System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving medical condition information from a subject; means for obtaining hospital details from a database; means for analyzing and matching the subject's medical condition information and the hospital details using a generated AI; and means for suggesting an optimal hospital for the subject.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 modern society, choosing a reliable hospital is difficult for many people. It is especially important to select an appropriate medical institution quickly and accurately when the patient is seriously ill. However, there are currently few hospital rating websites or word-of-mouth reviews, and appropriate information is lacking. This can lead to confusion and anxiety when choosing a hospital, and as a result, there is a risk that patients will not receive appropriate medical care. There is a need for a system to solve this problem. [Means for solving the problem]

[0005] This invention relates to a system that uses generative AI to recommend the most suitable hospital for a patient. Specifically, it provides a means for receiving medical condition information from patients, retrieving detailed hospital data (specialties, up-to-dateness of medical equipment, staff evaluations, etc.) from a database, and using generative AI to analyze and match the patient's medical condition information with the detailed hospital data. It also provides a means for recommending the most suitable hospital for patients and supporting patients in making reservations at the hospital of their choice. This system is expected to enable patients to quickly and reliably find the appropriate medical institution, reducing the stress and anxiety of choosing a hospital.

[0006] A "patient" refers to a person who has a problem with their physical condition or illness and uses a medical institution.

[0007] "Medical Condition Information" refers to detailed information about a patient's physical condition, including any symptoms or special needs the patient may have.

[0008] A "hospital" is a facility that provides medical care and treatment and is staffed by professionals from different medical fields.

[0009] "Generative AI" refers to a system that uses artificial intelligence technology to analyze and match data.

[0010] "Hospital detailed data" refers to information about each hospital, such as its areas of expertise, the state of the art of medical equipment, and staff evaluations.

[0011] "Database" refers to a data repository for systematically storing and managing detailed hospital data.

[0012] "Analysis and matching" refers to the process of comparing a patient's medical condition information with detailed hospital data to find the most suitable hospital.

[0013] "Suggestion" refers to the generative AI suggesting the most suitable hospital for a patient based on the results of analysis and matching.

[0014] "Reservation support" refers to functions and services that support the patient's reservation process at the hospital of their choice. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

[0023] [First embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0036] This invention relates to a system that uses generative AI to suggest the best hospital for a patient. The details of the interactions between the user, terminal, and server, as well as each function, are explained below.

[0037] 1. User login and information entry

[0038] Terminal

[0039] The user logs in to the hospital search system using a terminal. The login screen is displayed and the user enters their user ID and password. After input, the terminal sends this information to the server.

[0040] server

[0041] The server verifies the received login information and authenticates the user. If authentication is successful, the user proceeds to the next step.

[0042] Specific examples

[0043] When User A logs in to the hospital search system and enters his / her user ID and password, authentication is performed and the user is then directed to a screen for entering medical condition information.

[0044] 2. Enter patient information

[0045] Terminal

[0046] After authentication is complete, the user enters their symptoms and special needs, and the terminal transmits the medical condition information received from the user to the server.

[0047] server

[0048] The server receives the patient's medical condition information and stores it in a database, which is then ready for the next analysis step.

[0049] Specific examples

[0050] User A enters "headache" and "nausea" and sends the information to the server, which stores it.

[0051] 3. Hospital data collection and analysis

[0052] server

[0053] The server retrieves detailed hospital data (specialties, state-of-the-art medical equipment, staff evaluations, etc.) from a database, and then uses generative AI to analyze and match the patient's condition information and hospital details to the most suitable hospital.

[0054] Specific examples

[0055] The server collects a large amount of hospital data and lists hospitals that specialize in "neurology" or have "the latest CT scan equipment." The generative AI analyzes this data and finds the best hospital for the patient.

[0056] 4. Hospital list suggestions

[0057] server

[0058] Based on the analysis results of the generative AI, the server creates a list of hospitals that are best suited to the patient and sends it to the user's device in real time.

[0059] Terminal

[0060] The terminal displays the received list of hospitals to the user, which includes basic information and characteristics of the hospitals, allowing the user to check them.

[0061] Specific examples

[0062] User A's device displays a list of hospitals that specialize in "neurology" and hospitals that have "the latest CT scan equipment." The list also displays each hospital's specialty and evaluation points.

[0063] 5. Doctor's appointment

[0064] User

[0065] The user selects the most suitable hospital from the list and enters the desired reservation date. The terminal sends this information to the server and completes the reservation procedure.

[0066] server

[0067] The server receives the user's reservation information and sends the information to the selected hospital's reservation system to confirm the reservation.

[0068] Specific examples

[0069] When User A selects "Hospital B" from the list and enters the desired reservation date, the server sends the information to Hospital B's reservation system and confirms the reservation.

[0070] 6. Collecting reviews and ratings

[0071] Terminal

[0072] After the consultation, the user uses the terminal to enter reviews and ratings for the hospital.

[0073] server

[0074] The server stores the received reviews and ratings in a database and uses them for future analysis.

[0075] Specific examples

[0076] After User A visits Hospital B, he or she enters a rating (4 out of 5) and a review such as "The doctor was kind," and the server stores this in a database for future analysis.

[0077] With the above-described configuration, the present invention is a system that enables patients to quickly and reliably find an appropriate medical institution, thereby reducing the stress and anxiety of choosing a hospital.

[0078] The processing flow will be explained below.

[0079] Step 1:

[0080] The user accesses the hospital search system using a terminal and enters their user ID and password on the login screen. The terminal then sends the entered login information to the server.

[0081] Step 2:

[0082] The server authenticates the user by checking the user information in the database based on the received user ID and password. If authentication is successful, the user is guided to a screen to input medical condition information to proceed to the next step.

[0083] Step 3:

[0084] The user uses the terminal to input information about their medical condition and special needs, for example, inputting symptoms such as "headache" and "nausea." The terminal then transmits the input information to the server.

[0085] Step 4:

[0086] The server stores the received medical condition information in a database, and then queries the database to obtain detailed data about the hospital (such as areas of expertise, the up-to-dateness of medical equipment, and staff ratings).

[0087] Step 5:

[0088] The server analyzes the patient and hospital information using the generated AI based on the acquired detailed hospital data. The generated AI then creates a list of hospitals that are best suited to the patient's condition and ranks the list.

[0089] Step 6:

[0090] The server sends the hospital list ranked by the generation AI to the user's device in real time, and the device displays the hospital list to the user.

[0091] Step 7:

[0092] The user uses the terminal to check the displayed list of hospitals and select the one that best suits them. The user then inputs the desired reservation date for the selected hospital. The terminal then sends this reservation information to the server.

[0093] Step 8:

[0094] The server sends the received reservation information to the hospital's reservation system and confirms the reservation. The confirmed reservation information is notified to both the user and the hospital.

[0095] Step 9:

[0096] After the consultation, the user uses the terminal to input their rating and review of the hospital, which is then sent to the server.

[0097] Step 10:

[0098] The server stores the received ratings and reviews in a database and uses them as data for future hospital recommendations, improving the accuracy and reliability of the entire system.

[0099] Example 1

[0100] 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."

[0101] Conventional hospital search systems make it difficult for patients to find the medical institution that best suits their symptoms, and choosing a hospital requires a great deal of time and effort. Furthermore, they lack appropriate reservation systems and do not effectively utilize feedback after consultations. Therefore, there is a need for a system that allows patients to quickly find the appropriate medical institution.

[0102] 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.

[0103] In this invention, the server includes means for receiving and verifying login information for user authentication, means for receiving medical condition information from the patient and storing it in a database, means for acquiring detailed hospital data from the database, means for analyzing and matching the patient's medical condition information and detailed hospital data using a generative AI model, means for creating and transmitting a list of hospitals optimal for the patient based on the analysis results of the generative AI, means for receiving reservation information for the hospital selected by the patient and confirming the reservation, and means for receiving evaluations and reviews of the hospital from the patient and storing them in a database. This enables patients to quickly find the optimal hospital, reducing the stress and anxiety of choosing a hospital.

[0104] "User authentication" is the process of verifying authentication information such as user ID and password when a user accesses a system, and confirming whether the user is a legitimate user.

[0105] "Medical condition information" refers to information provided by patients about their symptoms and special needs, and is the basic data that enables the system to recommend the most suitable hospital.

[0106] The "database" is a high-performance information management system for systematically managing and storing various data required by the system, such as patient information, detailed hospital data, authentication information, ratings and reviews.

[0107] A "generative AI model" is an artificial intelligence model that uses machine learning and natural language processing to analyze data and perform specific tasks (such as matching hospitals).

[0108] "Hospital detailed data" is information about a specific medical institution, including areas of expertise, the latest state of medical equipment, and staff evaluations.

[0109] "Analysis and matching" is a data analysis process in which the generative AI model selects the most suitable hospital based on the patient's medical condition information and detailed hospital data.

[0110] The "hospital list" is a list of the most suitable medical institutions presented to patients based on the analysis results of the generative AI model, and includes basic information and characteristics of each hospital.

[0111] "Reservation information" is information relating to the patient's desired consultation date and time and the selected hospital, and is data necessary to confirm the reservation via the server.

[0112] "Evaluations and reviews" refer to the evaluations and opinions that patients provide to hospitals after their consultations, and are feedback data that the system can use for future analysis.

[0113] This invention relates to a system that uses a generative AI model to recommend the most suitable hospital for a patient. This system operates using a user's terminal, a server, and a database.

[0114] 1. User Login

[0115] Terminal

[0116] The user logs in to the hospital search system using a terminal. The login screen is displayed and the user enters their user ID and password. After inputting the information, the terminal sends this information to the server.

[0117] server

[0118] The server verifies the received login information and authenticates the user. If authentication is successful, the user can proceed to the next step.

[0119] Specific examples

[0120] When User A logs in to the hospital search system and enters his / her user ID and password, authentication is performed and the user is then directed to a screen for entering medical condition information.

[0121] 2. Enter patient information

[0122] Terminal

[0123] After authentication is complete, the user inputs their symptoms and special needs, and the terminal transmits the medical condition information received from the user to the server.

[0124] server

[0125] The server receives the patient's medical condition information and stores it in a database, which is then ready for the next analysis step.

[0126] Specific examples

[0127] User A enters "headache" and "nausea" and sends the information to the server, which stores it.

[0128] 3. Hospital data collection and analysis

[0129] server

[0130] The server retrieves detailed hospital data (specialties, state-of-the-art medical equipment, staff evaluations, etc.) from a database, and then uses a generative AI model to analyze and match the patient's condition information and hospital details to the most suitable hospital.

[0131] Specific examples

[0132] The server collects a large amount of hospital data and lists hospitals that specialize in "neurology" or have "the latest CT scan equipment." The generative AI model analyzes this data and finds the best hospital for the patient.

[0133] Example prompt sentence:

[0134] "Please suggest the best hospital based on the symptom information provided by the user."

[0135] 4. Hospital list suggestions

[0136] server

[0137] Based on the analysis results of the generative AI model, the server creates a list of hospitals that are best suited to the patient and sends it to the user's device in real time.

[0138] Terminal

[0139] The terminal displays the received list of hospitals to the user, which includes basic information and characteristics of the hospitals, allowing the user to check them.

[0140] Specific examples

[0141] User A's device displays a list of hospitals that specialize in "neurology" and hospitals that have "the latest CT scan equipment." The list also displays each hospital's specialty and evaluation points.

[0142] 5. Doctor Appointments

[0143] User

[0144] The user selects the most suitable hospital from the list and enters the desired reservation date. The terminal sends this information to the server and completes the reservation procedure.

[0145] server

[0146] The server receives the user's reservation information and sends the information to the selected hospital's reservation system to confirm the reservation.

[0147] Specific examples

[0148] When User A selects "Hospital B" from the list and enters the desired reservation date, the server sends the information to Hospital B's reservation system and confirms the reservation.

[0149] 6. Collecting reviews and ratings

[0150] Terminal

[0151] After the consultation, the user uses the terminal to enter reviews and ratings for the hospital.

[0152] server

[0153] The server stores the received reviews and ratings in a database and uses them for future analysis.

[0154] Specific examples

[0155] After User A visits Hospital B, he or she enters a rating (4 out of 5) and a review such as "The doctor was kind," and the server stores this in a database for future analysis.

[0156] This system allows patients to quickly and reliably find the appropriate medical institution, reducing the stress and anxiety of choosing a hospital.

[0157] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0158] Step 1:

[0159] User authentication

[0160] The user enters their user ID and password on the login screen of the hospital search system on their terminal. The terminal sends the entered login information to the server. The server authenticates the user by comparing the received login information with the authentication information in the database. If authentication is successful, the user can proceed to the next step.

[0161] Input: User ID and password

[0162] Data processing: The device encodes the login information and sends it to the server

[0163] Data calculation: The server checks the data against the database

[0164] Output: Authentication result

[0165] Specific operation: The user enters login information and clicks the "Login" button. The device sends the user ID and password to the server, and the server returns the authentication result to the device.

[0166] Step 2:

[0167] Enter medical condition information

[0168] After completing the authentication, the user inputs their medical condition and special needs. The terminal sends the medical condition information received from the user to the server. The server stores the medical condition information from the patient in a database.

[0169] Input: Medical condition information (e.g., "headache," "nausea")

[0170] Data processing: The device sends the input information

[0171] Data calculation: The server stores the information in a database

[0172] Output: Saved results

[0173] Specific operation: The user enters the medical condition and clicks the "Submit" button. The terminal sends the medical condition information to the server, which then stores it in the database.

[0174] Step 3:

[0175] Hospital Data Collection and Analysis

[0176] The server retrieves detailed hospital data (specialties, state-of-the-art medical equipment, staff evaluations, etc.) from the database, and then uses a generative AI model to analyze and match the most suitable hospital based on the patient's medical condition information and the hospital's detailed data.

[0177] Input: Hospital details and medical condition information in the database

[0178] Data processing: Acquire detailed hospital data and integrate it with medical condition information

[0179] Data Computing: Analysis and Matching with Generative AI Models

[0180] Output: A list of suitable hospitals

[0181] Specific operation: The server executes a database query to retrieve hospital data. The generative AI model is given the prompt "Symptoms: headache, nausea, Specialty: neurology, Equipment: latest CT scan." The generative AI model then outputs a list of the most suitable hospitals.

[0182] Step 4:

[0183] Hospital list suggestions

[0184] The server creates a list of hospitals that are optimal for the patient based on the analysis results of the generative AI model. The server then sends the list to the user's device, which then displays the received list to the user.

[0185] Input: Analysis results of the generative AI model

[0186] Data processing: Formatting hospital list

[0187] Data calculation: Creating and sending a list

[0188] Output: Hospital list

[0189] Specific operation: The server formats the analysis results of the generated AI model and creates a list of hospitals. The server then sends the list of hospitals to the device, which then displays it to the user.

[0190] Step 5:

[0191] Hospital appointment

[0192] The user selects the desired hospital from the list and enters the desired reservation date. The terminal sends the entered reservation information to the server. The server then sends the received reservation information to the reservation system of the selected hospital and confirms the reservation.

[0193] Input: Reservation information (selected hospital, desired date)

[0194] Data processing: Sending reservation information

[0195] Data calculation: Sending and confirming reservation information

[0196] Output: Reservation confirmation

[0197] Specific operation: The user selects a hospital, enters the desired appointment date, and clicks the "Book" button. The terminal sends the appointment information to the server, which then sends the information to the hospital's appointment system and confirms the appointment.

[0198] Step 6:

[0199] Collecting reviews and ratings

[0200] After a consultation, the user uses the device to input reviews and ratings for the hospital. The device then sends the input reviews and ratings to the server. The server then stores the received reviews and ratings in a database and uses them for future analysis.

[0201] Input: Rating and review (e.g., "Rating: 4 points, Comment: The doctor was kind.")

[0202] Data Processing: Submitting Reviews and Ratings

[0203] Data calculation: Saving to database

[0204] Output: Saved results

[0205] Specific operation: The user enters a rating and review and clicks the "Submit" button. The device sends the rating and review to the server, which stores them in a database.

[0206] (Application example 1)

[0207] 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."

[0208] In today's food delivery services, users often have difficulty finding restaurants that meet their dietary preferences and restrictions, and they need the information they need quickly. Furthermore, the process of making reservations at the restaurant of their choice can be cumbersome. A system that solves these problems is needed.

[0209] 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.

[0210] In this invention, the server includes a means for receiving preference information from a user, a means for acquiring detailed restaurant data from a database, and a means for analyzing and matching the user's preference information with the detailed restaurant data using a generating AI. This makes it possible to recommend the most suitable restaurant for the user. In addition, by assisting the user in making a reservation at the restaurant selected by the user, the complexity of the reservation procedure can be reduced.

[0211] "User" means an individual who uses the food delivery system to search for and make reservations at restaurants.

[0212] "Preference information" refers to information about dietary preferences and restrictions entered by the user, specifically dietary restrictions and preferred types of cuisine.

[0213] "Restaurant detailed data" refers to data that includes information about each restaurant, such as its specialty, the latest equipment, and staff ratings.

[0214] "Generative AI" is an artificial intelligence technology used to match users with the best restaurants based on their preferences and detailed restaurant data.

[0215] The "suggestion method" is a method for presenting a list of restaurants suitable for the user based on the analysis results of the generative AI.

[0216] "Reservation Process" means the process by which a User makes an online reservation with a Restaurant of their choice.

[0217] This invention is a system that allows users to use a food delivery system to search for and make reservations at the most suitable restaurant based on their preferences. This system mainly consists of the following steps.

[0218] 1. In the step of receiving preference information from a user, the user uses a terminal to input preference information such as dietary preferences and dietary restrictions. The terminal then transmits this preference information to a server.

[0219] 2. The server obtains the authentication token and uses an API to collect the restaurant details from a database. The API used here is a publicly available API of a food delivery service.

[0220] 3. Using the generation AI, the server analyzes and matches the user's preference information with the collected restaurant details. This generation AI uses an artificial intelligence model such as OpenAI's GPT-4 (registered trademark). Specifically, matching is performed by inputting the following prompt sentence into the generation AI:

[0221] "User preferences: ['vegan', 'low-carb']\nLocation: 'Tokyo'\nFind restaurants that match these preferences."

[0222] 4. Based on the analysis results of the generation AI, the server creates a list of restaurant recommendations that are best suited to the user and sends it to the user's device in real time. The device then displays the received restaurant list to the user, allowing the user to select a restaurant that best suits their preferences.

[0223] 5. The user selects a specific restaurant from the list and enters the date and time they wish to make a reservation. The device then sends this information to the server, which then sends it to the selected restaurant's reservation system to assist with the reservation process.

[0224] 6. After using the service, the user enters reviews and ratings of the restaurant through the terminal. The server stores these reviews and ratings in a database for future analysis.

[0225] This allows users to quickly and reliably find restaurants that suit their preferences and smoothly complete the reservation process. As a concrete example, if a vegan or low-carb user is looking for a restaurant in Tokyo, they can provide the following prompt to the generative AI model:

[0226] "User preferences: ['vegan', 'low-carb']\nLocation: 'Tokyo'\nFind restaurants that match these preferences."

[0227] Based on these prompts, the generative AI can suggest the most suitable restaurant, greatly improving user convenience.

[0228] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0229] Step 1:

[0230] Users log in using their device and enter their preferences. Specifically, users enter their food preferences, dietary restrictions, and desired location. Input data includes "vegan," "low carb," and "Tokyo." This information is sent to the server. The system receives the user's preferences and login information as input and sends it to the server.

[0231] Step 2:

[0232] The server receives the received preference information and performs the necessary steps to obtain an authentication token, which is required for API calls. The server then sends a request to the public food delivery service's API to collect restaurant details. It receives the preference information and authentication information as input and sends a request to the API to retrieve restaurant information from the database.

[0233] Step 3:

[0234] The server uses the generative AI model to analyze and match the restaurant's detailed data with the user's preferences based on the restaurant's detailed data obtained from the API, and generates a prompt sentence to input into the generative AI model.

[0235] An example of a specific prompt is:

[0236] "User preferences: ['vegan', 'low-carb']\nLocation: 'Tokyo'\nFind restaurants that match these preferences."

[0237] It takes user preference information and restaurant details as input, sends prompts to a generative AI model, and outputs a list of optimal restaurants.

[0238] Step 4:

[0239] The server receives the analysis results of the generative AI model and creates a list of restaurants that are optimal for the user based on the results. This list includes the restaurant's name, location, rating, specialties, etc. The server sends this information to the user's device in real time. It receives the analysis results of the generative AI model as input and creates and sends a list of restaurants for the user as output.

[0240] Step 5:

[0241] The user checks the list of suggested restaurants on the device and inputs the restaurant of their choice and the desired reservation date and time. The device sends this information to the server. The device receives the user's selection and reservation information as input and sends it to the server.

[0242] Step 6:

[0243] The server receives the reservation information and sends it to the selected restaurant's reservation system to complete the reservation process. It receives the user's reservation information as input and sends the data to the restaurant's reservation system as output to complete the reservation.

[0244] Step 7:

[0245] After using a restaurant's service, a user uses the device to enter reviews and ratings for the restaurant. The device sends this information to the server. The server stores the received reviews and ratings in a database for future analysis. The server receives the user's reviews and ratings as input and stores them in a database.

[0246] 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.

[0247] This invention relates to a system that uses generative AI and an emotion engine to suggest the most suitable hospital for a patient. The details of the interactions between the user, terminal, and server, as well as each function, are explained below.

[0248] 1. User login and information entry

[0249] Terminal

[0250] The user logs in to the hospital search system using a terminal. The login screen is displayed and the user enters their user ID and password. After inputting the information, the terminal sends this information to the server.

[0251] server

[0252] The server verifies the received login information and authenticates the user. If authentication is successful, the user proceeds to the next step.

[0253] Specific examples

[0254] When User A logs in to the hospital search system and enters his / her user ID and password, authentication is performed and the user is then directed to a screen for entering medical condition information.

[0255] 2. Entering patient information and recognizing emotions

[0256] Terminal

[0257] After successful authentication, the user inputs their symptoms and special needs. In addition, the device is equipped with an emotion engine that can analyze the user's emotional state from their input and voice.

[0258] server

[0259] The server stores the received medical condition information and the emotion analysis results from the emotion engine in a database, which is then ready for the next analysis step.

[0260] Specific examples

[0261] User A inputs "headache" and "nausea," and the emotion engine analyzes the user's stress level while inputting. The server stores this information.

[0262] 3. Hospital data collection and analysis

[0263] server

[0264] The server retrieves detailed hospital data (specialties, state-of-the-art medical equipment, staff evaluations, etc.) from a database, and uses generative AI to perform an analysis to match the patient with the most suitable hospital based on the patient's medical condition information, detailed hospital data, and sentiment analysis results.

[0265] Specific examples

[0266] The server collects a large amount of hospital data and lists hospitals that specialize in "neurology" or have "the latest CT scan equipment." The generative AI analyzes the user's gender and also considers their stress level to find the best hospital.

[0267] 4. Hospital list suggestions

[0268] server

[0269] Based on the analysis results of the generative AI, the server creates a list of hospitals that are best suited to the patient and sends it to the user's device in real time.

[0270] Terminal

[0271] The device displays the received list of hospitals to the user, including basic information and characteristics of the hospitals, as well as additional suggestions based on the results of the emotion engine analysis.

[0272] Specific examples

[0273] User A's device will display a list of hospitals that specialize in "neurology" and have "the latest CT scan equipment," and will also suggest hospitals that provide mental care to address User A's high stress levels.

[0274] 5. Doctor's appointment

[0275] User

[0276] The user uses the terminal to select the most suitable hospital from the displayed list of hospitals and enters the desired reservation date. The terminal then sends this reservation information to the server.

[0277] server

[0278] The server sends the received reservation information to the hospital's reservation system and confirms the reservation. The confirmed reservation information is notified to both the user and the hospital.

[0279] Specific examples

[0280] When User A selects "Hospital B" from the list and enters the desired reservation date, the server sends the information to Hospital B's reservation system and confirms the reservation.

[0281] 6. Collecting reviews and ratings

[0282] Terminal

[0283] After the consultation, the user can use the terminal to input their evaluation and review of the hospital. In addition, if any emotional changes during the consultation have been recorded, they can also input that data.

[0284] server

[0285] The server stores the received reviews, ratings, and emotional data in a database and uses them to suggest hospitals in the future.

[0286] Specific examples

[0287] When User A enters a rating for "Hospital B" (4 out of 5) and a review such as "The doctor was kind," the server stores these in a database and uses them for future analysis.

[0288] By having the above-described configuration, the present invention is a system that enables patients to quickly and reliably find an appropriate medical institution, and further, by taking into account the user's emotional state, can improve the accuracy of hospital selection.

[0289] The processing flow will be explained below.

[0290] Step 1:

[0291] The user accesses the hospital search system using a terminal and enters their user ID and password on the login screen. The terminal then sends the entered login information to the server.

[0292] Step 2:

[0293] The server authenticates the user by checking the user information in the database based on the received user ID and password. If authentication is successful, the user is guided to a screen to input medical condition information to proceed to the next step.

[0294] Step 3:

[0295] The user uses the device to input information about their medical condition and special needs. For example, they input symptoms such as "headache" and "nausea." The device then sends the input information to the server. The device then uses an emotion engine to analyze emotions from the user's input and voice.

[0296] Step 4:

[0297] The server stores the received medical condition information and the emotion analysis results from the emotion engine in a database, which prepares the database for the next analysis step.

[0298] Step 5:

[0299] The server retrieves detailed hospital data (areas of expertise, up-to-dateness of medical equipment, staff evaluations, etc.) from a database, and then uses generative AI to analyze and match the patient's medical condition information, detailed hospital data, and sentiment analysis results.

[0300] Step 6:

[0301] The server sends the hospital list ranked by the generative AI to the user's device in real time, including suggestions that take into account the results of sentiment analysis.

[0302] Step 7:

[0303] A list of hospitals is displayed on the terminal, and the user selects the most suitable hospital from the list. The user then inputs the desired reservation date for the selected hospital. The terminal then sends this reservation information to the server.

[0304] Step 8:

[0305] The server sends the received reservation information to the hospital's reservation system and confirms the reservation. The confirmed reservation information is notified to both the user and the hospital.

[0306] Step 9:

[0307] After the consultation, the user uses the device to input their evaluation and review of the hospital. Emotional data may also be recorded during input. This information is also sent to the server.

[0308] Step 10:

[0309] The server stores the received ratings, reviews, and emotion data in a database and uses it to make future hospital recommendations, improving the accuracy and reliability of the entire system.

[0310] Example 2

[0311] 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."

[0312] Conventional hospital search systems have the problem of being unable to suggest hospitals that take into account the patient's emotional state, and therefore unable to address the patient's psychological needs.In addition, they do not provide enough information necessary to select an appropriate hospital, making it difficult for patients to quickly find the hospital that is best suited to them.

[0313] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for receiving medical condition information from a patient, a means for analyzing an emotional state based on the medical condition information, a means for acquiring detailed hospital data from a database, a means for analyzing and matching the patient's medical condition information, the analysis results of the emotional state, and the detailed hospital data using a generation AI, and a means for suggesting the most suitable hospital for the patient. This enables the patient to quickly and appropriately find a hospital that is best suited to their medical condition and emotional state.

[0314] "Patient" refers to an individual who provides medical information and seeks appropriate medical care.

[0315] "Medical condition information" refers to information necessary for selecting a medical institution, such as a patient's current symptoms, special needs, and medical history.

[0316] "Emotional state" refers to information that indicates the patient's psychological state and emotional level, and is analyzed by the emotion engine.

[0317] "Hospital detailed data" refers to various information related to the hospital, such as the content of medical services provided by the hospital, the up-to-dateness of medical equipment, and evaluations of medical staff.

[0318] "Generative AI" refers to artificial intelligence that uses machine learning and natural language processing to analyze data and generate optimal suggestions.

[0319] "Analysis and matching" refers to the process of using generative AI to select the most suitable hospital based on the patient's medical condition information, the analysis results of their emotional state, and detailed hospital data.

[0320] "Proposal means" refers to the method or process of providing patients with the analysis and matching results using generative AI.

[0321] This invention is a system that suggests the most suitable hospital for a patient, and the details of the interactions between the user, terminal, and server, as well as each function, will be explained below.

[0322] User logs in and enters information

[0323] Terminal

[0324] The user logs in to the hospital search system using a terminal. The login screen is displayed and the user enters their user ID and password. After inputting the information, the terminal sends this information to the server.

[0325] server

[0326] The server verifies the received login information and authenticates the user. If authentication is successful, the user proceeds to the next step.

[0327] Entering patient information and recognizing emotions

[0328] Terminal

[0329] After successful authentication, the user inputs their symptoms and special needs. In addition, the device is equipped with an emotion engine that can analyze the user's emotional state from their input and voice.

[0330] server

[0331] The server stores the received medical condition information and the emotion analysis results from the emotion engine in a database.

[0332] Hospital data collection and analysis

[0333] server

[0334] The server retrieves detailed hospital data (specialties, state-of-the-art medical equipment, staff evaluations, etc.) from a database, and then uses a generative AI model to perform an analysis to match the patient with the most suitable hospital based on the patient's medical condition information, the results of an analysis of their emotional state, and the detailed hospital data.

[0335] Hospital list suggestions

[0336] server

[0337] Based on the analysis results of the generative AI model, the server creates a list of hospitals that are best suited to the patient and sends it to the user's device in real time.

[0338] Terminal

[0339] The device displays the received list of hospitals to the user, including basic information and characteristics of the hospitals, as well as additional suggestions based on the results of the emotion engine analysis.

[0340] Clinic appointment

[0341] User

[0342] The user uses the terminal to select the most suitable hospital from the displayed list of hospitals and enters the desired reservation date. The terminal then sends this reservation information to the server.

[0343] server

[0344] The server sends the received reservation information to the hospital's reservation system and confirms the reservation. The confirmed reservation information is notified to both the user and the hospital.

[0345] Collecting reviews and ratings

[0346] Terminal

[0347] After the consultation, the user can use the terminal to input their evaluation and review of the hospital. In addition, if any emotional changes during the consultation are recorded, they can also input that data.

[0348] server

[0349] The server stores the received reviews, ratings, and emotional data in a database and uses them to suggest hospitals in the future.

[0350] Hardware and software used

[0351] Device: Smartphone, tablet, or PC.

[0352] Server: A high-performance database server (e.g., MySQL (registered trademark), PostgreSQL).

[0353] Emotion engine: Natural language processing libraries and machine learning models (e.g., TENSORFLOW®, PyTorch).

[0354] Generative AI models: AI models used for data analysis and text generation (e.g., GPT-3 (registered trademark), BERT).

[0355] Examples of concrete examples and prompts

[0356] Specific examples

[0357] User A logs into the hospital search system and inputs his / her symptoms of headache and nausea, while the emotion engine analyzes his / her stress level. The server stores this information, and the generative AI model compares it with hospital data to identify the most suitable hospital. As a result, a list of appropriate hospitals is displayed on User A's device, and an appointment at the selected hospital is confirmed. After the consultation, User A inputs his / her rating and reviews of the hospital, which are used to make suggestions for future visits.

[0358] Prompt Sentence Examples

[0359] Symptoms: Headache and nausea

[0360] Special Needs: Stress Management

[0361] Emotional state: High stress levels

[0362] Specialty: Neurology

[0363] Medical equipment: Latest CT scan equipment

[0364] Staff rating: Highly rated

[0365] This system allows patients to quickly and reliably find the right medical institution, and by taking into account the patient's emotional state, it can improve the accuracy of hospital selection.

[0366] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0367] Step 1:

[0368] User login and information entry

[0369] (input)

[0370] The user enters the user ID and password into the terminal.

[0371] (Specific actions)

[0372] The user logs in to the hospital search system using a terminal. The login screen is displayed and the user enters their user ID and password.

[0373] (Data processing and calculation)

[0374] The terminal sends the entered user ID and password to the server.

[0375] (output)

[0376] The server verifies the received login information and sends the authentication result to the terminal.

[0377] Step 2:

[0378] Entering patient information and recognizing emotions

[0379] (input)

[0380] Users enter medical conditions and special needs into the terminal and also use voice to indicate their emotional state.

[0381] (Specific actions)

[0382] After successful authentication, the user inputs their symptoms and special needs, and the device is equipped with an emotion engine that analyzes their emotional state from the input and voice.

[0383] (Data processing and calculation)

[0384] The device sends the medical condition information and emotion analysis results to the server.

[0385] (output)

[0386] The server stores the received medical condition information and emotion analysis results in a database.

[0387] Step 3:

[0388] Hospital data collection and analysis

[0389] (input)

[0390] The server retrieves the hospital details from the database.

[0391] (Specific actions)

[0392] The server retrieves detailed hospital data (areas of expertise, up-to-dateness of medical equipment, staff evaluations, etc.) from a database.

[0393] (Data processing and calculation)

[0394] Using a generative AI model, an analysis is performed to match the most suitable hospital based on medical condition information, emotional state analysis results, and detailed hospital data.

[0395] (output)

[0396] The server generates a list of optimal hospitals as a result of the analysis.

[0397] Step 4:

[0398] Hospital list suggestions

[0399] (input)

[0400] The server generates a list of the best hospitals.

[0401] (Specific actions)

[0402] Based on the analysis results of the generative AI, a list of optimal hospitals is created and sent to the user's device.

[0403] (Data processing and calculation)

[0404] The server transmits the hospital list to the user terminal.

[0405] (output)

[0406] The terminal displays the received hospital list to the user.

[0407] Step 5:

[0408] Clinic appointment

[0409] (input)

[0410] The user selects a hospital from the list of hospitals and enters the desired appointment date.

[0411] (Specific actions)

[0412] The user selects the most suitable hospital from the displayed list and enters the desired reservation date. The terminal then sends this reservation information to the server.

[0413] (Data processing and calculation)

[0414] The server sends the received reservation information to the hospital's reservation system and confirms the reservation.

[0415] (output)

[0416] The server notifies both the user and the hospital of the confirmed reservation information.

[0417] Step 6:

[0418] Collecting reviews and ratings

[0419] (input)

[0420] Users enter their ratings and reviews of the hospital after their consultation.

[0421] (Specific actions)

[0422] After the consultation, the user can enter their evaluation and review of the hospital. If any emotional changes have been recorded, they can also enter that data.

[0423] (Data processing and calculation)

[0424] The device sends the ratings, reviews, and emotion data to the server.

[0425] (output)

[0426] The server stores the received reviews, ratings, and emotional data in a database, which will be used to recommend hospitals in the future.

[0427] (Application example 2)

[0428] 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."

[0429] Many conventional hospital recommendation systems suggest medical institutions based on a patient's medical condition information. However, these suggestions are made without taking into account the patient's emotional state or current mood, so the options are not necessarily comfortable for the patient. Furthermore, particularly in food delivery, there are no systems that can suggest optimal meals tailored to the user's emotional state, making it difficult to increase user satisfaction. Therefore, the present invention aims to provide a system that combines a patient's medical condition information and emotional state to make more accurate hospital and meal recommendations.

[0430] 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.

[0431] In this invention, the server includes means for receiving medical condition information from a patient, means for acquiring detailed hospital data from a database, means for analyzing and matching the patient's medical condition information and the detailed hospital data using a generative AI, means for analyzing the user's emotional state and using the analysis results to suggest the optimal meal for the patient, and means for suggesting the optimal hospital for the patient. This enables the patient to select a hospital and meal that are appropriate for their emotional state at the time, thereby improving user satisfaction and quality of life.

[0432] "Patient" refers to a person receiving medical examination or treatment.

[0433] "Medical condition information" refers to information about the symptoms or illnesses a patient is suffering from.

[0434] "Hospital detailed data" refers to information such as the hospital's areas of expertise, the state of the art of medical equipment, and staff evaluations.

[0435] "Generative AI" is a type of artificial intelligence that refers to a program that has the ability to generate new information based on large amounts of data.

[0436] "Emotional state" refers to the user's mood and psychological state at any given time.

[0437] An "emotion engine" refers to a system for analyzing emotions from user input and voice.

[0438] A "database" refers to a collection of data organized to allow efficient searching and management.

[0439] "Matching" refers to the process of comparing multiple items and finding the combination that best suits their respective characteristics.

[0440] "Suggestion" refers to the act of prompting a user for a particular option or piece of information.

[0441] "Food delivery" refers to a service that delivers meals ordered by a user to a specific location.

[0442] "Meal suggestions" refers to providing optimal meals based on the user's preferences and emotional state.

[0443] This invention is a system that suggests optimal hospitals and meals based on a user's medical condition information and emotional state. This system is realized using hardware and software components such as a smartphone application and a backend server.

[0444] System configuration

[0445] The system includes the following major components:

[0446] 1. User terminal: Implemented as a smartphone application, it provides an interface for users to input information about their medical condition and emotional state.

[0447] 2. Server: The backend server, using the Django framework, processes data from users, connects with the database for analysis, and includes generative AI models for generating meal and hospital recommendations.

[0448] 3. Database: Used to store detailed hospital data and users' medical condition and emotion data.

[0449] Explanation of program processing

[0450] User login and information entry

[0451] The user terminal provides an interface for the user to log in to the system. The user enters their ID and password, and the login information is sent to the server for authentication.

[0452] Entering medical information and emotional state

[0453] After successful authentication, the user device displays a screen prompting the user to enter their medical condition information and emotional state. As the user enters their medical condition information, the emotional engine analyzes their emotional state and transmits the data to the server.

[0454] Hospital data collection and analysis

[0455] The server retrieves detailed hospital data from the database and uses generative AI to analyze and match the user's medical condition information with the detailed hospital data. The generative AI model is implemented using PyTorch, TensorFlow, and other tools. Emotional state data is also included in the analysis to identify the most suitable hospital.

[0456] Meal suggestions

[0457] It also makes meal suggestions based on the user's emotional state.Generation AI is also used to generate meal suggestions, analyzing prompts based on user input to suggest appropriate meals.

[0458] Specific examples

[0459] For example, if User A logs in and inputs "headache" and "nausea," and the emotion engine detects high stress, the server will input a prompt to the generation AI saying, "Please suggest the best food for the user currently feeling stressed." The generation AI will then make suggestions such as "green tea," "healthy salad," and "fruit," and display them on the user's device.

[0460] Examples of prompts are:

[0461] "Please suggest the best foods for the user if they are currently feeling stressed."

[0462] As a result, the system takes into account the user's emotional state to suggest hospitals and meals that are best suited to the user's condition, improving the user's satisfaction and quality of life.

[0463] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0464] Step 1:

[0465] A user logs in to a terminal.

[0466] Input: User ID, Password

[0467] Specific operation: The user starts the smartphone application and enters their user ID and password on the login screen. The entered information is sent to the server.

[0468] Output: Authentication success or failure response

[0469] Step 2:

[0470] The server authenticates the user.

[0471] Input: User ID, Password

[0472] Specific operation: The server checks the received user ID and password against the database and performs authentication. If authentication is successful, it proceeds to the next step.

[0473] Output: Authentication result (success or failure)

[0474] Step 3:

[0475] The user inputs medical condition information and emotional state.

[0476] Input: medical condition information, emotional data

[0477] Specific operation: After successful authentication, the user will proceed to the screen to input their medical condition information. At the same time, the device will analyze the user's emotional data from their voice and text, and send this information to the server.

[0478] Output: medical condition information, emotional data

[0479] Step 4:

[0480] The server collects and analyzes hospital data.

[0481] Input: medical condition information, emotional data

[0482] How it works: The server retrieves detailed hospital data (specialties, state-of-the-art medical equipment, staff ratings, etc.) from the database, then uses a generative AI model to analyze and match the patient's condition information with the hospital data. Sentiment data is also added to this analysis to identify the most suitable hospital.

[0483] Output: List of best hospitals

[0484] Step 5:

[0485] Servers will make meal suggestions.

[0486] Input: Emotion data

[0487] Specific operation: Based on the emotion data, the server inputs the prompt sentence "Please suggest the best food for the user if he / she is currently feeling stressed" into the generative AI model. The generative AI generates meal suggestions based on this prompt sentence.

[0488] Output: A list of meal suggestions

[0489] Step 6:

[0490] The server sends the optimal hospital and meal recommendations to the user terminal.

[0491] Input: List of best hospitals, list of meal suggestions

[0492] Specific operation: The server sends the generated list of optimal hospitals and meal recommendations to the user's device, which receives it and displays it on the screen.

[0493] Output: Display of hospital list and meal suggestions

[0494] Step 7:

[0495] The user selects a hospital and makes a reservation.

[0496] Input: Selected hospital, desired appointment date

[0497] Specific operation: The user selects a hospital from the displayed list of hospitals and enters the desired reservation date. This information is sent to the server, which then sends the reservation information to the hospital's reservation system and confirms the reservation.

[0498] Output: Reservation confirmation information

[0499] Step 8:

[0500] Users enter ratings and reviews after their consultation.

[0501] Input: Post-consultation evaluation, reviews, emotional change data

[0502] Specific operation: After the consultation, the user uses the device to enter their evaluation and review of the hospital. In addition, if any emotional changes during the consultation were recorded, they can also enter that data. The server stores this information in a database and uses it to make suggestions for future visits.

[0503] Output: Ratings, reviews, and sentiment data

[0504] 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.

[0505] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0506] 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.

[0507] [Second embodiment]

[0508] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0509] 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.

[0510] 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).

[0511] 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.

[0512] 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.

[0513] 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).

[0514] 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.

[0515] 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.

[0516] 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.

[0517] 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.

[0518] In the smart glasses 214, 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.

[0519] 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."

[0520] This invention relates to a system that uses generative AI to suggest the best hospital for a patient. The details of the interactions between the user, terminal, and server, as well as each function, are explained below.

[0521] 1. User login and information entry

[0522] Terminal

[0523] The user logs in to the hospital search system using a terminal. The login screen is displayed and the user enters their user ID and password. After input, the terminal sends this information to the server.

[0524] server

[0525] The server verifies the received login information and authenticates the user. If authentication is successful, the user proceeds to the next step.

[0526] Specific examples

[0527] When User A logs in to the hospital search system and enters his / her user ID and password, authentication is performed and the user is then directed to a screen for entering medical condition information.

[0528] 2. Enter patient information

[0529] Terminal

[0530] After authentication is complete, the user enters their symptoms and special needs, and the terminal transmits the medical condition information received from the user to the server.

[0531] server

[0532] The server receives the patient's medical condition information and stores it in a database, which is then ready for the next analysis step.

[0533] Specific examples

[0534] User A enters "headache" and "nausea" and sends the information to the server, which stores it.

[0535] 3. Hospital data collection and analysis

[0536] server

[0537] The server retrieves detailed hospital data (specialties, state-of-the-art medical equipment, staff evaluations, etc.) from a database, and then uses generative AI to analyze and match the patient's condition information and hospital details to the most suitable hospital.

[0538] Specific examples

[0539] The server collects a large amount of hospital data and lists hospitals that specialize in "neurology" or have "the latest CT scan equipment." The generative AI analyzes this data and finds the best hospital for the patient.

[0540] 4. Hospital list suggestions

[0541] server

[0542] Based on the analysis results of the generative AI, the server creates a list of hospitals that are best suited to the patient and sends it to the user's device in real time.

[0543] Terminal

[0544] The terminal displays the received list of hospitals to the user, which includes basic information and characteristics of the hospitals, allowing the user to check them.

[0545] Specific examples

[0546] User A's device displays a list of hospitals that specialize in "neurology" and hospitals that have "the latest CT scan equipment." The list also displays each hospital's specialty and evaluation points.

[0547] 5. Doctor's appointment

[0548] User

[0549] The user selects the most suitable hospital from the list and enters the desired reservation date. The terminal sends this information to the server and completes the reservation procedure.

[0550] server

[0551] The server receives the user's reservation information and sends the information to the selected hospital's reservation system to confirm the reservation.

[0552] Specific examples

[0553] When User A selects "Hospital B" from the list and enters the desired reservation date, the server sends the information to Hospital B's reservation system and confirms the reservation.

[0554] 6. Collecting reviews and ratings

[0555] Terminal

[0556] After the consultation, the user uses the terminal to enter reviews and ratings for the hospital.

[0557] server

[0558] The server stores the received reviews and ratings in a database and uses them for future analysis.

[0559] Specific examples

[0560] After User A visits Hospital B, he or she enters a rating (4 out of 5) and a review such as "The doctor was kind," and the server stores this in a database for future analysis.

[0561] With the above-described configuration, the present invention is a system that enables patients to quickly and reliably find an appropriate medical institution, thereby reducing the stress and anxiety of choosing a hospital.

[0562] The processing flow will be explained below.

[0563] Step 1:

[0564] The user accesses the hospital search system using a terminal and enters their user ID and password on the login screen. The terminal then sends the entered login information to the server.

[0565] Step 2:

[0566] The server authenticates the user by checking the user information in the database based on the received user ID and password. If authentication is successful, the user is guided to a screen to input medical condition information to proceed to the next step.

[0567] Step 3:

[0568] The user uses the terminal to input information about their medical condition and special needs, for example, inputting symptoms such as "headache" and "nausea." The terminal then transmits the input information to the server.

[0569] Step 4:

[0570] The server stores the received medical condition information in a database, and then queries the database to obtain detailed data about the hospital (such as areas of expertise, the up-to-dateness of medical equipment, and staff ratings).

[0571] Step 5:

[0572] The server analyzes the patient and hospital information using the generated AI based on the acquired detailed hospital data. The generated AI then creates a list of hospitals that are best suited to the patient's condition and ranks the list.

[0573] Step 6:

[0574] The server sends the hospital list ranked by the generation AI to the user's device in real time, and the device displays the hospital list to the user.

[0575] Step 7:

[0576] The user uses the terminal to check the displayed list of hospitals and select the one that best suits them. The user then inputs the desired reservation date for the selected hospital. The terminal then sends this reservation information to the server.

[0577] Step 8:

[0578] The server sends the received reservation information to the hospital's reservation system and confirms the reservation. The confirmed reservation information is notified to both the user and the hospital.

[0579] Step 9:

[0580] After the consultation, the user uses the terminal to input their rating and review of the hospital, which is then sent to the server.

[0581] Step 10:

[0582] The server stores the received ratings and reviews in a database and uses them as data for future hospital recommendations, improving the accuracy and reliability of the entire system.

[0583] Example 1

[0584] 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."

[0585] Conventional hospital search systems make it difficult for patients to find the medical institution that best suits their symptoms, and choosing a hospital requires a great deal of time and effort. Furthermore, they lack appropriate reservation systems and do not effectively utilize feedback after consultations. Therefore, there is a need for a system that allows patients to quickly find the appropriate medical institution.

[0586] 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.

[0587] In this invention, the server includes means for receiving and verifying login information for user authentication, means for receiving medical condition information from the patient and storing it in a database, means for acquiring detailed hospital data from the database, means for analyzing and matching the patient's medical condition information and detailed hospital data using a generative AI model, means for creating and transmitting a list of hospitals optimal for the patient based on the analysis results of the generative AI, means for receiving reservation information for the hospital selected by the patient and confirming the reservation, and means for receiving evaluations and reviews of the hospital from the patient and storing them in a database. This enables patients to quickly find the optimal hospital, reducing the stress and anxiety of choosing a hospital.

[0588] "User authentication" is the process of verifying authentication information such as user ID and password when a user accesses a system, and confirming whether the user is a legitimate user.

[0589] "Medical condition information" refers to information provided by patients about their symptoms and special needs, and is the basic data that enables the system to recommend the most suitable hospital.

[0590] The "database" is a high-performance information management system for systematically managing and storing various data required by the system, such as patient information, detailed hospital data, authentication information, ratings and reviews.

[0591] A "generative AI model" is an artificial intelligence model that uses machine learning and natural language processing to analyze data and perform specific tasks (such as matching hospitals).

[0592] "Hospital detailed data" is information about a specific medical institution, including areas of expertise, the latest state of medical equipment, and staff evaluations.

[0593] "Analysis and matching" is a data analysis process in which the generative AI model selects the most suitable hospital based on the patient's medical condition information and detailed hospital data.

[0594] The "hospital list" is a list of the most suitable medical institutions presented to patients based on the analysis results of the generative AI model, and includes basic information and characteristics of each hospital.

[0595] "Reservation information" is information relating to the patient's desired consultation date and time and the selected hospital, and is data necessary to confirm the reservation via the server.

[0596] "Evaluations and reviews" refer to the evaluations and opinions that patients provide to hospitals after their consultations, and are feedback data that the system can use for future analysis.

[0597] This invention relates to a system that uses a generative AI model to recommend the most suitable hospital for a patient. This system operates using a user's terminal, a server, and a database.

[0598] 1. User Login

[0599] Terminal

[0600] The user logs in to the hospital search system using a terminal. The login screen is displayed and the user enters their user ID and password. After inputting the information, the terminal sends this information to the server.

[0601] server

[0602] The server verifies the received login information and authenticates the user. If authentication is successful, the user can proceed to the next step.

[0603] Specific examples

[0604] When User A logs in to the hospital search system and enters his / her user ID and password, authentication is performed and the user is then directed to a screen for entering medical condition information.

[0605] 2. Enter patient information

[0606] Terminal

[0607] After authentication is complete, the user inputs their symptoms and special needs, and the terminal transmits the medical condition information received from the user to the server.

[0608] server

[0609] The server receives the patient's medical condition information and stores it in a database, which is then ready for the next analysis step.

[0610] Specific examples

[0611] User A enters "headache" and "nausea" and sends the information to the server, which stores it.

[0612] 3. Hospital data collection and analysis

[0613] server

[0614] The server retrieves detailed hospital data (specialties, state-of-the-art medical equipment, staff evaluations, etc.) from a database, and then uses a generative AI model to analyze and match the patient's condition information and hospital details to the most suitable hospital.

[0615] Specific examples

[0616] The server collects a large amount of hospital data and lists hospitals that specialize in "neurology" or have "the latest CT scan equipment." The generative AI model analyzes this data and finds the best hospital for the patient.

[0617] Example prompt sentence:

[0618] "Please suggest the best hospital based on the symptom information provided by the user."

[0619] 4. Hospital list suggestions

[0620] server

[0621] Based on the analysis results of the generative AI model, the server creates a list of hospitals that are best suited to the patient and sends it to the user's device in real time.

[0622] Terminal

[0623] The terminal displays the received list of hospitals to the user, which includes basic information and characteristics of the hospitals, allowing the user to check them.

[0624] Specific examples

[0625] User A's device displays a list of hospitals that specialize in "neurology" and hospitals that have "the latest CT scan equipment." The list also displays each hospital's specialty and evaluation points.

[0626] 5. Doctor Appointments

[0627] User

[0628] The user selects the most suitable hospital from the list and enters the desired reservation date. The terminal sends this information to the server and completes the reservation procedure.

[0629] server

[0630] The server receives the user's reservation information and sends the information to the selected hospital's reservation system to confirm the reservation.

[0631] Specific examples

[0632] When User A selects "Hospital B" from the list and enters the desired reservation date, the server sends the information to Hospital B's reservation system and confirms the reservation.

[0633] 6. Collecting reviews and ratings

[0634] Terminal

[0635] After the consultation, the user uses the terminal to enter reviews and ratings for the hospital.

[0636] server

[0637] The server stores the received reviews and ratings in a database and uses them for future analysis.

[0638] Specific examples

[0639] After User A visits Hospital B, he or she enters a rating (4 out of 5) and a review such as "The doctor was kind," and the server stores this in a database for future analysis.

[0640] This system allows patients to quickly and reliably find the appropriate medical institution, reducing the stress and anxiety of choosing a hospital.

[0641] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0642] Step 1:

[0643] User authentication

[0644] The user enters their user ID and password on the login screen of the hospital search system on their terminal. The terminal sends the entered login information to the server. The server authenticates the user by comparing the received login information with the authentication information in the database. If authentication is successful, the user can proceed to the next step.

[0645] Input: User ID and password

[0646] Data processing: The device encodes the login information and sends it to the server

[0647] Data calculation: The server checks the data against the database

[0648] Output: Authentication result

[0649] Specific operation: The user enters login information and clicks the "Login" button. The device sends the user ID and password to the server, and the server returns the authentication result to the device.

[0650] Step 2:

[0651] Enter medical condition information

[0652] After completing the authentication, the user inputs their medical condition and special needs. The terminal sends the medical condition information received from the user to the server. The server stores the medical condition information from the patient in a database.

[0653] Input: Medical condition information (e.g., "headache," "nausea")

[0654] Data processing: The device sends the input information

[0655] Data calculation: The server stores the information in a database

[0656] Output: Saved results

[0657] Specific operation: The user enters the medical condition and clicks the "Submit" button. The terminal sends the medical condition information to the server, which then stores it in the database.

[0658] Step 3:

[0659] Hospital Data Collection and Analysis

[0660] The server retrieves detailed hospital data (specialties, state-of-the-art medical equipment, staff evaluations, etc.) from the database, and then uses a generative AI model to analyze and match the most suitable hospital based on the patient's medical condition information and the hospital's detailed data.

[0661] Input: Hospital details and medical condition information in the database

[0662] Data processing: Acquire detailed hospital data and integrate it with medical condition information

[0663] Data Computing: Analysis and Matching with Generative AI Models

[0664] Output: A list of suitable hospitals

[0665] Specific operation: The server executes a database query to retrieve hospital data. The generative AI model is given the prompt "Symptoms: headache, nausea, Specialty: neurology, Equipment: latest CT scan." The generative AI model then outputs a list of the most suitable hospitals.

[0666] Step 4:

[0667] Hospital list suggestions

[0668] The server creates a list of hospitals that are optimal for the patient based on the analysis results of the generative AI model. The server then sends the list to the user's device, which then displays the received list to the user.

[0669] Input: Analysis results of the generative AI model

[0670] Data processing: Formatting hospital list

[0671] Data calculation: Creating and sending a list

[0672] Output: Hospital list

[0673] Specific operation: The server formats the analysis results of the generated AI model and creates a list of hospitals. The server then sends the list of hospitals to the device, which then displays it to the user.

[0674] Step 5:

[0675] Hospital appointment

[0676] The user selects the desired hospital from the list and enters the desired reservation date. The terminal sends the entered reservation information to the server. The server then sends the received reservation information to the reservation system of the selected hospital and confirms the reservation.

[0677] Input: Reservation information (selected hospital, desired date)

[0678] Data processing: Sending reservation information

[0679] Data calculation: Sending and confirming reservation information

[0680] Output: Reservation confirmation

[0681] Specific operation: The user selects a hospital, enters the desired appointment date, and clicks the "Book" button. The terminal sends the appointment information to the server, which then sends the information to the hospital's appointment system and confirms the appointment.

[0682] Step 6:

[0683] Collecting reviews and ratings

[0684] After a consultation, the user uses the device to input reviews and ratings for the hospital. The device then sends the input reviews and ratings to the server. The server then stores the received reviews and ratings in a database and uses them for future analysis.

[0685] Input: Rating and review (e.g., "Rating: 4 points, Comment: The doctor was kind.")

[0686] Data Processing: Submitting Reviews and Ratings

[0687] Data calculation: Saving to database

[0688] Output: Saved results

[0689] Specific operation: The user enters a rating and review and clicks the "Submit" button. The device sends the rating and review to the server, which stores them in a database.

[0690] (Application example 1)

[0691] 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."

[0692] In today's food delivery services, users often have difficulty finding restaurants that meet their dietary preferences and restrictions, and they need the information they need quickly. Furthermore, the process of making reservations at the restaurant of their choice can be cumbersome. A system that solves these problems is needed.

[0693] 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.

[0694] In this invention, the server includes a means for receiving preference information from a user, a means for acquiring detailed restaurant data from a database, and a means for analyzing and matching the user's preference information with the detailed restaurant data using a generating AI. This makes it possible to recommend the most suitable restaurant for the user. In addition, by assisting the user in making a reservation at the restaurant selected by the user, the complexity of the reservation procedure can be reduced.

[0695] "User" means an individual who uses the food delivery system to search for and make reservations at restaurants.

[0696] "Preference information" refers to information about dietary preferences and restrictions entered by the user, specifically dietary restrictions and preferred types of cuisine.

[0697] "Restaurant detailed data" refers to data that includes information about each restaurant, such as its specialty, the latest equipment, and staff ratings.

[0698] "Generative AI" is an artificial intelligence technology used to match users with the best restaurants based on their preferences and detailed restaurant data.

[0699] The "suggestion method" is a method for presenting a list of restaurants suitable for the user based on the analysis results of the generative AI.

[0700] "Reservation Process" means the process by which a User makes an online reservation with a Restaurant of their choice.

[0701] This invention is a system that allows users to use a food delivery system to search for and make reservations at the most suitable restaurant based on their preferences. This system mainly consists of the following steps.

[0702] 1. In the step of receiving preference information from a user, the user uses a terminal to input preference information such as dietary preferences and dietary restrictions. The terminal then transmits this preference information to a server.

[0703] 2. The server obtains the authentication token and uses an API to collect the restaurant details from a database. The API used here is a publicly available API of a food delivery service.

[0704] 3. Using the generation AI, the server analyzes and matches the user's preference information with the collected restaurant details. This generation AI uses an artificial intelligence model such as OpenAI's GPT-4. Specifically, matching is performed by inputting the following prompt sentence into the generation AI:

[0705] "User preferences: ['vegan', 'low-carb']\nLocation: 'Tokyo'\nFind restaurants that match these preferences."

[0706] 4. Based on the analysis results of the generation AI, the server creates a list of restaurant recommendations that are best suited to the user and sends it to the user's device in real time. The device then displays the received restaurant list to the user, allowing the user to select a restaurant that best suits their preferences.

[0707] 5. The user selects a specific restaurant from the list and enters the date and time they wish to make a reservation. The device then sends this information to the server, which then sends it to the selected restaurant's reservation system to assist with the reservation process.

[0708] 6. After using the service, the user enters reviews and ratings of the restaurant through the terminal. The server stores these reviews and ratings in a database for future analysis.

[0709] This allows users to quickly and reliably find restaurants that suit their preferences and smoothly complete the reservation process. As a concrete example, if a vegan or low-carb user is looking for a restaurant in Tokyo, they can provide the following prompt to the generative AI model:

[0710] "User preferences: ['vegan', 'low-carb']\nLocation: 'Tokyo'\nFind restaurants that match these preferences."

[0711] Based on these prompts, the generative AI can suggest the most suitable restaurant, greatly improving user convenience.

[0712] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0713] Step 1:

[0714] Users log in using their device and enter their preferences. Specifically, users enter their food preferences, dietary restrictions, and desired location. Input data includes "vegan," "low carb," and "Tokyo." This information is sent to the server. The system receives the user's preferences and login information as input and sends it to the server.

[0715] Step 2:

[0716] The server receives the received preference information and performs the necessary steps to obtain an authentication token, which is required for API calls. The server then sends a request to the public food delivery service's API to collect restaurant details. It receives the preference information and authentication information as input and sends a request to the API to retrieve restaurant information from the database.

[0717] Step 3:

[0718] The server uses the generative AI model to analyze and match the restaurant's detailed data with the user's preferences based on the restaurant's detailed data obtained from the API, and generates a prompt sentence to input into the generative AI model.

[0719] An example of a specific prompt is:

[0720] "User preferences: ['vegan', 'low-carb']\nLocation: 'Tokyo'\nFind restaurants that match these preferences."

[0721] It takes user preference information and restaurant details as input, sends prompts to a generative AI model, and outputs a list of optimal restaurants.

[0722] Step 4:

[0723] The server receives the analysis results of the generative AI model and creates a list of restaurants that are optimal for the user based on the results. This list includes the restaurant's name, location, rating, specialties, etc. The server sends this information to the user's device in real time. It receives the analysis results of the generative AI model as input and creates and sends a list of restaurants for the user as output.

[0724] Step 5:

[0725] The user checks the list of suggested restaurants on the device and inputs the restaurant of their choice and the desired reservation date and time. The device sends this information to the server. The device receives the user's selection and reservation information as input and sends it to the server.

[0726] Step 6:

[0727] The server receives the reservation information and sends it to the selected restaurant's reservation system to complete the reservation process. It receives the user's reservation information as input and sends the data to the restaurant's reservation system as output to complete the reservation.

[0728] Step 7:

[0729] After using a restaurant's service, a user uses the device to enter reviews and ratings for the restaurant. The device sends this information to the server. The server stores the received reviews and ratings in a database for future analysis. The server receives the user's reviews and ratings as input and stores them in a database.

[0730] 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.

[0731] This invention relates to a system that uses generative AI and an emotion engine to suggest the most suitable hospital for a patient. The details of the interactions between the user, terminal, and server, as well as each function, are explained below.

[0732] 1. User login and information entry

[0733] Terminal

[0734] The user logs in to the hospital search system using a terminal. The login screen is displayed and the user enters their user ID and password. After inputting the information, the terminal sends this information to the server.

[0735] server

[0736] The server verifies the received login information and authenticates the user. If authentication is successful, the user proceeds to the next step.

[0737] Specific examples

[0738] When User A logs in to the hospital search system and enters his / her user ID and password, authentication is performed and the user is then directed to a screen for entering medical condition information.

[0739] 2. Entering patient information and recognizing emotions

[0740] Terminal

[0741] After successful authentication, the user inputs their symptoms and special needs. In addition, the device is equipped with an emotion engine that can analyze the user's emotional state from their input and voice.

[0742] server

[0743] The server stores the received medical condition information and the emotion analysis results from the emotion engine in a database, which is then ready for the next analysis step.

[0744] Specific examples

[0745] User A inputs "headache" and "nausea," and the emotion engine analyzes the user's stress level while inputting. The server stores this information.

[0746] 3. Hospital data collection and analysis

[0747] server

[0748] The server retrieves detailed hospital data (specialties, state-of-the-art medical equipment, staff evaluations, etc.) from a database, and uses generative AI to perform an analysis to match the patient with the most suitable hospital based on the patient's medical condition information, detailed hospital data, and sentiment analysis results.

[0749] Specific examples

[0750] The server collects a large amount of hospital data and lists hospitals that specialize in "neurology" or have "the latest CT scan equipment." The generative AI analyzes the user's gender and also considers their stress level to find the best hospital.

[0751] 4. Hospital list suggestions

[0752] server

[0753] Based on the analysis results of the generative AI, the server creates a list of hospitals that are best suited to the patient and sends it to the user's device in real time.

[0754] Terminal

[0755] The device displays the received list of hospitals to the user, including basic information and characteristics of the hospitals, as well as additional suggestions based on the results of the emotion engine analysis.

[0756] Specific examples

[0757] User A's device will display a list of hospitals that specialize in "neurology" and have "the latest CT scan equipment," and will also suggest hospitals that provide mental care to address User A's high stress levels.

[0758] 5. Doctor's appointment

[0759] User

[0760] The user uses the terminal to select the most suitable hospital from the displayed list of hospitals and enters the desired reservation date. The terminal then sends this reservation information to the server.

[0761] server

[0762] The server sends the received reservation information to the hospital's reservation system and confirms the reservation. The confirmed reservation information is notified to both the user and the hospital.

[0763] Specific examples

[0764] When User A selects "Hospital B" from the list and enters the desired reservation date, the server sends the information to Hospital B's reservation system and confirms the reservation.

[0765] 6. Collecting reviews and ratings

[0766] Terminal

[0767] After the consultation, the user can use the terminal to input their evaluation and review of the hospital. In addition, if any emotional changes during the consultation have been recorded, they can also input that data.

[0768] server

[0769] The server stores the received reviews, ratings, and emotional data in a database and uses them to suggest hospitals in the future.

[0770] Specific examples

[0771] When User A enters a rating for "Hospital B" (4 out of 5) and a review such as "The doctor was kind," the server stores these in a database and uses them for future analysis.

[0772] By having the above-described configuration, the present invention is a system that enables patients to quickly and reliably find an appropriate medical institution, and further, by taking into account the user's emotional state, can improve the accuracy of hospital selection.

[0773] The processing flow will be explained below.

[0774] Step 1:

[0775] The user accesses the hospital search system using a terminal and enters their user ID and password on the login screen. The terminal then sends the entered login information to the server.

[0776] Step 2:

[0777] The server authenticates the user by checking the user information in the database based on the received user ID and password. If authentication is successful, the user is guided to a screen to input medical condition information to proceed to the next step.

[0778] Step 3:

[0779] The user uses the device to input information about their medical condition and special needs. For example, they input symptoms such as "headache" and "nausea." The device then sends the input information to the server. The device then uses an emotion engine to analyze emotions from the user's input and voice.

[0780] Step 4:

[0781] The server stores the received medical condition information and the emotion analysis results from the emotion engine in a database, which prepares the database for the next analysis step.

[0782] Step 5:

[0783] The server retrieves detailed hospital data (areas of expertise, up-to-dateness of medical equipment, staff evaluations, etc.) from a database, and then uses generative AI to analyze and match the patient's medical condition information, detailed hospital data, and sentiment analysis results.

[0784] Step 6:

[0785] The server sends the hospital list ranked by the generative AI to the user's device in real time, including suggestions that take into account the results of sentiment analysis.

[0786] Step 7:

[0787] A list of hospitals is displayed on the terminal, and the user selects the most suitable hospital from the list. The user then inputs the desired reservation date for the selected hospital. The terminal then sends this reservation information to the server.

[0788] Step 8:

[0789] The server sends the received reservation information to the hospital's reservation system and confirms the reservation. The confirmed reservation information is notified to both the user and the hospital.

[0790] Step 9:

[0791] After the consultation, the user uses the device to input their evaluation and review of the hospital. Emotional data may also be recorded during input. This information is also sent to the server.

[0792] Step 10:

[0793] The server stores the received ratings, reviews, and emotion data in a database and uses it to make future hospital recommendations, improving the accuracy and reliability of the entire system.

[0794] Example 2

[0795] 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."

[0796] Conventional hospital search systems have the problem of being unable to suggest hospitals that take into account the patient's emotional state, and therefore unable to address the patient's psychological needs.In addition, they do not provide enough information necessary to select an appropriate hospital, making it difficult for patients to quickly find the hospital that is best suited to them.

[0797] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for receiving medical condition information from a patient, a means for analyzing an emotional state based on the medical condition information, a means for acquiring detailed hospital data from a database, a means for analyzing and matching the patient's medical condition information, the analysis results of the emotional state, and the detailed hospital data using a generation AI, and a means for suggesting the most suitable hospital for the patient. This enables the patient to quickly and appropriately find a hospital that is best suited to their medical condition and emotional state.

[0798] "Patient" refers to an individual who provides medical information and seeks appropriate medical care.

[0799] "Medical condition information" refers to information necessary for selecting a medical institution, such as a patient's current symptoms, special needs, and medical history.

[0800] "Emotional state" refers to information that indicates the patient's psychological state and emotional level, and is analyzed by the emotion engine.

[0801] "Hospital detailed data" refers to various information related to the hospital, such as the content of medical services provided by the hospital, the up-to-dateness of medical equipment, and evaluations of medical staff.

[0802] "Generative AI" refers to artificial intelligence that uses machine learning and natural language processing to analyze data and generate optimal suggestions.

[0803] "Analysis and matching" refers to the process of using generative AI to select the most suitable hospital based on the patient's medical condition information, the analysis results of their emotional state, and detailed hospital data.

[0804] "Proposal means" refers to the method or process of providing patients with the analysis and matching results using generative AI.

[0805] This invention is a system that suggests the most suitable hospital for a patient, and the details of the interactions between the user, terminal, and server, as well as each function, will be explained below.

[0806] User logs in and enters information

[0807] Terminal

[0808] The user logs in to the hospital search system using a terminal. The login screen is displayed and the user enters their user ID and password. After inputting the information, the terminal sends this information to the server.

[0809] server

[0810] The server verifies the received login information and authenticates the user. If authentication is successful, the user proceeds to the next step.

[0811] Entering patient information and recognizing emotions

[0812] Terminal

[0813] After successful authentication, the user inputs their symptoms and special needs. In addition, the device is equipped with an emotion engine that can analyze the user's emotional state from their input and voice.

[0814] server

[0815] The server stores the received medical condition information and the emotion analysis results from the emotion engine in a database.

[0816] Hospital data collection and analysis

[0817] server

[0818] The server retrieves detailed hospital data (specialties, state-of-the-art medical equipment, staff evaluations, etc.) from a database, and then uses a generative AI model to perform an analysis to match the patient with the most suitable hospital based on the patient's medical condition information, the results of an analysis of their emotional state, and the detailed hospital data.

[0819] Hospital list suggestions

[0820] server

[0821] Based on the analysis results of the generative AI model, the server creates a list of hospitals that are best suited to the patient and sends it to the user's device in real time.

[0822] Terminal

[0823] The device displays the received list of hospitals to the user, including basic information and characteristics of the hospitals, as well as additional suggestions based on the results of the emotion engine analysis.

[0824] Clinic appointment

[0825] User

[0826] The user uses the terminal to select the most suitable hospital from the displayed list of hospitals and enters the desired reservation date. The terminal then sends this reservation information to the server.

[0827] server

[0828] The server sends the received reservation information to the hospital's reservation system and confirms the reservation. The confirmed reservation information is notified to both the user and the hospital.

[0829] Collecting reviews and ratings

[0830] Terminal

[0831] After the consultation, the user can use the terminal to input their evaluation and review of the hospital. In addition, if any emotional changes during the consultation are recorded, they can also input that data.

[0832] server

[0833] The server stores the received reviews, ratings, and emotional data in a database and uses them to suggest hospitals in the future.

[0834] Hardware and software used

[0835] Device: Smartphone, tablet, or PC.

[0836] Server: A high-performance database server (e.g. MySQL, PostgreSQL).

[0837] Sentiment engine: Natural language processing libraries and machine learning models (e.g., TensorFlow, PyTorch).

[0838] Generative AI models: AI models used for data analysis and text generation (e.g., GPT-3, BERT).

[0839] Examples of concrete examples and prompts

[0840] Specific examples

[0841] User A logs into the hospital search system and inputs his / her symptoms of headache and nausea, while the emotion engine analyzes his / her stress level. The server stores this information, and the generative AI model compares it with hospital data to identify the most suitable hospital. As a result, a list of appropriate hospitals is displayed on User A's device, and an appointment at the selected hospital is confirmed. After the consultation, User A inputs his / her rating and reviews of the hospital, which are used to make suggestions for future visits.

[0842] Prompt Sentence Examples

[0843] Symptoms: Headache and nausea

[0844] Special Needs: Stress Management

[0845] Emotional state: High stress levels

[0846] Specialty: Neurology

[0847] Medical equipment: Latest CT scan equipment

[0848] Staff rating: Highly rated

[0849] This system allows patients to quickly and reliably find the right medical institution, and by taking into account the patient's emotional state, it can improve the accuracy of hospital selection.

[0850] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0851] Step 1:

[0852] User login and information entry

[0853] (input)

[0854] The user enters the user ID and password into the terminal.

[0855] (Specific actions)

[0856] The user logs in to the hospital search system using a terminal. The login screen is displayed and the user enters their user ID and password.

[0857] (Data processing and calculation)

[0858] The terminal sends the entered user ID and password to the server.

[0859] (output)

[0860] The server verifies the received login information and sends the authentication result to the terminal.

[0861] Step 2:

[0862] Entering patient information and recognizing emotions

[0863] (input)

[0864] Users enter medical conditions and special needs into the terminal and also use voice to indicate their emotional state.

[0865] (Specific actions)

[0866] After successful authentication, the user inputs their symptoms and special needs, and the device is equipped with an emotion engine that analyzes their emotional state from the input and voice.

[0867] (Data processing and calculation)

[0868] The device sends the medical condition information and emotion analysis results to the server.

[0869] (output)

[0870] The server stores the received medical condition information and emotion analysis results in a database.

[0871] Step 3:

[0872] Hospital data collection and analysis

[0873] (input)

[0874] The server retrieves the hospital details from the database.

[0875] (Specific actions)

[0876] The server retrieves detailed hospital data (areas of expertise, up-to-dateness of medical equipment, staff evaluations, etc.) from a database.

[0877] (Data processing and calculation)

[0878] Using a generative AI model, an analysis is performed to match the most suitable hospital based on medical condition information, emotional state analysis results, and detailed hospital data.

[0879] (output)

[0880] The server generates a list of optimal hospitals as a result of the analysis.

[0881] Step 4:

[0882] Hospital list suggestions

[0883] (input)

[0884] The server generates a list of the best hospitals.

[0885] (Specific actions)

[0886] Based on the analysis results of the generative AI, a list of optimal hospitals is created and sent to the user's device.

[0887] (Data processing and calculation)

[0888] The server transmits the hospital list to the user terminal.

[0889] (output)

[0890] The terminal displays the received hospital list to the user.

[0891] Step 5:

[0892] Clinic appointment

[0893] (input)

[0894] The user selects a hospital from the list of hospitals and enters the desired appointment date.

[0895] (Specific actions)

[0896] The user selects the most suitable hospital from the displayed list and enters the desired reservation date. The terminal then sends this reservation information to the server.

[0897] (Data processing and calculation)

[0898] The server sends the received reservation information to the hospital's reservation system and confirms the reservation.

[0899] (output)

[0900] The server notifies both the user and the hospital of the confirmed reservation information.

[0901] Step 6:

[0902] Collecting reviews and ratings

[0903] (input)

[0904] Users enter their ratings and reviews of the hospital after their consultation.

[0905] (Specific actions)

[0906] After the consultation, the user can enter their evaluation and review of the hospital. If any emotional changes have been recorded, they can also enter that data.

[0907] (Data processing and calculation)

[0908] The device sends the ratings, reviews, and emotion data to the server.

[0909] (output)

[0910] The server stores the received reviews, ratings, and emotional data in a database, which will be used to recommend hospitals in the future.

[0911] (Application example 2)

[0912] 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."

[0913] Many conventional hospital recommendation systems suggest medical institutions based on a patient's medical condition information. However, these suggestions are made without taking into account the patient's emotional state or current mood, so the options are not necessarily comfortable for the patient. Furthermore, particularly in food delivery, there are no systems that can suggest optimal meals tailored to the user's emotional state, making it difficult to increase user satisfaction. Therefore, the present invention aims to provide a system that combines a patient's medical condition information and emotional state to make more accurate hospital and meal recommendations.

[0914] 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.

[0915] In this invention, the server includes means for receiving medical condition information from a patient, means for acquiring detailed hospital data from a database, means for analyzing and matching the patient's medical condition information and the detailed hospital data using a generative AI, means for analyzing the user's emotional state and using the analysis results to suggest the optimal meal for the patient, and means for suggesting the optimal hospital for the patient. This enables the patient to select a hospital and meal that are appropriate for their emotional state at the time, thereby improving user satisfaction and quality of life.

[0916] "Patient" refers to a person receiving medical examination or treatment.

[0917] "Medical condition information" refers to information about the symptoms or illnesses a patient is suffering from.

[0918] "Hospital detailed data" refers to information such as the hospital's areas of expertise, the state of the art of medical equipment, and staff evaluations.

[0919] "Generative AI" is a type of artificial intelligence that refers to a program that has the ability to generate new information based on large amounts of data.

[0920] "Emotional state" refers to the user's mood and psychological state at any given time.

[0921] An "emotion engine" refers to a system for analyzing emotions from user input and voice.

[0922] A "database" refers to a collection of data organized to allow efficient searching and management.

[0923] "Matching" refers to the process of comparing multiple items and finding the combination that best suits their respective characteristics.

[0924] "Suggestion" refers to the act of prompting a user for a particular option or piece of information.

[0925] "Food delivery" refers to a service that delivers meals ordered by a user to a specific location.

[0926] "Meal suggestions" refers to providing optimal meals based on the user's preferences and emotional state.

[0927] This invention is a system that suggests optimal hospitals and meals based on a user's medical condition information and emotional state. This system is realized using hardware and software components such as a smartphone application and a backend server.

[0928] System configuration

[0929] The system includes the following major components:

[0930] 1. User terminal: Implemented as a smartphone application, it provides an interface for users to input information about their medical condition and emotional state.

[0931] 2. Server: The backend server, using the Django framework, processes data from users, connects with the database for analysis, and includes generative AI models for generating meal and hospital recommendations.

[0932] 3. Database: Used to store detailed hospital data and users' medical condition and emotion data.

[0933] Explanation of program processing

[0934] User login and information entry

[0935] The user terminal provides an interface for the user to log in to the system. The user enters their ID and password, and the login information is sent to the server for authentication.

[0936] Entering medical information and emotional state

[0937] After successful authentication, the user device displays a screen prompting the user to enter their medical condition information and emotional state. As the user enters their medical condition information, the emotional engine analyzes their emotional state and transmits the data to the server.

[0938] Hospital data collection and analysis

[0939] The server retrieves detailed hospital data from the database and uses generative AI to analyze and match the user's medical condition information with the detailed hospital data. The generative AI model is implemented using PyTorch, TensorFlow, and other tools. Emotional state data is also included in the analysis to identify the most suitable hospital.

[0940] Meal suggestions

[0941] It also makes meal suggestions based on the user's emotional state.Generation AI is also used to generate meal suggestions, analyzing prompts based on user input to suggest appropriate meals.

[0942] Specific examples

[0943] For example, if User A logs in and inputs "headache" and "nausea," and the emotion engine detects high stress, the server will input a prompt to the generation AI saying, "Please suggest the best food for the user currently feeling stressed." The generation AI will then make suggestions such as "green tea," "healthy salad," and "fruit," and display them on the user's device.

[0944] Examples of prompts are:

[0945] "Please suggest the best foods for the user if they are currently feeling stressed."

[0946] As a result, the system takes into account the user's emotional state to suggest hospitals and meals that are best suited to the user's condition, improving the user's satisfaction and quality of life.

[0947] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0948] Step 1:

[0949] A user logs in to a terminal.

[0950] Input: User ID, Password

[0951] Specific operation: The user starts the smartphone application and enters their user ID and password on the login screen. The entered information is sent to the server.

[0952] Output: Authentication success or failure response

[0953] Step 2:

[0954] The server authenticates the user.

[0955] Input: User ID, Password

[0956] Specific operation: The server checks the received user ID and password against the database and performs authentication. If authentication is successful, it proceeds to the next step.

[0957] Output: Authentication result (success or failure)

[0958] Step 3:

[0959] The user inputs medical condition information and emotional state.

[0960] Input: medical condition information, emotional data

[0961] Specific operation: After successful authentication, the user will proceed to the screen to input their medical condition information. At the same time, the device will analyze the user's emotional data from their voice and text, and send this information to the server.

[0962] Output: medical condition information, emotional data

[0963] Step 4:

[0964] The server collects and analyzes hospital data.

[0965] Input: medical condition information, emotional data

[0966] How it works: The server retrieves detailed hospital data (specialties, state-of-the-art medical equipment, staff ratings, etc.) from the database, then uses a generative AI model to analyze and match the patient's condition information with the hospital data. Sentiment data is also added to this analysis to identify the most suitable hospital.

[0967] Output: List of best hospitals

[0968] Step 5:

[0969] Servers will make meal suggestions.

[0970] Input: Emotion data

[0971] Specific operation: Based on the emotion data, the server inputs the prompt sentence "Please suggest the best food for the user if he / she is currently feeling stressed" into the generative AI model. The generative AI generates meal suggestions based on this prompt sentence.

[0972] Output: A list of meal suggestions

[0973] Step 6:

[0974] The server sends the optimal hospital and meal recommendations to the user terminal.

[0975] Input: List of best hospitals, list of meal suggestions

[0976] Specific operation: The server sends the generated list of optimal hospitals and meal recommendations to the user's device, which receives it and displays it on the screen.

[0977] Output: Display of hospital list and meal suggestions

[0978] Step 7:

[0979] The user selects a hospital and makes a reservation.

[0980] Input: Selected hospital, desired appointment date

[0981] Specific operation: The user selects a hospital from the displayed list of hospitals and enters the desired reservation date. This information is sent to the server, which then sends the reservation information to the hospital's reservation system and confirms the reservation.

[0982] Output: Reservation confirmation information

[0983] Step 8:

[0984] Users enter ratings and reviews after their consultation.

[0985] Input: Post-consultation evaluation, reviews, emotional change data

[0986] Specific operation: After the consultation, the user uses the device to enter their evaluation and review of the hospital. In addition, if any emotional changes during the consultation were recorded, they can also enter that data. The server stores this information in a database and uses it to make suggestions for future visits.

[0987] Output: Ratings, reviews, and sentiment data

[0988] 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.

[0989] 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.

[0990] 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.

[0991] [Third embodiment]

[0992] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0993] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0994] 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).

[0995] 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.

[0996] 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.

[0997] 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).

[0998] 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.

[0999] 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.

[1000] 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.

[1001] 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.

[1002] 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.

[1003] 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."

[1004] This invention relates to a system that uses generative AI to suggest the best hospital for a patient. The details of the interactions between the user, terminal, and server, as well as each function, are explained below.

[1005] 1. User login and information entry

[1006] Terminal

[1007] The user logs in to the hospital search system using a terminal. The login screen is displayed and the user enters their user ID and password. After input, the terminal sends this information to the server.

[1008] server

[1009] The server verifies the received login information and authenticates the user. If authentication is successful, the user proceeds to the next step.

[1010] Specific examples

[1011] When User A logs in to the hospital search system and enters his / her user ID and password, authentication is performed and the user is then directed to a screen for entering medical condition information.

[1012] 2. Enter patient information

[1013] Terminal

[1014] After authentication is complete, the user enters their symptoms and special needs, and the terminal transmits the medical condition information received from the user to the server.

[1015] server

[1016] The server receives the patient's medical condition information and stores it in a database, which is then ready for the next analysis step.

[1017] Specific examples

[1018] User A enters "headache" and "nausea" and sends the information to the server, which stores it.

[1019] 3. Hospital data collection and analysis

[1020] server

[1021] The server retrieves detailed hospital data (specialties, state-of-the-art medical equipment, staff evaluations, etc.) from a database, and then uses generative AI to analyze and match the patient's condition information and hospital details to the most suitable hospital.

[1022] Specific examples

[1023] The server collects a large amount of hospital data and lists hospitals that specialize in "neurology" or have "the latest CT scan equipment." The generative AI analyzes this data and finds the best hospital for the patient.

[1024] 4. Hospital list suggestions

[1025] server

[1026] Based on the analysis results of the generative AI, the server creates a list of hospitals that are best suited to the patient and sends it to the user's device in real time.

[1027] Terminal

[1028] The terminal displays the received list of hospitals to the user, which includes basic information and characteristics of the hospitals, allowing the user to check them.

[1029] Specific examples

[1030] User A's device displays a list of hospitals that specialize in "neurology" and hospitals that have "the latest CT scan equipment." The list also displays each hospital's specialty and evaluation points.

[1031] 5. Doctor's appointment

[1032] User

[1033] The user selects the most suitable hospital from the list and enters the desired reservation date. The terminal sends this information to the server and completes the reservation procedure.

[1034] server

[1035] The server receives the user's reservation information and sends the information to the selected hospital's reservation system to confirm the reservation.

[1036] Specific examples

[1037] When User A selects "Hospital B" from the list and enters the desired reservation date, the server sends the information to Hospital B's reservation system and confirms the reservation.

[1038] 6. Collecting reviews and ratings

[1039] Terminal

[1040] After the consultation, the user uses the terminal to enter reviews and ratings for the hospital.

[1041] server

[1042] The server stores the received reviews and ratings in a database and uses them for future analysis.

[1043] Specific examples

[1044] After User A visits Hospital B, he or she enters a rating (4 out of 5) and a review such as "The doctor was kind," and the server stores this in a database for future analysis.

[1045] With the above-described configuration, the present invention is a system that enables patients to quickly and reliably find an appropriate medical institution, thereby reducing the stress and anxiety of choosing a hospital.

[1046] The processing flow will be explained below.

[1047] Step 1:

[1048] The user accesses the hospital search system using a terminal and enters their user ID and password on the login screen. The terminal then sends the entered login information to the server.

[1049] Step 2:

[1050] The server authenticates the user by checking the user information in the database based on the received user ID and password. If authentication is successful, the user is guided to a screen to input medical condition information to proceed to the next step.

[1051] Step 3:

[1052] The user uses the terminal to input information about their medical condition and special needs, for example, inputting symptoms such as "headache" and "nausea." The terminal then transmits the input information to the server.

[1053] Step 4:

[1054] The server stores the received medical condition information in a database, and then queries the database to obtain detailed data about the hospital (such as areas of expertise, the up-to-dateness of medical equipment, and staff ratings).

[1055] Step 5:

[1056] The server analyzes the patient and hospital information using the generated AI based on the acquired detailed hospital data. The generated AI then creates a list of hospitals that are best suited to the patient's condition and ranks the list.

[1057] Step 6:

[1058] The server sends the hospital list ranked by the generation AI to the user's device in real time, and the device displays the hospital list to the user.

[1059] Step 7:

[1060] The user uses the terminal to check the displayed list of hospitals and select the one that best suits them. The user then inputs the desired reservation date for the selected hospital. The terminal then sends this reservation information to the server.

[1061] Step 8:

[1062] The server sends the received reservation information to the hospital's reservation system and confirms the reservation. The confirmed reservation information is notified to both the user and the hospital.

[1063] Step 9:

[1064] After the consultation, the user uses the terminal to input their rating and review of the hospital, which is then sent to the server.

[1065] Step 10:

[1066] The server stores the received ratings and reviews in a database and uses them as data for future hospital recommendations, improving the accuracy and reliability of the entire system.

[1067] Example 1

[1068] 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."

[1069] Conventional hospital search systems make it difficult for patients to find the medical institution that best suits their symptoms, and choosing a hospital requires a great deal of time and effort. Furthermore, they lack appropriate reservation systems and do not effectively utilize feedback after consultations. Therefore, there is a need for a system that allows patients to quickly find the appropriate medical institution.

[1070] 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.

[1071] In this invention, the server includes means for receiving and verifying login information for user authentication, means for receiving medical condition information from the patient and storing it in a database, means for acquiring detailed hospital data from the database, means for analyzing and matching the patient's medical condition information and detailed hospital data using a generative AI model, means for creating and transmitting a list of hospitals optimal for the patient based on the analysis results of the generative AI, means for receiving reservation information for the hospital selected by the patient and confirming the reservation, and means for receiving evaluations and reviews of the hospital from the patient and storing them in a database. This enables patients to quickly find the optimal hospital, reducing the stress and anxiety of choosing a hospital.

[1072] "User authentication" is the process of verifying authentication information such as user ID and password when a user accesses a system, and confirming whether the user is a legitimate user.

[1073] "Medical condition information" refers to information provided by patients about their symptoms and special needs, and is the basic data that enables the system to recommend the most suitable hospital.

[1074] The "database" is a high-performance information management system for systematically managing and storing various data required by the system, such as patient information, detailed hospital data, authentication information, ratings and reviews.

[1075] A "generative AI model" is an artificial intelligence model that uses machine learning and natural language processing to analyze data and perform specific tasks (such as matching hospitals).

[1076] "Hospital detailed data" is information about a specific medical institution, including areas of expertise, the latest state of medical equipment, and staff evaluations.

[1077] "Analysis and matching" is a data analysis process in which the generative AI model selects the most suitable hospital based on the patient's medical condition information and detailed hospital data.

[1078] The "hospital list" is a list of the most suitable medical institutions presented to patients based on the analysis results of the generative AI model, and includes basic information and characteristics of each hospital.

[1079] "Reservation information" is information relating to the patient's desired consultation date and time and the selected hospital, and is data necessary to confirm the reservation via the server.

[1080] "Evaluations and reviews" refer to the evaluations and opinions that patients provide to hospitals after their consultations, and are feedback data that the system can use for future analysis.

[1081] This invention relates to a system that uses a generative AI model to recommend the most suitable hospital for a patient. This system operates using a user's terminal, a server, and a database.

[1082] 1. User Login

[1083] Terminal

[1084] The user logs in to the hospital search system using a terminal. The login screen is displayed and the user enters their user ID and password. After inputting the information, the terminal sends this information to the server.

[1085] server

[1086] The server verifies the received login information and authenticates the user. If authentication is successful, the user can proceed to the next step.

[1087] Specific examples

[1088] When User A logs in to the hospital search system and enters his / her user ID and password, authentication is performed and the user is then directed to a screen for entering medical condition information.

[1089] 2. Enter patient information

[1090] Terminal

[1091] After authentication is complete, the user inputs their symptoms and special needs, and the terminal transmits the medical condition information received from the user to the server.

[1092] server

[1093] The server receives the patient's medical condition information and stores it in a database, which is then ready for the next analysis step.

[1094] Specific examples

[1095] User A enters "headache" and "nausea" and sends the information to the server, which stores it.

[1096] 3. Hospital data collection and analysis

[1097] server

[1098] The server retrieves detailed hospital data (specialties, state-of-the-art medical equipment, staff evaluations, etc.) from a database, and then uses a generative AI model to analyze and match the patient's condition information and hospital details to the most suitable hospital.

[1099] Specific examples

[1100] The server collects a large amount of hospital data and lists hospitals that specialize in "neurology" or have "the latest CT scan equipment." The generative AI model analyzes this data and finds the best hospital for the patient.

[1101] Example prompt sentence:

[1102] "Please suggest the best hospital based on the symptom information provided by the user."

[1103] 4. Hospital list suggestions

[1104] server

[1105] Based on the analysis results of the generative AI model, the server creates a list of hospitals that are best suited to the patient and sends it to the user's device in real time.

[1106] Terminal

[1107] The terminal displays the received list of hospitals to the user, which includes basic information and characteristics of the hospitals, allowing the user to check them.

[1108] Specific examples

[1109] User A's device displays a list of hospitals that specialize in "neurology" and hospitals that have "the latest CT scan equipment." The list also displays each hospital's specialty and evaluation points.

[1110] 5. Doctor Appointments

[1111] User

[1112] The user selects the most suitable hospital from the list and enters the desired reservation date. The terminal sends this information to the server and completes the reservation procedure.

[1113] server

[1114] The server receives the user's reservation information and sends the information to the selected hospital's reservation system to confirm the reservation.

[1115] Specific examples

[1116] When User A selects "Hospital B" from the list and enters the desired reservation date, the server sends the information to Hospital B's reservation system and confirms the reservation.

[1117] 6. Collecting reviews and ratings

[1118] Terminal

[1119] After the consultation, the user uses the terminal to enter reviews and ratings for the hospital.

[1120] server

[1121] The server stores the received reviews and ratings in a database and uses them for future analysis.

[1122] Specific examples

[1123] After User A visits Hospital B, he or she enters a rating (4 out of 5) and a review such as "The doctor was kind," and the server stores this in a database for future analysis.

[1124] This system allows patients to quickly and reliably find the appropriate medical institution, reducing the stress and anxiety of choosing a hospital.

[1125] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1126] Step 1:

[1127] User authentication

[1128] The user enters their user ID and password on the login screen of the hospital search system on their terminal. The terminal sends the entered login information to the server. The server authenticates the user by comparing the received login information with the authentication information in the database. If authentication is successful, the user can proceed to the next step.

[1129] Input: User ID and password

[1130] Data processing: The device encodes the login information and sends it to the server

[1131] Data calculation: The server checks the data against the database

[1132] Output: Authentication result

[1133] Specific operation: The user enters login information and clicks the "Login" button. The device sends the user ID and password to the server, and the server returns the authentication result to the device.

[1134] Step 2:

[1135] Enter medical condition information

[1136] After completing the authentication, the user inputs their medical condition and special needs. The terminal sends the medical condition information received from the user to the server. The server stores the medical condition information from the patient in a database.

[1137] Input: Medical condition information (e.g., "headache," "nausea")

[1138] Data processing: The device sends the input information

[1139] Data calculation: The server stores the information in a database

[1140] Output: Saved results

[1141] Specific operation: The user enters the medical condition and clicks the "Submit" button. The terminal sends the medical condition information to the server, which then stores it in the database.

[1142] Step 3:

[1143] Hospital Data Collection and Analysis

[1144] The server retrieves detailed hospital data (specialties, state-of-the-art medical equipment, staff evaluations, etc.) from the database, and then uses a generative AI model to analyze and match the most suitable hospital based on the patient's medical condition information and the hospital's detailed data.

[1145] Input: Hospital details and medical condition information in the database

[1146] Data processing: Acquire detailed hospital data and integrate it with medical condition information

[1147] Data Computing: Analysis and Matching with Generative AI Models

[1148] Output: A list of suitable hospitals

[1149] Specific operation: The server executes a database query to retrieve hospital data. The generative AI model is given the prompt "Symptoms: headache, nausea, Specialty: neurology, Equipment: latest CT scan." The generative AI model then outputs a list of the most suitable hospitals.

[1150] Step 4:

[1151] Hospital list suggestions

[1152] The server creates a list of hospitals that are optimal for the patient based on the analysis results of the generative AI model. The server then sends the list to the user's device, which then displays the received list to the user.

[1153] Input: Analysis results of the generative AI model

[1154] Data processing: Formatting hospital list

[1155] Data calculation: Creating and sending a list

[1156] Output: Hospital list

[1157] Specific operation: The server formats the analysis results of the generated AI model and creates a list of hospitals. The server then sends the list of hospitals to the device, which then displays it to the user.

[1158] Step 5:

[1159] Hospital appointment

[1160] The user selects the desired hospital from the list and enters the desired reservation date. The terminal sends the entered reservation information to the server. The server then sends the received reservation information to the reservation system of the selected hospital and confirms the reservation.

[1161] Input: Reservation information (selected hospital, desired date)

[1162] Data processing: Sending reservation information

[1163] Data calculation: Sending and confirming reservation information

[1164] Output: Reservation confirmation

[1165] Specific operation: The user selects a hospital, enters the desired appointment date, and clicks the "Book" button. The terminal sends the appointment information to the server, which then sends the information to the hospital's appointment system and confirms the appointment.

[1166] Step 6:

[1167] Collecting reviews and ratings

[1168] After a consultation, the user uses the device to input reviews and ratings for the hospital. The device then sends the input reviews and ratings to the server. The server then stores the received reviews and ratings in a database and uses them for future analysis.

[1169] Input: Rating and review (e.g., "Rating: 4 points, Comment: The doctor was kind.")

[1170] Data Processing: Submitting Reviews and Ratings

[1171] Data calculation: Saving to database

[1172] Output: Saved results

[1173] Specific operation: The user enters a rating and review and clicks the "Submit" button. The device sends the rating and review to the server, which stores them in a database.

[1174] (Application example 1)

[1175] 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."

[1176] In today's food delivery services, users often have difficulty finding restaurants that meet their dietary preferences and restrictions, and they need the information they need quickly. Furthermore, the process of making reservations at the restaurant of their choice can be cumbersome. A system that solves these problems is needed.

[1177] 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.

[1178] In this invention, the server includes a means for receiving preference information from a user, a means for acquiring detailed restaurant data from a database, and a means for analyzing and matching the user's preference information with the detailed restaurant data using a generating AI. This makes it possible to recommend the most suitable restaurant for the user. In addition, by assisting the user in making a reservation at the restaurant selected by the user, the complexity of the reservation procedure can be reduced.

[1179] "User" means an individual who uses the food delivery system to search for and make reservations at restaurants.

[1180] "Preference information" refers to information about dietary preferences and restrictions entered by the user, specifically dietary restrictions and preferred types of cuisine.

[1181] "Restaurant detailed data" refers to data that includes information about each restaurant, such as its specialty, the latest equipment, and staff ratings.

[1182] "Generative AI" is an artificial intelligence technology used to match users with the best restaurants based on their preferences and detailed restaurant data.

[1183] The "suggestion method" is a method for presenting a list of restaurants suitable for the user based on the analysis results of the generative AI.

[1184] "Reservation Process" means the process by which a User makes an online reservation with a Restaurant of their choice.

[1185] This invention is a system that allows users to use a food delivery system to search for and make reservations at the most suitable restaurant based on their preferences. This system mainly consists of the following steps.

[1186] 1. In the step of receiving preference information from a user, the user uses a terminal to input preference information such as dietary preferences and dietary restrictions. The terminal then transmits this preference information to a server.

[1187] 2. The server obtains the authentication token and uses an API to collect the restaurant details from a database. The API used here is a publicly available API of a food delivery service.

[1188] 3. Using the generation AI, the server analyzes and matches the user's preference information with the collected restaurant details. This generation AI uses an artificial intelligence model such as OpenAI's GPT-4. Specifically, matching is performed by inputting the following prompt sentence into the generation AI:

[1189] "User preferences: ['vegan', 'low-carb']\nLocation: 'Tokyo'\nFind restaurants that match these preferences."

[1190] 4. Based on the analysis results of the generation AI, the server creates a list of restaurant recommendations that are best suited to the user and sends it to the user's device in real time. The device then displays the received restaurant list to the user, allowing the user to select a restaurant that best suits their preferences.

[1191] 5. The user selects a specific restaurant from the list and enters the date and time they wish to make a reservation. The device then sends this information to the server, which then sends it to the selected restaurant's reservation system to assist with the reservation process.

[1192] 6. After using the service, the user enters reviews and ratings of the restaurant through the terminal. The server stores these reviews and ratings in a database for future analysis.

[1193] This allows users to quickly and reliably find restaurants that suit their preferences and smoothly complete the reservation process. As a concrete example, if a vegan or low-carb user is looking for a restaurant in Tokyo, they can provide the following prompt to the generative AI model:

[1194] "User preferences: ['vegan', 'low-carb']\nLocation: 'Tokyo'\nFind restaurants that match these preferences."

[1195] Based on these prompts, the generative AI can suggest the most suitable restaurant, greatly improving user convenience.

[1196] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1197] Step 1:

[1198] Users log in using their device and enter their preferences. Specifically, users enter their food preferences, dietary restrictions, and desired location. Input data includes "vegan," "low carb," and "Tokyo." This information is sent to the server. The system receives the user's preferences and login information as input and sends it to the server.

[1199] Step 2:

[1200] The server receives the received preference information and performs the necessary steps to obtain an authentication token, which is required for API calls. The server then sends a request to the public food delivery service's API to collect restaurant details. It receives the preference information and authentication information as input and sends a request to the API to retrieve restaurant information from the database.

[1201] Step 3:

[1202] The server uses the generative AI model to analyze and match the restaurant's detailed data with the user's preferences based on the restaurant's detailed data obtained from the API, and generates a prompt sentence to input into the generative AI model.

[1203] An example of a specific prompt is:

[1204] "User preferences: ['vegan', 'low-carb']\nLocation: 'Tokyo'\nFind restaurants that match these preferences."

[1205] It takes user preference information and restaurant details as input, sends prompts to a generative AI model, and outputs a list of optimal restaurants.

[1206] Step 4:

[1207] The server receives the analysis results of the generative AI model and creates a list of restaurants that are optimal for the user based on the results. This list includes the restaurant's name, location, rating, specialties, etc. The server sends this information to the user's device in real time. It receives the analysis results of the generative AI model as input and creates and sends a list of restaurants for the user as output.

[1208] Step 5:

[1209] The user checks the list of suggested restaurants on the device and inputs the restaurant of their choice and the desired reservation date and time. The device sends this information to the server. The device receives the user's selection and reservation information as input and sends it to the server.

[1210] Step 6:

[1211] The server receives the reservation information and sends it to the selected restaurant's reservation system to complete the reservation process. It receives the user's reservation information as input and sends the data to the restaurant's reservation system as output to complete the reservation.

[1212] Step 7:

[1213] After using a restaurant's service, a user uses the device to enter reviews and ratings for the restaurant. The device sends this information to the server. The server stores the received reviews and ratings in a database for future analysis. The server receives the user's reviews and ratings as input and stores them in a database.

[1214] 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.

[1215] This invention relates to a system that uses generative AI and an emotion engine to suggest the most suitable hospital for a patient. The details of the interactions between the user, terminal, and server, as well as each function, are explained below.

[1216] 1. User login and information entry

[1217] Terminal

[1218] The user logs in to the hospital search system using a terminal. The login screen is displayed and the user enters their user ID and password. After inputting the information, the terminal sends this information to the server.

[1219] server

[1220] The server verifies the received login information and authenticates the user. If authentication is successful, the user proceeds to the next step.

[1221] Specific examples

[1222] When User A logs in to the hospital search system and enters his / her user ID and password, authentication is performed and the user is then directed to a screen for entering medical condition information.

[1223] 2. Entering patient information and recognizing emotions

[1224] Terminal

[1225] After successful authentication, the user inputs their symptoms and special needs. In addition, the device is equipped with an emotion engine that can analyze the user's emotional state from their input and voice.

[1226] server

[1227] The server stores the received medical condition information and the emotion analysis results from the emotion engine in a database, which is then ready for the next analysis step.

[1228] Specific examples

[1229] User A inputs "headache" and "nausea," and the emotion engine analyzes the user's stress level while inputting. The server stores this information.

[1230] 3. Hospital data collection and analysis

[1231] server

[1232] The server retrieves detailed hospital data (specialties, state-of-the-art medical equipment, staff evaluations, etc.) from a database, and uses generative AI to perform an analysis to match the patient with the most suitable hospital based on the patient's medical condition information, detailed hospital data, and sentiment analysis results.

[1233] Specific examples

[1234] The server collects a large amount of hospital data and lists hospitals that specialize in "neurology" or have "the latest CT scan equipment." The generative AI analyzes the user's gender and also considers their stress level to find the best hospital.

[1235] 4. Hospital list suggestions

[1236] server

[1237] Based on the analysis results of the generative AI, the server creates a list of hospitals that are best suited to the patient and sends it to the user's device in real time.

[1238] Terminal

[1239] The device displays the received list of hospitals to the user, including basic information and characteristics of the hospitals, as well as additional suggestions based on the results of the emotion engine analysis.

[1240] Specific examples

[1241] User A's device will display a list of hospitals that specialize in "neurology" and have "the latest CT scan equipment," and will also suggest hospitals that provide mental care to address User A's high stress levels.

[1242] 5. Doctor's appointment

[1243] User

[1244] The user uses the terminal to select the most suitable hospital from the displayed list of hospitals and enters the desired reservation date. The terminal then sends this reservation information to the server.

[1245] server

[1246] The server sends the received reservation information to the hospital's reservation system and confirms the reservation. The confirmed reservation information is notified to both the user and the hospital.

[1247] Specific examples

[1248] When User A selects "Hospital B" from the list and enters the desired reservation date, the server sends the information to Hospital B's reservation system and confirms the reservation.

[1249] 6. Collecting reviews and ratings

[1250] Terminal

[1251] After the consultation, the user can use the terminal to input their evaluation and review of the hospital. In addition, if any emotional changes during the consultation have been recorded, they can also input that data.

[1252] server

[1253] The server stores the received reviews, ratings, and emotional data in a database and uses them to suggest hospitals in the future.

[1254] Specific examples

[1255] When User A enters a rating for "Hospital B" (4 out of 5) and a review such as "The doctor was kind," the server stores these in a database and uses them for future analysis.

[1256] By having the above-described configuration, the present invention is a system that enables patients to quickly and reliably find an appropriate medical institution, and further, by taking into account the user's emotional state, can improve the accuracy of hospital selection.

[1257] The processing flow will be explained below.

[1258] Step 1:

[1259] The user accesses the hospital search system using a terminal and enters their user ID and password on the login screen. The terminal then sends the entered login information to the server.

[1260] Step 2:

[1261] The server authenticates the user by checking the user information in the database based on the received user ID and password. If authentication is successful, the user is guided to a screen to input medical condition information to proceed to the next step.

[1262] Step 3:

[1263] The user uses the device to input information about their medical condition and special needs. For example, they input symptoms such as "headache" and "nausea." The device then sends the input information to the server. The device then uses an emotion engine to analyze emotions from the user's input and voice.

[1264] Step 4:

[1265] The server stores the received medical condition information and the emotion analysis results from the emotion engine in a database, which prepares the database for the next analysis step.

[1266] Step 5:

[1267] The server retrieves detailed hospital data (areas of expertise, up-to-dateness of medical equipment, staff evaluations, etc.) from a database, and then uses generative AI to analyze and match the patient's medical condition information, detailed hospital data, and sentiment analysis results.

[1268] Step 6:

[1269] The server sends the hospital list ranked by the generative AI to the user's device in real time, including suggestions that take into account the results of sentiment analysis.

[1270] Step 7:

[1271] A list of hospitals is displayed on the terminal, and the user selects the most suitable hospital from the list. The user then inputs the desired reservation date for the selected hospital. The terminal then sends this reservation information to the server.

[1272] Step 8:

[1273] The server sends the received reservation information to the hospital's reservation system and confirms the reservation. The confirmed reservation information is notified to both the user and the hospital.

[1274] Step 9:

[1275] After the consultation, the user uses the device to input their evaluation and review of the hospital. Emotional data may also be recorded during input. This information is also sent to the server.

[1276] Step 10:

[1277] The server stores the received ratings, reviews, and emotion data in a database and uses it to make future hospital recommendations, improving the accuracy and reliability of the entire system.

[1278] Example 2

[1279] 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."

[1280] Conventional hospital search systems have the problem of being unable to suggest hospitals that take into account the patient's emotional state, and therefore unable to address the patient's psychological needs.In addition, they do not provide enough information necessary to select an appropriate hospital, making it difficult for patients to quickly find the hospital that is best suited to them.

[1281] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for receiving medical condition information from a patient, a means for analyzing an emotional state based on the medical condition information, a means for acquiring detailed hospital data from a database, a means for analyzing and matching the patient's medical condition information, the analysis results of the emotional state, and the detailed hospital data using a generation AI, and a means for suggesting the most suitable hospital for the patient. This enables the patient to quickly and appropriately find a hospital that is best suited to their medical condition and emotional state.

[1282] "Patient" refers to an individual who provides medical information and seeks appropriate medical care.

[1283] "Medical condition information" refers to information necessary for selecting a medical institution, such as a patient's current symptoms, special needs, and medical history.

[1284] "Emotional state" refers to information that indicates the patient's psychological state and emotional level, and is analyzed by the emotion engine.

[1285] "Hospital detailed data" refers to various information related to the hospital, such as the content of medical services provided by the hospital, the up-to-dateness of medical equipment, and evaluations of medical staff.

[1286] "Generative AI" refers to artificial intelligence that uses machine learning and natural language processing to analyze data and generate optimal suggestions.

[1287] "Analysis and matching" refers to the process of using generative AI to select the most suitable hospital based on the patient's medical condition information, the analysis results of their emotional state, and detailed hospital data.

[1288] "Proposal means" refers to the method or process of providing patients with the analysis and matching results using generative AI.

[1289] This invention is a system that suggests the most suitable hospital for a patient, and the details of the interactions between the user, terminal, and server, as well as each function, will be explained below.

[1290] User logs in and enters information

[1291] Terminal

[1292] The user logs in to the hospital search system using a terminal. The login screen is displayed and the user enters their user ID and password. After inputting the information, the terminal sends this information to the server.

[1293] server

[1294] The server verifies the received login information and authenticates the user. If authentication is successful, the user proceeds to the next step.

[1295] Entering patient information and recognizing emotions

[1296] Terminal

[1297] After successful authentication, the user inputs their symptoms and special needs. In addition, the device is equipped with an emotion engine that can analyze the user's emotional state from their input and voice.

[1298] server

[1299] The server stores the received medical condition information and the emotion analysis results from the emotion engine in a database.

[1300] Hospital data collection and analysis

[1301] server

[1302] The server retrieves detailed hospital data (specialties, state-of-the-art medical equipment, staff evaluations, etc.) from a database, and then uses a generative AI model to perform an analysis to match the patient with the most suitable hospital based on the patient's medical condition information, the results of an analysis of their emotional state, and the detailed hospital data.

[1303] Hospital list suggestions

[1304] server

[1305] Based on the analysis results of the generative AI model, the server creates a list of hospitals that are best suited to the patient and sends it to the user's device in real time.

[1306] Terminal

[1307] The device displays the received list of hospitals to the user, including basic information and characteristics of the hospitals, as well as additional suggestions based on the results of the emotion engine analysis.

[1308] Clinic appointment

[1309] User

[1310] The user uses the terminal to select the most suitable hospital from the displayed list of hospitals and enters the desired reservation date. The terminal then sends this reservation information to the server.

[1311] server

[1312] The server sends the received reservation information to the hospital's reservation system and confirms the reservation. The confirmed reservation information is notified to both the user and the hospital.

[1313] Collecting reviews and ratings

[1314] Terminal

[1315] After the consultation, the user can use the terminal to input their evaluation and review of the hospital. In addition, if any emotional changes during the consultation are recorded, they can also input that data.

[1316] server

[1317] The server stores the received reviews, ratings, and emotional data in a database and uses them to suggest hospitals in the future.

[1318] Hardware and software used

[1319] Device: Smartphone, tablet, or PC.

[1320] Server: A high-performance database server (e.g. MySQL, PostgreSQL).

[1321] Sentiment engine: Natural language processing libraries and machine learning models (e.g., TensorFlow, PyTorch).

[1322] Generative AI models: AI models used for data analysis and text generation (e.g., GPT-3, BERT).

[1323] Examples of concrete examples and prompts

[1324] Specific examples

[1325] User A logs into the hospital search system and inputs his / her symptoms of headache and nausea, while the emotion engine analyzes his / her stress level. The server stores this information, and the generative AI model compares it with hospital data to identify the most suitable hospital. As a result, a list of appropriate hospitals is displayed on User A's device, and an appointment at the selected hospital is confirmed. After the consultation, User A inputs his / her rating and reviews of the hospital, which are used to make suggestions for future visits.

[1326] Prompt Sentence Examples

[1327] Symptoms: Headache and nausea

[1328] Special Needs: Stress Management

[1329] Emotional state: High stress levels

[1330] Specialty: Neurology

[1331] Medical equipment: Latest CT scan equipment

[1332] Staff rating: Highly rated

[1333] This system allows patients to quickly and reliably find the right medical institution, and by taking into account the patient's emotional state, it can improve the accuracy of hospital selection.

[1334] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1335] Step 1:

[1336] User login and information entry

[1337] (input)

[1338] The user enters the user ID and password into the terminal.

[1339] (Specific actions)

[1340] The user logs in to the hospital search system using a terminal. The login screen is displayed and the user enters their user ID and password.

[1341] (Data processing and calculation)

[1342] The terminal sends the entered user ID and password to the server.

[1343] (output)

[1344] The server verifies the received login information and sends the authentication result to the terminal.

[1345] Step 2:

[1346] Entering patient information and recognizing emotions

[1347] (input)

[1348] Users enter medical conditions and special needs into the terminal and also use voice to indicate their emotional state.

[1349] (Specific actions)

[1350] After successful authentication, the user inputs their symptoms and special needs, and the device is equipped with an emotion engine that analyzes their emotional state from the input and voice.

[1351] (Data processing and calculation)

[1352] The device sends the medical condition information and emotion analysis results to the server.

[1353] (output)

[1354] The server stores the received medical condition information and emotion analysis results in a database.

[1355] Step 3:

[1356] Hospital data collection and analysis

[1357] (input)

[1358] The server retrieves the hospital details from the database.

[1359] (Specific actions)

[1360] The server retrieves detailed hospital data (areas of expertise, up-to-dateness of medical equipment, staff evaluations, etc.) from a database.

[1361] (Data processing and calculation)

[1362] Using a generative AI model, an analysis is performed to match the most suitable hospital based on medical condition information, emotional state analysis results, and detailed hospital data.

[1363] (output)

[1364] The server generates a list of optimal hospitals as a result of the analysis.

[1365] Step 4:

[1366] Hospital list suggestions

[1367] (input)

[1368] The server generates a list of the best hospitals.

[1369] (Specific actions)

[1370] Based on the analysis results of the generative AI, a list of optimal hospitals is created and sent to the user's device.

[1371] (Data processing and calculation)

[1372] The server transmits the hospital list to the user terminal.

[1373] (output)

[1374] The terminal displays the received hospital list to the user.

[1375] Step 5:

[1376] Clinic appointment

[1377] (input)

[1378] The user selects a hospital from the list of hospitals and enters the desired appointment date.

[1379] (Specific actions)

[1380] The user selects the most suitable hospital from the displayed list and enters the desired reservation date. The terminal then sends this reservation information to the server.

[1381] (Data processing and calculation)

[1382] The server sends the received reservation information to the hospital's reservation system and confirms the reservation.

[1383] (output)

[1384] The server notifies both the user and the hospital of the confirmed reservation information.

[1385] Step 6:

[1386] Collecting reviews and ratings

[1387] (input)

[1388] Users enter their ratings and reviews of the hospital after their consultation.

[1389] (Specific actions)

[1390] After the consultation, the user can enter their evaluation and review of the hospital. If any emotional changes have been recorded, they can also enter that data.

[1391] (Data processing and calculation)

[1392] The device sends the ratings, reviews, and emotion data to the server.

[1393] (output)

[1394] The server stores the received reviews, ratings, and emotional data in a database, which will be used to recommend hospitals in the future.

[1395] (Application example 2)

[1396] 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."

[1397] Many conventional hospital recommendation systems suggest medical institutions based on a patient's medical condition information. However, these suggestions are made without taking into account the patient's emotional state or current mood, so the options are not necessarily comfortable for the patient. Furthermore, particularly in food delivery, there are no systems that can suggest optimal meals tailored to the user's emotional state, making it difficult to increase user satisfaction. Therefore, the present invention aims to provide a system that combines a patient's medical condition information and emotional state to make more accurate hospital and meal recommendations.

[1398] 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.

[1399] In this invention, the server includes means for receiving medical condition information from a patient, means for acquiring detailed hospital data from a database, means for analyzing and matching the patient's medical condition information and the detailed hospital data using a generative AI, means for analyzing the user's emotional state and using the analysis results to suggest the optimal meal for the patient, and means for suggesting the optimal hospital for the patient. This enables the patient to select a hospital and meal that are appropriate for their emotional state at the time, thereby improving user satisfaction and quality of life.

[1400] "Patient" refers to a person receiving medical examination or treatment.

[1401] "Medical condition information" refers to information about the symptoms or illnesses a patient is suffering from.

[1402] "Hospital detailed data" refers to information such as the hospital's areas of expertise, the state of the art of medical equipment, and staff evaluations.

[1403] "Generative AI" is a type of artificial intelligence that refers to a program that has the ability to generate new information based on large amounts of data.

[1404] "Emotional state" refers to the user's mood and psychological state at any given time.

[1405] An "emotion engine" refers to a system for analyzing emotions from user input and voice.

[1406] A "database" refers to a collection of data organized to allow efficient searching and management.

[1407] "Matching" refers to the process of comparing multiple items and finding the combination that best suits their respective characteristics.

[1408] "Suggestion" refers to the act of prompting a user for a particular option or piece of information.

[1409] "Food delivery" refers to a service that delivers meals ordered by a user to a specific location.

[1410] "Meal suggestions" refers to providing optimal meals based on the user's preferences and emotional state.

[1411] This invention is a system that suggests optimal hospitals and meals based on a user's medical condition information and emotional state. This system is realized using hardware and software components such as a smartphone application and a backend server.

[1412] System configuration

[1413] The system includes the following major components:

[1414] 1. User terminal: Implemented as a smartphone application, it provides an interface for users to input information about their medical condition and emotional state.

[1415] 2. Server: The backend server, using the Django framework, processes data from users, connects with the database for analysis, and includes generative AI models for generating meal and hospital recommendations.

[1416] 3. Database: Used to store detailed hospital data and users' medical condition and emotion data.

[1417] Explanation of program processing

[1418] User login and information entry

[1419] The user terminal provides an interface for the user to log in to the system. The user enters their ID and password, and the login information is sent to the server for authentication.

[1420] Entering medical information and emotional state

[1421] After successful authentication, the user device displays a screen prompting the user to enter their medical condition information and emotional state. As the user enters their medical condition information, the emotional engine analyzes their emotional state and transmits the data to the server.

[1422] Hospital data collection and analysis

[1423] The server retrieves detailed hospital data from the database and uses generative AI to analyze and match the user's medical condition information with the detailed hospital data. The generative AI model is implemented using PyTorch, TensorFlow, and other tools. Emotional state data is also included in the analysis to identify the most suitable hospital.

[1424] Meal suggestions

[1425] It also makes meal suggestions based on the user's emotional state.Generation AI is also used to generate meal suggestions, analyzing prompts based on user input to suggest appropriate meals.

[1426] Specific examples

[1427] For example, if User A logs in and inputs "headache" and "nausea," and the emotion engine detects high stress, the server will input a prompt to the generation AI saying, "Please suggest the best food for the user currently feeling stressed." The generation AI will then make suggestions such as "green tea," "healthy salad," and "fruit," and display them on the user's device.

[1428] Examples of prompts are:

[1429] "Please suggest the best foods for the user if they are currently feeling stressed."

[1430] As a result, the system takes into account the user's emotional state to suggest hospitals and meals that are best suited to the user's condition, improving the user's satisfaction and quality of life.

[1431] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1432] Step 1:

[1433] A user logs in to a terminal.

[1434] Input: User ID, Password

[1435] Specific operation: The user starts the smartphone application and enters their user ID and password on the login screen. The entered information is sent to the server.

[1436] Output: Authentication success or failure response

[1437] Step 2:

[1438] The server authenticates the user.

[1439] Input: User ID, Password

[1440] Specific operation: The server checks the received user ID and password against the database and performs authentication. If authentication is successful, it proceeds to the next step.

[1441] Output: Authentication result (success or failure)

[1442] Step 3:

[1443] The user inputs medical condition information and emotional state.

[1444] Input: medical condition information, emotional data

[1445] Specific operation: After successful authentication, the user will proceed to the screen to input their medical condition information. At the same time, the device will analyze the user's emotional data from their voice and text, and send this information to the server.

[1446] Output: medical condition information, emotional data

[1447] Step 4:

[1448] The server collects and analyzes hospital data.

[1449] Input: medical condition information, emotional data

[1450] How it works: The server retrieves detailed hospital data (specialties, state-of-the-art medical equipment, staff ratings, etc.) from the database, then uses a generative AI model to analyze and match the patient's condition information with the hospital data. Sentiment data is also added to this analysis to identify the most suitable hospital.

[1451] Output: List of best hospitals

[1452] Step 5:

[1453] Servers will make meal suggestions.

[1454] Input: Emotion data

[1455] Specific operation: Based on the emotion data, the server inputs the prompt sentence "Please suggest the best food for the user if he / she is currently feeling stressed" into the generative AI model. The generative AI generates meal suggestions based on this prompt sentence.

[1456] Output: A list of meal suggestions

[1457] Step 6:

[1458] The server sends the optimal hospital and meal recommendations to the user terminal.

[1459] Input: List of best hospitals, list of meal suggestions

[1460] Specific operation: The server sends the generated list of optimal hospitals and meal recommendations to the user's device, which receives it and displays it on the screen.

[1461] Output: Display of hospital list and meal suggestions

[1462] Step 7:

[1463] The user selects a hospital and makes a reservation.

[1464] Input: Selected hospital, desired appointment date

[1465] Specific operation: The user selects a hospital from the displayed list of hospitals and enters the desired reservation date. This information is sent to the server, which then sends the reservation information to the hospital's reservation system and confirms the reservation.

[1466] Output: Reservation confirmation information

[1467] Step 8:

[1468] Users enter ratings and reviews after their consultation.

[1469] Input: Post-consultation evaluation, reviews, emotional change data

[1470] Specific operation: After the consultation, the user uses the device to enter their evaluation and review of the hospital. In addition, if any emotional changes during the consultation were recorded, they can also enter that data. The server stores this information in a database and uses it to make suggestions for future visits.

[1471] Output: Ratings, reviews, and sentiment data

[1472] 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.

[1473] 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.

[1474] 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.

[1475] [Fourth embodiment]

[1476] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1477] 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.

[1478] 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).

[1479] 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.

[1480] 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.

[1481] 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).

[1482] 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.

[1483] 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.

[1484] 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.

[1485] 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.

[1486] 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.

[1487] 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.

[1488] 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."

[1489] This invention relates to a system that uses generative AI to suggest the best hospital for a patient. The details of the interactions between the user, terminal, and server, as well as each function, are explained below.

[1490] 1. User login and information entry

[1491] Terminal

[1492] The user logs in to the hospital search system using a terminal. The login screen is displayed and the user enters their user ID and password. After input, the terminal sends this information to the server.

[1493] server

[1494] The server verifies the received login information and authenticates the user. If authentication is successful, the user proceeds to the next step.

[1495] Specific examples

[1496] When User A logs in to the hospital search system and enters his / her user ID and password, authentication is performed and the user is then directed to a screen for entering medical condition information.

[1497] 2. Enter patient information

[1498] Terminal

[1499] After authentication is complete, the user enters their symptoms and special needs, and the terminal transmits the medical condition information received from the user to the server.

[1500] server

[1501] The server receives the patient's medical condition information and stores it in a database, which is then ready for the next analysis step.

[1502] Specific examples

[1503] User A enters "headache" and "nausea" and sends the information to the server, which stores it.

[1504] 3. Hospital data collection and analysis

[1505] server

[1506] The server retrieves detailed hospital data (specialties, state-of-the-art medical equipment, staff evaluations, etc.) from a database, and then uses generative AI to analyze and match the patient's condition information and hospital details to the most suitable hospital.

[1507] Specific examples

[1508] The server collects a large amount of hospital data and lists hospitals that specialize in "neurology" or have "the latest CT scan equipment." The generative AI analyzes this data and finds the best hospital for the patient.

[1509] 4. Hospital list suggestions

[1510] server

[1511] Based on the analysis results of the generative AI, the server creates a list of hospitals that are best suited to the patient and sends it to the user's device in real time.

[1512] Terminal

[1513] The terminal displays the received list of hospitals to the user, which includes basic information and characteristics of the hospitals, allowing the user to check them.

[1514] Specific examples

[1515] User A's device displays a list of hospitals that specialize in "neurology" and hospitals that have "the latest CT scan equipment." The list also displays each hospital's specialty and evaluation points.

[1516] 5. Doctor's appointment

[1517] User

[1518] The user selects the most suitable hospital from the list and enters the desired reservation date. The terminal sends this information to the server and completes the reservation procedure.

[1519] server

[1520] The server receives the user's reservation information and sends the information to the selected hospital's reservation system to confirm the reservation.

[1521] Specific examples

[1522] When User A selects "Hospital B" from the list and enters the desired reservation date, the server sends the information to Hospital B's reservation system and confirms the reservation.

[1523] 6. Collecting reviews and ratings

[1524] Terminal

[1525] After the consultation, the user uses the terminal to enter reviews and ratings for the hospital.

[1526] server

[1527] The server stores the received reviews and ratings in a database and uses them for future analysis.

[1528] Specific examples

[1529] After User A visits Hospital B, he or she enters a rating (4 out of 5) and a review such as "The doctor was kind," and the server stores this in a database for future analysis.

[1530] With the above-described configuration, the present invention is a system that enables patients to quickly and reliably find an appropriate medical institution, thereby reducing the stress and anxiety of choosing a hospital.

[1531] The processing flow will be explained below.

[1532] Step 1:

[1533] The user accesses the hospital search system using a terminal and enters their user ID and password on the login screen. The terminal then sends the entered login information to the server.

[1534] Step 2:

[1535] The server authenticates the user by checking the user information in the database based on the received user ID and password. If authentication is successful, the user is guided to a screen to input medical condition information to proceed to the next step.

[1536] Step 3:

[1537] The user uses the terminal to input information about their medical condition and special needs, for example, inputting symptoms such as "headache" and "nausea." The terminal then transmits the input information to the server.

[1538] Step 4:

[1539] The server stores the received medical condition information in a database, and then queries the database to obtain detailed data about the hospital (such as areas of expertise, the up-to-dateness of medical equipment, and staff ratings).

[1540] Step 5:

[1541] The server analyzes the patient and hospital information using the generated AI based on the acquired detailed hospital data. The generated AI then creates a list of hospitals that are best suited to the patient's condition and ranks the list.

[1542] Step 6:

[1543] The server sends the hospital list ranked by the generation AI to the user's device in real time, and the device displays the hospital list to the user.

[1544] Step 7:

[1545] The user uses the terminal to check the displayed list of hospitals and select the one that best suits them. The user then inputs the desired reservation date for the selected hospital. The terminal then sends this reservation information to the server.

[1546] Step 8:

[1547] The server sends the received reservation information to the hospital's reservation system and confirms the reservation. The confirmed reservation information is notified to both the user and the hospital.

[1548] Step 9:

[1549] After the consultation, the user uses the terminal to input their rating and review of the hospital, which is then sent to the server.

[1550] Step 10:

[1551] The server stores the received ratings and reviews in a database and uses them as data for future hospital recommendations, improving the accuracy and reliability of the entire system.

[1552] Example 1

[1553] 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."

[1554] Conventional hospital search systems make it difficult for patients to find the medical institution that best suits their symptoms, and choosing a hospital requires a great deal of time and effort. Furthermore, they lack appropriate reservation systems and do not effectively utilize feedback after consultations. Therefore, there is a need for a system that allows patients to quickly find the appropriate medical institution.

[1555] 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.

[1556] In this invention, the server includes means for receiving and verifying login information for user authentication, means for receiving medical condition information from the patient and storing it in a database, means for acquiring detailed hospital data from the database, means for analyzing and matching the patient's medical condition information and detailed hospital data using a generative AI model, means for creating and transmitting a list of hospitals optimal for the patient based on the analysis results of the generative AI, means for receiving reservation information for the hospital selected by the patient and confirming the reservation, and means for receiving evaluations and reviews of the hospital from the patient and storing them in a database. This enables patients to quickly find the optimal hospital, reducing the stress and anxiety of choosing a hospital.

[1557] "User authentication" is the process of verifying authentication information such as user ID and password when a user accesses a system, and confirming whether the user is a legitimate user.

[1558] "Medical condition information" refers to information provided by patients about their symptoms and special needs, and is the basic data that enables the system to recommend the most suitable hospital.

[1559] The "database" is a high-performance information management system for systematically managing and storing various data required by the system, such as patient information, detailed hospital data, authentication information, ratings and reviews.

[1560] A "generative AI model" is an artificial intelligence model that uses machine learning and natural language processing to analyze data and perform specific tasks (such as matching hospitals).

[1561] "Hospital detailed data" is information about a specific medical institution, including areas of expertise, the latest state of medical equipment, and staff evaluations.

[1562] "Analysis and matching" is a data analysis process in which the generative AI model selects the most suitable hospital based on the patient's medical condition information and detailed hospital data.

[1563] The "hospital list" is a list of the most suitable medical institutions presented to patients based on the analysis results of the generative AI model, and includes basic information and characteristics of each hospital.

[1564] "Reservation information" is information relating to the patient's desired consultation date and time and the selected hospital, and is data necessary to confirm the reservation via the server.

[1565] "Evaluations and reviews" refer to the evaluations and opinions that patients provide to hospitals after their consultations, and are feedback data that the system can use for future analysis.

[1566] This invention relates to a system that uses a generative AI model to recommend the most suitable hospital for a patient. This system operates using a user's terminal, a server, and a database.

[1567] 1. User Login

[1568] Terminal

[1569] The user logs in to the hospital search system using a terminal. The login screen is displayed and the user enters their user ID and password. After inputting the information, the terminal sends this information to the server.

[1570] server

[1571] The server verifies the received login information and authenticates the user. If authentication is successful, the user can proceed to the next step.

[1572] Specific examples

[1573] When User A logs in to the hospital search system and enters his / her user ID and password, authentication is performed and the user is then directed to a screen for entering medical condition information.

[1574] 2. Enter patient information

[1575] Terminal

[1576] After authentication is complete, the user inputs their symptoms and special needs, and the terminal transmits the medical condition information received from the user to the server.

[1577] server

[1578] The server receives the patient's medical condition information and stores it in a database, which is then ready for the next analysis step.

[1579] Specific examples

[1580] User A enters "headache" and "nausea" and sends the information to the server, which stores it.

[1581] 3. Hospital data collection and analysis

[1582] server

[1583] The server retrieves detailed hospital data (specialties, state-of-the-art medical equipment, staff evaluations, etc.) from a database, and then uses a generative AI model to analyze and match the patient's condition information and hospital details to the most suitable hospital.

[1584] Specific examples

[1585] The server collects a large amount of hospital data and lists hospitals that specialize in "neurology" or have "the latest CT scan equipment." The generative AI model analyzes this data and finds the best hospital for the patient.

[1586] Example prompt sentence:

[1587] "Please suggest the best hospital based on the symptom information provided by the user."

[1588] 4. Hospital list suggestions

[1589] server

[1590] Based on the analysis results of the generative AI model, the server creates a list of hospitals that are best suited to the patient and sends it to the user's device in real time.

[1591] Terminal

[1592] The terminal displays the received list of hospitals to the user, which includes basic information and characteristics of the hospitals, allowing the user to check them.

[1593] Specific examples

[1594] User A's device displays a list of hospitals that specialize in "neurology" and hospitals that have "the latest CT scan equipment." The list also displays each hospital's specialty and evaluation points.

[1595] 5. Doctor Appointments

[1596] User

[1597] The user selects the most suitable hospital from the list and enters the desired reservation date. The terminal sends this information to the server and completes the reservation procedure.

[1598] server

[1599] The server receives the user's reservation information and sends the information to the selected hospital's reservation system to confirm the reservation.

[1600] Specific examples

[1601] When User A selects "Hospital B" from the list and enters the desired reservation date, the server sends the information to Hospital B's reservation system and confirms the reservation.

[1602] 6. Collecting reviews and ratings

[1603] Terminal

[1604] After the consultation, the user uses the terminal to enter reviews and ratings for the hospital.

[1605] server

[1606] The server stores the received reviews and ratings in a database and uses them for future analysis.

[1607] Specific examples

[1608] After User A visits Hospital B, he or she enters a rating (4 out of 5) and a review such as "The doctor was kind," and the server stores this in a database for future analysis.

[1609] This system allows patients to quickly and reliably find the appropriate medical institution, reducing the stress and anxiety of choosing a hospital.

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

[1611] Step 1:

[1612] User authentication

[1613] The user enters their user ID and password on the login screen of the hospital search system on their terminal. The terminal sends the entered login information to the server. The server authenticates the user by comparing the received login information with the authentication information in the database. If authentication is successful, the user can proceed to the next step.

[1614] Input: User ID and password

[1615] Data processing: The device encodes the login information and sends it to the server

[1616] Data calculation: The server checks the data against the database

[1617] Output: Authentication result

[1618] Specific operation: The user enters login information and clicks the "Login" button. The device sends the user ID and password to the server, and the server returns the authentication result to the device.

[1619] Step 2:

[1620] Enter medical condition information

[1621] After completing the authentication, the user inputs their medical condition and special needs. The terminal sends the medical condition information received from the user to the server. The server stores the medical condition information from the patient in a database.

[1622] Input: Medical condition information (e.g., "headache," "nausea")

[1623] Data processing: The device sends the input information

[1624] Data calculation: The server stores the information in a database

[1625] Output: Saved results

[1626] Specific operation: The user enters the medical condition and clicks the "Submit" button. The terminal sends the medical condition information to the server, which then stores it in the database.

[1627] Step 3:

[1628] Hospital Data Collection and Analysis

[1629] The server retrieves detailed hospital data (specialties, state-of-the-art medical equipment, staff evaluations, etc.) from the database, and then uses a generative AI model to analyze and match the most suitable hospital based on the patient's medical condition information and the hospital's detailed data.

[1630] Input: Hospital details and medical condition information in the database

[1631] Data processing: Acquire detailed hospital data and integrate it with medical condition information

[1632] Data Computing: Analysis and Matching with Generative AI Models

[1633] Output: A list of suitable hospitals

[1634] Specific operation: The server executes a database query to retrieve hospital data. The generative AI model is given the prompt "Symptoms: headache, nausea, Specialty: neurology, Equipment: latest CT scan." The generative AI model then outputs a list of the most suitable hospitals.

[1635] Step 4:

[1636] Hospital list suggestions

[1637] The server creates a list of hospitals that are optimal for the patient based on the analysis results of the generative AI model. The server then sends the list to the user's device, which then displays the received list to the user.

[1638] Input: Analysis results of the generative AI model

[1639] Data processing: Formatting hospital list

[1640] Data calculation: Creating and sending a list

[1641] Output: Hospital list

[1642] Specific operation: The server formats the analysis results of the generated AI model and creates a list of hospitals. The server then sends the list of hospitals to the device, which then displays it to the user.

[1643] Step 5:

[1644] Hospital appointment

[1645] The user selects the desired hospital from the list and enters the desired reservation date. The terminal sends the entered reservation information to the server. The server then sends the received reservation information to the reservation system of the selected hospital and confirms the reservation.

[1646] Input: Reservation information (selected hospital, desired date)

[1647] Data processing: Sending reservation information

[1648] Data calculation: Sending and confirming reservation information

[1649] Output: Reservation confirmation

[1650] Specific operation: The user selects a hospital, enters the desired appointment date, and clicks the "Book" button. The terminal sends the appointment information to the server, which then sends the information to the hospital's appointment system and confirms the appointment.

[1651] Step 6:

[1652] Collecting reviews and ratings

[1653] After a consultation, the user uses the device to input reviews and ratings for the hospital. The device then sends the input reviews and ratings to the server. The server then stores the received reviews and ratings in a database and uses them for future analysis.

[1654] Input: Rating and review (e.g., "Rating: 4 points, Comment: The doctor was kind.")

[1655] Data Processing: Submitting Reviews and Ratings

[1656] Data calculation: Saving to database

[1657] Output: Saved results

[1658] Specific operation: The user enters a rating and review and clicks the "Submit" button. The device sends the rating and review to the server, which stores them in a database.

[1659] (Application example 1)

[1660] 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."

[1661] In today's food delivery services, users often have difficulty finding restaurants that meet their dietary preferences and restrictions, and they need the information they need quickly. Furthermore, the process of making reservations at the restaurant of their choice can be cumbersome. A system that solves these problems is needed.

[1662] 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.

[1663] In this invention, the server includes a means for receiving preference information from a user, a means for acquiring detailed restaurant data from a database, and a means for analyzing and matching the user's preference information with the detailed restaurant data using a generating AI. This makes it possible to recommend the most suitable restaurant for the user. In addition, by assisting the user in making a reservation at the restaurant selected by the user, the complexity of the reservation procedure can be reduced.

[1664] "User" means an individual who uses the food delivery system to search for and make reservations at restaurants.

[1665] "Preference information" refers to information about dietary preferences and restrictions entered by the user, specifically dietary restrictions and preferred types of cuisine.

[1666] "Restaurant detailed data" refers to data that includes information about each restaurant, such as its specialty, the latest equipment, and staff ratings.

[1667] "Generative AI" is an artificial intelligence technology used to match users with the best restaurants based on their preferences and detailed restaurant data.

[1668] The "suggestion method" is a method for presenting a list of restaurants suitable for the user based on the analysis results of the generative AI.

[1669] "Reservation Process" means the process by which a User makes an online reservation with a Restaurant of their choice.

[1670] This invention is a system that allows users to use a food delivery system to search for and make reservations at the most suitable restaurant based on their preferences. This system mainly consists of the following steps.

[1671] 1. In the step of receiving preference information from a user, the user uses a terminal to input preference information such as dietary preferences and dietary restrictions. The terminal then transmits this preference information to a server.

[1672] 2. The server obtains the authentication token and uses an API to collect the restaurant details from a database. The API used here is a publicly available API of a food delivery service.

[1673] 3. Using the generation AI, the server analyzes and matches the user's preference information with the collected restaurant details. This generation AI uses an artificial intelligence model such as OpenAI's GPT-4. Specifically, matching is performed by inputting the following prompt sentence into the generation AI:

[1674] "User preferences: ['vegan', 'low-carb']\nLocation: 'Tokyo'\nFind restaurants that match these preferences."

[1675] 4. Based on the analysis results of the generation AI, the server creates a list of restaurant recommendations that are best suited to the user and sends it to the user's device in real time. The device then displays the received restaurant list to the user, allowing the user to select a restaurant that best suits their preferences.

[1676] 5. The user selects a specific restaurant from the list and enters the date and time they wish to make a reservation. The device then sends this information to the server, which then sends it to the selected restaurant's reservation system to assist with the reservation process.

[1677] 6. After using the service, the user enters reviews and ratings of the restaurant through the terminal. The server stores these reviews and ratings in a database for future analysis.

[1678] This allows users to quickly and reliably find restaurants that suit their preferences and smoothly complete the reservation process. As a concrete example, if a vegan or low-carb user is looking for a restaurant in Tokyo, they can provide the following prompt to the generative AI model:

[1679] "User preferences: ['vegan', 'low-carb']\nLocation: 'Tokyo'\nFind restaurants that match these preferences."

[1680] Based on these prompts, the generative AI can suggest the most suitable restaurant, greatly improving user convenience.

[1681] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1682] Step 1:

[1683] Users log in using their device and enter their preferences. Specifically, users enter their food preferences, dietary restrictions, and desired location. Input data includes "vegan," "low carb," and "Tokyo." This information is sent to the server. The system receives the user's preferences and login information as input and sends it to the server.

[1684] Step 2:

[1685] The server receives the received preference information and performs the necessary steps to obtain an authentication token, which is required for API calls. The server then sends a request to the public food delivery service's API to collect restaurant details. It receives the preference information and authentication information as input and sends a request to the API to retrieve restaurant information from the database.

[1686] Step 3:

[1687] The server uses the generative AI model to analyze and match the restaurant's detailed data with the user's preferences based on the restaurant's detailed data obtained from the API, and generates a prompt sentence to input into the generative AI model.

[1688] An example of a specific prompt is:

[1689] "User preferences: ['vegan', 'low-carb']\nLocation: 'Tokyo'\nFind restaurants that match these preferences."

[1690] It takes user preference information and restaurant details as input, sends prompts to a generative AI model, and outputs a list of optimal restaurants.

[1691] Step 4:

[1692] The server receives the analysis results of the generative AI model and creates a list of restaurants that are optimal for the user based on the results. This list includes the restaurant's name, location, rating, specialties, etc. The server sends this information to the user's device in real time. It receives the analysis results of the generative AI model as input and creates and sends a list of restaurants for the user as output.

[1693] Step 5:

[1694] The user checks the list of suggested restaurants on the device and inputs the restaurant of their choice and the desired reservation date and time. The device sends this information to the server. The device receives the user's selection and reservation information as input and sends it to the server.

[1695] Step 6:

[1696] The server receives the reservation information and sends it to the selected restaurant's reservation system to complete the reservation process. It receives the user's reservation information as input and sends the data to the restaurant's reservation system as output to complete the reservation.

[1697] Step 7:

[1698] After using a restaurant's service, a user uses the device to enter reviews and ratings for the restaurant. The device sends this information to the server. The server stores the received reviews and ratings in a database for future analysis. The server receives the user's reviews and ratings as input and stores them in a database.

[1699] 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.

[1700] This invention relates to a system that uses generative AI and an emotion engine to suggest the most suitable hospital for a patient. The details of the interactions between the user, terminal, and server, as well as each function, are explained below.

[1701] 1. User login and information entry

[1702] Terminal

[1703] The user logs in to the hospital search system using a terminal. The login screen is displayed and the user enters their user ID and password. After inputting the information, the terminal sends this information to the server.

[1704] server

[1705] The server verifies the received login information and authenticates the user. If authentication is successful, the user proceeds to the next step.

[1706] Specific examples

[1707] When User A logs in to the hospital search system and enters his / her user ID and password, authentication is performed and the user is then directed to a screen for entering medical condition information.

[1708] 2. Entering patient information and recognizing emotions

[1709] Terminal

[1710] After successful authentication, the user inputs their symptoms and special needs. In addition, the device is equipped with an emotion engine that can analyze the user's emotional state from their input and voice.

[1711] server

[1712] The server stores the received medical condition information and the emotion analysis results from the emotion engine in a database, which is then ready for the next analysis step.

[1713] Specific examples

[1714] User A inputs "headache" and "nausea," and the emotion engine analyzes the user's stress level while inputting. The server stores this information.

[1715] 3. Hospital data collection and analysis

[1716] server

[1717] The server retrieves detailed hospital data (specialties, state-of-the-art medical equipment, staff evaluations, etc.) from a database, and uses generative AI to perform an analysis to match the patient with the most suitable hospital based on the patient's medical condition information, detailed hospital data, and sentiment analysis results.

[1718] Specific examples

[1719] The server collects a large amount of hospital data and lists hospitals that specialize in "neurology" or have "the latest CT scan equipment." The generative AI analyzes the user's gender and also considers their stress level to find the best hospital.

[1720] 4. Hospital list suggestions

[1721] server

[1722] Based on the analysis results of the generative AI, the server creates a list of hospitals that are best suited to the patient and sends it to the user's device in real time.

[1723] Terminal

[1724] The device displays the received list of hospitals to the user, including basic information and characteristics of the hospitals, as well as additional suggestions based on the results of the emotion engine analysis.

[1725] Specific examples

[1726] User A's device will display a list of hospitals that specialize in "neurology" and have "the latest CT scan equipment," and will also suggest hospitals that provide mental care to address User A's high stress levels.

[1727] 5. Doctor's appointment

[1728] User

[1729] The user uses the terminal to select the most suitable hospital from the displayed list of hospitals and enters the desired reservation date. The terminal then sends this reservation information to the server.

[1730] server

[1731] The server sends the received reservation information to the hospital's reservation system and confirms the reservation. The confirmed reservation information is notified to both the user and the hospital.

[1732] Specific examples

[1733] When User A selects "Hospital B" from the list and enters the desired reservation date, the server sends the information to Hospital B's reservation system and confirms the reservation.

[1734] 6. Collecting reviews and ratings

[1735] Terminal

[1736] After the consultation, the user can use the terminal to input their evaluation and review of the hospital. In addition, if any emotional changes during the consultation have been recorded, they can also input that data.

[1737] server

[1738] The server stores the received reviews, ratings, and emotional data in a database and uses them to suggest hospitals in the future.

[1739] Specific examples

[1740] When User A enters a rating for "Hospital B" (4 out of 5) and a review such as "The doctor was kind," the server stores these in a database and uses them for future analysis.

[1741] By having the above-described configuration, the present invention is a system that enables patients to quickly and reliably find an appropriate medical institution, and further, by taking into account the user's emotional state, can improve the accuracy of hospital selection.

[1742] The processing flow will be explained below.

[1743] Step 1:

[1744] The user accesses the hospital search system using a terminal and enters their user ID and password on the login screen. The terminal then sends the entered login information to the server.

[1745] Step 2:

[1746] The server authenticates the user by checking the user information in the database based on the received user ID and password. If authentication is successful, the user is guided to a screen to input medical condition information to proceed to the next step.

[1747] Step 3:

[1748] The user uses the device to input information about their medical condition and special needs. For example, they input symptoms such as "headache" and "nausea." The device then sends the input information to the server. The device then uses an emotion engine to analyze emotions from the user's input and voice.

[1749] Step 4:

[1750] The server stores the received medical condition information and the emotion analysis results from the emotion engine in a database, which prepares the database for the next analysis step.

[1751] Step 5:

[1752] The server retrieves detailed hospital data (areas of expertise, up-to-dateness of medical equipment, staff evaluations, etc.) from a database, and then uses generative AI to analyze and match the patient's medical condition information, detailed hospital data, and sentiment analysis results.

[1753] Step 6:

[1754] The server sends the hospital list ranked by the generative AI to the user's device in real time, including suggestions that take into account the results of sentiment analysis.

[1755] Step 7:

[1756] A list of hospitals is displayed on the terminal, and the user selects the most suitable hospital from the list. The user then inputs the desired reservation date for the selected hospital. The terminal then sends this reservation information to the server.

[1757] Step 8:

[1758] The server sends the received reservation information to the hospital's reservation system and confirms the reservation. The confirmed reservation information is notified to both the user and the hospital.

[1759] Step 9:

[1760] After the consultation, the user uses the device to input their evaluation and review of the hospital. Emotional data may also be recorded during input. This information is also sent to the server.

[1761] Step 10:

[1762] The server stores the received ratings, reviews, and emotion data in a database and uses it to make future hospital recommendations, improving the accuracy and reliability of the entire system.

[1763] Example 2

[1764] 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."

[1765] Conventional hospital search systems have the problem of being unable to suggest hospitals that take into account the patient's emotional state, and therefore unable to address the patient's psychological needs.In addition, they do not provide enough information necessary to select an appropriate hospital, making it difficult for patients to quickly find the hospital that is best suited to them.

[1766] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for receiving medical condition information from a patient, a means for analyzing an emotional state based on the medical condition information, a means for acquiring detailed hospital data from a database, a means for analyzing and matching the patient's medical condition information, the analysis results of the emotional state, and the detailed hospital data using a generation AI, and a means for suggesting the most suitable hospital for the patient. This enables the patient to quickly and appropriately find a hospital that is best suited to their medical condition and emotional state.

[1767] "Patient" refers to an individual who provides medical information and seeks appropriate medical care.

[1768] "Medical condition information" refers to information necessary for selecting a medical institution, such as a patient's current symptoms, special needs, and medical history.

[1769] "Emotional state" refers to information that indicates the patient's psychological state and emotional level, and is analyzed by the emotion engine.

[1770] "Hospital detailed data" refers to various information related to the hospital, such as the content of medical services provided by the hospital, the up-to-dateness of medical equipment, and evaluations of medical staff.

[1771] "Generative AI" refers to artificial intelligence that uses machine learning and natural language processing to analyze data and generate optimal suggestions.

[1772] "Analysis and matching" refers to the process of using generative AI to select the most suitable hospital based on the patient's medical condition information, the analysis results of their emotional state, and detailed hospital data.

[1773] "Proposal means" refers to the method or process of providing patients with the analysis and matching results using generative AI.

[1774] This invention is a system that suggests the most suitable hospital for a patient, and the details of the interactions between the user, terminal, and server, as well as each function, will be explained below.

[1775] User logs in and enters information

[1776] Terminal

[1777] The user logs in to the hospital search system using a terminal. The login screen is displayed and the user enters their user ID and password. After inputting the information, the terminal sends this information to the server.

[1778] server

[1779] The server verifies the received login information and authenticates the user. If authentication is successful, the user proceeds to the next step.

[1780] Entering patient information and recognizing emotions

[1781] Terminal

[1782] After successful authentication, the user inputs their symptoms and special needs. In addition, the device is equipped with an emotion engine that can analyze the user's emotional state from their input and voice.

[1783] server

[1784] The server stores the received medical condition information and the emotion analysis results from the emotion engine in a database.

[1785] Hospital data collection and analysis

[1786] server

[1787] The server retrieves detailed hospital data (specialties, state-of-the-art medical equipment, staff evaluations, etc.) from a database, and then uses a generative AI model to perform an analysis to match the patient with the most suitable hospital based on the patient's medical condition information, the results of an analysis of their emotional state, and the detailed hospital data.

[1788] Hospital list suggestions

[1789] server

[1790] Based on the analysis results of the generative AI model, the server creates a list of hospitals that are best suited to the patient and sends it to the user's device in real time.

[1791] Terminal

[1792] The device displays the received list of hospitals to the user, including basic information and characteristics of the hospitals, as well as additional suggestions based on the results of the emotion engine analysis.

[1793] Clinic appointment

[1794] User

[1795] The user uses the terminal to select the most suitable hospital from the displayed list of hospitals and enters the desired reservation date. The terminal then sends this reservation information to the server.

[1796] server

[1797] The server sends the received reservation information to the hospital's reservation system and confirms the reservation. The confirmed reservation information is notified to both the user and the hospital.

[1798] Collecting reviews and ratings

[1799] Terminal

[1800] After the consultation, the user can use the terminal to input their evaluation and review of the hospital. In addition, if any emotional changes during the consultation are recorded, they can also input that data.

[1801] server

[1802] The server stores the received reviews, ratings, and emotional data in a database and uses them to suggest hospitals in the future.

[1803] Hardware and software used

[1804] Device: Smartphone, tablet, or PC.

[1805] Server: A high-performance database server (e.g. MySQL, PostgreSQL).

[1806] Sentiment engine: Natural language processing libraries and machine learning models (e.g., TensorFlow, PyTorch).

[1807] Generative AI models: AI models used for data analysis and text generation (e.g., GPT-3, BERT).

[1808] Examples of concrete examples and prompts

[1809] Specific examples

[1810] User A logs into the hospital search system and inputs his / her symptoms of headache and nausea, while the emotion engine analyzes his / her stress level. The server stores this information, and the generative AI model compares it with hospital data to identify the most suitable hospital. As a result, a list of appropriate hospitals is displayed on User A's device, and an appointment at the selected hospital is confirmed. After the consultation, User A inputs his / her rating and reviews of the hospital, which are used to make suggestions for future visits.

[1811] Prompt Sentence Examples

[1812] Symptoms: Headache and nausea

[1813] Special Needs: Stress Management

[1814] Emotional state: High stress levels

[1815] Specialty: Neurology

[1816] Medical equipment: Latest CT scan equipment

[1817] Staff rating: Highly rated

[1818] This system allows patients to quickly and reliably find the right medical institution, and by taking into account the patient's emotional state, it can improve the accuracy of hospital selection.

[1819] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1820] Step 1:

[1821] User login and information entry

[1822] (input)

[1823] The user enters the user ID and password into the terminal.

[1824] (Specific actions)

[1825] The user logs in to the hospital search system using a terminal. The login screen is displayed and the user enters their user ID and password.

[1826] (Data processing and calculation)

[1827] The terminal sends the entered user ID and password to the server.

[1828] (output)

[1829] The server verifies the received login information and sends the authentication result to the terminal.

[1830] Step 2:

[1831] Entering patient information and recognizing emotions

[1832] (input)

[1833] Users enter medical conditions and special needs into the terminal and also use voice to indicate their emotional state.

[1834] (Specific actions)

[1835] After successful authentication, the user inputs their symptoms and special needs, and the device is equipped with an emotion engine that analyzes their emotional state from the input and voice.

[1836] (Data processing and calculation)

[1837] The device sends the medical condition information and emotion analysis results to the server.

[1838] (output)

[1839] The server stores the received medical condition information and emotion analysis results in a database.

[1840] Step 3:

[1841] Hospital data collection and analysis

[1842] (input)

[1843] The server retrieves the hospital details from the database.

[1844] (Specific actions)

[1845] The server retrieves detailed hospital data (areas of expertise, up-to-dateness of medical equipment, staff evaluations, etc.) from a database.

[1846] (Data processing and calculation)

[1847] Using a generative AI model, an analysis is performed to match the most suitable hospital based on medical condition information, emotional state analysis results, and detailed hospital data.

[1848] (output)

[1849] The server generates a list of optimal hospitals as a result of the analysis.

[1850] Step 4:

[1851] Hospital list suggestions

[1852] (input)

[1853] The server generates a list of the best hospitals.

[1854] (Specific actions)

[1855] Based on the analysis results of the generative AI, a list of optimal hospitals is created and sent to the user's device.

[1856] (Data processing and calculation)

[1857] The server transmits the hospital list to the user terminal.

[1858] (output)

[1859] The terminal displays the received hospital list to the user.

[1860] Step 5:

[1861] Clinic appointment

[1862] (input)

[1863] The user selects a hospital from the list of hospitals and enters the desired appointment date.

[1864] (Specific actions)

[1865] The user selects the most suitable hospital from the displayed list and enters the desired reservation date. The terminal then sends this reservation information to the server.

[1866] (Data processing and calculation)

[1867] The server sends the received reservation information to the hospital's reservation system and confirms the reservation.

[1868] (output)

[1869] The server notifies both the user and the hospital of the confirmed reservation information.

[1870] Step 6:

[1871] Collecting reviews and ratings

[1872] (input)

[1873] Users enter their ratings and reviews of the hospital after their consultation.

[1874] (Specific actions)

[1875] After the consultation, the user can enter their evaluation and review of the hospital. If any emotional changes have been recorded, they can also enter that data.

[1876] (Data processing and calculation)

[1877] The device sends the ratings, reviews, and emotion data to the server.

[1878] (output)

[1879] The server stores the received reviews, ratings, and emotional data in a database, which will be used to recommend hospitals in the future.

[1880] (Application example 2)

[1881] 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."

[1882] Many conventional hospital recommendation systems suggest medical institutions based on a patient's medical condition information. However, these suggestions are made without taking into account the patient's emotional state or current mood, so the options are not necessarily comfortable for the patient. Furthermore, particularly in food delivery, there are no systems that can suggest optimal meals tailored to the user's emotional state, making it difficult to increase user satisfaction. Therefore, the present invention aims to provide a system that combines a patient's medical condition information and emotional state to make more accurate hospital and meal recommendations.

[1883] 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.

[1884] In this invention, the server includes means for receiving medical condition information from a patient, means for acquiring detailed hospital data from a database, means for analyzing and matching the patient's medical condition information and the detailed hospital data using a generative AI, means for analyzing the user's emotional state and using the analysis results to suggest the optimal meal for the patient, and means for suggesting the optimal hospital for the patient. This enables the patient to select a hospital and meal that are appropriate for their emotional state at the time, thereby improving user satisfaction and quality of life.

[1885] "Patient" refers to a person receiving medical examination or treatment.

[1886] "Medical condition information" refers to information about the symptoms or illnesses a patient is suffering from.

[1887] "Hospital detailed data" refers to information such as the hospital's areas of expertise, the state of the art of medical equipment, and staff evaluations.

[1888] "Generative AI" is a type of artificial intelligence that refers to a program that has the ability to generate new information based on large amounts of data.

[1889] "Emotional state" refers to the user's mood and psychological state at any given time.

[1890] An "emotion engine" refers to a system for analyzing emotions from user input and voice.

[1891] A "database" refers to a collection of data organized to allow efficient searching and management.

[1892] "Matching" refers to the process of comparing multiple items and finding the combination that best suits their respective characteristics.

[1893] "Suggestion" refers to the act of prompting a user for a particular option or piece of information.

[1894] "Food delivery" refers to a service that delivers meals ordered by a user to a specific location.

[1895] "Meal suggestions" refers to providing optimal meals based on the user's preferences and emotional state.

[1896] This invention is a system that suggests optimal hospitals and meals based on a user's medical condition information and emotional state. This system is realized using hardware and software components such as a smartphone application and a backend server.

[1897] System configuration

[1898] The system includes the following major components:

[1899] 1. User terminal: Implemented as a smartphone application, it provides an interface for users to input information about their medical condition and emotional state.

[1900] 2. Server: The backend server, using the Django framework, processes data from users, connects with the database for analysis, and includes generative AI models for generating meal and hospital recommendations.

[1901] 3. Database: Used to store detailed hospital data and users' medical condition and emotion data.

[1902] Explanation of program processing

[1903] User login and information entry

[1904] The user terminal provides an interface for the user to log in to the system. The user enters their ID and password, and the login information is sent to the server for authentication.

[1905] Entering medical information and emotional state

[1906] After successful authentication, the user device displays a screen prompting the user to enter their medical condition information and emotional state. As the user enters their medical condition information, the emotional engine analyzes their emotional state and transmits the data to the server.

[1907] Hospital data collection and analysis

[1908] The server retrieves detailed hospital data from the database and uses generative AI to analyze and match the user's medical condition information with the detailed hospital data. The generative AI model is implemented using PyTorch, TensorFlow, and other tools. Emotional state data is also included in the analysis to identify the most suitable hospital.

[1909] Meal suggestions

[1910] It also makes meal suggestions based on the user's emotional state.Generation AI is also used to generate meal suggestions, analyzing prompts based on user input to suggest appropriate meals.

[1911] Specific examples

[1912] For example, if User A logs in and inputs "headache" and "nausea," and the emotion engine detects high stress, the server will input a prompt to the generation AI saying, "Please suggest the best food for the user currently feeling stressed." The generation AI will then make suggestions such as "green tea," "healthy salad," and "fruit," and display them on the user's device.

[1913] Examples of prompts are:

[1914] "Please suggest the best foods for the user if they are currently feeling stressed."

[1915] As a result, the system takes into account the user's emotional state to suggest hospitals and meals that are best suited to the user's condition, improving the user's satisfaction and quality of life.

[1916] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1917] Step 1:

[1918] A user logs in to a terminal.

[1919] Input: User ID, Password

[1920] Specific operation: The user starts the smartphone application and enters their user ID and password on the login screen. The entered information is sent to the server.

[1921] Output: Authentication success or failure response

[1922] Step 2:

[1923] The server authenticates the user.

[1924] Input: User ID, Password

[1925] Specific operation: The server checks the received user ID and password against the database and performs authentication. If authentication is successful, it proceeds to the next step.

[1926] Output: Authentication result (success or failure)

[1927] Step 3:

[1928] The user inputs medical condition information and emotional state.

[1929] Input: medical condition information, emotional data

[1930] Specific operation: After successful authentication, the user will proceed to the screen to input their medical condition information. At the same time, the device will analyze the user's emotional data from their voice and text, and send this information to the server.

[1931] Output: medical condition information, emotional data

[1932] Step 4:

[1933] The server collects and analyzes hospital data.

[1934] Input: medical condition information, emotional data

[1935] How it works: The server retrieves detailed hospital data (specialties, state-of-the-art medical equipment, staff ratings, etc.) from the database, then uses a generative AI model to analyze and match the patient's condition information with the hospital data. Sentiment data is also added to this analysis to identify the most suitable hospital.

[1936] Output: List of best hospitals

[1937] Step 5:

[1938] Servers will make meal suggestions.

[1939] Input: Emotion data

[1940] Specific operation: Based on the emotion data, the server inputs the prompt sentence "Please suggest the best food for the user if he / she is currently feeling stressed" into the generative AI model. The generative AI generates meal suggestions based on this prompt sentence.

[1941] Output: A list of meal suggestions

[1942] Step 6:

[1943] The server sends the optimal hospital and meal recommendations to the user terminal.

[1944] Input: List of best hospitals, list of meal suggestions

[1945] Specific operation: The server sends the generated list of optimal hospitals and meal recommendations to the user's device, which receives it and displays it on the screen.

[1946] Output: Display of hospital list and meal suggestions

[1947] Step 7:

[1948] The user selects a hospital and makes a reservation.

[1949] Input: Selected hospital, desired appointment date

[1950] Specific operation: The user selects a hospital from the displayed list of hospitals and enters the desired reservation date. This information is sent to the server, which then sends the reservation information to the hospital's reservation system and confirms the reservation.

[1951] Output: Reservation confirmation information

[1952] Step 8:

[1953] Users enter ratings and reviews after their consultation.

[1954] Input: Post-consultation evaluation, reviews, emotional change data

[1955] Specific operation: After the consultation, the user uses the device to enter their evaluation and review of the hospital. In addition, if any emotional changes during the consultation were recorded, they can also enter that data. The server stores this information in a database and uses it to make suggestions for future visits.

[1956] Output: Ratings, reviews, and sentiment data

[1957] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1958] 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.

[1959] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1960] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1961] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1962] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1963] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1964] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1965] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1966] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1967] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1968] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1969] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1970] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1971] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1972] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1973] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1974] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1975] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1976] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1977] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1978] The following is further disclosed regarding the above embodiment.

[1979] (Claim 1)

[1980] means for receiving medical condition information from a patient;

[1981] A means of obtaining detailed hospital data from the database;

[1982] A method for analyzing and matching patient condition information and detailed hospital data using generative AI,

[1983] A means to recommend the most suitable hospital for patients,

[1984] A system including:

[1985] (Claim 2)

[1986] The system according to claim 1, wherein the detailed hospital data includes the hospital's areas of expertise, the state of the art of medical equipment, and staff evaluations.

[1987] (Claim 3)

[1988] 10. The system of claim 1, further comprising means for assisting a patient in making an appointment at a hospital of their choice.

[1989] "Example 1"

[1990] (Claim 1)

[1991] A means for receiving and verifying login information for user authentication;

[1992] means for receiving and storing medical condition information from a patient in a database;

[1993] a means for retrieving hospital details from the database;

[1994] A means of analyzing and matching patient condition information and detailed hospital data using a generative AI model;

[1995] A means to create and send a list of hospitals that are best suited to the patient based on the analysis results of the generative AI;

[1996] A means for the patient to receive appointment information at the selected hospital and confirm the appointment;

[1997] A means of receiving and storing patient reviews and comments on hospitals in a database;

[1998] A system including:

[1999] (Claim 2)

[2000] The system according to claim 1, wherein the detailed hospital data includes the hospital's areas of expertise, the state of the art of medical equipment, and staff evaluations.

[2001] (Claim 3)

[2002] 10. The system of claim 1, further comprising means for assisting a patient in making an appointment at a hospital of their choice.

[2003] "Application Example 1"

[2004] (Claim 1)

[2005] means for receiving preference information from a user;

[2006] A means of retrieving restaurant details from a database;

[2007] A method for analyzing and matching user preference information with detailed restaurant data using generative AI,

[2008] A way to suggest the best restaurants to users,

[2009] A system including:

[2010] (Claim 2)

[2011] 2. The system of claim 1, wherein the restaurant details include restaurant specialties, equipment modernity, and staff ratings.

[2012] (Claim 3)

[2013] 10. The system of claim 1, further comprising means for assisting a user in making a reservation at a restaurant selected by the user.

[2014] "Example 2: Combining Emotion Engines"

[2015] (Claim 1)

[2016] means for receiving medical condition information from a patient;

[2017] means for analyzing an emotional state based on the medical condition information;

[2018] A means for retrieving hospital details from the database;

[2019] A means of analyzing and matching patient information, emotional state analysis results, and detailed hospital data using generative AI;

[2020] A means to suggest the most suitable hospital for patients,

[2021] A system including:

[2022] (Claim 2)

[2023] The system according to claim 1, wherein the detailed hospital data includes the hospital's areas of expertise, the state of the art of medical equipment, and staff evaluations.

[2024] (Claim 3)

[2025] 10. The system of claim 1, further comprising means for assisting a patient in making an appointment at a hospital of their choice.

[2026] "Application example 2 when combining emotion engines"

[2027] (Claim 1)

[2028] means for receiving medical condition information from a patient;

[2029] A means for retrieving hospital details from the database;

[2030] A method for analyzing and matching patient condition information and detailed hospital data using generative AI,

[2031] A means for analyzing the emotional state of a user and using the analysis results to suggest an optimal diet for the patient;

[2032] A means to suggest the most suitable hospital for patients,

[2033] A system including:

[2034] (Claim 2)

[2035] The system according to claim 1, wherein the detailed hospital data includes the hospital's areas of expertise, the state of the art of medical equipment, and staff evaluations.

[2036] (Claim 3)

[2037] 10. The system of claim 1, further comprising means for providing dietary suggestions based on the user's emotional state, thereby assisting in dietary optimization. [Explanation of symbols]

[2038] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving medical condition information from a patient; A means of obtaining detailed hospital data from the database; A method for analyzing and matching patient condition information and detailed hospital data using generative AI, A means to recommend the most suitable hospital for patients, A system including:

2. The system according to claim 1 , wherein the detailed hospital data includes the hospital's areas of expertise, the state of the art of medical equipment, and staff evaluations.

3. The system of claim 1 further comprising means for assisting the patient in making an appointment at a hospital of his choice.

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