data

[0501] The server stores the user's first consultation information as electronic data.

[0502] The server shares necessary medical data between different medical institutions.

[0503] Medical institutions provide medical treatment based on the shared data.

[0504] Input: Initial consultation information (electronic data format), sharing request.

[0505] Output: Electronic data storage, data sharing with different medical institutions.

[0506] The above is the specific processing flow of the program in the present invention. This system makes it possible to provide efficient and appropriate medical services based on the symptom information and emotional information of the patient.

[0507] (Application example 2)

[0508] 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."

[0509] In conventional medical systems, even if patients input symptom information before visiting a hospital, it is difficult to select the appropriate medical institution or medical professional based on that information alone. Furthermore, since medical treatment suggestions do not take into account the patient's emotional state, it is difficult to provide the optimal medical service for the patient. Furthermore, data is not shared efficiently between medical institutions, which makes it difficult for patients to receive medical treatment smoothly.

[0510] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0511] In this invention, the server includes: means for having the patient input symptom information before visiting the hospital; means for analyzing the input symptom information and emotional information to search for appropriate medical institutions and medical professionals; means for presenting information on multiple medical institutions and medical professionals based on the analysis results and allowing the patient to select one; means for confirming an appointment with the selected medical institution or medical professional; means for saving the patient's medical data as electronic data and sharing the data between medical institutions; means for generating analysis results using a generative AI model and proposing information on multiple medical institutions; means for presenting estimated medical costs based on the input symptom information and emotional information; and means for transmitting the patient's initial visit information and emotional information to the systems of each medical institution. This enables appropriate medical treatment suggestions that take the patient's emotional state into consideration and enables efficient data sharing between medical institutions.

[0512] "Symptom information" is detailed data about the patient's perceived poor health and illness.

[0513] "Emotional information" is data that indicates a patient's psychological or emotional state. Specific techniques are used to analyze emotions and assess levels of stress, anxiety, etc.

[0514] "Medical institution" refers to a facility that provides medical services, such as a hospital, medical office, or clinic.

[0515] "Healthcare professionals" are professionals who provide medical support, such as doctors, nurses, and medical technicians.

[0516] A "generative AI model" is an information generation technology that uses artificial intelligence, and specifically refers to algorithms that perform natural language processing and data analysis.

[0517] "Data sharing" is the process of efficiently exchanging electronic data between different medical institutions, making the information necessary for medical treatment mutually available.

[0518] "Confirming a reservation" means officially deciding to make a medical appointment with the medical institution or medical professional selected by the patient.

[0519] "Analysis results" refer to the results derived by generative AI models or other analysis engines based on the input symptom information and emotional information.

[0520] A "system" is a collection of processes and technologies in which the above-mentioned means work together.

[0521] "Proposals using generative AI models" refers to the process of analyzing a patient's symptom and emotional information to determine the most appropriate medical institution and treatment method.

[0522] This invention is a system that allows patients to input symptom and emotional information before visiting a hospital, analyzes that information, searches for appropriate medical institutions and medical professionals, and even makes recommendations and reservations. This system includes the following components and processes.

[0523] System Configuration

[0524] 1. User Device

[0525] A device where patients enter information and receive results, such as a smartphone or tablet.

[0526] 2. Server

[0527] A central system that analyzes the entered information and searches a database for appropriate medical institutions and medical professionals. It uses a cloud-based server (e.g., AWS, Google Cloud).

[0528] 3. Sentiment Analysis Engine

[0529] A system that analyzes emotions from patients' tone of voice and text, utilizing Microsoft Azure Cognitive Services and IBM Watson.

[0530] 4. Generative AI Models

[0531] An artificial intelligence model for generating analysis results of input information. Uses OpenAI's GPT-4, etc.

[0532] 5. Database

[0533] A system that manages data on medical institutions, medical professionals, and patients, using database management systems such as MySQL and PostgreSQL.

[0534] Program processing explanation

[0535] User terminal

[0536] The user (patient) launches the smartphone app and inputs symptom information and emotional information.

[0537] Using an emotion analysis engine, emotional information is collected through the user's tone of voice and text analysis.

[0538] The collected information is sent to a server.

[0539] server

[0540] The server analyzes the received symptom and emotion information using a generative AI model.

[0541] Search for appropriate medical departments, medical institutions, and medical professionals from medical databases.

[0542] Search results include treatment methods at each medical institution, estimated medical costs, and sentiment analysis results.

[0543] Information on multiple medical institutions and medical professionals is sent to the user terminal.

[0544] User terminal

[0545] The search results are presented to the user and an appointment is confirmed at the selected medical institution.

[0546] The confirmed reservation information is sent to the medical institution via the server.

[0547] Data Management

[0548] Patient medical data is stored electronically on a server.

[0549] When a user visits another medical institution, relevant medical data is shared with the new medical institution.

[0550] Specific examples

[0551] Example 1: Common cold symptoms

[0552] The user launches the app and types, "I have a bad cough and a sore throat."

[0553] The emotion analysis engine analyzes the user's emotions from the text they input and their tone of voice, and determines that their anxiety level is high.

[0554] The terminal transmits the input information and emotion information to the server.

[0555] The server analyzes the information and lists multiple medical institutions that primarily provide internal medicine care.

[0556] The device displays a list of medical institutions, estimated treatment costs, and sentiment analysis results.

[0557] The user selects the nearest clinic and confirms the appointment.

[0558] The server confirms the appointment and shares the data with the clinic.

[0559] When a user visits the clinic, they receive prompt medical treatment based on the information shared in advance.

[0560] Example 2: Treating long-term back pain

[0561] Users use the app to enter their past medical history and current symptoms.

[0562] The sentiment analysis engine recognizes from the input data that the user's stress level is high.

[0563] The terminal sends the information to the server.

[0564] The server analyzes the information and suggests multiple medical institutions that specialize in treating lower back pain.

[0565] The device displays a list of medical institutions, treatment costs, past reviews, and sentiment analysis results.

[0566] The user selects a specific orthopedic surgeon and confirms the appointment.

[0567] The server shares past medical data and emotional information with the orthopedic system.

[0568] The user visits an orthopedic clinic, and the new doctor creates an appropriate treatment plan based on past treatment data and emotional data.

[0569] Prompt Sentence Examples

[0570] When a user enters "I have a headache and feel nauseous," analyze the emotion they are feeling and generate a prompt to suggest the best medical institution that can also provide stress management.

[0571] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0572] Step 1:

[0573] The user launches the smartphone app.

[0574] Input: The user launches the app.

[0575] Output: The app's home screen is displayed.

[0576] Specific behavior: When a user taps the app icon on their smartphone, the app launches and the login screen appears.

[0577] Step 2:

[0578] The user inputs symptom information and emotional information.

[0579] Input: The user enters the symptoms they are experiencing in a text box (e.g., "I have a headache" or "I have a bad cough") and records their current feelings via voice as emotional information.

[0580] Output: Collected symptom and emotion information.

[0581] Specific operation: Users enter their symptoms in text in the app's input form, press the voice input button, and speak their feelings to collect voice data.

[0582] Step 3:

[0583] The device sends the collected information to an emotion analysis engine.

[0584] Input: Text data for symptom information and audio data for emotion information.

[0585] Output: Analysis results from the sentiment analysis engine.

[0586] Specific operation: The app sends the collected symptom information and voice data to the server, and the server passes the data to the emotion analysis engine for processing.

[0587] Step 4:

[0588] The emotion analysis engine analyzes the user's emotions and sends the results to the server.

[0589] Input: Audio data.

[0590] Output: Sentiment analysis result (e.g. "High Anxiety Level").

[0591] What it does: The emotion analysis engine processes the audio data, identifies the emotional state (e.g., "high anxiety"), and sends the results back to the server.

[0592] Step 5:

[0593] The server analyzes symptom information and emotional information using the generated AI model.

[0594] Input: Symptom information, sentiment analysis results.

[0595] Output: A list of appropriate medical institutions and medical professionals.

[0596] How it works: The server uses a generative AI model (e.g., OpenAI GPT-4) to analyze symptom and emotion data, and then searches and selects the most suitable medical institution and medical professional from a medical database.

[0597] Step 6:

[0598] The server sends a list of selected medical institutions and medical professionals to the terminal.

[0599] Input: Parsed result by the server.

[0600] Output: A list of medical institutions and medical professionals on the user's device.

[0601] Specific operation: The server sends the analysis results to the user's device, and the app displays the results to the user in list form.

[0602] Step 7:

[0603] The user selects a medical institution or medical professional and confirms the appointment.

[0604] Input: List of medical institutions, user selection.

[0605] Output: Confirmation of reservation, reservation details.

[0606] Specific operation: When the user selects the desired medical institution from the list and presses the reservation button, the reservation confirmation process begins and the reservation information is sent to the server.

[0607] Step 8:

[0608] The server confirms the appointment with the selected medical institution or medical professional and sends the appointment information to the medical institution's system.

[0609] Input: User selection information.

[0610] Output: A confirmation of the appointment is sent to the medical institution.

[0611] Specific operation: The server sends the reservation information to the relevant medical institution's reservation system, confirms the reservation, and sends a reservation confirmation notification to the user.

[0612] Step 9:

[0613] The server stores patient medical data as electronic data and shares the data between medical institutions.

[0614] Input: Medical data, medical results.

[0615] Output: Recorded in a medical database and saved in a format that can be used by other medical institutions.

[0616] Specific operations: The server stores the patient's medical information and medical results as electronic data and takes steps to share it with other medical institutions as necessary.

[0617] 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.

[0618] 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.

[0619] 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.

[0620] [Second embodiment]

[0621] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0622] 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.

[0623] 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).

[0624] 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.

[0625] 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.

[0626] 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).

[0627] 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.

[0628] 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.

[0629] 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.

[0630] 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.

[0631] 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.

[0632] 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."

[0633] The present invention is a system for enabling patients to receive medical services efficiently, and embodiments thereof will be described in detail below.

[0634] 1. System Overview

[0635] This system allows patients to input symptom information from devices such as smartphones or computers, and based on that information, searches for and suggests appropriate medical institutions and medical professionals, and then completes the process of booking an appointment.It also has the ability to store patients' medical data electronically and share it between different medical institutions.

[0636] 2. System Configuration

[0637] The system mainly consists of the following components:

[0638] User terminal: A device where the patient enters information and receives results.

[0639] Server: A central system that analyzes the input information and generates appropriate suggestions.

[0640] Medical institution system: A system that receives medical data and uses it for medical treatment.

[0641] 3. Program Processing

[0642] Processing on the user terminal

[0643] The user launches the dedicated app and enters symptoms and basic information, which is then sent to a server running the AI.

[0644] Processing on the server

[0645] The server analyzes the received information and searches the database for appropriate medical institutions and medical professionals. The search results include information on multiple medical institutions and medical professionals, including each institution's treatment methods and estimated medical costs.

[0646] Displaying the proposal results on the user's device

[0647] The server sends information about the selected medical institutions and medical professionals to the user's device, where the user can check the information and select the medical institution of their choice.

[0648] Confirmation of reservation

[0649] Once the user selects the desired medical institution, the reservation confirmation process begins. The reservation information is sent via the server to the medical institution's reservation system, and the reservation is confirmed. A reservation completion notice is sent to the user's terminal, and confirmation information is displayed.

[0650] Medical data storage and sharing

[0651] The patient's initial consultation information and subsequent medical information are stored as electronic data on a server. When the user visits another medical institution, the relevant medical data is shared with the new institution, making medical treatment more efficient.

[0652] Specific examples

[0653] Example 1: Common cold symptoms

[0654] The user launches the app and types, "I have a bad cough and a sore throat."

[0655] The terminal transmits the input information to the server.

[0656] The server analyzes the information and lists multiple medical institutions that primarily provide internal medicine care.

[0657] The device displays a list of medical institutions and estimated treatment costs.

[0658] The user selects the nearest clinic and confirms the appointment.

[0659] The server confirms the appointment and shares the data with the clinic.

[0660] When a user visits the clinic, they receive prompt medical treatment based on the information shared in advance.

[0661] Example 2: Treating long-term back pain

[0662] Users use the app to enter their past medical history and current symptoms.

[0663] The terminal sends the information to the server.

[0664] The server analyzes the information and suggests multiple medical institutions that specialize in treating lower back pain.

[0665] The device displays a list of medical institutions, treatment costs, and past reviews.

[0666] The user selects a specific orthopedic surgeon and confirms the appointment.

[0667] The server shares past medical data with the orthopedic system.

[0668] The user visits an orthopedic clinic, and the new doctor creates an appropriate treatment plan based on past treatment data.

[0669] The above is an embodiment of the present invention, which constitutes a system that reduces the burden on patients and provides efficient medical services.

[0670] The processing flow will be explained below.

[0671] Step 1:

[0672] The user launches an application on their smartphone or computer.

[0673] Step 2:

[0674] The device uses generative AI to prompt the user with questions such as, "What are your symptoms today?"

[0675] Step 3:

[0676] The user types, "I have a headache."

[0677] Step 4:

[0678] The device then asks, "How long have you been experiencing these symptoms?"

[0679] Step 5:

[0680] The user responds, "From three days ago."

[0681] Step 6:

[0682] The terminal collects and formats the information entered.

[0683] Step 7:

[0684] The device sends the collected information to the server.

[0685] Step 8:

[0686] The server analyzes the received symptom information.

[0687] Step 9:

[0688] The server identifies the corresponding medical department (e.g., internal medicine, neurology, etc.) and searches the database for medical institutions and medical professionals in that department.

[0689] Step 10:

[0690] The server collects information such as each medical institution's areas of expertise, past treatment results, patient reviews, and estimated medical costs.

[0691] Step 11:

[0692] Based on the data collected by the server, several of the most appropriate medical institutions and medical professionals are selected.

[0693] Step 12:

[0694] The server sends information about the selected medical institution and medical personnel to the terminal.

[0695] Step 13:

[0696] The terminal displays the information received from the server to the user.

[0697] Step 14:

[0698] The user reviews a list of suggested medical institutions and practitioners.

[0699] Step 15:

[0700] The user selects the desired medical institution or medical professional. For example, select "Clinic B."

[0701] Step 16:

[0702] The terminal displays a reservation button to the user.

[0703] Step 17:

[0704] The user taps the reservation button.

[0705] Step 18:

[0706] The terminal transmits the reservation information to the server.

[0707] Step 19:

[0708] The server communicates with the medical institution's reservation system and confirms the reservation.

[0709] Step 20:

[0710] The server sends a reservation completion notification to the terminal.

[0711] Step 21:

[0712] The terminal displays the reservation confirmation information to the user.

[0713] Step 22:

[0714] The user's initial consultation information and subsequent medical information are stored as electronic data on the server.

[0715] Step 23:

[0716] The user makes an appointment and visits the medical institution.

[0717] Step 24:

[0718] The medical institution's system retrieves the electronic data from the server.

[0719] Step 25:

[0720] The server sends the user's symptom information and basic information to the medical institution.

[0721] Step 26:

[0722] Medical institutions provide medical treatment based on information shared in advance.

[0723] Step 27:

[0724] After a diagnosis or treatment is performed, new medical data is updated from the medical institution's system to the server.

[0725] Step 28:

[0726] The server updates the user's personal data with the new medical data.

[0727] Step 29:

[0728] When the user visits another medical institution, the server shares the relevant medical data with the new medical institution.

[0729] Step 30:

[0730] The new medical institution receives the data from the server and provides efficient medical care.

[0731] Example 1

[0732] 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."

[0733] In conventional medical systems, when a patient visits multiple medical institutions, they must re-enter their initial consultation information and medical treatment information at each institution, which is time-consuming and can lead to information inconsistencies and delays. It also makes it difficult to select the appropriate medical institution, and confirmation and adjustments when making appointments are cumbersome. These issues reduce the efficiency of medical treatment for patients and make it difficult to provide prompt medical care.

[0734] 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.

[0735] In this invention, the server includes a terminal for patients to input symptom information, a means for transmitting the input symptom information to the server, and a means for the server to analyze the received information and search for appropriate medical institutions and medical professionals using a generative AI model. This allows patients to quickly and accurately input symptom information and select an appropriate medical institution. The server also includes a means for transmitting and displaying information on multiple searched medical institutions and medical professionals to the terminal, a means for confirming an appointment with the medical institution or medical professional selected by the patient, a means for sending a reservation completion notification to the terminal, and a means for storing the patient's medical data as electronic data and sharing it among medical institutions. This allows information entered by a patient once to be shared among multiple medical institutions, eliminating the need for time-consuming information input and reducing the complexity of appointment confirmation and adjustment, thereby significantly improving medical treatment efficiency.

[0736] A "terminal" is a device used by a patient to input symptom information, and includes smartphones, personal computers, etc.

[0737] The "server" is a central system that receives symptom information sent by patients, analyzes it, and searches for appropriate medical institutions and medical professionals.

[0738] A "generative AI model" is an artificial intelligence model used to analyze received information and suggest appropriate medical institutions and medical professionals, including, for example, advanced natural language processing models such as GPT-3.

[0739] "Symptom information" refers to specific information about a patient's health condition or illness that the patient enters into the device. This includes specific symptoms such as "I have a bad cough and a sore throat."

[0740] A "medical institution" is a facility where patients can receive medical treatment, and includes hospitals, clinics, and medical offices.

[0741] "Healthcare professionals" are professionals who are qualified to perform medical procedures on patients at medical institutions, and include doctors, nurses, pharmacists, etc.

[0742] A "reservation" is the act of a patient specifying a date and time to receive medical treatment from a medical institution or medical professional of their choice in advance.

[0743] "Electronic data" refers to data used to store and manage patient symptom information, medical records, and other information in digital format.

[0744] "Sharing" refers to electronic data being transmitted between different medical institutions and being made available to each other.

[0745] "Expected medical expenses" are a guideline for predicted medical expenses based on symptom information and medical treatment details.

[0746] This invention is a system that enables patients to receive medical services efficiently. This system allows patients to input symptom information from devices such as smartphones or computers, and based on that information, the system searches for and suggests appropriate medical institutions and medical professionals, and then completes the process of booking appointments. It also has the function of storing patients' medical data electronically and sharing the data between different medical institutions.

[0747] 1. System Overview

[0748] The user launches the dedicated app and enters symptoms and basic information. This input information is sent to a server running the generative AI model. The server analyzes the received information and searches a database for appropriate medical institutions and medical professionals. The server then sends the search results to the user's device, allowing the user to review the information and select the medical institution of their choice. Finally, the server confirms the reservation and sends a reservation completion notification to the user's device. The patient's medical data is also stored as electronic data and shared between medical institutions.

[0749] 2. Hardware and Software Used

[0750] User device: The device where the user enters information and receives the results (smartphone, PC, etc.)

[0751] Server: A central system that analyzes input information and generates appropriate suggestions. An environment for running Python or Java programs.

[0752] Database management system: A system that stores and manages patient medical data (e.g., MySQL)

[0753] Generative AI model: An artificial intelligence model (such as GPT-3) that analyzes the received information and suggests appropriate medical institutions and medical professionals.

[0754] 3. Specific Examples

[0755] A specific example of use is shown below.

[0756] Example 1: Common cold symptoms

[0757] The user launches the app and types, "I have a bad cough and a sore throat."

[0758] The terminal transmits the input information to the server.

[0759] The server analyzes the information and lists multiple medical institutions that primarily provide internal medicine care.

[0760] The device displays a list of medical institutions and estimated treatment costs.

[0761] The user selects the nearest clinic and confirms the appointment.

[0762] The server confirms the appointment and shares the data with the clinic.

[0763] When a user visits the clinic, they receive prompt medical treatment based on the information shared in advance.

[0764] Example 2: Treating long-term back pain

[0765] Users use the app to enter their past medical history and current symptoms.

[0766] The terminal sends the information to the server.

[0767] The server analyzes the information and suggests multiple medical institutions that specialize in treating lower back pain.

[0768] The device displays a list of medical institutions, treatment costs, and past reviews.

[0769] The user selects a specific orthopedic surgeon and confirms the appointment.

[0770] The server shares past medical data with the orthopedic system.

[0771] The user visits an orthopedic clinic, and the new doctor creates an appropriate treatment plan based on past treatment data.

[0772] Prompt Sentence Examples

[0773] Below is an example of a prompt sentence to input to the generative AI model.

[0774] Symptom information that makes diagnosis easier: "I have a bad cough and a sore throat. I'm looking for an internist."

[0775] Generated medical institution suggestions: "The following internal medicine clinics are nearby: XX Clinic, △△ Clinic, and □□ Hospital. The treatment costs for each are..."

[0776] Appointment confirmation process: "Please make an appointment at XX Clinic." -> "The appointment has been completed."

[0777] The above is an embodiment of the present invention, which constitutes a system that reduces the burden on patients and provides efficient medical services.

[0778] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0779] Step 1: Enter user information

[0780] The user launches the app and inputs their symptoms and basic information, including specific symptoms such as "severe cough and sore throat." This generates data on the user's health condition.

[0781] Input: Symptom information (e.g., "I have a bad cough and a sore throat")

[0782] Output: Symptom information data entered

[0783] Step 2: Sending information to the server

[0784] The device encrypts the input symptom information and sends it to the server. At this time, encryption protocols such as SSL / TLS are used to ensure the security of the communication. The transmitted data is then received by the server.

[0785] Input: Symptom information data entered

[0786] Output: Data sent to the server

[0787] Step 3: Analyze the received information

[0788] The server analyzes the received symptom information using Python or Java scripts. A generative AI model (e.g., GPT-3) is used to calculate the data and suggest appropriate medical institutions and medical professionals. Specifically, a prompt is input into the generative AI model, and the analysis results are obtained.

[0789] Input: Symptom information data received by the server

[0790] Output: Proposed results data from the generative AI model

[0791] Step 4: Search and select a medical institution

[0792] The server searches and selects appropriate medical institutions and medical professionals from the database based on the results of the generative AI model. A list is generated that includes each medical institution's treatment method, estimated medical costs, address, etc. This is done using SQL queries.

[0793] Input: Proposal result data of generative AI model

[0794] Output: List of medical institutions

[0795] Step 5: Submit search results

[0796] The server packages the searched and selected information on medical institutions and medical professionals in JSON format and sends it to the user's device, where the user receives detailed information on selectable medical institutions.

[0797] Input: List of medical institutions

[0798] Output: Data sent to the terminal

[0799] Step 6: Review and select the suggestions

[0800] The user can then view information about medical institutions and medical professionals based on the search results on their device, including the name, address, treatment details, and reviews of each medical institution. The user can then select the medical institution of their choice.

[0801] Input: Medical institution information displayed on the terminal

[0802] Output: User selection data

[0803] Step 7: Submit your reservation information

[0804] The terminal sends the reservation information of the medical institution selected by the user to the server. The information is sent to the server in JSON format. The server receives the information and processes it to confirm the reservation at the selected medical institution.

[0805] Input: User selected data

[0806] Output: Data scheduled to be sent to the server

[0807] Step 8: Send a reservation completion notification

[0808] The server checks the reservation information database to confirm that the reservation has been confirmed, and sends a reservation completion notice to the user's terminal, allowing the user to confirm that their reservation has been confirmed.

[0809] Input: Confirmed reservation data

[0810] Output: Reservation completion notification data to the terminal

[0811] Step 9: Storing and sharing medical data

[0812] The server stores the patient's initial consultation information and subsequent medical information as electronic data. The data is managed using a database management system (e.g., MySQL). When a user receives treatment at another medical institution, the relevant medical data is encrypted and sent to the new medical institution.

[0813] Input: Patient medical information

[0814] Output: Stored electronic data and shared data

[0815] (Application example 1)

[0816] 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."

[0817] Conventional medical systems lack an integrated means for patients to effectively select appropriate medical institutions and medical professionals and quickly confirm appointments. Furthermore, it is difficult to share medical data between different medical institutions, resulting in inefficient medical treatment. Furthermore, security services require real-time information analysis and communication methods to detect suspicious individuals and respond quickly to emergencies. A system that can solve these issues in an integrated manner is needed.

[0818] 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.

[0819] In this invention, the server includes means for having a patient input symptom information before visiting a hospital, means for analyzing the input symptom information and searching for an appropriate medical institution or medical professional, means for presenting information on multiple medical institutions and medical professionals based on the analysis results and having the patient select one, means for confirming an appointment with the selected medical institution or medical professional, means for saving the patient's medical data as electronic data and sharing the data between medical institutions, means for having a user input information on abnormal behavior or suspicious individuals and analyzing the information to generate appropriate suggestions, and means for contacting a security team or the police based on the analysis results. This not only streamlines the patient's medical treatment process and makes it easier to share data between medical institutions, but also enables quick and appropriate responses in security services.

[0820] definition statement

[0821] "Means for patients to input symptom information before visiting the hospital" refers to a function that allows patients to input their symptoms and basic information using their own devices (smartphones, computers, etc.).

[0822] "Means of analyzing input symptom information and searching for appropriate medical institutions and medical professionals" is a function that identifies and suggests the most appropriate medical institutions and medical professionals from a database based on the symptom information input by the patient.

[0823] "Means of presenting information on multiple medical institutions and medical professionals based on the analysis results and allowing the patient to make a selection" is a function that provides the patient with multiple appropriate options for medical institutions and medical professionals based on the analyzed information, allowing the patient to make the selection themselves.

[0824] "Means for confirming appointments with selected medical institutions and healthcare professionals" refers to a function that allows a patient to securely confirm appointments with the medical institutions and healthcare professionals selected by the patient within the system and send a confirmation notification.

[0825] "Means for storing patient medical data as electronic data and sharing the data between medical institutions" refers to a function that allows patients' medical information to be recorded electronically and the data to be shared with different medical institutions as needed.

[0826] "A means for having users input information about abnormal behavior or suspicious individuals, analyzing that information, and generating appropriate suggestions" is a function in security services that allows users to report suspicious activity or abnormal behavior, analyze that information, and suggest optimal countermeasures.

[0827] "Means of contacting security teams or police based on analysis results" is a function that allows for quick contact with specialized security teams or police as necessary based on the analyzed information.

[0828] MODE FOR CARRYING OUT THE INVENTION

[0829] This invention is an integrated system that enables patients to receive medical services efficiently and also enables prompt response in security. This system is designed for use in Japanese medical institutions, but can also be applied in other countries.

[0830] Overall system configuration

[0831] The system mainly consists of the following components:

[0832] User terminal: A device, such as a smartphone or smart glasses, through which the patient or user enters information and receives results.

[0833] Server: A central system that analyzes input information and generates appropriate suggestions, using generative AI models, image recognition, natural language processing, etc.

[0834] Medical institution system: A system that receives medical data and uses it for medical treatment. It mainly works in conjunction with electronic medical record systems and reservation systems.

[0835] Program Generation

[0836] The system program is generated as follows:

[0837] Processing on the user terminal

[0838] The user launches a dedicated application using a smartphone or smart glasses, and inputs information about symptoms and suspicious individuals into the application. This information is then sent to the server.

[0839] Processing on the server

[0840] The server has the following features:

[0841] Symptom analysis: Analyzes the input symptom information and suggests appropriate medical institutions and medical professionals. A generative AI model is used for the analysis.

[0842] Image Recognition: Analyzes images of suspicious individuals and detects abnormalities and suspicious movements. Uses OpenCV.

[0843] Natural Language Processing: Analyzes input text information and generates appropriate suggestions. Uses the transformers library.

[0844] Recommendation generation: Based on the analysis results, appropriate medical institutions and action instructions are suggested to the user.

[0845] Specific examples

[0846] Examples of medical services

[0847] 1. The user launches the app and types, "I have a bad cough and a sore throat."

[0848] 2. The terminal sends the entered information to the server.

[0849] 3. The server analyzes the information and lists multiple medical institutions that primarily provide internal medicine care.

[0850] 4. The device will display a list of medical institutions and estimated treatment costs.

[0851] 5. The user selects the nearest clinic and confirms the appointment.

[0852] 6. The server confirms the appointment and shares the data with the clinic.

[0853] 7. When the user visits the clinic, they receive prompt medical treatment based on the information shared in advance.

[0854] Security Service Examples

[0855] 1. The user uses smart glasses to capture a photo of a suspicious person and sends the data to the server via the app.

[0856] 2. The server analyzes the image and, if it determines that the person is suspicious, it instructs the user to contact the police immediately.

[0857] 3. Based on this, users can take prompt action and implement appropriate security measures.

[0858] Prompt Sentence Examples

[0859] "Enter an image and generate a suspicious person identification and necessary action instructions."

[0860] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0861] Program processing flow

[0862] The process by which users enter their medical information and make appointments

[0863] Step 1:

[0864] User enters symptom information

[0865] Input: The user launches a dedicated application on a smartphone or smart glasses and enters symptom information such as "I have a bad cough and a sore throat."

[0866] Processing: The application temporarily stores the entered information and converts it into the required format.

[0867] Output: Formatted symptom information data.

[0868] Step 2:

[0869] The device sends the information to the server

[0870] Input: Formatted symptom information data.

[0871] Processing: The device sends data to the server over the network using an HTTP POST request.

[0872] Output: The data sent to the server.

[0873] Step 3:

[0874] The server analyzes the symptom information

[0875] Input: Submitted symptom information data.

[0876] Processing: Generative AI models are used to analyze the symptom information provided, using natural language processing libraries to identify symptom patterns.

[0877] Output: A list of appropriate medical institutions and medical professionals.

[0878] Step 4:

[0879] The server sends the proposal results to the user's device.

[0880] Input: A list of appropriate medical institutions and practitioners.

[0881] Processing: The server sends the analysis results to the user's device, again using an HTTP POST request.

[0882] Output: A list of medical institutions and estimated treatment costs displayed on the user's terminal.

[0883] Step 5:

[0884] The user selects a medical institution and confirms the appointment

[0885] Input: A list of medical institutions and estimated treatment costs displayed on the user's terminal.

[0886] Processing: The user selects the desired medical institution and taps the reservation button. The device resends the reservation information to the server.

[0887] Output: Reservation information sent to the server.

[0888] Step 6:

[0889] The server confirms the reservation and shares the data with the medical institution.

[0890] Input: Reservation information submitted by the user.

[0891] Processing: The server accesses the reservation system and confirms the reservation. At the same time, it sends the reservation information and the patient's symptom information to the medical institution's system.

[0892] Output: Appointment confirmation notice and medical information stored in the medical institution's system.

[0893] Flow of how users enter security information and take emergency action

[0894] Step 1:

[0895] The user captures and enters the suspicious person information

[0896] Input: The user uses the smart glasses to capture a photo of the suspicious person and input it into the security app.

[0897] Processing: The app temporarily stores the image data.

[0898] Output: Saved image data.

[0899] Step 2:

[0900] The device sends the image data to the server.

[0901] Input: Saved image data.

[0902] Processing: The device sends the image data to the server over the network, again using an HTTP POST request.

[0903] Output: Image data sent to the server.

[0904] Step 3:

[0905] The server analyzes the image data

[0906] Input: The submitted image data.

[0907] Processing: Image data is analyzed using OpenCV. This analysis identifies suspicious individuals and abnormal behavior.

[0908] Output: Analysis results, including whether suspicious activity was detected and recommended actions.

[0909] Step 4:

[0910] The server sends the proposal results to the user's device.

[0911] Input: Analysis results.

[0912] Processing: The server sends the analysis results and instructions to the user's device using an HTTP POST request.

[0913] Output: Instructions to be displayed on the user's device. In the case of a suspicious person, instructions to contact the police, etc.

[0914] Step 5:

[0915] User takes action based on the suggestion

[0916] Input: Response instructions.

[0917] Action: The user follows the instructions on the smart device and takes appropriate action, such as contacting the police. The device collects a log of the action.

[0918] Output: Action log data and emergency response implementation.

[0919] Prompt Sentence Examples

[0920] "Enter an image and generate a suspicious person identification and necessary action instructions."

[0921] 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.

[0922] The present invention is a system that allows patients to input symptom information before visiting a hospital, analyzes that information to search for appropriate medical institutions and medical professionals, and further combines it with an emotion engine that recognizes the user's emotions to make more appropriate medical recommendations. The following describes in detail an embodiment of the system.

[0923] 1. System Overview

[0924] This system allows patients to input symptom and emotional information from devices such as smartphones or PCs, and based on that information, the system searches for and suggests appropriate medical institutions and medical professionals, and then confirms appointments.It also has the function of storing patients' medical data electronically and sharing it between different medical institutions.

[0925] 2. System Configuration

[0926] The system mainly consists of the following components:

[0927] User terminal: A device where patients input information and receive results. It also collects emotional information.

[0928] Server: A central system that analyzes the input information and generates appropriate suggestions.

[0929] Medical institution system: A system that receives medical data and uses it for medical treatment.

[0930] Emotion engine: A system that analyzes the user's emotions and suggests medical treatment priorities and appropriate medical institutions.

[0931] 3. Program Processing

[0932] Processing on the user terminal

[0933] The user launches the app and inputs symptoms and basic information. Along with this input information, the emotion engine collects emotional information through tone of voice and text analysis. The collected information is sent to a server where the generative AI runs.

[0934] Processing on the server

[0935] The server analyzes the received symptom and emotion information and searches the database for the appropriate medical department, medical institution, and medical professional. The search results include information on multiple medical institutions and medical professionals. This information also reflects each institution's treatment method, estimated medical costs, and the analysis of the user's stress level and urgency using an emotion engine.

[0936] Displaying the proposal results on the user's device

[0937] The server sends information about the selected medical institutions and medical professionals to the user's device. The user can then check this information on the device and select the medical institution of their choice. The analysis results of the emotion engine are also displayed.

[0938] Confirmation of reservation

[0939] Once the user selects the desired medical institution, the reservation confirmation process begins. The reservation information is sent via the server to the medical institution's reservation system, and the reservation is confirmed. A reservation completion notice is sent to the user's terminal, and confirmation information is displayed.

[0940] Medical data storage and sharing

[0941] The patient's initial consultation information and subsequent medical information are stored as electronic data on a server. When the user visits another medical institution, the relevant medical data is shared with the new institution, making medical treatment more efficient.

[0942] Specific examples

[0943] Example 1: Common cold symptoms

[0944] The user launches the app and types, "I have a bad cough and a sore throat."

[0945] The emotion engine analyzes emotions from the user's input text and tone of voice and determines that the anxiety level is high.

[0946] The terminal transmits the input information and emotion information to the server.

[0947] The server analyzes the information and lists multiple medical institutions that primarily provide internal medicine care.

[0948] The device displays a list of medical institutions, estimated treatment costs, and sentiment analysis results.

[0949] The user selects the nearest clinic and confirms the appointment.

[0950] The server confirms the appointment and shares the data with the clinic.

[0951] When a user visits the clinic, they receive prompt medical treatment based on the information shared in advance.

[0952] Example 2: Treating long-term back pain

[0953] Users use the app to enter their past medical history and current symptoms.

[0954] The emotion engine recognizes from the input data that the user's stress level is high.

[0955] The terminal sends the information to the server.

[0956] The server analyzes the information and suggests multiple medical institutions that specialize in treating lower back pain.

[0957] The device displays a list of medical institutions, treatment costs, past reviews, and sentiment analysis results.

[0958] The user selects a specific orthopedic surgeon and confirms the appointment.

[0959] The server shares past medical data and emotional information with the orthopedic system.

[0960] The user visits an orthopedic clinic, and the new doctor creates an appropriate treatment plan based on past treatment data and emotional data.

[0961] The above is an embodiment of the present invention, which constitutes a system that reduces the burden on patients and provides efficient and appropriate medical services that take into account their emotional state.

[0962] The processing flow will be explained below.

[0963] Step 1:

[0964] The user launches an application on their smartphone or computer.

[0965] Step 2:

[0966] The device uses generative AI and an emotion engine to prompt the user with questions such as, "What are your symptoms today?"

[0967] Step 3:

[0968] The user types, "I have a headache."

[0969] Step 4:

[0970] The emotion engine analyzes emotions from the user's input text and tone of voice and determines that the anxiety level is high.

[0971] Step 5:

[0972] The device will ask, "How long have you been experiencing these symptoms?"

[0973] Step 6:

[0974] The user responds, "From three days ago."

[0975] Step 7:

[0976] The device collects and formats the entered symptom information and emotion analysis results.

[0977] Step 8:

[0978] The device sends the collected information to the server.

[0979] Step 9:

[0980] The server analyzes the received symptom information and emotion information.

[0981] Step 10:

[0982] The server identifies the corresponding medical department (e.g., internal medicine, neurology, etc.) and searches the database for medical institutions and medical professionals in that department.

[0983] Step 11:

[0984] The server collects each medical institution's areas of expertise, past treatment records, patient reviews, estimated medical costs, and sentiment analysis results.

[0985] Step 12:

[0986] Based on the data collected by the server, several of the most appropriate medical institutions and medical professionals are selected.

[0987] Step 13:

[0988] The server sends information about the selected medical institution and medical personnel to the terminal.

[0989] Step 14:

[0990] The device displays the information received from the server to the user, including the results of emotion analysis.

[0991] Step 15:

[0992] The user reviews a list of suggested medical institutions and practitioners.

[0993] Step 16:

[0994] The user selects the desired medical institution or medical professional. For example, select "Clinic B."

[0995] Step 17:

[0996] The terminal displays a reservation button to the user.

[0997] Step 18:

[0998] The user taps the reservation button.

[0999] Step 19:

[1000] The terminal transmits the reservation information to the server.

[1001] Step 20:

[1002] The server communicates with the medical institution's reservation system and confirms the reservation.

[1003] Step 21:

[1004] The server sends a reservation completion notification to the terminal.

[1005] Step 22:

[1006] The terminal displays the reservation confirmation information to the user.

[1007] Step 23:

[1008] The user's initial consultation information and subsequent medical information are stored as electronic data on the server.

[1009] Step 24:

[1010] The user visits the medical institution for which they have made an appointment.

[1011] Step 25:

[1012] The medical institution's system retrieves electronic data and emotional information from the server.

[1013] Step 26:

[1014] The server transmits the user's symptom information and emotional information to the medical institution.

[1015] Step 27:

[1016] Medical institutions provide medical treatment based on information shared in advance.

[1017] Step 28:

[1018] After a diagnosis or treatment is performed, new medical data is updated from the medical institution's system to the server.

[1019] Step 29:

[1020] The server updates the user's personal data with the new medical data.

[1021] Step 30:

[1022] When the user visits another medical institution, the server shares relevant medical data and emotional information with the new medical institution.

[1023] Step 31:

[1024] The new medical institution receives the data from the server and provides efficient medical care.

[1025] Example 2

[1026] 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."

[1027] The current medical system faces the problem of not providing patients with enough information to select the appropriate medical institution or medical professional when making a medical appointment. Furthermore, medical treatment recommendations do not take into account the patient's emotional state, which can increase anxiety and stress. Furthermore, medical data is not shared enough, hindering efficient medical care delivery between different medical institutions. To solve these issues, appropriate medical treatment recommendations based on the patient's symptom and emotional information, as well as effective management and sharing of medical data, are needed.

[1028] 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.

[1029] In this invention, the server includes a means for having the patient input symptom information and emotional information before visiting the hospital, a means for analyzing the input symptom information and emotional information to search for appropriate medical institutions and medical professionals, and a means for presenting information on multiple medical institutions and medical professionals based on the analysis results and allowing the patient to select one. This makes it possible to propose appropriate medical care that takes into account not only the patient's symptoms but also their emotional information. In addition, by providing a means for storing patient medical data as electronic data and sharing the data between medical institutions, smooth and efficient medical care can be provided.

[1030] "Symptom information" is data that indicates physical discomfort or pain that a patient is experiencing.

[1031] "Emotional information" is data obtained by analyzing a patient's psychological state and emotions.

[1032] A "medical institution" is an organization or facility that provides medical services.

[1033] "Medical professionals" are medical professionals such as doctors and nurses who provide medical care.

[1034] "Analysis" is the process of processing input data and extracting meaningful information.

[1035] "Searching" is the process of finding information from a database based on specific criteria.

[1036] "Presentation" means displaying the analyzed information in a way that is easy for the user to understand.

[1037] "Confirming an appointment" means officially deciding on a date and time for consultation with the selected medical institution or healthcare professional.

[1038] "Electronic data" means information stored in digital form.

[1039] "Sharing" means exchanging and making available data between different medical institutions.

[1040] This system allows patients to input symptom and emotional information before visiting a hospital, analyzes that information, searches for appropriate medical institutions and medical professionals, and confirms appointments, thereby reducing the burden on patients and providing efficient medical services. The system includes a user terminal that runs a dedicated application, a server that performs data analysis, and an emotion engine that processes emotional information.

[1041] Hardware and software used

[1042] User terminal

[1043] Hardware: Smartphones, PCs

[1044] Software: Dedicated application, emotion engine

[1045] The user launches the dedicated app and inputs symptom and emotional information. The emotion engine analyzes the user's tone of voice and input text to collect emotional information. The device then sends the collected information to the server.

[1046] server

[1047] Hardware: Server system

[1048] Software: Database system, analysis software, emotion engine

[1049] The server analyzes the received symptom and emotion information and searches the database for appropriate medical institutions and medical professionals. Based on the analysis results, it generates a list of search results, including treatment methods, estimated medical costs, and the analysis results of the emotion engine.

[1050] Confirmed reservations and data sharing

[1051] Medical Institution System

[1052] The server sends information about the selected medical institutions and medical professionals to the user's terminal, and once the user selects the desired medical institution, the reservation confirmation process begins. The reservation information is sent via the server to the medical institution's reservation system, and the reservation is confirmed. The confirmed reservation information is shared with the medical institution's system. Furthermore, the patient's medical data is stored as electronic data and shared so that it can be used between different medical institutions.

[1053] Specific operation example

[1054] Example 1: Common cold symptoms

[1055] The user launches the app and types, "I have a bad cough and a sore throat."

[1056] The emotion engine analyzes emotions from the user's input text and tone of voice and determines that the anxiety level is high.

[1057] The terminal transmits the input information and emotion information to the server.

[1058] The server analyzes the symptom information and emotional information and lists multiple medical institutions that primarily provide internal medicine care.

[1059] The device displays a list of medical institutions, estimated treatment costs, and sentiment analysis results.

[1060] The user selects the nearest clinic and confirms the appointment.

[1061] The server confirms the appointment and shares the data with the clinic.

[1062] When a user visits the clinic, they receive prompt medical treatment based on the information shared in advance.

[1063] Prompt Sentence Examples

[1064] "The user enters 'severe cough, sore throat' into a dedicated app, and the emotion engine determines that the anxiety level is high. The server then lists medical institutions that primarily provide internal medicine care, and the user confirms an appointment. The server then shares information to ensure prompt treatment when visiting the clinic."

[1065] As described above, the present invention is a system that realizes the provision of efficient and appropriate medical services based on the symptom information and emotional information of patients.

[1066] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1067] Program processing flow

[1068] Step 1: Enter your user information

[1069] The user launches the dedicated app.

[1070] The user enters symptom information (e.g., "bad cough, sore throat").

[1071] The device receives the input symptom information and prompts the user to input emotional information.

[1072] The emotion engine analyzes the user's tone of voice and input text to generate emotion information (e.g., "high anxiety level").

[1073] The symptom information and emotion information collected by the terminal are transmitted to a server.

[1074] Input: Symptom information (text), emotion information (analysis results).

[1075] Output: Symptom and emotion information sent to the server.

[1076] Step 2: Information analysis

[1077] The server analyzes the received symptom information and emotion information.

[1078] The server determines the predicted medical specialty based on the symptom information (e.g., "internal medicine").

[1079] The server evaluates the user's level of urgency based on emotional information (e.g., "high anxiety level").

[1080] The server searches the database for appropriate medical institutions and medical professionals.

[1081] The server lists the search results and generates information including treatment methods, estimated medical costs, and sentiment analysis results.

[1082] Input: Symptom information, emotion information.

[1083] Output: List of medical institutions, treatment methods, medical costs, and sentiment analysis results.

[1084] Step 3: Viewing the Suggestion Results

[1085] The server sends the generated list of medical institutions to the user terminal.

[1086] The device displays to the user a list of medical institutions, estimated treatment costs, and sentiment analysis results.

[1087] The user selects the desired medical institution from the displayed list.

[1088] Input: List of medical institutions, treatment methods, medical costs, and sentiment analysis results.

[1089] Output: Display to user, user selection.

[1090] Step 4: Confirm your reservation

[1091] The terminal receives the user's selection and transmits information about the selected medical institution to the server.

[1092] The server sends the reservation information to the medical institution's reservation system and confirms the reservation.

[1093] The server receives the reservation confirmation notice and sends it to the user terminal.

[1094] The terminal displays a reservation completion notice to the user.

[1095] Input: User selection information, medical institution reservation system.

[1096] Output: Reservation confirmation notice, user notification.

[1097] Step 5: Managing and sharing medical data

[1098] The server stores the user's first consultation information as electronic data.

[1099] The server shares necessary medical data between different medical institutions.

[1100] Medical institutions provide medical treatment based on the shared data.

[1101] Input: Initial consultation information (electronic data format), sharing request.

[1102] Output: Electronic data storage, data sharing with different medical institutions.

[1103] The above is the specific processing flow of the program in the present invention. This system makes it possible to provide efficient and appropriate medical services based on the symptom information and emotional information of the patient.

[1104] (Application example 2)

[1105] 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."

[1106] In conventional medical systems, even if patients input symptom information before visiting a hospital, it is difficult to select the appropriate medical institution or medical professional based on that information alone. Furthermore, since medical treatment suggestions do not take into account the patient's emotional state, it is difficult to provide the optimal medical service for the patient. Furthermore, data is not shared efficiently between medical institutions, which makes it difficult for patients to receive medical treatment smoothly.

[1107] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1108] In this invention, the server includes: means for having the patient input symptom information before visiting the hospital; means for analyzing the input symptom information and emotional information to search for appropriate medical institutions and medical professionals; means for presenting information on multiple medical institutions and medical professionals based on the analysis results and allowing the patient to select one; means for confirming an appointment with the selected medical institution or medical professional; means for saving the patient's medical data as electronic data and sharing the data between medical institutions; means for generating analysis results using a generative AI model and proposing information on multiple medical institutions; means for presenting estimated medical costs based on the input symptom information and emotional information; and means for transmitting the patient's initial visit information and emotional information to the systems of each medical institution. This enables appropriate medical treatment suggestions that take the patient's emotional state into consideration and enables efficient data sharing between medical institutions.

[1109] "Symptom information" is detailed data about the patient's perceived poor health and illness.

[1110] "Emotional information" is data that indicates a patient's psychological or emotional state. Specific techniques are used to analyze emotions and assess levels of stress, anxiety, etc.

[1111] "Medical institution" refers to a facility that provides medical services, such as a hospital, medical office, or clinic.

[1112] "Healthcare professionals" are professionals who provide medical support, such as doctors, nurses, and medical technicians.

[1113] A "generative AI model" is an information generation technology that uses artificial intelligence, and specifically refers to algorithms that perform natural language processing and data analysis.

[1114] "Data sharing" is the process of efficiently exchanging electronic data between different medical institutions, making the information necessary for medical treatment mutually available.

[1115] "Confirming a reservation" means officially deciding to make a medical appointment with the medical institution or medical professional selected by the patient.

[1116] "Analysis results" refer to the results derived by generative AI models or other analysis engines based on the input symptom information and emotional information.

[1117] A "system" is a collection of processes and technologies in which the above-mentioned means work together.

[1118] "Proposals using generative AI models" refers to the process of analyzing a patient's symptom and emotional information to determine the most appropriate medical institution and treatment method.

[1119] This invention is a system that allows patients to input symptom and emotional information before visiting a hospital, analyzes that information, searches for appropriate medical institutions and medical professionals, and even makes recommendations and reservations. This system includes the following components and processes.

[1120] System Configuration

[1121] 1. User Device

[1122] A device where patients enter information and receive results, such as a smartphone or tablet.

[1123] 2. Server

[1124] A central system that analyzes the entered information and searches a database for appropriate medical institutions and medical professionals. It uses a cloud-based server (e.g., AWS, Google Cloud).

[1125] 3. Sentiment Analysis Engine

[1126] A system that analyzes emotions from patients' tone of voice and text, utilizing Microsoft Azure Cognitive Services and IBM Watson.

[1127] 4. Generative AI Models

[1128] An artificial intelligence model for generating analysis results of input information. Uses OpenAI's GPT-4, etc.

[1129] 5. Database

[1130] A system that manages data on medical institutions, medical professionals, and patients, using database management systems such as MySQL and PostgreSQL.

[1131] Program processing explanation

[1132] User terminal

[1133] The user (patient) launches the smartphone app and inputs symptom information and emotional information.

[1134] Using an emotion analysis engine, emotional information is collected through the user's tone of voice and text analysis.

[1135] The collected information is sent to a server.

[1136] server

[1137] The server analyzes the received symptom and emotion information using a generative AI model.

[1138] Search for appropriate medical departments, medical institutions, and medical professionals from medical databases.

[1139] Search results include treatment methods at each medical institution, estimated medical costs, and sentiment analysis results.

[1140] Information on multiple medical institutions and medical professionals is sent to the user terminal.

[1141] User terminal

[1142] The search results are presented to the user and an appointment is confirmed at the selected medical institution.

[1143] The confirmed reservation information is sent to the medical institution via the server.

[1144] Data Management

[1145] Patient medical data is stored electronically on a server.

[1146] When a user visits another medical institution, relevant medical data is shared with the new medical institution.

[1147] Specific examples

[1148] Example 1: Common cold symptoms

[1149] The user launches the app and types, "I have a bad cough and a sore throat."

[1150] The emotion analysis engine analyzes the user's emotions from the text they input and their tone of voice, and determines that their anxiety level is high.

[1151] The terminal transmits the input information and emotion information to the server.

[1152] The server analyzes the information and lists multiple medical institutions that primarily provide internal medicine care.

[1153] The device displays a list of medical institutions, estimated treatment costs, and sentiment analysis results.

[1154] The user selects the nearest clinic and confirms the appointment.

[1155] The server confirms the appointment and shares the data with the clinic.

[1156] When a user visits the clinic, they receive prompt medical treatment based on the information shared in advance.

[1157] Example 2: Treating long-term back pain

[1158] Users use the app to enter their past medical history and current symptoms.

[1159] The sentiment analysis engine recognizes from the input data that the user's stress level is high.

[1160] The terminal sends the information to the server.

[1161] The server analyzes the information and suggests multiple medical institutions that specialize in treating lower back pain.

[1162] The device displays a list of medical institutions, treatment costs, past reviews, and sentiment analysis results.

[1163] The user selects a specific orthopedic surgeon and confirms the appointment.

[1164] The server shares past medical data and emotional information with the orthopedic system.

[1165] The user visits an orthopedic clinic, and the new doctor creates an appropriate treatment plan based on past treatment data and emotional data.

[1166] Prompt Sentence Examples

[1167] When a user enters "I have a headache and feel nauseous," analyze the emotion they are feeling and generate a prompt to suggest the best medical institution that can also provide stress management.

[1168] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1169] Step 1:

[1170] The user launches the smartphone app.

[1171] Input: The user launches the app.

[1172] Output: The app's home screen is displayed.

[1173] Specific behavior: When a user taps the app icon on their smartphone, the app launches and the login screen appears.

[1174] Step 2:

[1175] The user inputs symptom information and emotional information.

[1176] Input: The user enters the symptoms they are experiencing in a text box (e.g., "I have a headache" or "I have a bad cough") and records their current feelings via voice as emotional information.

[1177] Output: Collected symptom and emotion information.

[1178] Specific operation: Users enter their symptoms in text in the app's input form, press the voice input button, and speak their feelings to collect voice data.

[1179] Step 3:

[1180] The device sends the collected information to an emotion analysis engine.

[1181] Input: Text data for symptom information and audio data for emotion information.

[1182] Output: Analysis results from the sentiment analysis engine.

[1183] Specific operation: The app sends the collected symptom information and voice data to the server, and the server passes the data to the emotion analysis engine for processing.

[1184] Step 4:

[1185] The emotion analysis engine analyzes the user's emotions and sends the results to the server.

[1186] Input: Audio data.

[1187] Output: Sentiment analysis result (e.g. "High Anxiety Level").

[1188] What it does: The emotion analysis engine processes the audio data, identifies the emotional state (e.g., "high anxiety"), and sends the results back to the server.

[1189] Step 5:

[1190] The server analyzes symptom information and emotional information using the generated AI model.

[1191] Input: Symptom information, sentiment analysis results.

[1192] Output: A list of appropriate medical institutions and medical professionals.

[1193] How it works: The server uses a generative AI model (e.g., OpenAI GPT-4) to analyze symptom and emotion data, and then searches and selects the most suitable medical institution and medical professional from a medical database.

[1194] Step 6:

[1195] The server sends a list of selected medical institutions and medical professionals to the terminal.

[1196] Input: Parsed result by the server.

[1197] Output: A list of medical institutions and medical professionals on the user's device.

[1198] Specific operation: The server sends the analysis results to the user's device, and the app displays the results to the user in list form.

[1199] Step 7:

[1200] The user selects a medical institution or medical professional and confirms the appointment.

[1201] Input: List of medical institutions, user selection.

[1202] Output: Confirmation of reservation, reservation details.

[1203] Specific operation: When the user selects the desired medical institution from the list and presses the reservation button, the reservation confirmation process begins and the reservation information is sent to the server.

[1204] Step 8:

[1205] The server confirms the appointment with the selected medical institution or medical professional and sends the appointment information to the medical institution's system.

[1206] Input: User selection information.

[1207] Output: A confirmation of the appointment is sent to the medical institution.

[1208] Specific operation: The server sends the reservation information to the relevant medical institution's reservation system, confirms the reservation, and sends a reservation confirmation notification to the user.

[1209] Step 9:

[1210] The server stores patient medical data as electronic data and shares the data between medical institutions.

[1211] Input: Medical data, medical results.

[1212] Output: Recorded in a medical database and saved in a format that can be used by other medical institutions.

[1213] Specific operations: The server stores the patient's medical information and medical results as electronic data and takes steps to share it with other medical institutions as necessary.

[1214] 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.

[1215] 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.

[1216] 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.

[1217] [Third embodiment]

[1218] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1219] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1220] 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).

[1221] 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.

[1222] 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.

[1223] 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).

[1224] 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.

[1225] 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.

[1226] 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.

[1227] 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.

[1228] 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.

[1229] 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."

[1230] The present invention is a system for enabling patients to receive medical services efficiently, and embodiments thereof will be described in detail below.

[1231] 1. System Overview

[1232] This system allows patients to input symptom information from devices such as smartphones or computers, and based on that information, searches for and suggests appropriate medical institutions and medical professionals, and then completes the process of booking an appointment.It also has the ability to store patients' medical data electronically and share it between different medical institutions.

[1233] 2. System Configuration

[1234] The system mainly consists of the following components:

[1235] User terminal: A device where the patient enters information and receives results.

[1236] Server: A central system that analyzes the input information and generates appropriate suggestions.

[1237] Medical institution system: A system that receives medical data and uses it for medical treatment.

[1238] 3. Program Processing

[1239] Processing on the user terminal

[1240] The user launches the dedicated app and enters symptoms and basic information, which is then sent to a server running the AI.

[1241] Processing on the server

[1242] The server analyzes the received information and searches the database for appropriate medical institutions and medical professionals. The search results include information on multiple medical institutions and medical professionals, including each institution's treatment methods and estimated medical costs.

[1243] Displaying the proposal results on the user's device

[1244] The server sends information about the selected medical institutions and medical professionals to the user's device, where the user can check the information and select the medical institution of their choice.

[1245] Confirmation of reservation

[1246] Once the user selects the desired medical institution, the reservation confirmation process begins. The reservation information is sent via the server to the medical institution's reservation system, and the reservation is confirmed. A reservation completion notice is sent to the user's terminal, and confirmation information is displayed.

[1247] Medical data storage and sharing

[1248] The patient's initial consultation information and subsequent medical information are stored as electronic data on a server. When the user visits another medical institution, the relevant medical data is shared with the new institution, making medical treatment more efficient.

[1249] Specific examples

[1250] Example 1: Common cold symptoms

[1251] The user launches the app and types, "I have a bad cough and a sore throat."

[1252] The terminal transmits the input information to the server.

[1253] The server analyzes the information and lists multiple medical institutions that primarily provide internal medicine care.

[1254] The device displays a list of medical institutions and estimated treatment costs.

[1255] The user selects the nearest clinic and confirms the appointment.

[1256] The server confirms the appointment and shares the data with the clinic.

[1257] When a user visits the clinic, they receive prompt medical treatment based on the information shared in advance.

[1258] Example 2: Treating long-term back pain

[1259] Users use the app to enter their past medical history and current symptoms.

[1260] The terminal sends the information to the server.

[1261] The server analyzes the information and suggests multiple medical institutions that specialize in treating lower back pain.

[1262] The device displays a list of medical institutions, treatment costs, and past reviews.

[1263] The user selects a specific orthopedic surgeon and confirms the appointment.

[1264] The server shares past medical data with the orthopedic system.

[1265] The user visits an orthopedic clinic, and the new doctor creates an appropriate treatment plan based on past treatment data.

[1266] The above is an embodiment of the present invention, which constitutes a system that reduces the burden on patients and provides efficient medical services.

[1267] The processing flow will be explained below.

[1268] Step 1:

[1269] The user launches an application on their smartphone or computer.

[1270] Step 2:

[1271] The device uses generative AI to prompt the user with questions such as, "What are your symptoms today?"

[1272] Step 3:

[1273] The user types, "I have a headache."

[1274] Step 4:

[1275] The device then asks, "How long have you been experiencing these symptoms?"

[1276] Step 5:

[1277] The user responds, "From three days ago."

[1278] Step 6:

[1279] The terminal collects and formats the information entered.

[1280] Step 7:

[1281] The device sends the collected information to the server.

[1282] Step 8:

[1283] The server analyzes the received symptom information.

[1284] Step 9:

[1285] The server identifies the corresponding medical department (e.g., internal medicine, neurology, etc.) and searches the database for medical institutions and medical professionals in that department.

[1286] Step 10:

[1287] The server collects information such as each medical institution's areas of expertise, past treatment results, patient reviews, and estimated medical costs.

[1288] Step 11:

[1289] Based on the data collected by the server, several of the most appropriate medical institutions and medical professionals are selected.

[1290] Step 12:

[1291] The server sends information about the selected medical institution and medical personnel to the terminal.

[1292] Step 13:

[1293] The terminal displays the information received from the server to the user.

[1294] Step 14:

[1295] The user reviews a list of suggested medical institutions and practitioners.

[1296] Step 15:

[1297] The user selects the desired medical institution or medical professional. For example, select "Clinic B."

[1298] Step 16:

[1299] The terminal displays a reservation button to the user.

[1300] Step 17:

[1301] The user taps the reservation button.

[1302] Step 18:

[1303] The terminal transmits the reservation information to the server.

[1304] Step 19:

[1305] The server communicates with the medical institution's reservation system and confirms the reservation.

[1306] Step 20:

[1307] The server sends a reservation completion notification to the terminal.

[1308] Step 21:

[1309] The terminal displays the reservation confirmation information to the user.

[1310] Step 22:

[1311] The user's initial consultation information and subsequent medical information are stored as electronic data on the server.

[1312] Step 23:

[1313] The user makes an appointment and visits the medical institution.

[1314] Step 24:

[1315] The medical institution's system retrieves the electronic data from the server.

[1316] Step 25:

[1317] The server sends the user's symptom information and basic information to the medical institution.

[1318] Step 26:

[1319] Medical institutions provide medical treatment based on information shared in advance.

[1320] Step 27:

[1321] After a diagnosis or treatment is performed, new medical data is updated from the medical institution's system to the server.

[1322] Step 28:

[1323] The server updates the user's personal data with the new medical data.

[1324] Step 29:

[1325] When the user visits another medical institution, the server shares the relevant medical data with the new medical institution.

[1326] Step 30:

[1327] The new medical institution receives the data from the server and provides efficient medical care.

[1328] Example 1

[1329] 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."

[1330] In conventional medical systems, when a patient visits multiple medical institutions, they must re-enter their initial consultation information and medical treatment information at each institution, which is time-consuming and can lead to information inconsistencies and delays. It also makes it difficult to select the appropriate medical institution, and confirmation and adjustments when making appointments are cumbersome. These issues reduce the efficiency of medical treatment for patients and make it difficult to provide prompt medical care.

[1331] 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.

[1332] In this invention, the server includes a terminal for patients to input symptom information, a means for transmitting the input symptom information to the server, and a means for the server to analyze the received information and search for appropriate medical institutions and medical professionals using a generative AI model. This allows patients to quickly and accurately input symptom information and select an appropriate medical institution. The server also includes a means for transmitting and displaying information on multiple searched medical institutions and medical professionals to the terminal, a means for confirming an appointment with the medical institution or medical professional selected by the patient, a means for sending a reservation completion notification to the terminal, and a means for storing the patient's medical data as electronic data and sharing it among medical institutions. This allows information entered by a patient once to be shared among multiple medical institutions, eliminating the need for time-consuming information input and reducing the complexity of appointment confirmation and adjustment, thereby significantly improving medical treatment efficiency.

[1333] A "terminal" is a device used by a patient to input symptom information, and includes smartphones, personal computers, etc.

[1334] The "server" is a central system that receives symptom information sent by patients, analyzes it, and searches for appropriate medical institutions and medical professionals.

[1335] A "generative AI model" is an artificial intelligence model used to analyze received information and suggest appropriate medical institutions and medical professionals, including, for example, advanced natural language processing models such as GPT-3.

[1336] "Symptom information" refers to specific information about a patient's health condition or illness that the patient enters into the device. This includes specific symptoms such as "I have a bad cough and a sore throat."

[1337] A "medical institution" is a facility where patients can receive medical treatment, and includes hospitals, clinics, and medical offices.

[1338] "Healthcare professionals" are professionals who are qualified to perform medical procedures on patients at medical institutions, and include doctors, nurses, pharmacists, etc.

[1339] A "reservation" is the act of a patient specifying a date and time to receive medical treatment from a medical institution or medical professional of their choice in advance.

[1340] "Electronic data" refers to data used to store and manage patient symptom information, medical records, and other information in digital format.

[1341] "Sharing" refers to electronic data being transmitted between different medical institutions and being made available to each other.

[1342] "Expected medical expenses" are a guideline for predicted medical expenses based on symptom information and medical treatment details.

[1343] This invention is a system that enables patients to receive medical services efficiently. This system allows patients to input symptom information from devices such as smartphones or computers, and based on that information, the system searches for and suggests appropriate medical institutions and medical professionals, and then completes the process of booking appointments. It also has the function of storing patients' medical data electronically and sharing the data between different medical institutions.

[1344] 1. System Overview

[1345] The user launches the dedicated app and enters symptoms and basic information. This input information is sent to a server running the generative AI model. The server analyzes the received information and searches a database for appropriate medical institutions and medical professionals. The server then sends the search results to the user's device, allowing the user to review the information and select the medical institution of their choice. Finally, the server confirms the reservation and sends a reservation completion notification to the user's device. The patient's medical data is also stored as electronic data and shared between medical institutions.

[1346] 2. Hardware and Software Used

[1347] User device: The device where the user enters information and receives the results (smartphone, PC, etc.)

[1348] Server: A central system that analyzes input information and generates appropriate suggestions. An environment for running Python or Java programs.

[1349] Database management system: A system that stores and manages patient medical data (e.g., MySQL)

[1350] Generative AI model: An artificial intelligence model (such as GPT-3) that analyzes the received information and suggests appropriate medical institutions and medical professionals.

[1351] 3. Specific Examples

[1352] A specific example of use is shown below.

[1353] Example 1: Common cold symptoms

[1354] The user launches the app and types, "I have a bad cough and a sore throat."

[1355] The terminal transmits the input information to the server.

[1356] The server analyzes the information and lists multiple medical institutions that primarily provide internal medicine care.

[1357] The device displays a list of medical institutions and estimated treatment costs.

[1358] The user selects the nearest clinic and confirms the appointment.

[1359] The server confirms the appointment and shares the data with the clinic.

[1360] When a user visits the clinic, they receive prompt medical treatment based on the information shared in advance.

[1361] Example 2: Treating long-term back pain

[1362] Users use the app to enter their past medical history and current symptoms.

[1363] The terminal sends the information to the server.

[1364] The server analyzes the information and suggests multiple medical institutions that specialize in treating lower back pain.

[1365] The device displays a list of medical institutions, treatment costs, and past reviews.

[1366] The user selects a specific orthopedic surgeon and confirms the appointment.

[1367] The server shares past medical data with the orthopedic system.

[1368] The user visits an orthopedic clinic, and the new doctor creates an appropriate treatment plan based on past treatment data.

[1369] Prompt Sentence Examples

[1370] Below is an example of a prompt sentence to input to the generative AI model.

[1371] Symptom information that makes diagnosis easier: "I have a bad cough and a sore throat. I'm looking for an internist."

[1372] Generated medical institution suggestions: "The following internal medicine clinics are nearby: XX Clinic, △△ Clinic, and □□ Hospital. The treatment costs for each are..."

[1373] Appointment confirmation process: "Please make an appointment at XX Clinic." -> "The appointment has been completed."

[1374] The above is an embodiment of the present invention, which constitutes a system that reduces the burden on patients and provides efficient medical services.

[1375] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1376] Step 1: Enter user information

[1377] The user launches the app and inputs their symptoms and basic information, including specific symptoms such as "severe cough and sore throat." This generates data on the user's health condition.

[1378] Input: Symptom information (e.g., "I have a bad cough and a sore throat")

[1379] Output: Symptom information data entered

[1380] Step 2: Sending information to the server

[1381] The device encrypts the input symptom information and sends it to the server. At this time, encryption protocols such as SSL / TLS are used to ensure the security of the communication. The transmitted data is then received by the server.

[1382] Input: Symptom information data entered

[1383] Output: Data sent to the server

[1384] Step 3: Analyze the received information

[1385] The server analyzes the received symptom information using Python or Java scripts. A generative AI model (e.g., GPT-3) is used to calculate the data and suggest appropriate medical institutions and medical professionals. Specifically, a prompt is input into the generative AI model, and the analysis results are obtained.

[1386] Input: Symptom information data received by the server

[1387] Output: Proposed results data from the generative AI model

[1388] Step 4: Search and select a medical institution

[1389] The server searches and selects appropriate medical institutions and medical professionals from the database based on the results of the generative AI model. A list is generated that includes each medical institution's treatment method, estimated medical costs, address, etc. This is done using SQL queries.

[1390] Input: Proposal result data of generative AI model

[1391] Output: List of medical institutions

[1392] Step 5: Submit search results

[1393] The server packages the searched and selected information on medical institutions and medical professionals in JSON format and sends it to the user's device, where the user receives detailed information on selectable medical institutions.

[1394] Input: List of medical institutions

[1395] Output: Data sent to the terminal

[1396] Step 6: Review and select the suggestions

[1397] The user can then view information about medical institutions and medical professionals based on the search results on their device, including the name, address, treatment details, and reviews of each medical institution. The user can then select the medical institution of their choice.

[1398] Input: Medical institution information displayed on the terminal

[1399] Output: User selection data

[1400] Step 7: Submit your reservation information

[1401] The terminal sends the reservation information of the medical institution selected by the user to the server. The information is sent to the server in JSON format. The server receives the information and processes it to confirm the reservation at the selected medical institution.

[1402] Input: User selected data

[1403] Output: Data scheduled to be sent to the server

[1404] Step 8: Send a reservation completion notification

[1405] The server checks the reservation information database to confirm that the reservation has been confirmed, and sends a reservation completion notice to the user's terminal, allowing the user to confirm that their reservation has been confirmed.

[1406] Input: Confirmed reservation data

[1407] Output: Reservation completion notification data to the terminal

[1408] Step 9: Storing and sharing medical data

[1409] The server stores the patient's initial consultation information and subsequent medical information as electronic data. The data is managed using a database management system (e.g., MySQL). When a user receives treatment at another medical institution, the relevant medical data is encrypted and sent to the new medical institution.

[1410] Input: Patient medical information

[1411] Output: Stored electronic data and shared data

[1412] (Application example 1)

[1413] 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."

[1414] Conventional medical systems lack an integrated means for patients to effectively select appropriate medical institutions and medical professionals and quickly confirm appointments. Furthermore, it is difficult to share medical data between different medical institutions, resulting in inefficient medical treatment. Furthermore, security services require real-time information analysis and communication methods to detect suspicious individuals and respond quickly to emergencies. A system that can solve these issues in an integrated manner is needed.

[1415] 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.

[1416] In this invention, the server includes means for having a patient input symptom information before visiting a hospital, means for analyzing the input symptom information and searching for an appropriate medical institution or medical professional, means for presenting information on multiple medical institutions and medical professionals based on the analysis results and having the patient select one, means for confirming an appointment with the selected medical institution or medical professional, means for saving the patient's medical data as electronic data and sharing the data between medical institutions, means for having a user input information on abnormal behavior or suspicious individuals and analyzing the information to generate appropriate suggestions, and means for contacting a security team or the police based on the analysis results. This not only streamlines the patient's medical treatment process and makes it easier to share data between medical institutions, but also enables quick and appropriate responses in security services.

[1417] definition statement

[1418] "Means for patients to input symptom information before visiting the hospital" refers to a function that allows patients to input their symptoms and basic information using their own devices (smartphones, computers, etc.).

[1419] "Means of analyzing input symptom information and searching for appropriate medical institutions and medical professionals" is a function that identifies and suggests the most appropriate medical institutions and medical professionals from a database based on the symptom information input by the patient.

[1420] "Means of presenting information on multiple medical institutions and medical professionals based on the analysis results and allowing the patient to make a selection" is a function that provides the patient with multiple appropriate options for medical institutions and medical professionals based on the analyzed information, allowing the patient to make the selection themselves.

[1421] "Means for confirming appointments with selected medical institutions and healthcare professionals" refers to a function that allows a patient to securely confirm appointments with the medical institutions and healthcare professionals selected by the patient within the system and send a confirmation notification.

[1422] "Means for storing patient medical data as electronic data and sharing the data between medical institutions" refers to a function that allows patients' medical information to be recorded electronically and the data to be shared with different medical institutions as needed.

[1423] "A means for having users input information about abnormal behavior or suspicious individuals, analyzing that information, and generating appropriate suggestions" is a function in security services that allows users to report suspicious activity or abnormal behavior, analyze that information, and suggest optimal countermeasures.

[1424] "Means of contacting security teams or police based on analysis results" is a function that allows for quick contact with specialized security teams or police as necessary based on the analyzed information.

[1425] MODE FOR CARRYING OUT THE INVENTION

[1426] This invention is an integrated system that enables patients to receive medical services efficiently and also enables prompt response in security. This system is designed for use in Japanese medical institutions, but can also be applied in other countries.

[1427] Overall system configuration

[1428] The system mainly consists of the following components:

[1429] User terminal: A device, such as a smartphone or smart glasses, through which the patient or user enters information and receives results.

[1430] Server: A central system that analyzes input information and generates appropriate suggestions, using generative AI models, image recognition, natural language processing, etc.

[1431] Medical institution system: A system that receives medical data and uses it for medical treatment. It mainly works in conjunction with electronic medical record systems and reservation systems.

[1432] Program Generation

[1433] The system program is generated as follows:

[1434] Processing on the user terminal

[1435] The user launches a dedicated application using a smartphone or smart glasses, and inputs information about symptoms and suspicious individuals into the application. This information is then sent to the server.

[1436] Processing on the server

[1437] The server has the following features:

[1438] Symptom analysis: Analyzes the input symptom information and suggests appropriate medical institutions and medical professionals. A generative AI model is used for the analysis.

[1439] Image Recognition: Analyzes images of suspicious individuals and detects abnormalities and suspicious movements. Uses OpenCV.

[1440] Natural Language Processing: Analyzes input text information and generates appropriate suggestions. Uses the transformers library.

[1441] Recommendation generation: Based on the analysis results, appropriate medical institutions and action instructions are suggested to the user.

[1442] Specific examples

[1443] Examples of medical services

[1444] 1. The user launches the app and types, "I have a bad cough and a sore throat."

[1445] 2. The terminal sends the entered information to the server.

[1446] 3. The server analyzes the information and lists multiple medical institutions that primarily provide internal medicine care.

[1447] 4. The device will display a list of medical institutions and estimated treatment costs.

[1448] 5. The user selects the nearest clinic and confirms the appointment.

[1449] 6. The server confirms the appointment and shares the data with the clinic.

[1450] 7. When the user visits the clinic, they receive prompt medical treatment based on the information shared in advance.

[1451] Security Service Examples

[1452] 1. The user uses smart glasses to capture a photo of a suspicious person and sends the data to the server via the app.

[1453] 2. The server analyzes the image and, if it determines that the person is suspicious, it instructs the user to contact the police immediately.

[1454] 3. Based on this, users can take prompt action and implement appropriate security measures.

[1455] Prompt Sentence Examples

[1456] "Enter an image and generate a suspicious person identification and necessary action instructions."

[1457] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1458] Program processing flow

[1459] The process by which users enter their medical information and make appointments

[1460] Step 1:

[1461] User enters symptom information

[1462] Input: The user launches a dedicated application on a smartphone or smart glasses and enters symptom information such as "I have a bad cough and a sore throat."

[1463] Processing: The application temporarily stores the entered information and converts it into the required format.

[1464] Output: Formatted symptom information data.

[1465] Step 2:

[1466] The device sends the information to the server

[1467] Input: Formatted symptom information data.

[1468] Processing: The device sends data to the server over the network using an HTTP POST request.

[1469] Output: The data sent to the server.

[1470] Step 3:

[1471] The server analyzes the symptom information

[1472] Input: Submitted symptom information data.

[1473] Processing: Generative AI models are used to analyze the symptom information provided, using natural language processing libraries to identify symptom patterns.

[1474] Output: A list of appropriate medical institutions and medical professionals.

[1475] Step 4:

[1476] The server sends the proposal results to the user's device.

[1477] Input: A list of appropriate medical institutions and practitioners.

[1478] Processing: The server sends the analysis results to the user's device, again using an HTTP POST request.

[1479] Output: A list of medical institutions and estimated treatment costs displayed on the user's terminal.

[1480] Step 5:

[1481] The user selects a medical institution and confirms the appointment

[1482] Input: A list of medical institutions and estimated treatment costs displayed on the user's terminal.

[1483] Processing: The user selects the desired medical institution and taps the reservation button. The device resends the reservation information to the server.

[1484] Output: Reservation information sent to the server.

[1485] Step 6:

[1486] The server confirms the reservation and shares the data with the medical institution.

[1487] Input: Reservation information submitted by the user.

[1488] Processing: The server accesses the reservation system and confirms the reservation. At the same time, it sends the reservation information and the patient's symptom information to the medical institution's system.

[1489] Output: Appointment confirmation notice and medical information stored in the medical institution's system.

[1490] Flow of how users enter security information and take emergency action

[1491] Step 1:

[1492] The user captures and enters the suspicious person information

[1493] Input: The user uses the smart glasses to capture a photo of the suspicious person and input it into the security app.

[1494] Processing: The app temporarily stores the image data.

[1495] Output: Saved image data.

[1496] Step 2:

[1497] The device sends the image data to the server.

[1498] Input: Saved image data.

[1499] Processing: The device sends the image data to the server over the network, again using an HTTP POST request.

[1500] Output: Image data sent to the server.

[1501] Step 3:

[1502] The server analyzes the image data

[1503] Input: The submitted image data.

[1504] Processing: Image data is analyzed using OpenCV. This analysis identifies suspicious individuals and abnormal behavior.

[1505] Output: Analysis results, including whether suspicious activity was detected and recommended actions.

[1506] Step 4:

[1507] The server sends the proposal results to the user's device.

[1508] Input: Analysis results.

[1509] Processing: The server sends the analysis results and instructions to the user's device using an HTTP POST request.

[1510] Output: Instructions to be displayed on the user's device. In the case of a suspicious person, instructions to contact the police, etc.

[1511] Step 5:

[1512] User takes action based on the suggestion

[1513] Input: Response instructions.

[1514] Action: The user follows the instructions on the smart device and takes appropriate action, such as contacting the police. The device collects a log of the action.

[1515] Output: Action log data and emergency response implementation.

[1516] Prompt Sentence Examples

[1517] "Enter an image and generate a suspicious person identification and necessary action instructions."

[1518] 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.

[1519] The present invention is a system that allows patients to input symptom information before visiting a hospital, analyzes that information to search for appropriate medical institutions and medical professionals, and further combines it with an emotion engine that recognizes the user's emotions to make more appropriate medical recommendations. The following describes in detail an embodiment of the system.

[1520] 1. System Overview

[1521] This system allows patients to input symptom and emotional information from devices such as smartphones or PCs, and based on that information, the system searches for and suggests appropriate medical institutions and medical professionals, and then confirms appointments.It also has the function of storing patients' medical data electronically and sharing it between different medical institutions.

[1522] 2. System Configuration

[1523] The system mainly consists of the following components:

[1524] User terminal: A device where patients input information and receive results. It also collects emotional information.

[1525] Server: A central system that analyzes the input information and generates appropriate suggestions.

[1526] Medical institution system: A system that receives medical data and uses it for medical treatment.

[1527] Emotion engine: A system that analyzes the user's emotions and suggests medical treatment priorities and appropriate medical institutions.

[1528] 3. Program Processing

[1529] Processing on the user terminal

[1530] The user launches the app and inputs symptoms and basic information. Along with this input information, the emotion engine collects emotional information through tone of voice and text analysis. The collected information is sent to a server where the generative AI runs.

[1531] Processing on the server

[1532] The server analyzes the received symptom and emotion information and searches the database for the appropriate medical department, medical institution, and medical professional. The search results include information on multiple medical institutions and medical professionals. This information also reflects each institution's treatment method, estimated medical costs, and the analysis of the user's stress level and urgency using an emotion engine.

[1533] Displaying the proposal results on the user's device

[1534] The server sends information about the selected medical institutions and medical professionals to the user's device. The user can then check this information on the device and select the medical institution of their choice. The analysis results of the emotion engine are also displayed.

[1535] Confirmation of reservation

[1536] Once the user selects the desired medical institution, the reservation confirmation process begins. The reservation information is sent via the server to the medical institution's reservation system, and the reservation is confirmed. A reservation completion notice is sent to the user's terminal, and confirmation information is displayed.

[1537] Medical data storage and sharing

[1538] The patient's initial consultation information and subsequent medical information are stored as electronic data on a server. When the user visits another medical institution, the relevant medical data is shared with the new institution, making medical treatment more efficient.

[1539] Specific examples

[1540] Example 1: Common cold symptoms

[1541] The user launches the app and types, "I have a bad cough and a sore throat."

[1542] The emotion engine analyzes emotions from the user's input text and tone of voice and determines that the anxiety level is high.

[1543] The terminal transmits the input information and emotion information to the server.

[1544] The server analyzes the information and lists multiple medical institutions that primarily provide internal medicine care.

[1545] The device displays a list of medical institutions, estimated treatment costs, and sentiment analysis results.

[1546] The user selects the nearest clinic and confirms the appointment.

[1547] The server confirms the appointment and shares the data with the clinic.

[1548] When a user visits the clinic, they receive prompt medical treatment based on the information shared in advance.

[1549] Example 2: Treating long-term back pain

[1550] Users use the app to enter their past medical history and current symptoms.

[1551] The emotion engine recognizes from the input data that the user's stress level is high.

[1552] The terminal sends the information to the server.

[1553] The server analyzes the information and suggests multiple medical institutions that specialize in treating lower back pain.

[1554] The device displays a list of medical institutions, treatment costs, past reviews, and sentiment analysis results.

[1555] The user selects a specific orthopedic surgeon and confirms the appointment.

[1556] The server shares past medical data and emotional information with the orthopedic system.

[1557] The user visits an orthopedic clinic, and the new doctor creates an appropriate treatment plan based on past treatment data and emotional data.

[1558] The above is an embodiment of the present invention, which constitutes a system that reduces the burden on patients and provides efficient and appropriate medical services that take into account their emotional state.

[1559] The processing flow will be explained below.

[1560] Step 1:

[1561] The user launches an application on their smartphone or computer.

[1562] Step 2:

[1563] The device uses generative AI and an emotion engine to prompt the user with questions such as, "What are your symptoms today?"

[1564] Step 3:

[1565] The user types, "I have a headache."

[1566] Step 4:

[1567] The emotion engine analyzes emotions from the user's input text and tone of voice and determines that the anxiety level is high.

[1568] Step 5:

[1569] The device will ask, "How long have you been experiencing these symptoms?"

[1570] Step 6:

[1571] The user responds, "From three days ago."

[1572] Step 7:

[1573] The device collects and formats the entered symptom information and emotion analysis results.

[1574] Step 8:

[1575] The device sends the collected information to the server.

[1576] Step 9:

[1577] The server analyzes the received symptom information and emotion information.

[1578] Step 10:

[1579] The server identifies the corresponding medical department (e.g., internal medicine, neurology, etc.) and searches the database for medical institutions and medical professionals in that department.

[1580] Step 11:

[1581] The server collects each medical institution's areas of expertise, past treatment records, patient reviews, estimated medical costs, and sentiment analysis results.

[1582] Step 12:

[1583] Based on the data collected by the server, several of the most appropriate medical institutions and medical professionals are selected.

[1584] Step 13:

[1585] The server sends information about the selected medical institution and medical personnel to the terminal.

[1586] Step 14:

[1587] The device displays the information received from the server to the user, including the results of emotion analysis.

[1588] Step 15:

[1589] The user reviews a list of suggested medical institutions and practitioners.

[1590] Step 16:

[1591] The user selects the desired medical institution or medical professional. For example, select "Clinic B."

[1592] Step 17:

[1593] The terminal displays a reservation button to the user.

[1594] Step 18:

[1595] The user taps the reservation button.

[1596] Step 19:

[1597] The terminal transmits the reservation information to the server.

[1598] Step 20:

[1599] The server communicates with the medical institution's reservation system and confirms the reservation.

[1600] Step 21:

[1601] The server sends a reservation completion notification to the terminal.

[1602] Step 22:

[1603] The terminal displays the reservation confirmation information to the user.

[1604] Step 23:

[1605] The user's initial consultation information and subsequent medical information are stored as electronic data on the server.

[1606] Step 24:

[1607] The user visits the medical institution for which they have made an appointment.

[1608] Step 25:

[1609] The medical institution's system retrieves electronic data and emotional information from the server.

[1610] Step 26:

[1611] The server transmits the user's symptom information and emotional information to the medical institution.

[1612] Step 27:

[1613] Medical institutions provide medical treatment based on information shared in advance.

[1614] Step 28:

[1615] After a diagnosis or treatment is performed, new medical data is updated from the medical institution's system to the server.

[1616] Step 29:

[1617] The server updates the user's personal data with the new medical data.

[1618] Step 30:

[1619] When the user visits another medical institution, the server shares relevant medical data and emotional information with the new medical institution.

[1620] Step 31:

[1621] The new medical institution receives the data from the server and provides efficient medical care.

[1622] Example 2

[1623] 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."

[1624] The current medical system faces the problem of not providing patients with enough information to select the appropriate medical institution or medical professional when making a medical appointment. Furthermore, medical treatment recommendations do not take into account the patient's emotional state, which can increase anxiety and stress. Furthermore, medical data is not shared enough, hindering efficient medical care delivery between different medical institutions. To solve these issues, appropriate medical treatment recommendations based on the patient's symptom and emotional information, as well as effective management and sharing of medical data, are needed.

[1625] 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.

[1626] In this invention, the server includes a means for having the patient input symptom information and emotional information before visiting the hospital, a means for analyzing the input symptom information and emotional information to search for appropriate medical institutions and medical professionals, and a means for presenting information on multiple medical institutions and medical professionals based on the analysis results and allowing the patient to select one. This makes it possible to propose appropriate medical care that takes into account not only the patient's symptoms but also their emotional information. In addition, by providing a means for storing patient medical data as electronic data and sharing the data between medical institutions, smooth and efficient medical care can be provided.

[1627] "Symptom information" is data that indicates physical discomfort or pain that a patient is experiencing.

[1628] "Emotional information" is data obtained by analyzing a patient's psychological state and emotions.

[1629] A "medical institution" is an organization or facility that provides medical services.

[1630] "Medical professionals" are medical professionals such as doctors and nurses who provide medical care.

[1631] "Analysis" is the process of processing input data and extracting meaningful information.

[1632] "Searching" is the process of finding information from a database based on specific criteria.

[1633] "Presentation" means displaying the analyzed information in a way that is easy for the user to understand.

[1634] "Confirming an appointment" means officially deciding on a date and time for consultation with the selected medical institution or healthcare professional.

[1635] "Electronic data" means information stored in digital form.

[1636] "Sharing" means exchanging and making available data between different medical institutions.

[1637] This system allows patients to input symptom and emotional information before visiting a hospital, analyzes that information, searches for appropriate medical institutions and medical professionals, and confirms appointments, thereby reducing the burden on patients and providing efficient medical services. The system includes a user terminal that runs a dedicated application, a server that performs data analysis, and an emotion engine that processes emotional information.

[1638] Hardware and software used

[1639] User terminal

[1640] Hardware: Smartphones, PCs

[1641] Software: Dedicated application, emotion engine

[1642] The user launches the dedicated app and inputs symptom and emotional information. The emotion engine analyzes the user's tone of voice and input text to collect emotional information. The device then sends the collected information to the server.

[1643] server

[1644] Hardware: Server system

[1645] Software: Database system, analysis software, emotion engine

[1646] The server analyzes the received symptom and emotion information and searches the database for appropriate medical institutions and medical professionals. Based on the analysis results, it generates a list of search results, including treatment methods, estimated medical costs, and the analysis results of the emotion engine.

[1647] Confirmed reservations and data sharing

[1648] Medical Institution System

[1649] The server sends information about the selected medical institutions and medical professionals to the user's terminal, and once the user selects the desired medical institution, the reservation confirmation process begins. The reservation information is sent via the server to the medical institution's reservation system, and the reservation is confirmed. The confirmed reservation information is shared with the medical institution's system. Furthermore, the patient's medical data is stored as electronic data and shared so that it can be used between different medical institutions.

[1650] Specific operation example

[1651] Example 1: Common cold symptoms

[1652] The user launches the app and types, "I have a bad cough and a sore throat."

[1653] The emotion engine analyzes emotions from the user's input text and tone of voice and determines that the anxiety level is high.

[1654] The terminal transmits the input information and emotion information to the server.

[1655] The server analyzes the symptom information and emotional information and lists multiple medical institutions that primarily provide internal medicine care.

[1656] The device displays a list of medical institutions, estimated treatment costs, and sentiment analysis results.

[1657] The user selects the nearest clinic and confirms the appointment.

[1658] The server confirms the appointment and shares the data with the clinic.

[1659] When a user visits the clinic, they receive prompt medical treatment based on the information shared in advance.

[1660] Prompt Sentence Examples

[1661] "The user enters 'severe cough, sore throat' into a dedicated app, and the emotion engine determines that the anxiety level is high. The server then lists medical institutions that primarily provide internal medicine care, and the user confirms an appointment. The server then shares information to ensure prompt treatment when visiting the clinic."

[1662] As described above, the present invention is a system that realizes the provision of efficient and appropriate medical services based on the symptom information and emotional information of patients.

[1663] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1664] Program processing flow

[1665] Step 1: Enter your user information

[1666] The user launches the dedicated app.

[1667] The user enters symptom information (e.g., "bad cough, sore throat").

[1668] The device receives the input symptom information and prompts the user to input emotional information.

[1669] The emotion engine analyzes the user's tone of voice and input text to generate emotion information (e.g., "high anxiety level").

[1670] The symptom information and emotion information collected by the terminal are transmitted to a server.

[1671] Input: Symptom information (text), emotion information (analysis results).

[1672] Output: Symptom and emotion information sent to the server.

[1673] Step 2: Information analysis

[1674] The server analyzes the received symptom information and emotion information.

[1675] The server determines the predicted medical specialty based on the symptom information (e.g., "internal medicine").

[1676] The server evaluates the user's level of urgency based on emotional information (e.g., "high anxiety level").

[1677] The server searches the database for appropriate medical institutions and medical professionals.

[1678] The server lists the search results and generates information including treatment methods, estimated medical costs, and sentiment analysis results.

[1679] Input: Symptom information, emotion information.

[1680] Output: List of medical institutions, treatment methods, medical costs, and sentiment analysis results.

[1681] Step 3: Viewing the Suggestion Results

[1682] The server sends the generated list of medical institutions to the user terminal.

[1683] The device displays to the user a list of medical institutions, estimated treatment costs, and sentiment analysis results.

[1684] The user selects the desired medical institution from the displayed list.

[1685] Input: List of medical institutions, treatment methods, medical costs, and sentiment analysis results.

[1686] Output: Display to user, user selection.

[1687] Step 4: Confirm your reservation

[1688] The terminal receives the user's selection and transmits information about the selected medical institution to the server.

[1689] The server sends the reservation information to the medical institution's reservation system and confirms the reservation.

[1690] The server receives the reservation confirmation notice and sends it to the user terminal.

[1691] The terminal displays a reservation completion notice to the user.

[1692] Input: User selection information, medical institution reservation system.

[1693] Output: Reservation confirmation notice, user notification.

[1694] Step 5: Managing and sharing medical data

[1695] The server stores the user's first consultation information as electronic data.

[1696] The server shares necessary medical data between different medical institutions.

[1697] Medical institutions provide medical treatment based on the shared data.

[1698] Input: Initial consultation information (electronic data format), sharing request.

[1699] Output: Electronic data storage, data sharing with different medical institutions.

[1700] The above is the specific processing flow of the program in the present invention. This system makes it possible to provide efficient and appropriate medical services based on the symptom information and emotional information of the patient.

[1701] (Application example 2)

[1702] 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."

[1703] In conventional medical systems, even if patients input symptom information before visiting a hospital, it is difficult to select the appropriate medical institution or medical professional based on that information alone. Furthermore, since medical treatment suggestions do not take into account the patient's emotional state, it is difficult to provide the optimal medical service for the patient. Furthermore, data is not shared efficiently between medical institutions, which makes it difficult for patients to receive medical treatment smoothly.

[1704] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1705] In this invention, the server includes: means for having the patient input symptom information before visiting the hospital; means for analyzing the input symptom information and emotional information to search for appropriate medical institutions and medical professionals; means for presenting information on multiple medical institutions and medical professionals based on the analysis results and allowing the patient to select one; means for confirming an appointment with the selected medical institution or medical professional; means for saving the patient's medical data as electronic data and sharing the data between medical institutions; means for generating analysis results using a generative AI model and proposing information on multiple medical institutions; means for presenting estimated medical costs based on the input symptom information and emotional information; and means for transmitting the patient's initial visit information and emotional information to the systems of each medical institution. This enables appropriate medical treatment suggestions that take the patient's emotional state into consideration and enables efficient data sharing between medical institutions.

[1706] "Symptom information" is detailed data about the patient's perceived poor health and illness.

[1707] "Emotional information" is data that indicates a patient's psychological or emotional state. Specific techniques are used to analyze emotions and assess levels of stress, anxiety, etc.

[1708] "Medical institution" refers to a facility that provides medical services, such as a hospital, medical office, or clinic.

[1709] "Healthcare professionals" are professionals who provide medical support, such as doctors, nurses, and medical technicians.

[1710] A "generative AI model" is an information generation technology that uses artificial intelligence, and specifically refers to algorithms that perform natural language processing and data analysis.

[1711] "Data sharing" is the process of efficiently exchanging electronic data between different medical institutions, making the information necessary for medical treatment mutually available.

[1712] "Confirming a reservation" means officially deciding to make a medical appointment with the medical institution or medical professional selected by the patient.

[1713] "Analysis results" refer to the results derived by generative AI models or other analysis engines based on the input symptom information and emotional information.

[1714] A "system" is a collection of processes and technologies in which the above-mentioned means work together.

[1715] "Proposals using generative AI models" refers to the process of analyzing a patient's symptom and emotional information to determine the most appropriate medical institution and treatment method.

[1716] This invention is a system that allows patients to input symptom and emotional information before visiting a hospital, analyzes that information, searches for appropriate medical institutions and medical professionals, and even makes recommendations and reservations. This system includes the following components and processes.

[1717] System Configuration

[1718] 1. User Device

[1719] A device where patients enter information and receive results, such as a smartphone or tablet.

[1720] 2. Server

[1721] A central system that analyzes the entered information and searches a database for appropriate medical institutions and medical professionals. It uses a cloud-based server (e.g., AWS, Google Cloud).

[1722] 3. Sentiment Analysis Engine

[1723] A system that analyzes emotions from patients' tone of voice and text, utilizing Microsoft Azure Cognitive Services and IBM Watson.

[1724] 4. Generative AI Models

[1725] An artificial intelligence model for generating analysis results of input information. Uses OpenAI's GPT-4, etc.

[1726] 5. Database

[1727] A system that manages data on medical institutions, medical professionals, and patients, using database management systems such as MySQL and PostgreSQL.

[1728] Program processing explanation

[1729] User terminal

[1730] The user (patient) launches the smartphone app and inputs symptom information and emotional information.

[1731] Using an emotion analysis engine, emotional information is collected through the user's tone of voice and text analysis.

[1732] The collected information is sent to a server.

[1733] server

[1734] The server analyzes the received symptom and emotion information using a generative AI model.

[1735] Search for appropriate medical departments, medical institutions, and medical professionals from medical databases.

[1736] Search results include treatment methods at each medical institution, estimated medical costs, and sentiment analysis results.

[1737] Information on multiple medical institutions and medical professionals is sent to the user terminal.

[1738] User terminal

[1739] The search results are presented to the user and an appointment is confirmed at the selected medical institution.

[1740] The confirmed reservation information is sent to the medical institution via the server.

[1741] Data Management

[1742] Patient medical data is stored electronically on a server.

[1743] When a user visits another medical institution, relevant medical data is shared with the new medical institution.

[1744] Specific examples

[1745] Example 1: Common cold symptoms

[1746] The user launches the app and types, "I have a bad cough and a sore throat."

[1747] The emotion analysis engine analyzes the user's emotions from the text they input and their tone of voice, and determines that their anxiety level is high.

[1748] The terminal transmits the input information and emotion information to the server.

[1749] The server analyzes the information and lists multiple medical institutions that primarily provide internal medicine care.

[1750] The device displays a list of medical institutions, estimated treatment costs, and sentiment analysis results.

[1751] The user selects the nearest clinic and confirms the appointment.

[1752] The server confirms the appointment and shares the data with the clinic.

[1753] When a user visits the clinic, they receive prompt medical treatment based on the information shared in advance.

[1754] Example 2: Treating long-term back pain

[1755] Users use the app to enter their past medical history and current symptoms.

[1756] The sentiment analysis engine recognizes from the input data that the user's stress level is high.

[1757] The terminal sends the information to the server.

[1758] The server analyzes the information and suggests multiple medical institutions that specialize in treating lower back pain.

[1759] The device displays a list of medical institutions, treatment costs, past reviews, and sentiment analysis results.

[1760] The user selects a specific orthopedic surgeon and confirms the appointment.

[1761] The server shares past medical data and emotional information with the orthopedic system.

[1762] The user visits an orthopedic clinic, and the new doctor creates an appropriate treatment plan based on past treatment data and emotional data.

[1763] Prompt Sentence Examples

[1764] When a user enters "I have a headache and feel nauseous," analyze the emotion they are feeling and generate a prompt to suggest the best medical institution that can also provide stress management.

[1765] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1766] Step 1:

[1767] The user launches the smartphone app.

[1768] Input: The user launches the app.

[1769] Output: The app's home screen is displayed.

[1770] Specific behavior: When a user taps the app icon on their smartphone, the app launches and the login screen appears.

[1771] Step 2:

[1772] The user inputs symptom information and emotional information.

[1773] Input: The user enters the symptoms they are experiencing in a text box (e.g., "I have a headache" or "I have a bad cough") and records their current feelings via voice as emotional information.

[1774] Output: Collected symptom and emotion information.

[1775] Specific operation: Users enter their symptoms in text in the app's input form, press the voice input button, and speak their feelings to collect voice data.

[1776] Step 3:

[1777] The device sends the collected information to an emotion analysis engine.

[1778] Input: Text data for symptom information and audio data for emotion information.

[1779] Output: Analysis results from the sentiment analysis engine.

[1780] Specific operation: The app sends the collected symptom information and voice data to the server, and the server passes the data to the emotion analysis engine for processing.

[1781] Step 4:

[1782] The emotion analysis engine analyzes the user's emotions and sends the results to the server.

[1783] Input: Audio data.

[1784] Output: Sentiment analysis result (e.g. "High Anxiety Level").

[1785] What it does: The emotion analysis engine processes the audio data, identifies the emotional state (e.g., "high anxiety"), and sends the results back to the server.

[1786] Step 5:

[1787] The server analyzes symptom information and emotional information using the generated AI model.

[1788] Input: Symptom information, sentiment analysis results.

[1789] Output: A list of appropriate medical institutions and medical professionals.

[1790] How it works: The server uses a generative AI model (e.g., OpenAI GPT-4) to analyze symptom and emotion data, and then searches and selects the most suitable medical institution and medical professional from a medical database.

[1791] Step 6:

[1792] The server sends a list of selected medical institutions and medical professionals to the terminal.

[1793] Input: Parsed result by the server.

[1794] Output: A list of medical institutions and medical professionals on the user's device.

[1795] Specific operation: The server sends the analysis results to the user's device, and the app displays the results to the user in list form.

[1796] Step 7:

[1797] The user selects a medical institution or medical professional and confirms the appointment.

[1798] Input: List of medical institutions, user selection.

[1799] Output: Confirmation of reservation, reservation details.

[1800] Specific operation: When the user selects the desired medical institution from the list and presses the reservation button, the reservation confirmation process begins and the reservation information is sent to the server.

[1801] Step 8:

[1802] The server confirms the appointment with the selected medical institution or medical professional and sends the appointment information to the medical institution's system.

[1803] Input: User selection information.

[1804] Output: A confirmation of the appointment is sent to the medical institution.

[1805] Specific operation: The server sends the reservation information to the relevant medical institution's reservation system, confirms the reservation, and sends a reservation confirmation notification to the user.

[1806] Step 9:

[1807] The server stores patient medical data as electronic data and shares the data between medical institutions.

[1808] Input: Medical data, medical results.

[1809] Output: Recorded in a medical database and saved in a format that can be used by other medical institutions.

[1810] Specific operations: The server stores the patient's medical information and medical results as electronic data and takes steps to share it with other medical institutions as necessary.

[1811] 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.

[1812] 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.

[1813] 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.

[1814] [Fourth embodiment]

[1815] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1816] 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.

[1817] 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).

[1818] 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.

[1819] 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.

[1820] 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).

[1821] 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.

[1822] 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.

[1823] 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.

[1824] 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.

[1825] 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.

[1826] 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.

[1827] 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."

[1828] The present invention is a system for enabling patients to receive medical services efficiently, and embodiments thereof will be described in detail below.

[1829] 1. System Overview

[1830] This system allows patients to input symptom information from devices such as smartphones or computers, and based on that information, searches for and suggests appropriate medical institutions and medical professionals, and then completes the process of booking an appointment.It also has the ability to store patients' medical data electronically and share it between different medical institutions.

[1831] 2. System Configuration

[1832] The system mainly consists of the following components:

[1833] User terminal: A device where the patient enters information and receives results.

[1834] Server: A central system that analyzes the input information and generates appropriate suggestions.

[1835] Medical institution system: A system that receives medical data and uses it for medical treatment.

[1836] 3. Program Processing

[1837] Processing on the user terminal

[1838] The user launches the dedicated app and enters symptoms and basic information, which is then sent to a server running the AI.

[1839] Processing on the server

[1840] The server analyzes the received information and searches the database for appropriate medical institutions and medical professionals. The search results include information on multiple medical institutions and medical professionals, including each institution's treatment methods and estimated medical costs.

[1841] Displaying the proposal results on the user's device

[1842] The server sends information about the selected medical institutions and medical professionals to the user's device, where the user can check the information and select the medical institution of their choice.

[1843] Confirmation of reservation

[1844] Once the user selects the desired medical institution, the reservation confirmation process begins. The reservation information is sent via the server to the medical institution's reservation system, and the reservation is confirmed. A reservation completion notice is sent to the user's terminal, and confirmation information is displayed.

[1845] Medical data storage and sharing

[1846] The patient's initial consultation information and subsequent medical information are stored as electronic data on a server. When the user visits another medical institution, the relevant medical data is shared with the new institution, making medical treatment more efficient.

[1847] Specific examples

[1848] Example 1: Common cold symptoms

[1849] The user launches the app and types, "I have a bad cough and a sore throat."

[1850] The terminal transmits the input information to the server.

[1851] The server analyzes the information and lists multiple medical institutions that primarily provide internal medicine care.

[1852] The device displays a list of medical institutions and estimated treatment costs.

[1853] The user selects the nearest clinic and confirms the appointment.

[1854] The server confirms the appointment and shares the data with the clinic.

[1855] When a user visits the clinic, they receive prompt medical treatment based on the information shared in advance.

[1856] Example 2: Treating long-term back pain

[1857] Users use the app to enter their past medical history and current symptoms.

[1858] The terminal sends the information to the server.

[1859] The server analyzes the information and suggests multiple medical institutions that specialize in treating lower back pain.

[1860] The device displays a list of medical institutions, treatment costs, and past reviews.

[1861] The user selects a specific orthopedic surgeon and confirms the appointment.

[1862] The server shares past medical data with the orthopedic system.

[1863] The user visits an orthopedic clinic, and the new doctor creates an appropriate treatment plan based on past treatment data.

[1864] The above is an embodiment of the present invention, which constitutes a system that reduces the burden on patients and provides efficient medical services.

[1865] The processing flow will be explained below.

[1866] Step 1:

[1867] The user launches an application on their smartphone or computer.

[1868] Step 2:

[1869] The device uses generative AI to prompt the user with questions such as, "What are your symptoms today?"

[1870] Step 3:

[1871] The user types, "I have a headache."

[1872] Step 4:

[1873] The device then asks, "How long have you been experiencing these symptoms?"

[1874] Step 5:

[1875] The user responds, "From three days ago."

[1876] Step 6:

[1877] The terminal collects and formats the information entered.

[1878] Step 7:

[1879] The device sends the collected information to the server.

[1880] Step 8:

[1881] The server analyzes the received symptom information.

[1882] Step 9:

[1883] The server identifies the corresponding medical department (e.g., internal medicine, neurology, etc.) and searches the database for medical institutions and medical professionals in that department.

[1884] Step 10:

[1885] The server collects information such as each medical institution's areas of expertise, past treatment results, patient reviews, and estimated medical costs.

[1886] Step 11:

[1887] Based on the data collected by the server, several of the most appropriate medical institutions and medical professionals are selected.

[1888] Step 12:

[1889] The server sends information about the selected medical institution and medical personnel to the terminal.

[1890] Step 13:

[1891] The terminal displays the information received from the server to the user.

[1892] Step 14:

[1893] The user reviews a list of suggested medical institutions and practitioners.

[1894] Step 15:

[1895] The user selects the desired medical institution or medical professional. For example, select "Clinic B."

[1896] Step 16:

[1897] The terminal displays a reservation button to the user.

[1898] Step 17:

[1899] The user taps the reservation button.

[1900] Step 18:

[1901] The terminal transmits the reservation information to the server.

[1902] Step 19:

[1903] The server communicates with the medical institution's reservation system and confirms the reservation.

[1904] Step 20:

[1905] The server sends a reservation completion notification to the terminal.

[1906] Step 21:

[1907] The terminal displays the reservation confirmation information to the user.

[1908] Step 22:

[1909] The user's initial consultation information and subsequent medical information are stored as electronic data on the server.

[1910] Step 23:

[1911] The user makes an appointment and visits the medical institution.

[1912] Step 24:

[1913] The medical institution's system retrieves the electronic data from the server.

[1914] Step 25:

[1915] The server sends the user's symptom information and basic information to the medical institution.

[1916] Step 26:

[1917] Medical institutions provide medical treatment based on information shared in advance.

[1918] Step 27:

[1919] After a diagnosis or treatment is performed, new medical data is updated from the medical institution's system to the server.

[1920] Step 28:

[1921] The server updates the user's personal data with the new medical data.

[1922] Step 29:

[1923] When the user visits another medical institution, the server shares the relevant medical data with the new medical institution.

[1924] Step 30:

[1925] The new medical institution receives the data from the server and provides efficient medical care.

[1926] Example 1

[1927] 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."

[1928] In conventional medical systems, when a patient visits multiple medical institutions, they must re-enter their initial consultation information and medical treatment information at each institution, which is time-consuming and can lead to information inconsistencies and delays. It also makes it difficult to select the appropriate medical institution, and confirmation and adjustments when making appointments are cumbersome. These issues reduce the efficiency of medical treatment for patients and make it difficult to provide prompt medical care.

[1929] 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.

[1930] In this invention, the server includes a terminal for patients to input symptom information, a means for transmitting the input symptom information to the server, and a means for the server to analyze the received information and search for appropriate medical institutions and medical professionals using a generative AI model. This allows patients to quickly and accurately input symptom information and select an appropriate medical institution. The server also includes a means for transmitting and displaying information on multiple searched medical institutions and medical professionals to the terminal, a means for confirming an appointment with the medical institution or medical professional selected by the patient, a means for sending a reservation completion notification to the terminal, and a means for storing the patient's medical data as electronic data and sharing it among medical institutions. This allows information entered by a patient once to be shared among multiple medical institutions, eliminating the need for time-consuming information input and reducing the complexity of appointment confirmation and adjustment, thereby significantly improving medical treatment efficiency.

[1931] A "terminal" is a device used by a patient to input symptom information, and includes smartphones, personal computers, etc.

[1932] The "server" is a central system that receives symptom information sent by patients, analyzes it, and searches for appropriate medical institutions and medical professionals.

[1933] A "generative AI model" is an artificial intelligence model used to analyze received information and suggest appropriate medical institutions and medical professionals, including, for example, advanced natural language processing models such as GPT-3.

[1934] "Symptom information" refers to specific information about a patient's health condition or illness that the patient enters into the device. This includes specific symptoms such as "I have a bad cough and a sore throat."

[1935] A "medical institution" is a facility where patients can receive medical treatment, and includes hospitals, clinics, and medical offices.

[1936] "Healthcare professionals" are professionals who are qualified to perform medical procedures on patients at medical institutions, and include doctors, nurses, pharmacists, etc.

[1937] A "reservation" is the act of a patient specifying a date and time to receive medical treatment from a medical institution or medical professional of their choice in advance.

[1938] "Electronic data" refers to data used to store and manage patient symptom information, medical records, and other information in digital format.

[1939] "Sharing" refers to electronic data being transmitted between different medical institutions and being made available to each other.

[1940] "Expected medical expenses" are a guideline for predicted medical expenses based on symptom information and medical treatment details.

[1941] This invention is a system that enables patients to receive medical services efficiently. This system allows patients to input symptom information from devices such as smartphones or computers, and based on that information, the system searches for and suggests appropriate medical institutions and medical professionals, and then completes the process of booking appointments. It also has the function of storing patients' medical data electronically and sharing the data between different medical institutions.

[1942] 1. System Overview

[1943] The user launches the dedicated app and enters symptoms and basic information. This input information is sent to a server running the generative AI model. The server analyzes the received information and searches a database for appropriate medical institutions and medical professionals. The server then sends the search results to the user's device, allowing the user to review the information and select the medical institution of their choice. Finally, the server confirms the reservation and sends a reservation completion notification to the user's device. The patient's medical data is also stored as electronic data and shared between medical institutions.

[1944] 2. Hardware and Software Used

[1945] User device: The device where the user enters information and receives the results (smartphone, PC, etc.)

[1946] Server: A central system that analyzes input information and generates appropriate suggestions. An environment for running Python or Java programs.

[1947] Database management system: A system that stores and manages patient medical data (e.g., MySQL)

[1948] Generative AI model: An artificial intelligence model (such as GPT-3) that analyzes the received information and suggests appropriate medical institutions and medical professionals.

[1949] 3. Specific Examples

[1950] A specific example of use is shown below.

[1951] Example 1: Common cold symptoms

[1952] The user launches the app and types, "I have a bad cough and a sore throat."

[1953] The terminal transmits the input information to the server.

[1954] The server analyzes the information and lists multiple medical institutions that primarily provide internal medicine care.

[1955] The device displays a list of medical institutions and estimated treatment costs.

[1956] The user selects the nearest clinic and confirms the appointment.

[1957] The server confirms the appointment and shares the data with the clinic.

[1958] When a user visits the clinic, they receive prompt medical treatment based on the information shared in advance.

[1959] Example 2: Treating long-term back pain

[1960] Users use the app to enter their past medical history and current symptoms.

[1961] The terminal sends the information to the server.

[1962] The server analyzes the information and suggests multiple medical institutions that specialize in treating lower back pain.

[1963] The device displays a list of medical institutions, treatment costs, and past reviews.

[1964] The user selects a specific orthopedic surgeon and confirms the appointment.

[1965] The server shares past medical data with the orthopedic system.

[1966] The user visits an orthopedic clinic, and the new doctor creates an appropriate treatment plan based on past treatment data.

[1967] Prompt Sentence Examples

[1968] Below is an example of a prompt sentence to input to the generative AI model.

[1969] Symptom information that makes diagnosis easier: "I have a bad cough and a sore throat. I'm looking for an internist."

[1970] Generated medical institution suggestions: "The following internal medicine clinics are nearby: XX Clinic, △△ Clinic, and □□ Hospital. The treatment costs for each are..."

[1971] Appointment confirmation process: "Please make an appointment at XX Clinic." -> "The appointment has been completed."

[1972] The above is an embodiment of the present invention, which constitutes a system that reduces the burden on patients and provides efficient medical services.

[1973] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1974] Step 1: Enter user information

[1975] The user launches the app and inputs their symptoms and basic information, including specific symptoms such as "severe cough and sore throat." This generates data on the user's health condition.

[1976] Input: Symptom information (e.g., "I have a bad cough and a sore throat")

[1977] Output: Symptom information data entered

[1978] Step 2: Sending information to the server

[1979] The device encrypts the input symptom information and sends it to the server. At this time, encryption protocols such as SSL / TLS are used to ensure the security of the communication. The transmitted data is then received by the server.

[1980] Input: Symptom information data entered

[1981] Output: Data sent to the server

[1982] Step 3: Analyze the received information

[1983] The server analyzes the received symptom information using Python or Java scripts. A generative AI model (e.g., GPT-3) is used to calculate the data and suggest appropriate medical institutions and medical professionals. Specifically, a prompt is input into the generative AI model, and the analysis results are obtained.

[1984] Input: Symptom information data received by the server

[1985] Output: Proposed results data from the generative AI model

[1986] Step 4: Search and select a medical institution

[1987] The server searches and selects appropriate medical institutions and medical professionals from the database based on the results of the generative AI model. A list is generated that includes each medical institution's treatment method, estimated medical costs, address, etc. This is done using SQL queries.

[1988] Input: Proposal result data of generative AI model

[1989] Output: List of medical institutions

[1990] Step 5: Submit search results

[1991] The server packages the searched and selected information on medical institutions and medical professionals in JSON format and sends it to the user's device, where the user receives detailed information on selectable medical institutions.

[1992] Input: List of medical institutions

[1993] Output: Data sent to the terminal

[1994] Step 6: Review and select the suggestions

[1995] The user can then view information about medical institutions and medical professionals based on the search results on their device, including the name, address, treatment details, and reviews of each medical institution. The user can then select the medical institution of their choice.

[1996] Input: Medical institution information displayed on the terminal

[1997] Output: User selection data

[1998] Step 7: Submit your reservation information

[1999] The terminal sends the reservation information of the medical institution selected by the user to the server. The information is sent to the server in JSON format. The server receives the information and processes it to confirm the reservation at the selected medical institution.

[2000] Input: User selected data

[2001] Output: Data scheduled to be sent to the server

[2002] Step 8: Send a reservation completion notification

[2003] The server checks the reservation information database to confirm that the reservation has been confirmed, and sends a reservation completion notice to the user's terminal, allowing the user to confirm that their reservation has been confirmed.

[2004] Input: Confirmed reservation data

[2005] Output: Reservation completion notification data to the terminal

[2006] Step 9: Storing and sharing medical data

[2007] The server stores the patient's initial consultation information and subsequent medical information as electronic data. The data is managed using a database management system (e.g., MySQL). When a user receives treatment at another medical institution, the relevant medical data is encrypted and sent to the new medical institution.

[2008] Input: Patient medical information

[2009] Output: Stored electronic data and shared data

[2010] (Application example 1)

[2011] 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."

[2012] Conventional medical systems lack an integrated means for patients to effectively select appropriate medical institutions and medical professionals and quickly confirm appointments. Furthermore, it is difficult to share medical data between different medical institutions, resulting in inefficient medical treatment. Furthermore, security services require real-time information analysis and communication methods to detect suspicious individuals and respond quickly to emergencies. A system that can solve these issues in an integrated manner is needed.

[2013] 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.

[2014] In this invention, the server includes means for having a patient input symptom information before visiting a hospital, means for analyzing the input symptom information and searching for an appropriate medical institution or medical professional, means for presenting information on multiple medical institutions and medical professionals based on the analysis results and having the patient select one, means for confirming an appointment with the selected medical institution or medical professional, means for saving the patient's medical data as electronic data and sharing the data between medical institutions, means for having a user input information on abnormal behavior or suspicious individuals and analyzing the information to generate appropriate suggestions, and means for contacting a security team or the police based on the analysis results. This not only streamlines the patient's medical treatment process and makes it easier to share data between medical institutions, but also enables quick and appropriate responses in security services.

[2015] definition statement

[2016] "Means for patients to input symptom information before visiting the hospital" refers to a function that allows patients to input their symptoms and basic information using their own devices (smartphones, computers, etc.).

[2017] "Means of analyzing input symptom information and searching for appropriate medical institutions and medical professionals" is a function that identifies and suggests the most appropriate medical institutions and medical professionals from a database based on the symptom information input by the patient.

[2018] "Means of presenting information on multiple medical institutions and medical professionals based on the analysis results and allowing the patient to make a selection" is a function that provides the patient with multiple appropriate options for medical institutions and medical professionals based on the analyzed information, allowing the patient to make the selection themselves.

[2019] "Means for confirming appointments with selected medical institutions and healthcare professionals" refers to a function that allows a patient to securely confirm appointments with the medical institutions and healthcare professionals selected by the patient within the system and send a confirmation notification.

[2020] "Means for storing patient medical data as electronic data and sharing the data between medical institutions" refers to a function that allows patients' medical information to be recorded electronically and the data to be shared with different medical institutions as needed.

[2021] "A means for having users input information about abnormal behavior or suspicious individuals, analyzing that information, and generating appropriate suggestions" is a function in security services that allows users to report suspicious activity or abnormal behavior, analyze that information, and suggest optimal countermeasures.

[2022] "Means of contacting security teams or police based on analysis results" is a function that allows for quick contact with specialized security teams or police as necessary based on the analyzed information.

[2023] MODE FOR CARRYING OUT THE INVENTION

[2024] This invention is an integrated system that enables patients to receive medical services efficiently and also enables prompt response in security. This system is designed for use in Japanese medical institutions, but can also be applied in other countries.

[2025] Overall system configuration

[2026] The system mainly consists of the following components:

[2027] User terminal: A device, such as a smartphone or smart glasses, through which the patient or user enters information and receives results.

[2028] Server: A central system that analyzes input information and generates appropriate suggestions, using generative AI models, image recognition, natural language processing, etc.

[2029] Medical institution system: A system that receives medical data and uses it for medical treatment. It mainly works in conjunction with electronic medical record systems and reservation systems.

[2030] Program Generation

[2031] The system program is generated as follows:

[2032] Processing on the user terminal

[2033] The user launches a dedicated application using a smartphone or smart glasses, and inputs information about symptoms and suspicious individuals into the application. This information is then sent to the server.

[2034] Processing on the server

[2035] The server has the following features:

[2036] Symptom analysis: Analyzes the input symptom information and suggests appropriate medical institutions and medical professionals. A generative AI model is used for the analysis.

[2037] Image Recognition: Analyzes images of suspicious individuals and detects abnormalities and suspicious movements. Uses OpenCV.

[2038] Natural Language Processing: Analyzes input text information and generates appropriate suggestions. Uses the transformers library.

[2039] Recommendation generation: Based on the analysis results, appropriate medical institutions and action instructions are suggested to the user.

[2040] Specific examples

[2041] Examples of medical services

[2042] 1. The user launches the app and types, "I have a bad cough and a sore throat."

[2043] 2. The terminal sends the entered information to the server.

[2044] 3. The server analyzes the information and lists multiple medical institutions that primarily provide internal medicine care.

[2045] 4. The device will display a list of medical institutions and estimated treatment costs.

[2046] 5. The user selects the nearest clinic and confirms the appointment.

[2047] 6. The server confirms the appointment and shares the data with the clinic.

[2048] 7. When the user visits the clinic, they receive prompt medical treatment based on the information shared in advance.

[2049] Security Service Examples

[2050] 1. The user uses smart glasses to capture a photo of a suspicious person and sends the data to the server via the app.

[2051] 2. The server analyzes the image and, if it determines that the person is suspicious, it instructs the user to contact the police immediately.

[2052] 3. Based on this, users can take prompt action and implement appropriate security measures.

[2053] Prompt Sentence Examples

[2054] "Enter an image and generate a suspicious person identification and necessary action instructions."

[2055] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2056] Program processing flow

[2057] The process by which users enter their medical information and make appointments

[2058] Step 1:

[2059] User enters symptom information

[2060] Input: The user launches a dedicated application on a smartphone or smart glasses and enters symptom information such as "I have a bad cough and a sore throat."

[2061] Processing: The application temporarily stores the entered information and converts it into the required format.

[2062] Output: Formatted symptom information data.

[2063] Step 2:

[2064] The device sends the information to the server

[2065] Input: Formatted symptom information data.

[2066] Processing: The device sends data to the server over the network using an HTTP POST request.

[2067] Output: The data sent to the server.

[2068] Step 3:

[2069] The server analyzes the symptom information

[2070] Input: Submitted symptom information data.

[2071] Processing: Generative AI models are used to analyze the symptom information provided, using natural language processing libraries to identify symptom patterns.

[2072] Output: A list of appropriate medical institutions and medical professionals.

[2073] Step 4:

[2074] The server sends the proposal results to the user's device.

[2075] Input: A list of appropriate medical institutions and practitioners.

[2076] Processing: The server sends the analysis results to the user's device, again using an HTTP POST request.

[2077] Output: A list of medical institutions and estimated treatment costs displayed on the user's terminal.

[2078] Step 5:

[2079] The user selects a medical institution and confirms the appointment

[2080] Input: A list of medical institutions and estimated treatment costs displayed on the user's terminal.

[2081] Processing: The user selects the desired medical institution and taps the reservation button. The device resends the reservation information to the server.

[2082] Output: Reservation information sent to the server.

[2083] Step 6:

[2084] The server confirms the reservation and shares the data with the medical institution.

[2085] Input: Reservation information submitted by the user.

[2086] Processing: The server accesses the reservation system and confirms the reservation. At the same time, it sends the reservation information and the patient's symptom information to the medical institution's system.

[2087] Output: Appointment confirmation notice and medical information stored in the medical institution's system.

[2088] Flow of how users enter security information and take emergency action

[2089] Step 1:

[2090] The user captures and enters the suspicious person information

[2091] Input: The user uses the smart glasses to capture a photo of the suspicious person and input it into the security app.

[2092] Processing: The app temporarily stores the image data.

[2093] Output: Saved image data.

[2094] Step 2:

[2095] The device sends the image data to the server.

[2096] Input: Saved image data.

[2097] Processing: The device sends the image data to the server over the network, again using an HTTP POST request.

[2098] Output: Image data sent to the server.

[2099] Step 3:

[2100] The server analyzes the image data

[2101] Input: The submitted image data.

[2102] Processing: Image data is analyzed using OpenCV. This analysis identifies suspicious individuals and abnormal behavior.

[2103] Output: Analysis results, including whether suspicious activity was detected and recommended actions.

[2104] Step 4:

[2105] The server sends the proposal results to the user's device.

[2106] Input: Analysis results.

[2107] Processing: The server sends the analysis results and instructions to the user's device using an HTTP POST request.

[2108] Output: Instructions to be displayed on the user's device. In the case of a suspicious person, instructions to contact the police, etc.

[2109] Step 5:

[2110] User takes action based on the suggestion

[2111] Input: Response instructions.

[2112] Action: The user follows the instructions on the smart device and takes appropriate action, such as contacting the police. The device collects a log of the action.

[2113] Output: Action log data and emergency response implementation.

[2114] Prompt Sentence Examples

[2115] "Enter an image and generate a suspicious person identification and necessary action instructions."

[2116] 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.

[2117] The present invention is a system that allows patients to input symptom information before visiting a hospital, analyzes that information to search for appropriate medical institutions and medical professionals, and further combines it with an emotion engine that recognizes the user's emotions to make more appropriate medical recommendations. The following describes in detail an embodiment of the system.

[2118] 1. System Overview

[2119] This system allows patients to input symptom and emotional information from devices such as smartphones or PCs, and based on that information, the system searches for and suggests appropriate medical institutions and medical professionals, and then confirms appointments.It also has the function of storing patients' medical data electronically and sharing it between different medical institutions.

[2120] 2. System Configuration

[2121] The system mainly consists of the following components:

[2122] User terminal: A device where patients input information and receive results. It also collects emotional information.

[2123] Server: A central system that analyzes the input information and generates appropriate suggestions.

[2124] Medical institution system: A system that receives medical data and uses it for medical treatment.

[2125] Emotion engine: A system that analyzes the user's emotions and suggests medical treatment priorities and appropriate medical institutions.

[2126] 3. Program Processing

[2127] Processing on the user terminal

[2128] The user launches the app and inputs symptoms and basic information. Along with this input information, the emotion engine collects emotional information through tone of voice and text analysis. The collected information is sent to a server where the generative AI runs.

[2129] Processing on the server

[2130] The server analyzes the received symptom and emotion information and searches the database for the appropriate medical department, medical institution, and medical professional. The search results include information on multiple medical institutions and medical professionals. This information also reflects each institution's treatment method, estimated medical costs, and the analysis of the user's stress level and urgency using an emotion engine.

[2131] Displaying the proposal results on the user's device

[2132] The server sends information about the selected medical institutions and medical professionals to the user's device. The user can then check this information on the device and select the medical institution of their choice. The analysis results of the emotion engine are also displayed.

[2133] Confirmation of reservation

[2134] Once the user selects the desired medical institution, the reservation confirmation process begins. The reservation information is sent via the server to the medical institution's reservation system, and the reservation is confirmed. A reservation completion notice is sent to the user's terminal, and confirmation information is displayed.

[2135] Medical data storage and sharing

[2136] The patient's initial consultation information and subsequent medical information are stored as electronic data on a server. When the user visits another medical institution, the relevant medical data is shared with the new institution, making medical treatment more efficient.

[2137] Specific examples

[2138] Example 1: Common cold symptoms

[2139] The user launches the app and types, "I have a bad cough and a sore throat."

[2140] The emotion engine analyzes emotions from the user's input text and tone of voice and determines that the anxiety level is high.

[2141] The terminal transmits the input information and emotion information to the server.

[2142] The server analyzes the information and lists multiple medical institutions that primarily provide internal medicine care.

[2143] The device displays a list of medical institutions, estimated treatment costs, and sentiment analysis results.

[2144] The user selects the nearest clinic and confirms the appointment.

[2145] The server confirms the appointment and shares the data with the clinic.

[2146] When a user visits the clinic, they receive prompt medical treatment based on the information shared in advance.

[2147] Example 2: Treating long-term back pain

[2148] Users use the app to enter their past medical history and current symptoms.

[2149] The emotion engine recognizes from the input data that the user's stress level is high.

[2150] The terminal sends the information to the server.

[2151] The server analyzes the information and suggests multiple medical institutions that specialize in treating lower back pain.

[2152] The device displays a list of medical institutions, treatment costs, past reviews, and sentiment analysis results.

[2153] The user selects a specific orthopedic surgeon and confirms the appointment.

[2154] The server shares past medical data and emotional information with the orthopedic system.

[2155] The user visits an orthopedic clinic, and the new doctor creates an appropriate treatment plan based on past treatment data and emotional data.

[2156] The above is an embodiment of the present invention, which constitutes a system that reduces the burden on patients and provides efficient and appropriate medical services that take into account their emotional state.

[2157] The processing flow will be explained below.

[2158] Step 1:

[2159] The user launches an application on their smartphone or computer.

[2160] Step 2:

[2161] The device uses generative AI and an emotion engine to prompt the user with questions such as, "What are your symptoms today?"

[2162] Step 3:

[2163] The user types, "I have a headache."

[2164] Step 4:

[2165] The emotion engine analyzes emotions from the user's input text and tone of voice and determines that the anxiety level is high.

[2166] Step 5:

[2167] The device will ask, "How long have you been experiencing these symptoms?"

[2168] Step 6:

[2169] The user responds, "From three days ago."

[2170] Step 7:

[2171] The device collects and formats the entered symptom information and emotion analysis results.

[2172] Step 8:

[2173] The device sends the collected information to the server.

[2174] Step 9:

[2175] The server analyzes the received symptom information and emotion information.

[2176] Step 10:

[2177] The server identifies the corresponding medical department (e.g., internal medicine, neurology, etc.) and searches the database for medical institutions and medical professionals in that department.

[2178] Step 11:

[2179] The server collects each medical institution's areas of expertise, past treatment records, patient reviews, estimated medical costs, and sentiment analysis results.

[2180] Step 12:

[2181] Based on the data collected by the server, several of the most appropriate medical institutions and medical professionals are selected.

[2182] Step 13:

[2183] The server sends information about the selected medical institution and medical personnel to the terminal.

[2184] Step 14:

[2185] The device displays the information received from the server to the user, including the results of emotion analysis.

[2186] Step 15:

[2187] The user reviews a list of suggested medical institutions and practitioners.

[2188] Step 16:

[2189] The user selects the desired medical institution or medical professional. For example, select "Clinic B."

[2190] Step 17:

[2191] The terminal displays a reservation button to the user.

[2192] Step 18:

[2193] The user taps the reservation button.

[2194] Step 19:

[2195] The terminal transmits the reservation information to the server.

[2196] Step 20:

[2197] The server communicates with the medical institution's reservation system and confirms the reservation.

[2198] Step 21:

[2199] The server sends a reservation completion notification to the terminal.

[2200] Step 22:

[2201] The terminal displays the reservation confirmation information to the user.

[2202] Step 23:

[2203] The user's initial consultation information and subsequent medical information are stored as electronic data on the server.

[2204] Step 24:

[2205] The user visits the medical institution for which they have made an appointment.

[2206] Step 25:

[2207] The medical institution's system retrieves electronic data and emotional information from the server.

[2208] Step 26:

[2209] The server transmits the user's symptom information and emotional information to the medical institution.

[2210] Step 27:

[2211] Medical institutions provide medical treatment based on information shared in advance.

[2212] Step 28:

[2213] After a diagnosis or treatment is performed, new medical data is updated from the medical institution's system to the server.

[2214] Step 29:

[2215] The server updates the user's personal data with the new medical data.

[2216] Step 30:

[2217] When the user visits another medical institution, the server shares relevant medical data and emotional information with the new medical institution.

[2218] Step 31:

[2219] The new medical institution receives the data from the server and provides efficient medical care.

[2220] Example 2

[2221] 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."

[2222] The current medical system faces the problem of not providing patients with enough information to select the appropriate medical institution or medical professional when making a medical appointment. Furthermore, medical treatment recommendations do not take into account the patient's emotional state, which can increase anxiety and stress. Furthermore, medical data is not shared enough, hindering efficient medical care delivery between different medical institutions. To solve these issues, appropriate medical treatment recommendations based on the patient's symptom and emotional information, as well as effective management and sharing of medical data, are needed.

[2223] 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.

[2224] In this invention, the server includes a means for having the patient input symptom information and emotional information before visiting the hospital, a means for analyzing the input symptom information and emotional information to search for appropriate medical institutions and medical professionals, and a means for presenting information on multiple medical institutions and medical professionals based on the analysis results and allowing the patient to select one. This makes it possible to propose appropriate medical care that takes into account not only the patient's symptoms but also their emotional information. In addition, by providing a means for storing patient medical data as electronic data and sharing the data between medical institutions, smooth and efficient medical care can be provided.

[2225] "Symptom information" is data that indicates physical discomfort or pain that a patient is experiencing.

[2226] "Emotional information" is data obtained by analyzing a patient's psychological state and emotions.

[2227] A "medical institution" is an organization or facility that provides medical services.

[2228] "Medical professionals" are medical professionals such as doctors and nurses who provide medical care.

[2229] "Analysis" is the process of processing input data and extracting meaningful information.

[2230] "Searching" is the process of finding information from a database based on specific criteria.

[2231] "Presentation" means displaying the analyzed information in a way that is easy for the user to understand.

[2232] "Confirming an appointment" means officially deciding on a date and time for consultation with the selected medical institution or healthcare professional.

[2233] "Electronic data" means information stored in digital form.

[2234] "Sharing" means exchanging and making available data between different medical institutions.

[2235] This system allows patients to input symptom and emotional information before visiting a hospital, analyzes that information, searches for appropriate medical institutions and medical professionals, and confirms appointments, thereby reducing the burden on patients and providing efficient medical services. The system includes a user terminal that runs a dedicated application, a server that performs data analysis, and an emotion engine that processes emotional information.

[2236] Hardware and software used

[2237] User terminal

[2238] Hardware: Smartphones, PCs

[2239] Software: Dedicated application, emotion engine

[2240] The user launches the dedicated app and inputs symptom and emotional information. The emotion engine analyzes the user's tone of voice and input text to collect emotional information. The device then sends the collected information to the server.

[2241] server

[2242] Hardware: Server system

[2243] Software: Database system, analysis software, emotion engine

[2244] The server analyzes the received symptom and emotion information and searches the database for appropriate medical institutions and medical professionals. Based on the analysis results, it generates a list of search results, including treatment methods, estimated medical costs, and the analysis results of the emotion engine.

[2245] Confirmed reservations and data sharing

[2246] Medical Institution System

[2247] The server sends information about the selected medical institutions and medical professionals to the user's terminal, and once the user selects the desired medical institution, the reservation confirmation process begins. The reservation information is sent via the server to the medical institution's reservation system, and the reservation is confirmed. The confirmed reservation information is shared with the medical institution's system. Furthermore, the patient's medical data is stored as electronic data and shared so that it can be used between different medical institutions.

[2248] Specific operation example

[2249] Example 1: Common cold symptoms

[2250] The user launches the app and types, "I have a bad cough and a sore throat."

[2251] The emotion engine analyzes emotions from the user's input text and tone of voice and determines that the anxiety level is high.

[2252] The terminal transmits the input information and emotion information to the server.

[2253] The server analyzes the symptom information and emotional information and lists multiple medical institutions that primarily provide internal medicine care.

[2254] The device displays a list of medical institutions, estimated treatment costs, and sentiment analysis results.

[2255] The user selects the nearest clinic and confirms the appointment.

[2256] The server confirms the appointment and shares the data with the clinic.

[2257] When a user visits the clinic, they receive prompt medical treatment based on the information shared in advance.

[2258] Prompt Sentence Examples

[2259] "The user enters 'severe cough, sore throat' into a dedicated app, and the emotion engine determines that the anxiety level is high. The server then lists medical institutions that primarily provide internal medicine care, and the user confirms an appointment. The server then shares information to ensure prompt treatment when visiting the clinic."

[2260] As described above, the present invention is a system that realizes the provision of efficient and appropriate medical services based on the symptom information and emotional information of patients.

[2261] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2262] Program processing flow

[2263] Step 1: Enter your user information

[2264] The user launches the dedicated app.

[2265] The user enters symptom information (e.g., "bad cough, sore throat").

[2266] The device receives the input symptom information and prompts the user to input emotional information.

[2267] The emotion engine analyzes the user's tone of voice and input text to generate emotion information (e.g., "high anxiety level").

[2268] The symptom information and emotion information collected by the terminal are transmitted to a server.

[2269] Input: Symptom information (text), emotion information (analysis results).

[2270] Output: Symptom and emotion information sent to the server.

[2271] Step 2: Information analysis

[2272] The server analyzes the received symptom information and emotion information.

[2273] The server determines the predicted medical specialty based on the symptom information (e.g., "internal medicine").

[2274] The server evaluates the user's level of urgency based on emotional information (e.g., "high anxiety level").

[2275] The server searches the database for appropriate medical institutions and medical professionals.

[2276] The server lists the search results and generates information including treatment methods, estimated medical costs, and sentiment analysis results.

[2277] Input: Symptom information, emotion information.

[2278] Output: List of medical institutions, treatment methods, medical costs, and sentiment analysis results.

[2279] Step 3: Viewing the Suggestion Results

[2280] The server sends the generated list of medical institutions to the user terminal.

[2281] The device displays to the user a list of medical institutions, estimated treatment costs, and sentiment analysis results.

[2282] The user selects the desired medical institution from the displayed list.

[2283] Input: List of medical institutions, treatment methods, medical costs, and sentiment analysis results.

[2284] Output: Display to user, user selection.

[2285] Step 4: Confirm your reservation

[2286] The terminal receives the user's selection and transmits information about the selected medical institution to the server.

[2287] The server sends the reservation information to the medical institution's reservation system and confirms the reservation.

[2288] The server receives the reservation confirmation notice and sends it to the user terminal.

[2289] The terminal displays a reservation completion notice to the user.

[2290] Input: User selection information, medical institution reservation system.

[2291] Output: Reservation confirmation notice, user notification.

[2292] Step 5: Managing and sharing medical data

[2293] The server stores the user's first consultation information as electronic data.

[2294] The server shares necessary medical data between different medical institutions.

[2295] Medical institutions provide medical treatment based on the shared data.

[2296] Input: Initial consultation information (electronic data format), sharing request.

[2297] Output: Electronic data storage, data sharing with different medical institutions.

[2298] The above is the specific processing flow of the program in the present invention. This system makes it possible to provide efficient and appropriate medical services based on the symptom information and emotional information of the patient.

[2299] (Application example 2)

[2300] 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."

[2301] In conventional medical systems, even if patients input symptom information before visiting a hospital, it is difficult to select the appropriate medical institution or medical professional based on that information alone. Furthermore, since medical treatment suggestions do not take into account the patient's emotional state, it is difficult to provide the optimal medical service for the patient. Furthermore, data is not shared efficiently between medical institutions, which makes it difficult for patients to receive medical treatment smoothly.

[2302] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[2303] In this invention, the server includes: means for having the patient input symptom information before visiting the hospital; means for analyzing the input symptom information and emotional information to search for appropriate medical institutions and medical professionals; means for presenting information on multiple medical institutions and medical professionals based on the analysis results and allowing the patient to select one; means for confirming an appointment with the selected medical institution or medical professional; means for saving the patient's medical data as electronic data and sharing the data between medical institutions; means for generating analysis results using a generative AI model and proposing information on multiple medical institutions; means for presenting estimated medical costs based on the input symptom information and emotional information; and means for transmitting the patient's initial visit information and emotional information to the systems of each medical institution. This enables appropriate medical treatment suggestions that take the patient's emotional state into consideration and enables efficient data sharing between medical institutions.

[2304] "Symptom information" is detailed data about the patient's perceived poor health and illness.

[2305] "Emotional information" is data that indicates a patient's psychological or emotional state. Specific techniques are used to analyze emotions and assess levels of stress, anxiety, etc.

[2306] "Medical institution" refers to a facility that provides medical services, such as a hospital, medical office, or clinic.

[2307] "Healthcare professionals" are professionals who provide medical support, such as doctors, nurses, and medical technicians.

[2308] A "generative AI model" is an information generation technology that uses artificial intelligence, and specifically refers to algorithms that perform natural language processing and data analysis.

[2309] "Data sharing" is the process of efficiently exchanging electronic data between different medical institutions, making the information necessary for medical treatment mutually available.

[2310] "Confirming a reservation" means officially deciding to make a medical appointment with the medical institution or medical professional selected by the patient.

[2311] "Analysis results" refer to the results derived by generative AI models or other analysis engines based on the input symptom information and emotional information.

[2312] A "system" is a collection of processes and technologies in which the above-mentioned means work together.

[2313] "Proposals using generative AI models" refers to the process of analyzing a patient's symptom and emotional information to determine the most appropriate medical institution and treatment method.

[2314] This invention is a system that allows patients to input symptom and emotional information before visiting a hospital, analyzes that information, searches for appropriate medical institutions and medical professionals, and even makes recommendations and reservations. This system includes the following components and processes.

[2315] System Configuration

[2316] 1. User Device

[2317] A device where patients enter information and receive results, such as a smartphone or tablet.

[2318] 2. Server

[2319] A central system that analyzes the entered information and searches a database for appropriate medical institutions and medical professionals. It uses a cloud-based server (e.g., AWS, Google Cloud).

[2320] 3. Sentiment Analysis Engine

[2321] A system that analyzes emotions from patients' tone of voice and text, utilizing Microsoft Azure Cognitive Services and IBM Watson.

[2322] 4. Generative AI Models

[2323] An artificial intelligence model for generating analysis results of input information. Uses OpenAI's GPT-4, etc.

[2324] 5. Database

[2325] A system that manages data on medical institutions, medical professionals, and patients, using database management systems such as MySQL and PostgreSQL.

[2326] Program processing explanation

[2327] User terminal

[2328] The user (patient) launches the smartphone app and inputs symptom information and emotional information.

[2329] Using an emotion analysis engine, emotional information is collected through the user's tone of voice and text analysis.

[2330] The collected information is sent to a server.

[2331] server

[2332] The server analyzes the received symptom and emotion information using a generative AI model.

[2333] Search for appropriate medical departments, medical institutions, and medical professionals from medical databases.

[2334] Search results include treatment methods at each medical institution, estimated medical costs, and sentiment analysis results.

[2335] Information on multiple medical institutions and medical professionals is sent to the user terminal.

[2336] User terminal

[2337] The search results are presented to the user and an appointment is confirmed at the selected medical institution.

[2338] The confirmed reservation information is sent to the medical institution via the server.

[2339] Data Management

[2340] Patient medical data is stored electronically on a server.

[2341] When a user visits another medical institution, relevant medical data is shared with the new medical institution.

[2342] Specific examples

[2343] Example 1: Common cold symptoms

[2344] The user launches the app and types, "I have a bad cough and a sore throat."

[2345] The emotion analysis engine analyzes the user's emotions from the text they input and their tone of voice, and determines that their anxiety level is high.

[2346] The terminal transmits the input information and emotion information to the server.

[2347] The server analyzes the information and lists multiple medical institutions that primarily provide internal medicine care.

[2348] The device displays a list of medical institutions, estimated treatment costs, and sentiment analysis results.

[2349] The user selects the nearest clinic and confirms the appointment.

[2350] The server confirms the appointment and shares the data with the clinic.

[2351] When a user visits the clinic, they receive prompt medical treatment based on the information shared in advance.

[2352] Example 2: Treating long-term back pain

[2353] Users use the app to enter their past medical history and current symptoms.

[2354] The sentiment analysis engine recognizes from the input data that the user's stress level is high.

[2355] The terminal sends the information to the server.

[2356] The server analyzes the information and suggests multiple medical institutions that specialize in treating lower back pain.

[2357] The device displays a list of medical institutions, treatment costs, past reviews, and sentiment analysis results.

[2358] The user selects a specific orthopedic surgeon and confirms the appointment.

[2359] The server shares past medical data and emotional information with the orthopedic system.

[2360] The user visits an orthopedic clinic, and the new doctor creates an appropriate treatment plan based on past treatment data and emotional data.

[2361] Prompt Sentence Examples

[2362] When a user enters "I have a headache and feel nauseous," analyze the emotion they are feeling and generate a prompt to suggest the best medical institution that can also provide stress management.

[2363] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2364] Step 1:

[2365] The user launches the smartphone app.

[2366] Input: The user launches the app.

[2367] Output: The app's home screen is displayed.

[2368] Specific behavior: When a user taps the app icon on their smartphone, the app launches and the login screen appears.

[2369] Step 2:

[2370] The user inputs symptom information and emotional information.

[2371] Input: The user enters the symptoms they are experiencing in a text box (e.g., "I have a headache" or "I have a bad cough") and records their current feelings via voice as emotional information.

[2372] Output: Collected symptom and emotion information.

[2373] Specific operation: Users enter their symptoms in text in the app's input form, press the voice input button, and speak their feelings to collect voice data.

[2374] Step 3:

[2375] The device sends the collected information to an emotion analysis engine.

[2376] Input: Text data for symptom information and audio data for emotion information.

[2377] Output: Analysis results from the sentiment analysis engine.

[2378] Specific operation: The app sends the collected symptom information and voice data to the server, and the server passes the data to the emotion analysis engine for processing.

[2379] Step 4:

[2380] The emotion analysis engine analyzes the user's emotions and sends the results to the server.

[2381] Input: Audio data.

[2382] Output: Sentiment analysis result (e.g. "High Anxiety Level").

[2383] What it does: The emotion analysis engine processes the audio data, identifies the emotional state (e.g., "high anxiety"), and sends the results back to the server.

[2384] Step 5:

[2385] The server analyzes symptom information and emotional information using the generated AI model.

[2386] Input: Symptom information, sentiment analysis results.

[2387] Output: A list of appropriate medical institutions and medical professionals.

[2388] How it works: The server uses a generative AI model (e.g., OpenAI GPT-4) to analyze symptom and emotion data, and then searches and selects the most suitable medical institution and medical professional from a medical database.

[2389] Step 6:

[2390] The server sends a list of selected medical institutions and medical professionals to the terminal.

[2391] Input: Parsed result by the server.

[2392] Output: A list of medical institutions and medical professionals on the user's device.

[2393] Specific operation: The server sends the analysis results to the user's device, and the app displays the results to the user in list form.

[2394] Step 7:

[2395] The user selects a medical institution or medical professional and confirms the appointment.

[2396] Input: List of medical institutions, user selection.

[2397] Output: Confirmation of reservation, reservation details.

[2398] Specific operation: When the user selects the desired medical institution from the list and presses the reservation button, the reservation confirmation process begins and the reservation information is sent to the server.

[2399] Step 8:

[2400] The server confirms the appointment with the selected medical institution or medical professional and sends the appointment information to the medical institution's system.

[2401] Input: User selection information.

[2402] Output: A confirmation of the appointment is sent to the medical institution.

[2403] Specific operation: The server sends the reservation information to the relevant medical institution's reservation system, confirms the reservation, and sends a reservation confirmation notification to the user.

[2404] Step 9:

[2405] The server stores patient medical data as electronic data and shares the data between medical institutions.

[2406] Input: Medical data, medical results.

[2407] Output: Recorded in a medical database and saved in a format that can be used by other medical institutions.

[2408] Specific operations: The server stores the patient's medical information and medical results as electronic data and takes steps to share it with other medical institutions as necessary.

[2409] 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 voi...

Claims

1. A means for having patients input symptom information before coming to the hospital; A means of analyzing the input symptom information and searching for appropriate medical institutions and medical professionals; A method for presenting information on multiple medical institutions and medical professionals based on the analysis results and allowing patients to select one; A means to confirm an appointment with the selected medical institution or healthcare professional; A means of storing patient medical data as electronic data and sharing the data between medical institutions; A system including:

2. The system according to claim 1 , further comprising means for presenting estimated medical costs based on the input symptom information.

3. 2. The system according to claim 1, further comprising means for transmitting electronic data including information on the patient's first visit to the system of each medical institution.

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