system

Abstract

Provide a system. A method for detecting a symptom includes: receiving symptom input from a user; a means for collecting health data from a wearable device; A means of analyzing the collected data and estimating possible pathologies; A means to search for appropriate medical institutions and make appointments; a means for notifying the user of the diagnostic results and reservation information; A system including:

Description

[Technical Field]

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

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

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

[0004] In modern healthcare, it is important for users to understand their own health conditions and receive prompt diagnosis and treatment at appropriate medical institutions. However, users often waste a lot of time and effort because they are unable to properly assess their own symptoms. Making appointments with medical institutions is also time-consuming and laborious, and there are many cases where patients are unable to receive treatment in a timely manner. Furthermore, there is an issue of a lack of systems that efficiently link individual health management with medical institutions by integrating data collected by wearable devices. [Means for solving the problem]

[0005] This invention is a system that receives symptom input from the user, collects health data from a wearable device, and uses AI-based data analysis to estimate possible medical conditions. Furthermore, this system searches for appropriate medical institutions, confirms appointments with the user, and notifies the user of diagnosis results and reservation information. This allows users to quickly receive diagnosis and treatment at the appropriate medical institution, improving the efficiency of health management. Furthermore, by linking with wearable devices, highly accurate medical condition estimation based on individual health data becomes possible.

[0006] "User" refers to an individual who uses the system to check their health status and input symptoms.

[0007] "Symptom input" refers to the act of a user entering their own physical condition or abnormalities in text or multiple choice format.

[0008] A "wearable device" refers to an electronic device that is worn by the user on a daily basis to collect health data.

[0009] "Health data" refers to physiological information such as heart rate, blood pressure, and sleep patterns collected by wearable devices.

[0010] "Data analysis" refers to the process of using AI and algorithms to analyze collected health data and estimate possible medical conditions.

[0011] "Condition" refers to a specific health condition or disease that is predicted based on a user's health data and symptom input.

[0012] "Medical institution" refers to a facility such as a hospital, clinic, or medical office where users can receive diagnosis or treatment.

[0013] "Reservation" refers to the act of reserving a date, time, and location for a user to receive diagnosis or treatment at a medical institution.

[0014] "Notification" refers to the act of the system informing the user of diagnosis results and reservation information via a device such as a smartphone.

[0015] "Notification module" refers to the part of the system that has the function of sending diagnostic results and reservation information to users via push notifications or SMS.

[0016] The "analysis module" refers to the part of the system that uses AI and algorithms to analyze the condition based on collected health data. [Brief explanation of the drawings]

[0017] [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

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] 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).

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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."

[0038] This system receives symptom input from the user, collects health data from a wearable device, and uses AI-based data analysis to predict possible illnesses. Furthermore, the system searches for appropriate medical institutions, confirms appointments with the user, and notifies the user of diagnosis results and appointment information.

[0039] User Input

[0040] The system begins by the user entering their symptoms into their smartphone, for example, "I have a headache," and pressing the "send" button to send the symptoms to the system.

[0041] User: Type "I have a headache" and press the "Send" button.

[0042] Data collection

[0043] The wearable device collects the user's daily health data, including heart rate, blood pressure, sleep patterns, etc. The collected data is sent from the device to a server.

[0044] Device: Wearable device collects heart rate, blood pressure, and sleep pattern data via Bluetooth.

[0045] Terminal: Sends collected data to the server via HTTP POST request.

[0046] Data analysis

[0047] The server passes the received data to an analysis module, which uses AI to analyze the data and predict possible medical conditions. For example, the AI ​​analyzes heart rate, blood pressure, and sleep patterns and determines that a person has a high probability of having a migraine.

[0048] Server: Passes the received data to the analysis module.

[0049] Server: Performs AI-based data analysis to identify possible medical conditions.

[0050] Collaboration with medical institutions

[0051] Based on the analysis results, the server searches for an appropriate medical institution. Based on the search results, the server sends a notification to the user requesting them to confirm an appointment with the medical institution. Once the user confirms the appointment, the server sends the appointment to the medical institution.

[0052] Server: Lists relevant medical institutions and selects the most suitable one.

[0053] Server: Prompts user, "Would you like to schedule an appointment with a neurologist?"

[0054] User: Check the notification and tap "Yes" to confirm the reservation.

[0055] Schedule execution and notification

[0056] The server sends the user's appointment to the medical institution and receives the appointment confirmation. Once confirmed, the server sends the appointment details to the notification module and notifies the user.

[0057] Server: Sends appointments to medical institutions and receives appointment confirmations.

[0058] Server: Sends the reservation details (date, time, location) to the notification module.

[0059] Device: Sends a push notification to the user saying, "Reservation completed. Date and time: XX / XX / XX, XX time, Location: XX Hospital."

[0060] Specific examples

[0061] 1. The user types "I have a headache" into their smartphone and sends it.

[0062] 2. The smartphone collects data from the wearable device, including heart rate (80 bpm), blood pressure (130 / 85), and sleep patterns (6 hours of light sleep), and sends it to the server.

[0063] 3. The server passes the received data to the analysis module, where the AI ​​analyzes it. The AI ​​determines that there is a high possibility of a migraine.

[0064] 4. The server searches for the nearest neurologist and notifies the user, "Would you like to make an appointment with a neurologist?"

[0065] 5. The user confirms the notification and taps "Yes" to confirm the reservation.

[0066] 6. The server sends the appointment to the medical institution and receives a confirmation of the appointment.

[0067] 7. The server sends the reservation details (date, time, location) to the notification module and notifies the user.

[0068] 8. A push notification will appear on the user's smartphone saying, "Reservation completed. Date and time: XX / XX / XX XX o'clock, Location: XX Hospital."

[0069] In this way, the present invention allows users to quickly check their health status and seek medical attention at an appropriate medical institution.

[0070] The processing flow will be explained below.

[0071] Step 1:

[0072] The user enters the symptom "headache" into their smartphone and presses the "send" button.

[0073] User: Type "I have a headache" and press the "Send" button.

[0074] Step 2:

[0075] The device collects health data from the wearable device, including heart rate, blood pressure, and sleep patterns, and transmits the collected data to a server.

[0076] Device: Heart rate (80 bpm), blood pressure (130 / 85), and sleep pattern (6 hours of light sleep) data are collected from the wearable device via Bluetooth.

[0077] Device: Health data and user symptom input are sent to the server via an HTTP POST request.

[0078] Step 3:

[0079] The server passes the received data to the analysis module, which then uses AI to analyze the data and estimate possible medical conditions.

[0080] Server: Passes the received data to the analysis module.

[0081] Server: The AI ​​analyzes the health data and symptom input it receives, compares it with past cases, and determines that there is a high possibility of a migraine.

[0082] Step 4:

[0083] The server searches for an appropriate medical institution based on the estimated medical condition, and then sends the user a notification requesting them to confirm an appointment.

[0084] Server: Lists relevant medical institutions such as internal medicine and neurology departments from a database and selects the nearest medical institution.

[0085] Server: Notifies the user of the information about the selected medical institution and sends a message asking, "Would you like to make an appointment with a neurologist?"

[0086] Step 5:

[0087] The user checks the notification on the device and confirms the medical appointment.

[0088] User: Check the notification and tap "Yes" to confirm the reservation.

[0089] Step 6:

[0090] The server sends the user's reservation information to the medical institution, and then receives reservation confirmation from the medical institution.

[0091] Server: Sends appointment information to the selected neurology department (using HTTP requests or a dedicated API).

[0092] Server: Receives the reservation confirmation response from the medical institution.

[0093] Step 7:

[0094] The server sends the reservation details (date, time, location) to the notification module, and finally the device notifies the user of the information.

[0095] Server: Sends reservation details such as "Reservation completed. Date and time: XX / XX / XX, Location: XX Hospital" to the notification module.

[0096] Device: The user will receive a push notification saying, "Reservation completed. Date and time: XX / XX / XX XX time, Location: XX Hospital."

[0097] Example 1

[0098] 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."

[0099] In modern society, there is a demand for systems that allow users to quickly understand their health status and receive treatment at the appropriate medical institution. However, it is extremely difficult for users to find a medical institution that is suitable for their symptoms and smoothly schedule an appointment. There is also a lack of systems that effectively utilize daily health data to realize preventive medicine. There is a need for a system that solves these problems and allows users to efficiently manage their health and receive appropriate medical care.

[0100] 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.

[0101] In this invention, the server includes means for receiving symptom input from the user, means for collecting biometric data from the wearable device, means for analyzing the collected data and identifying an estimated health condition, means for searching for an appropriate medical institution and making an appointment, and means for notifying the user of the diagnosis results and appointment information, thereby enabling the user to quickly check their own health condition and receive a medical examination at an appropriate medical institution.

[0102] "Means for receiving symptom input from the user" refers to a device or interface that acquires information about physical ailments and symptoms entered by the user and transmits it to the system.

[0103] "Means for collecting biometric data from wearable devices" refers to devices or methods that have the ability to obtain physical data such as heart rate, blood pressure, and sleep patterns from equipment worn by the user.

[0104] "Means for analyzing collected data and identifying estimated health status" refers to algorithms or systems that analyze collected biometric data and determine and identify health status from the results.

[0105] "Means for searching for and making appointments with appropriate medical institutions" refers to functions and systems that allow users to select the most appropriate medical institution based on their health condition and symptoms and make appointments for consultations.

[0106] "Means for notifying users of diagnostic results and appointment information" refers to systems or devices with notification functions for informing users of analysis results and appointment information with medical institutions.

[0107] "Means of using AI to analyze data and compare it with a database of past cases to estimate health status" refers to a system that uses artificial intelligence technology to analyze collected data and compare it with past case data to predict health status.

[0108] "Means for collecting data from a wearable device worn by a user via wireless communication" refers to a method or system for collecting data from a wearable device worn by a user using wireless communication technologies such as Bluetooth or Wi-Fi.

[0109] This invention is a system that receives symptom input from a user, collects biometric data from a wearable device, and estimates health status through data analysis using AI. This section describes the specific hardware and software configurations required to realize this system.

[0110] First, users use their smartphones to input their symptoms. A dedicated application is installed on the smartphone, and users enter their symptoms through this application and press the "send" button to send the symptoms to the system.

[0111] Next, wearable devices such as smartwatches and fitness trackers can be used. These devices connect to smartphones via Bluetooth and collect biometric data such as heart rate, blood pressure, sleep patterns, etc. The smartphone terminal acquires this data and sends it to a server using an HTTP POST request.

[0112] The server passes the received data to the analysis module, where an AI model analyzes the data. This AI model is a generative AI model that compares the data with a database of past cases to estimate the patient's health condition. The resulting estimate may be, for example, "high possibility of migraine."

[0113] Based on the analysis results, the server searches for an appropriate medical institution. It lists relevant medical institutions from a database of medical institutions and selects the most suitable one based on the user's location and symptoms. After selecting the institution, the server sends the user a notification asking, "Do you want to make an appointment with a neurologist?" The user confirms the notification and taps "Yes" to confirm the appointment.

[0114] The server sends the reservation details to the medical institution and receives a reservation confirmation. Once confirmation is received, the server passes the reservation details to the notification module and sends a push notification to the user. For example, the user's smartphone will display a message saying, "Reservation completed. Date and time: XX / XX / XX, Location: XX Hospital."

[0115] Specific examples

[0116] 1. User Input

[0117] A user types "I have a headache" into a smartphone app and presses the "send" button.

[0118] 2. Data Collection

[0119] The device collects data from the wearable device, including heart rate (80 bpm), blood pressure (130 / 85), and sleep patterns (6 hours of light sleep), and sends it to a server.

[0120] 3. Data Analysis

[0121] The server passes the received data to the analysis module, where the AI ​​analyzes it and determines that there is a high possibility of a migraine.

[0122] 4. Collaboration with medical institutions

[0123] The server searches for the nearest neurologist and prompts the user, "Would you like to make an appointment with a neurologist?"

[0124] The user sees the notification and taps "Yes" to confirm the booking.

[0125] 5. Reservation execution and notification

[0126] The server sends the appointment to the medical institution and receives the appointment confirmation.

[0127] The server sends the reservation details (date, time, location) to the notification module, which notifies the user.

[0128] A push notification will appear on the user's smartphone saying, "Reservation completed. Date and time: XX / XX / XX XX time, Location: XX Hospital."

[0129] In this way, users can quickly check their health status and seek medical attention at the appropriate medical institution.

[0130] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0131] Step 1: User Input

[0132] Specific explanation: The user enters their symptoms into a smartphone device and sends them to the system.

[0133] Operation and data processing: The user enters "I have a headache" into the text box of the app and presses the "Send" button. This input information is sent from the smartphone to the server via an HTTP POST request.

[0134] Input: Text data of symptoms entered by the user (e.g., "My head hurts")

[0135] Output: Text data of symptoms received by the server

[0136] Step 2: Data collection

[0137] Specific description: Biometric data is collected from a wearable device and sent to a server.

[0138] Operation and data processing: The device connects to the wearable device via Bluetooth and acquires data such as heart rate, blood pressure, and sleep patterns. The acquired data is then sent to the server via an HTTP POST request.

[0139] Input: Biometric data obtained from wearable devices (e.g., heart rate 80 bpm, blood pressure 130 / 85, sleep patterns)

[0140] Output: Biometric data received by the server

[0141] Step 3: Data analysis

[0142] Specifically: A server analyzes health data and identifies a probable health condition.

[0143] Operation and data processing: The server passes the received biometric data to the analysis module, where the AI ​​model analyzes it by comparing it with a database of past cases. The AI ​​analyzes the data and identifies a probable health condition.

[0144] Input: Biometric data and case database stored on the server

[0145] Output: Text data of the estimated health condition (e.g., "High probability of migraine")

[0146] Step 4: Collaboration with medical institutions

[0147] Specific explanation: Based on the analysis results, an appropriate medical institution is searched for and notified to the user.

[0148] Operation and data processing: Based on the data analysis results, the server searches the database for relevant medical institutions. It selects the most suitable medical institution from the search results and sends a notification to the user asking, "Would you like to make an appointment with a neurologist?"

[0149] Input: Data analysis results and medical institution database

[0150] Output: Notification data sent to the user

[0151] Step 5: User response

[0152] Specific Description: The user confirms the notification and confirms the reservation.

[0153] Operation and data processing: The user checks the notification on their smartphone and taps "Yes." This response is sent to the server, and the reservation is confirmed.

[0154] Input: User response data (e.g. "Yes")

[0155] Output: The response data received by the server

[0156] Step 6: Reservation execution and notification

[0157] Specific details: Sends appointment details to the medical institution and notifies the user.

[0158] Operation and data processing: The server sends the user's appointment information to the medical institution and receives the appointment confirmation. Once confirmed, the server passes the appointment details to the notification module and sends a push notification to the user.

[0159] Input: Reservation information and medical institution confirmation data

[0160] Output: Push notification data sent to the user (e.g., "Your reservation has been completed. Date and time: ____ month ____ day ____ time, location: ____ Hospital")

[0161] In this way, the system performs specific actions and processes data at each step, allowing users to efficiently manage their health.

[0162] (Application example 1)

[0163] 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."

[0164] In modern society, it is important for users to be able to quickly check their health status and access appropriate medical institutions. There is a particular need for systems that can respond quickly and accurately when symptoms suddenly appear or when users are unsure which medical institution to choose. Conventional methods require users to search for medical information online and make their own judgments when selecting a medical institution, which is time- and knowledge-intensive. Therefore, there is a need for a system that can automatically analyze the user's condition based on their symptoms and suggest and book an appropriate medical institution.

[0165] 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.

[0166] In this invention, the server includes means for receiving symptom input from the user, means for collecting health data from the wearable device, means for analyzing the collected data and estimating possible medical conditions, means for searching for an appropriate medical institution and making a reservation, means for notifying the user of the diagnosis results and reservation information, means for analyzing the medical condition using AI based on the symptoms entered by the user and the health data collected from the wearable device, and means for sending a notification confirming a reservation at an appropriate medical institution based on the analysis results, thereby enabling the user to receive prompt and appropriate medical treatment for their symptoms.

[0167] "Symptom input" refers to users inputting their own physical condition and symptoms into a digital device.

[0168] "Health Data" refers to information about a user's health, such as heart rate, blood pressure, and sleep patterns, obtained from wearable devices or other sensors.

[0169] "Analysis" refers to the process of using AI and databases to identify possible medical conditions based on collected health data and symptom input.

[0170] "Disease prediction" refers to the prediction of possible diseases or health problems obtained as a result of the analysis.

[0171] "Medical institution search" refers to searching for a medical facility that can provide appropriate examination and treatment based on an estimated medical condition.

[0172] "Reservation" refers to the procedure by which a user reserves a date and time for an appointment at a medical institution.

[0173] "Notification of diagnosis results" refers to informing the user of the estimated condition and reservation information obtained through analysis.

[0174] "Analyzing data using AI" refers to using artificial intelligence technology to analyze collected health data and symptom inputs to predict or identify medical conditions.

[0175] A "case database" is a database that compiles previously recorded case data and is a collection of information used to collate new symptoms and health data.

[0176] A "wearable device" is an electronic device that can be worn by a user and has the ability to collect health data such as heart rate, blood pressure, and sleep patterns.

[0177] "Bluetooth" is a short-range wireless communication technology and refers to a standard used to wirelessly exchange data between electronic devices.

[0178] "Wi-Fi" is one of the wireless LAN standards and refers to the technology for connecting to the Internet and exchanging data wirelessly.

[0179] The system for implementing this invention quickly evaluates the user's health condition and automatically schedules an appointment with an appropriate medical institution. Below, we will explain in detail how each means works and how the overall flow proceeds.

[0180] Hardware and Software Configuration

[0181] Hardware

[0182] Smartphone: A device for user input and health data collection.

[0183] Wearable devices: Devices used to collect health data such as heart rate, blood pressure, and sleep patterns (e.g., Apple Watch, Fitbit).

[0184] Server: A central device that handles analytics and database management.

[0185] software

[0186] AI analysis module: A module that analyzes health data and predicts disease conditions using machine learning libraries such as TENSORFLOW (registered trademark) and PyTorch.

[0187] Data collection application: An application for transferring health data from a wearable device to a smartphone and sending it to a server.

[0188] Medical institution search and reservation system: A system that uses medical APIs to search for appropriate medical institutions and make reservations.

[0189] Notification system: A system that uses push notifications to send users diagnostic results and appointment information.

[0190] Explanation of each method

[0191] Symptom input method

[0192] Users enter their symptoms in text form via their smartphone, and this information is used for subsequent analysis. For example, they might enter a symptom such as "I have a headache."

[0193] Health data collection methods

[0194] The smartphone uses Bluetooth or Wi-Fi to collect health data such as heart rate, blood pressure, and sleep patterns from the wearable device, which is then sent to a server for analysis.

[0195] Analysis means

[0196] The server uses AI to analyze the symptom input and health data it receives. The AI ​​analysis module uses TensorFlow and PyTorch to compare the data with a database of past cases and estimate the condition. For example, it analyzes data such as heart rate (80 bpm), blood pressure (130 / 85), and sleep patterns (six hours of light sleep) to determine a "high possibility of migraine."

[0197] Medical institution search and reservation method

[0198] Based on the analysis results, the server searches for an appropriate medical institution. Using a medical API, it lists the nearest medical institutions and sends the user a reservation confirmation notification for the appropriate medical institution. The user confirms the notification and selects "Yes" to confirm the reservation.

[0199] Diagnostic result notification method

[0200] Once the reservation is confirmed, the server sends the reservation details (date, time, location) to the user via push notification, and the user can check the reservation details on their smartphone.

[0201] Specific examples

[0202] 1. The user enters the symptom "headache" into their smartphone.

[0203] 2. The smartphone collects heart rate (80 bpm), blood pressure (130 / 85), and sleep pattern (6 hours of light sleep) from the wearable device and sends them to the server.

[0204] 3. The server analyzes the data received using an AI analysis module (TensorFlow, PyTorch) and determines that there is a high possibility of a migraine.

[0205] 4. Search for the nearest neurologist through a healthcare API and send a push notification to the user asking, "Would you like to make an appointment with a neurologist?"

[0206] 5. The user confirms the notification and selects "Yes." The server sends the appointment to the medical institution and receives a confirmation of the appointment.

[0207] 6. Reservation details (date, time, location) will be pushed to the user's smartphone.

[0208] Prompt Sentence Examples

[0209] When a user inputs the symptom "headache," analyze the heart rate (80 bpm), blood pressure (130 / 85), and sleep pattern (6 hours of light sleep) collected from the wearable device to deduce possible medical conditions, find the most appropriate medical institution, and ask the user to confirm an appointment.

[0210] This invention allows users to quickly and appropriately assess their health status and receive the necessary medical services.

[0211] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0212] Step 1:

[0213] The user launches the smartphone application and inputs their symptoms (e.g., "I have a headache"). The input symptom data is stored within the application and used in the next data collection step. Input is in text format and can be completed intuitively by the user.

[0214] Step 2:

[0215] The device (smartphone) collects health data from the user's wearable device (e.g., heart rate, blood pressure, sleep patterns) via Bluetooth or Wi-Fi. The collected data is stored within the application and then sent to a server. The input is sensor data, and the output is the collected biometric data.

[0216] Step 3:

[0217] The device organizes the collected health data and sends it to the server along with the user's symptom data via an HTTP POST request. The data is sent to the server in JSON format and prepared for processing by the analysis module. The input is the organized health data and symptom data pair, and the output is the data sent to the server.

[0218] Step 4:

[0219] The server passes the received data to the AI ​​analysis module, which uses TensorFlow or PyTorch to analyze the data and compare it with a database of past cases to estimate the condition. The AI ​​model then predicts the condition based on the generative AI model. The input is the user's health data and symptom data, and the output is a predicted condition.

[0220] Step 5:

[0221] The server uses a medical API to search for an appropriate medical institution based on the prediction results obtained from the AI ​​analysis module. Based on the analysis results, the most suitable medical institutions are listed, and from there the most suitable medical institution is selected based on criteria such as location, rating, and available hours. The input is the predicted condition result, and the output is information about the most suitable medical institution.

[0222] Step 6:

[0223] The server sends the user a reservation confirmation notification based on the information of the selected medical institution. This notification is sent to the user's smartphone as a push notification. The user can check the notification and choose whether to confirm the reservation. The input is medical institution information, and the output is a notification to the user.

[0224] Step 7:

[0225] If the user checks the notification and selects "Yes," the server sends the reservation to the medical institution and receives a reservation confirmation. This confirms the reservation. The input is the user's reservation confirmation, and the output is a reservation completion notification.

[0226] Step 8:

[0227] The server sends a push notification to the user's smartphone with appointment details (date, time, location). The user receives the notification and can check the appointment schedule. The input is appointment confirmation information from the medical institution, and the output is a notification of appointment details to the user.

[0228] Prompt Sentence Examples

[0229] When a user inputs the symptom "headache," analyze the heart rate (80 bpm), blood pressure (130 / 85), and sleep pattern (6 hours of light sleep) collected from the wearable device to deduce possible medical conditions, find the most appropriate medical institution, and ask the user to confirm an appointment.

[0230] 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.

[0231] This system receives symptom input from the user, collects health data from a wearable device, and uses AI-based data analysis to predict possible illnesses. It also uses an emotion engine to recognize the user's emotions and customizes the selection of medical institutions and notification content accordingly. The system searches for appropriate medical institutions, confirms appointments with the user, and notifies the user of diagnosis results and reservation information.

[0232] User Input

[0233] The system begins by the user entering their symptoms into their smartphone, for example, "I have a headache," and pressing the "send" button to send the symptoms to the system.

[0234] User: Type "I have a headache" and press the "Send" button.

[0235] Data collection

[0236] The wearable device collects the user's daily health data, including heart rate, blood pressure, sleep patterns, etc. The collected data is sent from the device to a server.

[0237] Device: Obtains heart rate, blood pressure, and sleep pattern data from a wearable device via Bluetooth.

[0238] Device: Health data and user symptom input are sent to the server via an HTTP POST request.

[0239] Data analysis and emotion recognition

[0240] The server passes the received data to the analysis module, which uses AI to analyze the data and estimate possible medical conditions. The emotion engine then uses the user's input symptoms and collected health data to recognize the user's emotions. For example, if the analysis module determines that there is a high possibility of a migraine, the emotion engine recognizes that the user is in an anxious state.

[0241] Server: Passes the received data to the analysis module.

[0242] Server: The AI ​​analyzes the health data and symptom input it receives, compares it with past cases, and determines that there is a high possibility of a migraine.

[0243] Server: The emotion engine analyzes the data and recognizes that the user is in an "anxious state."

[0244] Collaboration and optimization with medical institutions

[0245] Based on the analysis results and the emotion engine's recognition results, the server searches for an appropriate medical institution. Based on the search results, the server sends a notification to the user requesting them to confirm their appointment. For example, if the emotion engine detects the user's anxiety, it will prioritize medical institutions that can respond quickly.

[0246] Server: Lists relevant medical institutions from a database and selects the most suitable one.

[0247] Server: Sends a message to the user asking "Would you like to make an appointment with a neurologist?"

[0248] Server: When a user is in an "anxious state," the server prioritizes the selection of a medical institution that can respond quickly.

[0249] User reservation confirmation

[0250] The user checks the notification on their device and confirms the appointment with the medical institution. For example, the notification content is customized according to the user's emotion, and displays, "You have made an appointment with a medical institution that can respond quickly."

[0251] User: Check the notification and tap "Yes" to confirm the reservation.

[0252] User: Receives a customized notification that "Your appointment has been scheduled with a fast-acting provider."

[0253] Schedule execution and notification

[0254] The server sends the user's appointment information to the medical institution, then receives the appointment confirmation from the medical institution, and once the confirmation is received, the server sends the appointment details to the notification module and notifies the user.

[0255] Server: Sends appointment information to the selected neurology department (using HTTP requests or a dedicated API).

[0256] Server: Receives the reservation confirmation response from the medical institution.

[0257] Server: Sends the reservation details (date, time, location) to the notification module.

[0258] Device: The user will receive a push notification saying, "Reservation completed. Date and time: XX / XX / XX XX time, Location: XX Hospital."

[0259] Device: Provides customized lighting and messages based on the user's emotions.

[0260] Specific examples

[0261] 1. The user types "I have a headache" into their smartphone and sends it.

[0262] 2. The smartphone collects data from the wearable device, including heart rate (80 bpm), blood pressure (130 / 85), and sleep patterns (6 hours of light sleep), and sends it to the server.

[0263] 3. The server passes the received data to the analysis module, where the AI ​​analyzes it. The AI ​​determines that there is a high possibility of a migraine, and the emotion engine recognizes the user's anxiety.

[0264] 4. The server searches for the nearest neurology clinic and selects a medical institution that can respond quickly based on the recognition results of the emotion engine. The server then notifies the user, "Would you like to make an appointment with a neurology clinic?"

[0265] 5. The user confirms the notification and taps "Yes" to confirm the appointment. The notification then displays, "You have made an appointment with a medical institution that can respond promptly."

[0266] 6. The server sends the appointment to the medical institution and receives a confirmation of the appointment.

[0267] 7. The server sends the reservation details to the notification module, and the user is notified via push notification that "Reservation completed. Date and time: XX / XX / XX, Location: XX Hospital."

[0268] 8. The user's smartphone will display customized details and messages to alleviate anxiety.

[0269] In this way, the present invention allows users to quickly check their health status and seek medical advice from the most appropriate medical institution, taking into account their emotional state.

[0270] The processing flow will be explained below.

[0271] Step 1:

[0272] The user enters the symptom "headache" into their smartphone and presses the "send" button.

[0273] User: Type "I have a headache" and press the "Send" button.

[0274] Step 2:

[0275] The device collects health data from the wearable device, including heart rate, blood pressure, and sleep patterns, and transmits the collected data to a server.

[0276] Device: Heart rate (80 bpm), blood pressure (130 / 85), and sleep pattern (6 hours of light sleep) data are collected from the wearable device via Bluetooth.

[0277] Device: Health data and user symptom input are sent to the server via an HTTP POST request.

[0278] Step 3:

[0279] The server passes the received data to an analysis module, which uses AI to analyze the data and estimate possible medical conditions.

[0280] Server: Passes the received data to the analysis module.

[0281] Server: The AI ​​analyzes the health data and symptom input it receives, compares it with past cases, and determines that there is a high possibility of a migraine.

[0282] Step 4:

[0283] The emotion engine recognizes the user's emotional state based on the user's symptoms and collected health data. For example, it can recognize that the user is feeling anxious based on their symptoms and health data.

[0284] Server: The emotion engine analyzes the data and determines that the user is in an "anxious state."

[0285] Step 5:

[0286] Based on the analysis results and the emotion engine's recognition results, the server searches for an appropriate medical institution. For example, if the user is feeling anxious, it will prioritize medical institutions that can respond quickly.

[0287] Server: Lists relevant medical institutions from a database and selects the most suitable one.

[0288] Server: Based on the information of the selected medical institution, the server sends the user a message asking, "Would you like to make an appointment with a neurologist?"

[0289] Server: If a user is in an "anxious state," prioritize medical institutions that can respond quickly.

[0290] Step 6:

[0291] The user checks the notification on their device and confirms the appointment with the medical institution. For example, the notification content is customized according to the user's emotion, and displays, "You have made an appointment with a medical institution that can respond quickly."

[0292] User: Check the notification and tap "Yes" to confirm the reservation.

[0293] On the device: Display a customized notification to the user saying, "Your appointment has been made with a fast-acting provider."

[0294] Step 7:

[0295] The server sends the user's reservation information to the medical institution, and then receives reservation confirmation from the medical institution.

[0296] Server: Sends appointment information to the selected neurology department (using HTTP requests or a dedicated API).

[0297] Server: Receives the reservation confirmation response from the medical institution.

[0298] Step 8:

[0299] The server sends the reservation details (date, time, location) to the notification module. Finally, the device notifies the user of this information. For example, the user may receive a push notification saying, "Reservation completed. Date and time: XX / XX / XX, location: XX Hospital." Additionally, a message to alleviate anxiety may also be displayed.

[0300] Server: Sends reservation details such as "Reservation completed. Date and time: XX / XX / XX, Location: XX Hospital" to the notification module.

[0301] Device: The user will receive a push notification saying, "Reservation completed. Date and time: XX / XX / XX XX time, Location: XX Hospital."

[0302] Device: Depending on the user's emotions, customized messages such as "Don't worry, we'll respond quickly" are also displayed.

[0303] Example 2

[0304] 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."

[0305] In modern society, users need to quickly and accurately understand their health status and seek medical treatment at the appropriate medical institution. However, current systems select medical institutions without taking into account the user's emotional state, which can increase anxiety and stress. In addition, it is difficult for users to manually collect and analyze health data from wearable devices, making it difficult to complete the process of seeking medical treatment immediately.

[0306] 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 means for receiving symptom input from the user, means for collecting health data from the wearable device, means for analyzing the collected data and estimating possible medical conditions, means for recognizing the user's emotional state using an emotion engine, means for searching for and making an appointment with an appropriate medical institution based on the recognition result, and means for notifying the user of the diagnosis result and appointment information. This allows the user to quickly check their own health condition and receive a medical examination at the most appropriate medical institution taking their emotional state into consideration.

[0307] "User" refers to an individual who uses the System to manage their own health status and input symptoms.

[0308] "Symptom input" refers to the act of a user sending their symptoms of poor health or illness to the system using a device such as a smartphone or computer.

[0309] "Wearable devices" refer to devices worn by users on a daily basis that measure and record physical condition data, and specifically include smart watches, heart rate monitors, blood pressure monitors, etc.

[0310] "Health data" refers to data about a user's physical condition collected by wearable devices, including, for example, heart rate, blood pressure, and sleep patterns.

[0311] "Analysis Module" refers to software or algorithms used to analyze collected data and infer possible pathologies.

[0312] An "emotion engine" refers to software or algorithms that analyze user-entered symptoms and collected health data to recognize a user's emotional state.

[0313] "Medical institution" refers to a facility such as a hospital or clinic where users can receive medical examinations or treatment.

[0314] "Appointment confirmation" refers to the act of notifying a user that an appointment with a medical institution has been confirmed.

[0315] "Push notification" refers to a technology that instantly sends information to a device and notifies the user.

[0316] "Diagnosis result" refers to information about the medical condition estimated as a result of analysis performed by AI.

[0317] This system receives symptom input from the user, collects health data from a wearable device, and uses AI to analyze the data to predict the patient's condition. It also uses an emotion engine to recognize the user's emotions, and customizes the selection of medical institutions and notification content accordingly. The system searches for appropriate medical institutions, confirms appointments with the user, and notifies the user of diagnosis results and reservation information.

[0318] Users begin using the system by entering their symptoms into their smartphone. For example, they enter a specific symptom such as "headache," and then press the "send" button to send the information to the server.

[0319] The device collects health data from wearable devices using Bluetooth or Wi-Fi communication, including heart rate, blood pressure, and sleep patterns, and sends this data to the server via HTTP POST requests.

[0320] The server passes the received health data and symptom information to the analysis module. The analysis module uses AI to analyze the data and compare it with a database of past cases to estimate possible medical conditions. For example, it may diagnose "high probability of migraine." Furthermore, an emotion engine uses this data to recognize the user's emotional state (e.g., anxiety).

[0321] Based on the analysis results and the user's emotional state, the server searches for an appropriate medical institution. Taking into account information such as the user's location and the medical institution's response speed, it prioritizes medical institutions that can respond quickly. As a result, the server sends the user a confirmation message such as "Would you like to make an appointment with a neurologist?"

[0322] Once the user confirms the notification and confirms the appointment, the server sends the appointment information to the medical institution and receives a reservation confirmation response. If the appointment is confirmed, the server sends the appointment details (date, time, location, etc.) to the notification module and sends a push notification to the device stating, "Appointment completed. Date and time: XX / XX / XX, XX time, Location: XX Hospital." A customized message generated by the emotion engine is also displayed on the device, providing information to reduce anxiety.

[0323] For example, when a user types "I have a headache" into their smartphone and sends it, the smartphone sends health data collected from the wearable device, such as heart rate (80 bpm), blood pressure (130 / 85), and sleep pattern (6 hours of light sleep), to a server. The server analyzes this data and determines that there is a high possibility of a migraine, and the emotion engine recognizes the user's anxiety. The server searches for the nearest neurologist, selects a medical institution that can respond quickly, and notifies the user. When the user confirms an appointment, the server sends the appointment to the medical institution and notifies them of detailed information once the appointment is confirmed.

[0324] An example of a prompt for a generative AI model is, "Please tell me the steps to design a system that analyzes a user's symptoms and health data collected from a wearable device and selects the most appropriate medical institution." Such prompts can be used to extract information needed for system design.

[0325] In this way, the present invention allows users to quickly check their health status and seek appropriate medical attention, taking into account their emotional state.

[0326] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0327] Step 1:

[0328] Users enter their symptoms into a smartphone application.

[0329] User: Type "I have a headache" and press the "Send" button.

[0330] Specific action: Enter the words "I have a headache" into the text input field on your smartphone and tap the send button.

[0331] Input: User's symptoms (e.g. headache)

[0332] Output: Symptom data is sent to the server.

[0333] Step 2:

[0334] The terminal collects health data from wearable devices.

[0335] Device: Using Bluetooth or Wi-Fi communication, data on heart rate, blood pressure, and sleep patterns is collected from wearable devices.

[0336] Specific operation: By pressing the "Get health data" button, communication with the wearable device begins and data is collected.

[0337] Input: User's wearable device

[0338] Output: Health data (heart rate, blood pressure, sleep patterns) is sent to the device.

[0339] Step 3:

[0340] The device sends the collected health and symptom data to a server.

[0341] Device: Sends health data and symptom input to the server via an HTTP POST request.

[0342] Specific operation: The collected health and symptom data is converted into JSON format and sent to the specified endpoint on the server via an HTTP POST request.

[0343] Input: Health data, symptom data

[0344] Output: The server receives the health data and symptom data.

[0345] Step 4:

[0346] The server passes the received data to the analysis module.

[0347] Server: Passes health data and symptom data to the analysis module and prepares it for analysis.

[0348] Specific operation: Convert the received data into an appropriate format and input it into the analysis module.

[0349] Input: Received data (health data, symptom data)

[0350] Output: Data ready for analysis

[0351] Step 5:

[0352] The server uses an analysis module to analyze the data and estimate the disease state.

[0353] Server: Uses AI to analyze health data and symptom input, and compares it with a database of past cases to estimate the condition.

[0354] Specific operation: Pass data to the AI ​​model, perform pattern matching and collation, and obtain inference results.

[0355] Input: Data ready for analysis

[0356] Output: Estimated medical condition (e.g., high probability of migraine)

[0357] Step 6:

[0358] The server uses an emotion engine to recognize the user's emotional state.

[0359] Server: Passes the analysis results and symptom data to the emotion engine to recognize the user's emotional state.

[0360] Specific behavior: The emotion engine analyzes the data and classifies the emotional state.

[0361] Input: Analysis results, symptom data

[0362] Output: User's emotional state (e.g., anxious state)

[0363] Step 7:

[0364] The server searches for an appropriate medical institution taking into account the user's emotional state.

[0365] Server: Searches a medical institution database based on emotional state and location information to select the most suitable medical institution.

[0366] What it does: Scores and prioritizes medical facility listings based on user sentiment and location.

[0367] Input: Emotional state, location information

[0368] Output: Information on the best medical institution

[0369] Step 8:

[0370] The server sends the user a medical appointment confirmation notification.

[0371] Server: Sends a message to the user asking "Would you like to schedule an appointment with a neurologist?"

[0372] On the device: A push notification will be sent to the user to confirm.

[0373] Specific operation: A confirmation message is sent to the user's device via the notification API, allowing the user to confirm.

[0374] Input: Information on the most suitable medical institution

[0375] Output: A confirmation message is displayed on the user's terminal.

[0376] Step 9:

[0377] The user confirms the notification and confirms the reservation.

[0378] User: Check the notification and tap "Yes" to confirm the reservation.

[0379] Specific actions: Check the push notification and tap the displayed button.

[0380] Input:Confirmation message

[0381] Output: Reservation confirmation response

[0382] Step 10:

[0383] The server sends the appointment information to the medical facility and receives confirmation.

[0384] Server: Sends reservation information to the selected medical institution and receives a reservation confirmation response.

[0385] Specific operation: Access the medical institution's dedicated API, send the reservation information, and then receive a reservation confirmation response.

[0386] Input: Reservation confirmation response

[0387] Output: Appointment confirmation response from the medical institution

[0388] Step 11:

[0389] The server sends the reservation details to the notification module, which notifies the user.

[0390] Server: Sends the reservation details (date, time, location) to the notification module.

[0391] Device: A push notification will be sent stating, "Reservation completed. Date and time: XX / XX / XX XX time, Location: XX Hospital."

[0392] Specific operation: Send the reservation details to the user terminal via the notification module.

[0393] Input: Appointment confirmation response from medical institution

[0394] Output: Notification of reservation details to user

[0395] (Application example 2)

[0396] 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."

[0397] The challenge in autonomous vehicles is to quickly and accurately check the health status of drivers and passengers and provide optimal route guidance to medical institutions that users need. There is also a need for a means to take into account the user's emotional state and provide appropriate advice to reduce stress. Current systems have difficulty making appointments with medical institutions that correspond to individual health conditions or providing prompt diagnoses, making it difficult to ensure users' peace of mind.

[0398] 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.

[0399] In this invention, the server includes: means for receiving symptom input from the user; means for collecting health data from the wearable device; means for analyzing the collected data and estimating possible medical conditions; means for searching for an appropriate medical institution and making an appointment; means for notifying the user of diagnosis results and appointment information; means for collecting surrounding information from sensors inside the autonomous vehicle; means for calculating the optimal route to the medical institution based on the collected health data and location information; and means for recognizing the user's emotional state and customizing the notification content. This allows the user's health condition to be grasped in real time even inside the autonomous vehicle, and the user to be quickly guided to the most appropriate medical institution. Furthermore, appropriate messages based on the user's emotions can be provided to provide a sense of security.

[0400] "Means for receiving symptom input from the user" refers to an interface that allows the user to input symptoms related to their physical condition or health via a smartphone or other device.

[0401] "Means for collecting health data from wearable devices" refers to technology that collects physiological data such as heart rate, blood pressure, and sleep patterns from health management devices worn by users (e.g., smartwatches and fitness trackers).

[0402] "Means for analyzing collected data and inferring possible medical conditions" refers to algorithms and software that use AI to analyze collected physiological data and symptom input from the user, and infer possible medical conditions the user may be suffering from.

[0403] "Means for searching for an appropriate medical institution and making an appointment" refers to the process of selecting the most appropriate medical institution from a database based on the user's current location and estimated medical condition, and making an appointment for an examination.

[0404] "Means of notifying users of diagnosis results and reservation information" refers to methods of transmitting analyzed diagnosis results and information about the medical institution for which an appointment has been made to the user's device via push notification, email, SMS, etc.

[0405] "Means for collecting surrounding information from sensors within an autonomous vehicle" refers to technology that acquires information about the surrounding environment using sensors such as cameras, lidars, and radars installed in autonomous vehicles.

[0406] The "means for calculating the optimal route to a medical institution based on collected health data and location information" is an algorithm that uses collected health data and current location information to calculate the most efficient and safe route to the desired medical institution.

[0407] "Means for recognizing the user's emotional state and customizing notification content" refers to technology that infers the user's emotional state based on their input and physiological data, and then appropriately adjusts the notification content based on the results, providing the user with a sense of security.

[0408] The present invention provides a system that allows a user to check their health condition while in an autonomous vehicle and quickly access a medical institution. Specific embodiments for carrying out the present invention will be described below.

[0409] System Overview

[0410] It uses a smartphone application that allows users to input and check their health status inside the autonomous vehicle, as well as sensors and wearable devices installed in the autonomous vehicle. The system consists of the following main components:

[0411] 1. Symptom input method

[0412] Users enter their symptoms through a smartphone application, for example, "I have a headache," and press the "send" button to send the symptoms to the system.

[0413] 2. Health data collection methods

[0414] Health data, including heart rate, blood pressure, and sleep patterns, is collected from sensors and wearable devices inside the autonomous vehicle and transmitted to a smartphone via Bluetooth or Wi-Fi.

[0415] 3. Data Analysis Methods

[0416] Based on the health data and symptom input received by the server, the data is analyzed using AI to predict possible illnesses, which are also compared with a database of past cases.

[0417] 4. Emotion recognition means

[0418] The server also uses an emotion engine to recognize the user's emotional state, which is inferred from health data and symptom input.

[0419] 5. Medical institution search and reservation methods

[0420] Based on the analysis results and emotion recognition results, the server searches for an appropriate medical institution and makes an appointment. Taking into account surrounding information obtained from sensors inside the autonomous vehicle, the server calculates the optimal route to the medical institution.

[0421] 6. Means of notification

[0422] It notifies users of diagnosis results and medical appointment information, and the content of notifications is customized according to the user's emotional state.

[0423] Specific examples

[0424] 1. Cases where users input their symptoms

[0425] The user types "I have a headache" into their smartphone and sends it.

[0426] Sensors inside the self-driving vehicle capture the user's current location.

[0427] The smartphone collects heart rate (80 bpm), blood pressure (130 / 85), and sleep patterns (6 hours of light sleep) from the wearable device and sends them to a server.

[0428] 2. Data analysis and emotion recognition process

[0429] The server passes the received data to the analysis module, where the AI ​​analyzes it and determines that there is a high possibility of a migraine.

[0430] The emotion engine recognizes the user's anxiety.

[0431] 3. Searching for and booking medical appointments

[0432] The server searches for the nearest neurology department and selects a medical institution that can respond quickly.

[0433] The user is prompted, "Would you like to schedule an appointment with a neurologist?"

[0434] The user confirms the notification and taps "Yes" to confirm the appointment. The message "You have made an appointment with a medical institution that can respond promptly" is displayed.

[0435] 4. Notification Details

[0436] The server sends the appointment to the medical institution and receives the appointment confirmation.

[0437] The server sends the reservation details to the notification module, notifying the user that "Reservation completed. Date and time: XX / XX / XX, time: XX, location: XX Hospital."

[0438] It also displays customized messages based on the user's emotions to reduce anxiety.

[0439] Prompt Sentence Examples

[0440] "I'm thinking about developing a health check and optimal route application for self-driving vehicles. This app will allow drivers and passengers to input their own health status, and will collect health data from sensors in the vehicle and wearable devices worn by the user. Based on the collected data and input information, AI will make a diagnosis and suggest the optimal route to safely guide the user to the nearest medical institution. It will also take into account the user's emotional state and display warnings and calming messages to reassure them."

[0441] In this way, the system of the present invention can grasp the user's health condition in real time and provide prompt and appropriate guidance to medical institutions. Furthermore, it also takes into consideration the user's emotional state, improving their sense of security.

[0442] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0443] Step 1:

[0444] A user opens a smartphone application, enters their symptoms (e.g., "I have a headache"), and presses the send button. This sends the entered symptom data from the device to the server. The input is string data, and the output is sent to the server as an HTTP POST request.

[0445] Step 2:

[0446] The terminal collects health data from a wearable device. Specifically, a smartphone acquires heart rate, blood pressure, and sleep pattern data from the wearable device via Bluetooth or Wi-Fi and sends this data to a server. The input is physiological data from the wearable device, and the output is an HTTP POST request to the server.

[0447] Step 3:

[0448] The server passes the received symptom data and health data to the analysis module. Specifically, the data analysis algorithm uses AI to analyze this data and infer possible medical conditions. The input is text data and physiological data, and the output is an inferred medical condition (e.g., "High probability of migraine").

[0449] Step 4:

[0450] The server uses an emotion engine to recognize the user's emotional state. Based on the medical condition data and health data passed from the analysis module, the emotion engine estimates whether the user is anxious or unstable. The inputs are the analysis results and physiological data, and the output is the emotional state (e.g., "anxiety").

[0451] Step 5:

[0452] The server searches the database for appropriate medical institutions based on the analysis results and emotional state, and selects the most suitable one from the listed medical institutions. Specifically, it takes into account the user's current location information and prioritizes medical institutions that can respond quickly. The inputs are the user's emotional state, analysis results, and current location information, and the output is information about the selected medical institution.

[0453] Step 6:

[0454] The server sends the user a confirmation notification of the medical institution's appointment. Specifically, along with information about the selected medical institution, it sends a confirmation message such as "Do you want to make an appointment with a neurology specialist?" via push notification or email. The input is the information about the selected medical institution, and the output is a notification sent to the user's device.

[0455] Step 7:

[0456] The user confirms the notification on the device and taps "Yes" to confirm the reservation. This action causes the device to send the reservation confirmation data back to the server. The input is the user's selected data, and the output is an HTTP POST request to the server.

[0457] Step 8:

[0458] The server sends the reservation information to the medical institution and receives a reservation confirmation response. Specifically, the reservation information is transmitted to the medical institution using an HTTP request or a dedicated API, and a confirmation response is received from the medical institution. The input is the reservation confirmation data, and the output is the reservation confirmation response from the medical institution.

[0459] Step 9:

[0460] The server sends the reservation details (date, time, location) to the notification module and notifies the user. Specifically, it sends a push notification saying, "Reservation completed. Date and time: XX / XX / XX, XX time, Location: XX Hospital." The input is the reservation confirmation response from the medical institution, and the output is a push notification sent to the user's device.

[0461] Step 10:

[0462] The server customizes the notification content according to the user's emotions and adds a message to give a sense of security. For example, it sends a message such as, "We have made an appointment with a medical institution that can respond quickly. Please rest assured." The input is the user's emotional state, and the output is the customized notification message.

[0463] 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.

[0464] 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.

[0465] 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.

[0466] [Second embodiment]

[0467] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0468] 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.

[0469] 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).

[0470] 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.

[0471] 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.

[0472] 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).

[0473] 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. 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.

[0474] 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.

[0475] 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.

[0476] 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.

[0477] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0478] 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."

[0479] This system receives symptom input from the user, collects health data from a wearable device, and uses AI-based data analysis to predict possible illnesses. Furthermore, the system searches for appropriate medical institutions, confirms appointments with the user, and notifies the user of diagnosis results and appointment information.

[0480] User Input

[0481] The system begins by the user entering their symptoms into their smartphone, for example, "I have a headache," and pressing the "send" button to send the symptoms to the system.

[0482] User: Type "I have a headache" and press the "Send" button.

[0483] Data collection

[0484] The wearable device collects the user's daily health data, including heart rate, blood pressure, sleep patterns, etc. The collected data is sent from the device to a server.

[0485] Device: Wearable device collects heart rate, blood pressure, and sleep pattern data via Bluetooth.

[0486] Terminal: Sends collected data to the server via HTTP POST request.

[0487] Data analysis

[0488] The server passes the received data to an analysis module, which uses AI to analyze the data and predict possible medical conditions. For example, the AI ​​analyzes heart rate, blood pressure, and sleep patterns and determines that a person has a high probability of having a migraine.

[0489] Server: Passes the received data to the analysis module.

[0490] Server: Performs AI-based data analysis to identify possible medical conditions.

[0491] Collaboration with medical institutions

[0492] Based on the analysis results, the server searches for an appropriate medical institution. Based on the search results, the server sends a notification to the user requesting them to confirm an appointment with the medical institution. Once the user confirms the appointment, the server sends the appointment to the medical institution.

[0493] Server: Lists relevant medical institutions and selects the most suitable one.

[0494] Server: Prompts user, "Would you like to schedule an appointment with a neurologist?"

[0495] User: Check the notification and tap "Yes" to confirm the reservation.

[0496] Schedule execution and notification

[0497] The server sends the user's appointment to the medical institution and receives the appointment confirmation. Once confirmed, the server sends the appointment details to the notification module and notifies the user.

[0498] Server: Sends appointments to medical institutions and receives appointment confirmations.

[0499] Server: Sends the reservation details (date, time, location) to the notification module.

[0500] Device: Sends a push notification to the user saying, "Reservation completed. Date and time: XX / XX / XX, XX time, Location: XX Hospital."

[0501] Specific examples

[0502] 1. The user types "I have a headache" into their smartphone and sends it.

[0503] 2. The smartphone collects data from the wearable device, including heart rate (80 bpm), blood pressure (130 / 85), and sleep patterns (6 hours of light sleep), and sends it to the server.

[0504] 3. The server passes the received data to the analysis module, where the AI ​​analyzes it. The AI ​​determines that there is a high possibility of a migraine.

[0505] 4. The server searches for the nearest neurologist and notifies the user, "Would you like to make an appointment with a neurologist?"

[0506] 5. The user confirms the notification and taps "Yes" to confirm the reservation.

[0507] 6. The server sends the appointment to the medical institution and receives a confirmation of the appointment.

[0508] 7. The server sends the reservation details (date, time, location) to the notification module and notifies the user.

[0509] 8. A push notification will appear on the user's smartphone saying, "Reservation completed. Date and time: XX / XX / XX XX o'clock, Location: XX Hospital."

[0510] In this way, the present invention allows users to quickly check their health status and seek medical attention at an appropriate medical institution.

[0511] The processing flow will be explained below.

[0512] Step 1:

[0513] The user enters the symptom "headache" into their smartphone and presses the "send" button.

[0514] User: Type "I have a headache" and press the "Send" button.

[0515] Step 2:

[0516] The device collects health data from the wearable device, including heart rate, blood pressure, and sleep patterns, and transmits the collected data to a server.

[0517] Device: Heart rate (80 bpm), blood pressure (130 / 85), and sleep pattern (6 hours of light sleep) data are collected from the wearable device via Bluetooth.

[0518] Device: Health data and user symptom input are sent to the server via an HTTP POST request.

[0519] Step 3:

[0520] The server passes the received data to the analysis module, which then uses AI to analyze the data and estimate possible medical conditions.

[0521] Server: Passes the received data to the analysis module.

[0522] Server: The AI ​​analyzes the health data and symptom input it receives, compares it with past cases, and determines that there is a high possibility of a migraine.

[0523] Step 4:

[0524] The server searches for an appropriate medical institution based on the estimated medical condition, and then sends the user a notification requesting them to confirm an appointment.

[0525] Server: Lists relevant medical institutions such as internal medicine and neurology departments from a database and selects the nearest medical institution.

[0526] Server: Notifies the user of the information about the selected medical institution and sends a message asking, "Would you like to make an appointment with a neurologist?"

[0527] Step 5:

[0528] The user checks the notification on the device and confirms the medical appointment.

[0529] User: Check the notification and tap "Yes" to confirm the reservation.

[0530] Step 6:

[0531] The server sends the user's reservation information to the medical institution, and then receives reservation confirmation from the medical institution.

[0532] Server: Sends appointment information to the selected neurology department (using HTTP requests or a dedicated API).

[0533] Server: Receives the reservation confirmation response from the medical institution.

[0534] Step 7:

[0535] The server sends the reservation details (date, time, location) to the notification module, and finally the device notifies the user of the information.

[0536] Server: Sends reservation details such as "Reservation completed. Date and time: XX / XX / XX, Location: XX Hospital" to the notification module.

[0537] Device: The user will receive a push notification saying, "Reservation completed. Date and time: XX / XX / XX XX time, Location: XX Hospital."

[0538] Example 1

[0539] 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."

[0540] In modern society, there is a demand for systems that allow users to quickly understand their health status and receive treatment at the appropriate medical institution. However, it is extremely difficult for users to find a medical institution that is suitable for their symptoms and smoothly schedule an appointment. There is also a lack of systems that effectively utilize daily health data to realize preventive medicine. There is a need for a system that solves these problems and allows users to efficiently manage their health and receive appropriate medical care.

[0541] 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.

[0542] In this invention, the server includes means for receiving symptom input from the user, means for collecting biometric data from the wearable device, means for analyzing the collected data and identifying an estimated health condition, means for searching for an appropriate medical institution and making an appointment, and means for notifying the user of the diagnosis results and appointment information, thereby enabling the user to quickly check their own health condition and receive a medical examination at an appropriate medical institution.

[0543] "Means for receiving symptom input from the user" refers to a device or interface that acquires information about physical ailments and symptoms entered by the user and transmits it to the system.

[0544] "Means for collecting biometric data from wearable devices" refers to devices or methods that have the ability to obtain physical data such as heart rate, blood pressure, and sleep patterns from equipment worn by the user.

[0545] "Means for analyzing collected data and identifying estimated health status" refers to algorithms or systems that analyze collected biometric data and determine and identify health status from the results.

[0546] "Means for searching for and making appointments with appropriate medical institutions" refers to functions and systems that allow users to select the most appropriate medical institution based on their health condition and symptoms and make appointments for consultations.

[0547] "Means for notifying users of diagnostic results and appointment information" refers to systems or devices with notification functions for informing users of analysis results and appointment information with medical institutions.

[0548] "Means of using AI to analyze data and compare it with a database of past cases to estimate health status" refers to a system that uses artificial intelligence technology to analyze collected data and compare it with past case data to predict health status.

[0549] "Means for collecting data from a wearable device worn by a user via wireless communication" refers to a method or system for collecting data from a wearable device worn by a user using wireless communication technologies such as Bluetooth or Wi-Fi.

[0550] This invention is a system that receives symptom input from a user, collects biometric data from a wearable device, and estimates health status through data analysis using AI. This section describes the specific hardware and software configurations required to realize this system.

[0551] First, users use their smartphones to input their symptoms. A dedicated application is installed on the smartphone, and users enter their symptoms through this application and press the "send" button to send the symptoms to the system.

[0552] Next, wearable devices such as smartwatches and fitness trackers can be used. These devices connect to smartphones via Bluetooth and collect biometric data such as heart rate, blood pressure, sleep patterns, etc. The smartphone terminal acquires this data and sends it to a server using an HTTP POST request.

[0553] The server passes the received data to the analysis module, where an AI model analyzes the data. This AI model is a generative AI model that compares the data with a database of past cases to estimate the patient's health condition. The resulting estimate may be, for example, "high possibility of migraine."

[0554] Based on the analysis results, the server searches for an appropriate medical institution. It lists relevant medical institutions from a database of medical institutions and selects the most suitable one based on the user's location and symptoms. After selecting the institution, the server sends the user a notification asking, "Do you want to make an appointment with a neurologist?" The user confirms the notification and taps "Yes" to confirm the appointment.

[0555] The server sends the reservation details to the medical institution and receives a reservation confirmation. Once confirmation is received, the server passes the reservation details to the notification module and sends a push notification to the user. For example, the user's smartphone will display a message saying, "Reservation completed. Date and time: XX / XX / XX, Location: XX Hospital."

[0556] Specific examples

[0557] 1. User Input

[0558] A user types "I have a headache" into a smartphone app and presses the "send" button.

[0559] 2. Data Collection

[0560] The device collects data from the wearable device, including heart rate (80 bpm), blood pressure (130 / 85), and sleep patterns (6 hours of light sleep), and sends it to a server.

[0561] 3. Data Analysis

[0562] The server passes the received data to the analysis module, where the AI ​​analyzes it and determines that there is a high possibility of a migraine.

[0563] 4. Collaboration with medical institutions

[0564] The server searches for the nearest neurologist and prompts the user, "Would you like to make an appointment with a neurologist?"

[0565] The user sees the notification and taps "Yes" to confirm the booking.

[0566] 5. Reservation execution and notification

[0567] The server sends the appointment to the medical institution and receives the appointment confirmation.

[0568] The server sends the reservation details (date, time, location) to the notification module, which notifies the user.

[0569] A push notification will appear on the user's smartphone saying, "Reservation completed. Date and time: XX / XX / XX XX time, Location: XX Hospital."

[0570] In this way, users can quickly check their health status and seek medical attention at the appropriate medical institution.

[0571] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0572] Step 1: User Input

[0573] Specific explanation: The user enters their symptoms into a smartphone device and sends them to the system.

[0574] Operation and data processing: The user enters "I have a headache" into the text box of the app and presses the "Send" button. This input information is sent from the smartphone to the server via an HTTP POST request.

[0575] Input: Text data of symptoms entered by the user (e.g., "My head hurts")

[0576] Output: Text data of symptoms received by the server

[0577] Step 2: Data collection

[0578] Specific description: Biometric data is collected from a wearable device and sent to a server.

[0579] Operation and data processing: The device connects to the wearable device via Bluetooth and acquires data such as heart rate, blood pressure, and sleep patterns. The acquired data is then sent to the server via an HTTP POST request.

[0580] Input: Biometric data obtained from wearable devices (e.g., heart rate 80 bpm, blood pressure 130 / 85, sleep patterns)

[0581] Output: Biometric data received by the server

[0582] Step 3: Data analysis

[0583] Specifically: A server analyzes health data and identifies a probable health condition.

[0584] Operation and data processing: The server passes the received biometric data to the analysis module, where the AI ​​model analyzes it by comparing it with a database of past cases. The AI ​​analyzes the data and identifies a probable health condition.

[0585] Input: Biometric data and case database stored on the server

[0586] Output: Text data of the estimated health condition (e.g., "High probability of migraine")

[0587] Step 4: Collaboration with medical institutions

[0588] Specific explanation: Based on the analysis results, an appropriate medical institution is searched for and notified to the user.

[0589] Operation and data processing: Based on the data analysis results, the server searches the database for relevant medical institutions. It selects the most suitable medical institution from the search results and sends a notification to the user asking, "Would you like to make an appointment with a neurologist?"

[0590] Input: Data analysis results and medical institution database

[0591] Output: Notification data sent to the user

[0592] Step 5: User response

[0593] Specific Description: The user confirms the notification and confirms the reservation.

[0594] Operation and data processing: The user checks the notification on their smartphone and taps "Yes." This response is sent to the server, and the reservation is confirmed.

[0595] Input: User response data (e.g. "Yes")

[0596] Output: The response data received by the server

[0597] Step 6: Reservation execution and notification

[0598] Specific details: Sends appointment details to the medical institution and notifies the user.

[0599] Operation and data processing: The server sends the user's appointment information to the medical institution and receives the appointment confirmation. Once confirmed, the server passes the appointment details to the notification module and sends a push notification to the user.

[0600] Input: Reservation information and medical institution confirmation data

[0601] Output: Push notification data sent to the user (e.g., "Your reservation has been completed. Date and time: ____ month ____ day ____ time, location: ____ Hospital")

[0602] In this way, the system performs specific actions and processes data at each step, allowing users to efficiently manage their health.

[0603] (Application example 1)

[0604] 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."

[0605] In modern society, it is important for users to be able to quickly check their health status and access appropriate medical institutions. There is a particular need for systems that can respond quickly and accurately when symptoms suddenly appear or when users are unsure which medical institution to choose. Conventional methods require users to search for medical information online and make their own judgments when selecting a medical institution, which is time- and knowledge-intensive. Therefore, there is a need for a system that can automatically analyze the user's condition based on their symptoms and suggest and book an appropriate medical institution.

[0606] 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.

[0607] In this invention, the server includes means for receiving symptom input from the user, means for collecting health data from the wearable device, means for analyzing the collected data and estimating possible medical conditions, means for searching for an appropriate medical institution and making a reservation, means for notifying the user of the diagnosis results and reservation information, means for analyzing the medical condition using AI based on the symptoms entered by the user and the health data collected from the wearable device, and means for sending a notification confirming a reservation at an appropriate medical institution based on the analysis results, thereby enabling the user to receive prompt and appropriate medical treatment for their symptoms.

[0608] "Symptom input" refers to users inputting their own physical condition and symptoms into a digital device.

[0609] "Health Data" refers to information about a user's health, such as heart rate, blood pressure, and sleep patterns, obtained from wearable devices or other sensors.

[0610] "Analysis" refers to the process of using AI and databases to identify possible medical conditions based on collected health data and symptom input.

[0611] "Disease prediction" refers to the prediction of possible diseases or health problems obtained as a result of the analysis.

[0612] "Medical institution search" refers to searching for a medical facility that can provide appropriate examination and treatment based on an estimated medical condition.

[0613] "Reservation" refers to the procedure by which a user reserves a date and time for an appointment at a medical institution.

[0614] "Notification of diagnosis results" refers to informing the user of the estimated condition and reservation information obtained through analysis.

[0615] "Analyzing data using AI" refers to using artificial intelligence technology to analyze collected health data and symptom inputs to predict or identify medical conditions.

[0616] A "case database" is a database that compiles previously recorded case data and is a collection of information used to collate new symptoms and health data.

[0617] A "wearable device" is an electronic device that can be worn by a user and has the ability to collect health data such as heart rate, blood pressure, and sleep patterns.

[0618] "Bluetooth" is a short-range wireless communication technology and refers to a standard used to wirelessly exchange data between electronic devices.

[0619] "Wi-Fi" is one of the wireless LAN standards and refers to the technology for connecting to the Internet and exchanging data wirelessly.

[0620] The system for implementing this invention quickly evaluates the user's health condition and automatically schedules an appointment with an appropriate medical institution. Below, we will explain in detail how each means works and how the overall flow proceeds.

[0621] Hardware and Software Configuration

[0622] Hardware

[0623] Smartphone: A device for user input and health data collection.

[0624] Wearable devices: Devices used to collect health data such as heart rate, blood pressure, and sleep patterns (e.g., Apple Watch, Fitbit).

[0625] Server: A central device that handles analytics and database management.

[0626] software

[0627] AI analysis module: A module that analyzes health data and predicts disease conditions using machine learning libraries such as TensorFlow and PyTorch.

[0628] Data collection application: An application for transferring health data from a wearable device to a smartphone and sending it to a server.

[0629] Medical institution search and reservation system: A system that uses medical APIs to search for appropriate medical institutions and make reservations.

[0630] Notification system: A system that uses push notifications to send users diagnostic results and appointment information.

[0631] Explanation of each method

[0632] Symptom input method

[0633] Users enter their symptoms in text form via their smartphone, and this information is used for subsequent analysis. For example, they might enter a symptom such as "I have a headache."

[0634] Health data collection methods

[0635] The smartphone uses Bluetooth or Wi-Fi to collect health data such as heart rate, blood pressure, and sleep patterns from the wearable device, which is then sent to a server for analysis.

[0636] Analysis means

[0637] The server uses AI to analyze the symptom input and health data it receives. The AI ​​analysis module uses TensorFlow and PyTorch to compare the data with a database of past cases and estimate the condition. For example, it analyzes data such as heart rate (80 bpm), blood pressure (130 / 85), and sleep patterns (six hours of light sleep) to determine a "high possibility of migraine."

[0638] Medical institution search and reservation method

[0639] Based on the analysis results, the server searches for an appropriate medical institution. Using a medical API, it lists the nearest medical institutions and sends the user a reservation confirmation notification for the appropriate medical institution. The user confirms the notification and selects "Yes" to confirm the reservation.

[0640] Diagnostic result notification method

[0641] Once the reservation is confirmed, the server sends the reservation details (date, time, location) to the user via push notification, and the user can check the reservation details on their smartphone.

[0642] Specific examples

[0643] 1. The user enters the symptom "headache" into their smartphone.

[0644] 2. The smartphone collects heart rate (80 bpm), blood pressure (130 / 85), and sleep pattern (6 hours of light sleep) from the wearable device and sends them to the server.

[0645] 3. The server analyzes the data received using an AI analysis module (TensorFlow, PyTorch) and determines that there is a high possibility of a migraine.

[0646] 4. Search for the nearest neurologist through a healthcare API and send a push notification to the user asking, "Would you like to make an appointment with a neurologist?"

[0647] 5. The user confirms the notification and selects "Yes." The server sends the appointment to the medical institution and receives a confirmation of the appointment.

[0648] 6. Reservation details (date, time, location) will be pushed to the user's smartphone.

[0649] Prompt Sentence Examples

[0650] When a user inputs the symptom "headache," analyze the heart rate (80 bpm), blood pressure (130 / 85), and sleep pattern (6 hours of light sleep) collected from the wearable device to deduce possible medical conditions, find the most appropriate medical institution, and ask the user to confirm an appointment.

[0651] This invention allows users to quickly and appropriately assess their health status and receive the necessary medical services.

[0652] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0653] Step 1:

[0654] The user launches the smartphone application and inputs their symptoms (e.g., "I have a headache"). The input symptom data is stored within the application and used in the next data collection step. Input is in text format and can be completed intuitively by the user.

[0655] Step 2:

[0656] The device (smartphone) collects health data from the user's wearable device (e.g., heart rate, blood pressure, sleep patterns) via Bluetooth or Wi-Fi. The collected data is stored within the application and then sent to a server. The input is sensor data, and the output is the collected biometric data.

[0657] Step 3:

[0658] The device organizes the collected health data and sends it to the server along with the user's symptom data via an HTTP POST request. The data is sent to the server in JSON format and prepared for processing by the analysis module. The input is the organized health data and symptom data pair, and the output is the data sent to the server.

[0659] Step 4:

[0660] The server passes the received data to the AI ​​analysis module, which uses TensorFlow or PyTorch to analyze the data and compare it with a database of past cases to estimate the condition. The AI ​​model then predicts the condition based on the generative AI model. The input is the user's health data and symptom data, and the output is a predicted condition.

[0661] Step 5:

[0662] The server uses a medical API to search for an appropriate medical institution based on the prediction results obtained from the AI ​​analysis module. Based on the analysis results, the most suitable medical institutions are listed, and from there the most suitable medical institution is selected based on criteria such as location, rating, and available hours. The input is the predicted condition result, and the output is information about the most suitable medical institution.

[0663] Step 6:

[0664] The server sends the user a reservation confirmation notification based on the information of the selected medical institution. This notification is sent to the user's smartphone as a push notification. The user can check the notification and choose whether to confirm the reservation. The input is medical institution information, and the output is a notification to the user.

[0665] Step 7:

[0666] If the user checks the notification and selects "Yes," the server sends the reservation to the medical institution and receives a reservation confirmation. This confirms the reservation. The input is the user's reservation confirmation, and the output is a reservation completion notification.

[0667] Step 8:

[0668] The server sends a push notification to the user's smartphone with appointment details (date, time, location). The user receives the notification and can check the appointment schedule. The input is appointment confirmation information from the medical institution, and the output is a notification of appointment details to the user.

[0669] Prompt Sentence Examples

[0670] When a user inputs the symptom "headache," analyze the heart rate (80 bpm), blood pressure (130 / 85), and sleep pattern (6 hours of light sleep) collected from the wearable device to deduce possible medical conditions, find the most appropriate medical institution, and ask the user to confirm an appointment.

[0671] 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.

[0672] This system receives symptom input from the user, collects health data from a wearable device, and uses AI-based data analysis to predict possible illnesses. It also uses an emotion engine to recognize the user's emotions and customizes the selection of medical institutions and notification content accordingly. The system searches for appropriate medical institutions, confirms appointments with the user, and notifies the user of diagnosis results and reservation information.

[0673] User Input

[0674] The system begins by the user entering their symptoms into their smartphone, for example, "I have a headache," and pressing the "send" button to send the symptoms to the system.

[0675] User: Type "I have a headache" and press the "Send" button.

[0676] Data collection

[0677] The wearable device collects the user's daily health data, including heart rate, blood pressure, sleep patterns, etc. The collected data is sent from the device to a server.

[0678] Device: Obtains heart rate, blood pressure, and sleep pattern data from a wearable device via Bluetooth.

[0679] Device: Health data and user symptom input are sent to the server via an HTTP POST request.

[0680] Data analysis and emotion recognition

[0681] The server passes the received data to the analysis module, which uses AI to analyze the data and estimate possible medical conditions. The emotion engine then uses the user's input symptoms and collected health data to recognize the user's emotions. For example, if the analysis module determines that there is a high possibility of a migraine, the emotion engine recognizes that the user is in an anxious state.

[0682] Server: Passes the received data to the analysis module.

[0683] Server: The AI ​​analyzes the health data and symptom input it receives, compares it with past cases, and determines that there is a high possibility of a migraine.

[0684] Server: The emotion engine analyzes the data and recognizes that the user is in an "anxious state."

[0685] Collaboration and optimization with medical institutions

[0686] Based on the analysis results and the emotion engine's recognition results, the server searches for an appropriate medical institution. Based on the search results, the server sends a notification to the user requesting them to confirm their appointment. For example, if the emotion engine detects the user's anxiety, it will prioritize medical institutions that can respond quickly.

[0687] Server: Lists relevant medical institutions from a database and selects the most suitable one.

[0688] Server: Sends a message to the user asking "Would you like to make an appointment with a neurologist?"

[0689] Server: When a user is in an "anxious state," the server prioritizes the selection of a medical institution that can respond quickly.

[0690] User reservation confirmation

[0691] The user checks the notification on their device and confirms the appointment with the medical institution. For example, the notification content is customized according to the user's emotion, and displays, "You have made an appointment with a medical institution that can respond quickly."

[0692] User: Check the notification and tap "Yes" to confirm the reservation.

[0693] User: Receives a customized notification that "Your appointment has been scheduled with a fast-acting provider."

[0694] Schedule execution and notification

[0695] The server sends the user's appointment information to the medical institution, then receives the appointment confirmation from the medical institution, and once the confirmation is received, the server sends the appointment details to the notification module and notifies the user.

[0696] Server: Sends appointment information to the selected neurology department (using HTTP requests or a dedicated API).

[0697] Server: Receives the reservation confirmation response from the medical institution.

[0698] Server: Sends the reservation details (date, time, location) to the notification module.

[0699] Device: The user will receive a push notification saying, "Reservation completed. Date and time: XX / XX / XX XX time, Location: XX Hospital."

[0700] Device: Provides customized lighting and messages based on the user's emotions.

[0701] Specific examples

[0702] 1. The user types "I have a headache" into their smartphone and sends it.

[0703] 2. The smartphone collects data from the wearable device, including heart rate (80 bpm), blood pressure (130 / 85), and sleep patterns (6 hours of light sleep), and sends it to the server.

[0704] 3. The server passes the received data to the analysis module, where the AI ​​analyzes it. The AI ​​determines that there is a high possibility of a migraine, and the emotion engine recognizes the user's anxiety.

[0705] 4. The server searches for the nearest neurology clinic and selects a medical institution that can respond quickly based on the recognition results of the emotion engine. The server then notifies the user, "Would you like to make an appointment with a neurology clinic?"

[0706] 5. The user confirms the notification and taps "Yes" to confirm the appointment. The notification then displays, "You have made an appointment with a medical institution that can respond promptly."

[0707] 6. The server sends the appointment to the medical institution and receives a confirmation of the appointment.

[0708] 7. The server sends the reservation details to the notification module, and the user is notified via push notification that "Reservation completed. Date and time: XX / XX / XX, Location: XX Hospital."

[0709] 8. The user's smartphone will display customized details and messages to alleviate anxiety.

[0710] In this way, the present invention allows users to quickly check their health status and seek medical advice from the most appropriate medical institution, taking into account their emotional state.

[0711] The processing flow will be explained below.

[0712] Step 1:

[0713] The user enters the symptom "headache" into their smartphone and presses the "send" button.

[0714] User: Type "I have a headache" and press the "Send" button.

[0715] Step 2:

[0716] The device collects health data from the wearable device, including heart rate, blood pressure, and sleep patterns, and transmits the collected data to a server.

[0717] Device: Heart rate (80 bpm), blood pressure (130 / 85), and sleep pattern (6 hours of light sleep) data are collected from the wearable device via Bluetooth.

[0718] Device: Health data and user symptom input are sent to the server via an HTTP POST request.

[0719] Step 3:

[0720] The server passes the received data to an analysis module, which uses AI to analyze the data and estimate possible medical conditions.

[0721] Server: Passes the received data to the analysis module.

[0722] Server: The AI ​​analyzes the health data and symptom input it receives, compares it with past cases, and determines that there is a high possibility of a migraine.

[0723] Step 4:

[0724] The emotion engine recognizes the user's emotional state based on the user's symptoms and collected health data. For example, it can recognize that the user is feeling anxious based on their symptoms and health data.

[0725] Server: The emotion engine analyzes the data and determines that the user is in an "anxious state."

[0726] Step 5:

[0727] Based on the analysis results and the emotion engine's recognition results, the server searches for an appropriate medical institution. For example, if the user is feeling anxious, it will prioritize medical institutions that can respond quickly.

[0728] Server: Lists relevant medical institutions from a database and selects the most suitable one.

[0729] Server: Based on the information of the selected medical institution, the server sends the user a message asking, "Would you like to make an appointment with a neurologist?"

[0730] Server: If a user is in an "anxious state," prioritize medical institutions that can respond quickly.

[0731] Step 6:

[0732] The user checks the notification on their device and confirms the appointment with the medical institution. For example, the notification content is customized according to the user's emotion, and displays, "You have made an appointment with a medical institution that can respond quickly."

[0733] User: Check the notification and tap "Yes" to confirm the reservation.

[0734] On the device: Display a customized notification to the user saying, "Your appointment has been made with a fast-acting provider."

[0735] Step 7:

[0736] The server sends the user's reservation information to the medical institution, and then receives reservation confirmation from the medical institution.

[0737] Server: Sends appointment information to the selected neurology department (using HTTP requests or a dedicated API).

[0738] Server: Receives the reservation confirmation response from the medical institution.

[0739] Step 8:

[0740] The server sends the reservation details (date, time, location) to the notification module. Finally, the device notifies the user of this information. For example, the user may receive a push notification saying, "Reservation completed. Date and time: XX / XX / XX, location: XX Hospital." Additionally, a message to alleviate anxiety may also be displayed.

[0741] Server: Sends reservation details such as "Reservation completed. Date and time: XX / XX / XX, Location: XX Hospital" to the notification module.

[0742] Device: The user will receive a push notification saying, "Reservation completed. Date and time: XX / XX / XX XX time, Location: XX Hospital."

[0743] Device: Depending on the user's emotions, customized messages such as "Don't worry, we'll respond quickly" are also displayed.

[0744] Example 2

[0745] 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."

[0746] In modern society, users need to quickly and accurately understand their health status and seek medical treatment at the appropriate medical institution. However, current systems select medical institutions without taking into account the user's emotional state, which can increase anxiety and stress. In addition, it is difficult for users to manually collect and analyze health data from wearable devices, making it difficult to complete the process of seeking medical treatment immediately.

[0747] 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 means for receiving symptom input from the user, means for collecting health data from the wearable device, means for analyzing the collected data and estimating possible medical conditions, means for recognizing the user's emotional state using an emotion engine, means for searching for and making an appointment with an appropriate medical institution based on the recognition result, and means for notifying the user of the diagnosis result and appointment information. This allows the user to quickly check their own health condition and receive a medical examination at the most appropriate medical institution taking their emotional state into consideration.

[0748] "User" refers to an individual who uses the System to manage their own health status and input symptoms.

[0749] "Symptom input" refers to the act of a user sending their symptoms of poor health or illness to the system using a device such as a smartphone or computer.

[0750] "Wearable devices" refer to devices worn by users on a daily basis that measure and record physical condition data, and specifically include smart watches, heart rate monitors, blood pressure monitors, etc.

[0751] "Health data" refers to data about a user's physical condition collected by wearable devices, including, for example, heart rate, blood pressure, and sleep patterns.

[0752] "Analysis Module" refers to software or algorithms used to analyze collected data and infer possible pathologies.

[0753] An "emotion engine" refers to software or algorithms that analyze user-entered symptoms and collected health data to recognize a user's emotional state.

[0754] "Medical institution" refers to a facility such as a hospital or clinic where users can receive medical examinations or treatment.

[0755] "Appointment confirmation" refers to the act of notifying a user that an appointment with a medical institution has been confirmed.

[0756] "Push notification" refers to a technology that instantly sends information to a device and notifies the user.

[0757] "Diagnosis result" refers to information about the medical condition estimated as a result of analysis performed by AI.

[0758] This system receives symptom input from the user, collects health data from a wearable device, and uses AI to analyze the data to predict the patient's condition. It also uses an emotion engine to recognize the user's emotions, and customizes the selection of medical institutions and notification content accordingly. The system searches for appropriate medical institutions, confirms appointments with the user, and notifies the user of diagnosis results and reservation information.

[0759] Users begin using the system by entering their symptoms into their smartphone. For example, they enter a specific symptom such as "headache," and then press the "send" button to send the information to the server.

[0760] The device collects health data from wearable devices using Bluetooth or Wi-Fi communication, including heart rate, blood pressure, and sleep patterns, and sends this data to the server via HTTP POST requests.

[0761] The server passes the received health data and symptom information to the analysis module. The analysis module uses AI to analyze the data and compare it with a database of past cases to estimate possible medical conditions. For example, it may diagnose "high probability of migraine." Furthermore, an emotion engine uses this data to recognize the user's emotional state (e.g., anxiety).

[0762] Based on the analysis results and the user's emotional state, the server searches for an appropriate medical institution. Taking into account information such as the user's location and the medical institution's response speed, it prioritizes medical institutions that can respond quickly. As a result, the server sends the user a confirmation message such as "Would you like to make an appointment with a neurologist?"

[0763] Once the user confirms the notification and confirms the appointment, the server sends the appointment information to the medical institution and receives a reservation confirmation response. If the appointment is confirmed, the server sends the appointment details (date, time, location, etc.) to the notification module and sends a push notification to the device stating, "Appointment completed. Date and time: XX / XX / XX, XX time, Location: XX Hospital." A customized message generated by the emotion engine is also displayed on the device, providing information to reduce anxiety.

[0764] For example, when a user types "I have a headache" into their smartphone and sends it, the smartphone sends health data collected from the wearable device, such as heart rate (80 bpm), blood pressure (130 / 85), and sleep pattern (6 hours of light sleep), to a server. The server analyzes this data and determines that there is a high possibility of a migraine, and the emotion engine recognizes the user's anxiety. The server searches for the nearest neurologist, selects a medical institution that can respond quickly, and notifies the user. When the user confirms an appointment, the server sends the appointment to the medical institution and notifies them of detailed information once the appointment is confirmed.

[0765] An example of a prompt for a generative AI model is, "Please tell me the steps to design a system that analyzes a user's symptoms and health data collected from a wearable device and selects the most appropriate medical institution." Such prompts can be used to extract information needed for system design.

[0766] In this way, the present invention allows users to quickly check their health status and seek appropriate medical attention, taking into account their emotional state.

[0767] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0768] Step 1:

[0769] Users enter their symptoms into a smartphone application.

[0770] User: Type "I have a headache" and press the "Send" button.

[0771] Specific action: Enter the words "I have a headache" into the text input field on your smartphone and tap the send button.

[0772] Input: User's symptoms (e.g. headache)

[0773] Output: Symptom data is sent to the server.

[0774] Step 2:

[0775] The terminal collects health data from wearable devices.

[0776] Device: Using Bluetooth or Wi-Fi communication, data on heart rate, blood pressure, and sleep patterns is collected from wearable devices.

[0777] Specific operation: By pressing the "Get health data" button, communication with the wearable device begins and data is collected.

[0778] Input: User's wearable device

[0779] Output: Health data (heart rate, blood pressure, sleep patterns) is sent to the device.

[0780] Step 3:

[0781] The device sends the collected health and symptom data to a server.

[0782] Device: Sends health data and symptom input to the server via an HTTP POST request.

[0783] Specific operation: The collected health and symptom data is converted into JSON format and sent to the specified endpoint on the server via an HTTP POST request.

[0784] Input: Health data, symptom data

[0785] Output: The server receives the health data and symptom data.

[0786] Step 4:

[0787] The server passes the received data to the analysis module.

[0788] Server: Passes health data and symptom data to the analysis module and prepares it for analysis.

[0789] Specific operation: Convert the received data into an appropriate format and input it into the analysis module.

[0790] Input: Received data (health data, symptom data)

[0791] Output: Data ready for analysis

[0792] Step 5:

[0793] The server uses an analysis module to analyze the data and estimate the disease state.

[0794] Server: Uses AI to analyze health data and symptom input, and compares it with a database of past cases to estimate the condition.

[0795] Specific operation: Pass data to the AI ​​model, perform pattern matching and collation, and obtain inference results.

[0796] Input: Data ready for analysis

[0797] Output: Estimated medical condition (e.g., high probability of migraine)

[0798] Step 6:

[0799] The server uses an emotion engine to recognize the user's emotional state.

[0800] Server: Passes the analysis results and symptom data to the emotion engine to recognize the user's emotional state.

[0801] Specific behavior: The emotion engine analyzes the data and classifies the emotional state.

[0802] Input: Analysis results, symptom data

[0803] Output: User's emotional state (e.g., anxious state)

[0804] Step 7:

[0805] The server searches for an appropriate medical institution taking into account the user's emotional state.

[0806] Server: Searches a medical institution database based on emotional state and location information to select the most suitable medical institution.

[0807] What it does: Scores and prioritizes medical facility listings based on user sentiment and location.

[0808] Input: Emotional state, location information

[0809] Output: Information on the best medical institution

[0810] Step 8:

[0811] The server sends the user a medical appointment confirmation notification.

[0812] Server: Sends a message to the user asking "Would you like to schedule an appointment with a neurologist?"

[0813] On the device: A push notification will be sent to the user to confirm.

[0814] Specific operation: A confirmation message is sent to the user's device via the notification API, allowing the user to confirm.

[0815] Input: Information on the most suitable medical institution

[0816] Output: A confirmation message is displayed on the user's terminal.

[0817] Step 9:

[0818] The user confirms the notification and confirms the reservation.

[0819] User: Check the notification and tap "Yes" to confirm the reservation.

[0820] Specific actions: Check the push notification and tap the displayed button.

[0821] Input:Confirmation message

[0822] Output: Reservation confirmation response

[0823] Step 10:

[0824] The server sends the appointment information to the medical facility and receives confirmation.

[0825] Server: Sends reservation information to the selected medical institution and receives a reservation confirmation response.

[0826] Specific operation: Access the medical institution's dedicated API, send the reservation information, and then receive a reservation confirmation response.

[0827] Input: Reservation confirmation response

[0828] Output: Appointment confirmation response from the medical institution

[0829] Step 11:

[0830] The server sends the reservation details to the notification module, which notifies the user.

[0831] Server: Sends the reservation details (date, time, location) to the notification module.

[0832] Device: A push notification will be sent stating, "Reservation completed. Date and time: XX / XX / XX XX time, Location: XX Hospital."

[0833] Specific operation: Send the reservation details to the user terminal via the notification module.

[0834] Input: Appointment confirmation response from medical institution

[0835] Output: Notification of reservation details to user

[0836] (Application example 2)

[0837] 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."

[0838] The challenge in autonomous vehicles is to quickly and accurately check the health status of drivers and passengers and provide optimal route guidance to medical institutions that users need. There is also a need for a means to take into account the user's emotional state and provide appropriate advice to reduce stress. Current systems have difficulty making appointments with medical institutions that correspond to individual health conditions or providing prompt diagnoses, making it difficult to ensure users' peace of mind.

[0839] 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.

[0840] In this invention, the server includes: means for receiving symptom input from the user; means for collecting health data from the wearable device; means for analyzing the collected data and estimating possible medical conditions; means for searching for an appropriate medical institution and making an appointment; means for notifying the user of diagnosis results and appointment information; means for collecting surrounding information from sensors inside the autonomous vehicle; means for calculating the optimal route to the medical institution based on the collected health data and location information; and means for recognizing the user's emotional state and customizing the notification content. This allows the user's health condition to be grasped in real time even inside the autonomous vehicle, and the user to be quickly guided to the most appropriate medical institution. Furthermore, appropriate messages based on the user's emotions can be provided to provide a sense of security.

[0841] "Means for receiving symptom input from the user" refers to an interface that allows the user to input symptoms related to their physical condition or health via a smartphone or other device.

[0842] "Means for collecting health data from wearable devices" refers to technology that collects physiological data such as heart rate, blood pressure, and sleep patterns from health management devices worn by users (e.g., smartwatches and fitness trackers).

[0843] "Means for analyzing collected data and inferring possible medical conditions" refers to algorithms and software that use AI to analyze collected physiological data and symptom input from the user, and infer possible medical conditions the user may be suffering from.

[0844] "Means for searching for an appropriate medical institution and making an appointment" refers to the process of selecting the most appropriate medical institution from a database based on the user's current location and estimated medical condition, and making an appointment for an examination.

[0845] "Means of notifying users of diagnosis results and reservation information" refers to methods of transmitting analyzed diagnosis results and information about the medical institution for which an appointment has been made to the user's device via push notification, email, SMS, etc.

[0846] "Means for collecting surrounding information from sensors within an autonomous vehicle" refers to technology that acquires information about the surrounding environment using sensors such as cameras, lidars, and radars installed in autonomous vehicles.

[0847] The "means for calculating the optimal route to a medical institution based on collected health data and location information" is an algorithm that uses collected health data and current location information to calculate the most efficient and safe route to the desired medical institution.

[0848] "Means for recognizing the user's emotional state and customizing notification content" refers to technology that infers the user's emotional state based on their input and physiological data, and then appropriately adjusts the notification content based on the results, providing the user with a sense of security.

[0849] The present invention provides a system that allows a user to check their health condition while in an autonomous vehicle and quickly access a medical institution. Specific embodiments for carrying out the present invention will be described below.

[0850] System Overview

[0851] It uses a smartphone application that allows users to input and check their health status inside the autonomous vehicle, as well as sensors and wearable devices installed in the autonomous vehicle. The system consists of the following main components:

[0852] 1. Symptom input method

[0853] Users enter their symptoms through a smartphone application, for example, "I have a headache," and press the "send" button to send the symptoms to the system.

[0854] 2. Health data collection methods

[0855] Health data, including heart rate, blood pressure, and sleep patterns, is collected from sensors and wearable devices inside the autonomous vehicle and transmitted to a smartphone via Bluetooth or Wi-Fi.

[0856] 3. Data Analysis Methods

[0857] Based on the health data and symptom input received by the server, the data is analyzed using AI to predict possible illnesses, which are also compared with a database of past cases.

[0858] 4. Emotion recognition means

[0859] The server also uses an emotion engine to recognize the user's emotional state, which is inferred from health data and symptom input.

[0860] 5. Medical institution search and reservation methods

[0861] Based on the analysis results and emotion recognition results, the server searches for an appropriate medical institution and makes an appointment. Taking into account surrounding information obtained from sensors inside the autonomous vehicle, the server calculates the optimal route to the medical institution.

[0862] 6. Means of notification

[0863] It notifies users of diagnosis results and medical appointment information, and the content of notifications is customized according to the user's emotional state.

[0864] Specific examples

[0865] 1. Cases where users input their symptoms

[0866] The user types "I have a headache" into their smartphone and sends it.

[0867] Sensors inside the self-driving vehicle capture the user's current location.

[0868] The smartphone collects heart rate (80 bpm), blood pressure (130 / 85), and sleep patterns (6 hours of light sleep) from the wearable device and sends them to a server.

[0869] 2. Data analysis and emotion recognition process

[0870] The server passes the received data to the analysis module, where the AI ​​analyzes it and determines that there is a high possibility of a migraine.

[0871] The emotion engine recognizes the user's anxiety.

[0872] 3. Searching for and booking medical appointments

[0873] The server searches for the nearest neurology department and selects a medical institution that can respond quickly.

[0874] The user is prompted, "Would you like to schedule an appointment with a neurologist?"

[0875] The user confirms the notification and taps "Yes" to confirm the appointment. The message "You have made an appointment with a medical institution that can respond promptly" is displayed.

[0876] 4. Notification Details

[0877] The server sends the appointment to the medical institution and receives the appointment confirmation.

[0878] The server sends the reservation details to the notification module, notifying the user that "Reservation completed. Date and time: XX / XX / XX, time: XX, location: XX Hospital."

[0879] It also displays customized messages based on the user's emotions to reduce anxiety.

[0880] Prompt Sentence Examples

[0881] "I'm thinking about developing a health check and optimal route application for self-driving vehicles. This app will allow drivers and passengers to input their own health status, and will collect health data from sensors in the vehicle and wearable devices worn by the user. Based on the collected data and input information, AI will make a diagnosis and suggest the optimal route to safely guide the user to the nearest medical institution. It will also take into account the user's emotional state and display warnings and calming messages to reassure them."

[0882] In this way, the system of the present invention can grasp the user's health condition in real time and provide prompt and appropriate guidance to medical institutions. Furthermore, it also takes into consideration the user's emotional state, improving their sense of security.

[0883] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0884] Step 1:

[0885] A user opens a smartphone application, enters their symptoms (e.g., "I have a headache"), and presses the send button. This sends the entered symptom data from the device to the server. The input is string data, and the output is sent to the server as an HTTP POST request.

[0886] Step 2:

[0887] The terminal collects health data from a wearable device. Specifically, a smartphone acquires heart rate, blood pressure, and sleep pattern data from the wearable device via Bluetooth or Wi-Fi and sends this data to a server. The input is physiological data from the wearable device, and the output is an HTTP POST request to the server.

[0888] Step 3:

[0889] The server passes the received symptom data and health data to the analysis module. Specifically, the data analysis algorithm uses AI to analyze this data and infer possible medical conditions. The input is text data and physiological data, and the output is an inferred medical condition (e.g., "High probability of migraine").

[0890] Step 4:

[0891] The server uses an emotion engine to recognize the user's emotional state. Based on the medical condition data and health data passed from the analysis module, the emotion engine estimates whether the user is anxious or unstable. The inputs are the analysis results and physiological data, and the output is the emotional state (e.g., "anxiety").

[0892] Step 5:

[0893] The server searches the database for appropriate medical institutions based on the analysis results and emotional state, and selects the most suitable one from the listed medical institutions. Specifically, it takes into account the user's current location information and prioritizes medical institutions that can respond quickly. The inputs are the user's emotional state, analysis results, and current location information, and the output is information about the selected medical institution.

[0894] Step 6:

[0895] The server sends the user a confirmation notification of the medical institution's appointment. Specifically, along with information about the selected medical institution, it sends a confirmation message such as "Do you want to make an appointment with a neurology specialist?" via push notification or email. The input is the information about the selected medical institution, and the output is a notification sent to the user's device.

[0896] Step 7:

[0897] The user confirms the notification on the device and taps "Yes" to confirm the reservation. This action causes the device to send the reservation confirmation data back to the server. The input is the user's selected data, and the output is an HTTP POST request to the server.

[0898] Step 8:

[0899] The server sends the reservation information to the medical institution and receives a reservation confirmation response. Specifically, the reservation information is transmitted to the medical institution using an HTTP request or a dedicated API, and a confirmation response is received from the medical institution. The input is the reservation confirmation data, and the output is the reservation confirmation response from the medical institution.

[0900] Step 9:

[0901] The server sends the reservation details (date, time, location) to the notification module and notifies the user. Specifically, it sends a push notification saying, "Reservation completed. Date and time: XX / XX / XX, XX time, Location: XX Hospital." The input is the reservation confirmation response from the medical institution, and the output is a push notification sent to the user's device.

[0902] Step 10:

[0903] The server customizes the notification content according to the user's emotions and adds a message to give a sense of security. For example, it sends a message such as, "We have made an appointment with a medical institution that can respond quickly. Please rest assured." The input is the user's emotional state, and the output is the customized notification message.

[0904] 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.

[0905] 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.

[0906] 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.

[0907] [Third embodiment]

[0908] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0909] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0910] 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).

[0911] 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.

[0912] 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.

[0913] 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).

[0914] 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. 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.

[0915] 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.

[0916] 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.

[0917] 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.

[0918] 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.

[0919] 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."

[0920] This system receives symptom input from the user, collects health data from a wearable device, and uses AI-based data analysis to predict possible illnesses. Furthermore, the system searches for appropriate medical institutions, confirms appointments with the user, and notifies the user of diagnosis results and appointment information.

[0921] User Input

[0922] The system begins by the user entering their symptoms into their smartphone, for example, "I have a headache," and pressing the "send" button to send the symptoms to the system.

[0923] User: Type "I have a headache" and press the "Send" button.

[0924] Data collection

[0925] The wearable device collects the user's daily health data, including heart rate, blood pressure, sleep patterns, etc. The collected data is sent from the device to a server.

[0926] Device: Wearable device collects heart rate, blood pressure, and sleep pattern data via Bluetooth.

[0927] Terminal: Sends collected data to the server via HTTP POST request.

[0928] Data analysis

[0929] The server passes the received data to an analysis module, which uses AI to analyze the data and predict possible medical conditions. For example, the AI ​​analyzes heart rate, blood pressure, and sleep patterns and determines that a person has a high probability of having a migraine.

[0930] Server: Passes the received data to the analysis module.

[0931] Server: Performs AI-based data analysis to identify possible medical conditions.

[0932] Collaboration with medical institutions

[0933] Based on the analysis results, the server searches for an appropriate medical institution. Based on the search results, the server sends a notification to the user requesting them to confirm an appointment with the medical institution. Once the user confirms the appointment, the server sends the appointment to the medical institution.

[0934] Server: Lists relevant medical institutions and selects the most suitable one.

[0935] Server: Prompts user, "Would you like to schedule an appointment with a neurologist?"

[0936] User: Check the notification and tap "Yes" to confirm the reservation.

[0937] Schedule execution and notification

[0938] The server sends the user's appointment to the medical institution and receives the appointment confirmation. Once confirmed, the server sends the appointment details to the notification module and notifies the user.

[0939] Server: Sends appointments to medical institutions and receives appointment confirmations.

[0940] Server: Sends the reservation details (date, time, location) to the notification module.

[0941] Device: Sends a push notification to the user saying, "Reservation completed. Date and time: XX / XX / XX, XX time, Location: XX Hospital."

[0942] Specific examples

[0943] 1. The user types "I have a headache" into their smartphone and sends it.

[0944] 2. The smartphone collects data from the wearable device, including heart rate (80 bpm), blood pressure (130 / 85), and sleep patterns (6 hours of light sleep), and sends it to the server.

[0945] 3. The server passes the received data to the analysis module, where the AI ​​analyzes it. The AI ​​determines that there is a high possibility of a migraine.

[0946] 4. The server searches for the nearest neurologist and notifies the user, "Would you like to make an appointment with a neurologist?"

[0947] 5. The user confirms the notification and taps "Yes" to confirm the reservation.

[0948] 6. The server sends the appointment to the medical institution and receives a confirmation of the appointment.

[0949] 7. The server sends the reservation details (date, time, location) to the notification module and notifies the user.

[0950] 8. A push notification will appear on the user's smartphone saying, "Reservation completed. Date and time: XX / XX / XX XX o'clock, Location: XX Hospital."

[0951] In this way, the present invention allows users to quickly check their health status and seek medical attention at an appropriate medical institution.

[0952] The processing flow will be explained below.

[0953] Step 1:

[0954] The user enters the symptom "headache" into their smartphone and presses the "send" button.

[0955] User: Type "I have a headache" and press the "Send" button.

[0956] Step 2:

[0957] The device collects health data from the wearable device, including heart rate, blood pressure, and sleep patterns, and transmits the collected data to a server.

[0958] Device: Heart rate (80 bpm), blood pressure (130 / 85), and sleep pattern (6 hours of light sleep) data are collected from the wearable device via Bluetooth.

[0959] Device: Health data and user symptom input are sent to the server via an HTTP POST request.

[0960] Step 3:

[0961] The server passes the received data to the analysis module, which then uses AI to analyze the data and estimate possible medical conditions.

[0962] Server: Passes the received data to the analysis module.

[0963] Server: The AI ​​analyzes the health data and symptom input it receives, compares it with past cases, and determines that there is a high possibility of a migraine.

[0964] Step 4:

[0965] The server searches for an appropriate medical institution based on the estimated medical condition, and then sends the user a notification requesting them to confirm an appointment.

[0966] Server: Lists relevant medical institutions such as internal medicine and neurology departments from a database and selects the nearest medical institution.

[0967] Server: Notifies the user of the information about the selected medical institution and sends a message asking, "Would you like to make an appointment with a neurologist?"

[0968] Step 5:

[0969] The user checks the notification on the device and confirms the medical appointment.

[0970] User: Check the notification and tap "Yes" to confirm the reservation.

[0971] Step 6:

[0972] The server sends the user's reservation information to the medical institution, and then receives reservation confirmation from the medical institution.

[0973] Server: Sends appointment information to the selected neurology department (using HTTP requests or a dedicated API).

[0974] Server: Receives the reservation confirmation response from the medical institution.

[0975] Step 7:

[0976] The server sends the reservation details (date, time, location) to the notification module, and finally the device notifies the user of the information.

[0977] Server: Sends reservation details such as "Reservation completed. Date and time: XX / XX / XX, Location: XX Hospital" to the notification module.

[0978] Device: The user will receive a push notification saying, "Reservation completed. Date and time: XX / XX / XX XX time, Location: XX Hospital."

[0979] Example 1

[0980] 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."

[0981] In modern society, there is a demand for systems that allow users to quickly understand their health status and receive treatment at the appropriate medical institution. However, it is extremely difficult for users to find a medical institution that is suitable for their symptoms and smoothly schedule an appointment. There is also a lack of systems that effectively utilize daily health data to realize preventive medicine. There is a need for a system that solves these problems and allows users to efficiently manage their health and receive appropriate medical care.

[0982] 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.

[0983] In this invention, the server includes means for receiving symptom input from the user, means for collecting biometric data from the wearable device, means for analyzing the collected data and identifying an estimated health condition, means for searching for an appropriate medical institution and making an appointment, and means for notifying the user of the diagnosis results and appointment information, thereby enabling the user to quickly check their own health condition and receive a medical examination at an appropriate medical institution.

[0984] "Means for receiving symptom input from the user" refers to a device or interface that acquires information about physical ailments and symptoms entered by the user and transmits it to the system.

[0985] "Means for collecting biometric data from wearable devices" refers to devices or methods that have the ability to obtain physical data such as heart rate, blood pressure, and sleep patterns from equipment worn by the user.

[0986] "Means for analyzing collected data and identifying estimated health status" refers to algorithms or systems that analyze collected biometric data and determine and identify health status from the results.

[0987] "Means for searching for and making appointments with appropriate medical institutions" refers to functions and systems that allow users to select the most appropriate medical institution based on their health condition and symptoms and make appointments for consultations.

[0988] "Means for notifying users of diagnostic results and appointment information" refers to systems or devices with notification functions for informing users of analysis results and appointment information with medical institutions.

[0989] "Means of using AI to analyze data and compare it with a database of past cases to estimate health status" refers to a system that uses artificial intelligence technology to analyze collected data and compare it with past case data to predict health status.

[0990] "Means for collecting data from a wearable device worn by a user via wireless communication" refers to a method or system for collecting data from a wearable device worn by a user using wireless communication technologies such as Bluetooth or Wi-Fi.

[0991] This invention is a system that receives symptom input from a user, collects biometric data from a wearable device, and estimates health status through data analysis using AI. This section describes the specific hardware and software configurations required to realize this system.

[0992] First, users use their smartphones to input their symptoms. A dedicated application is installed on the smartphone, and users enter their symptoms through this application and press the "send" button to send the symptoms to the system.

[0993] Next, wearable devices such as smartwatches and fitness trackers can be used. These devices connect to smartphones via Bluetooth and collect biometric data such as heart rate, blood pressure, sleep patterns, etc. The smartphone terminal acquires this data and sends it to a server using an HTTP POST request.

[0994] The server passes the received data to the analysis module, where an AI model analyzes the data. This AI model is a generative AI model that compares the data with a database of past cases to estimate the patient's health condition. The resulting estimate may be, for example, "high possibility of migraine."

[0995] Based on the analysis results, the server searches for an appropriate medical institution. It lists relevant medical institutions from a database of medical institutions and selects the most suitable one based on the user's location and symptoms. After selecting the institution, the server sends the user a notification asking, "Do you want to make an appointment with a neurologist?" The user confirms the notification and taps "Yes" to confirm the appointment.

[0996] The server sends the reservation details to the medical institution and receives a reservation confirmation. Once confirmation is received, the server passes the reservation details to the notification module and sends a push notification to the user. For example, the user's smartphone will display a message saying, "Reservation completed. Date and time: XX / XX / XX, Location: XX Hospital."

[0997] Specific examples

[0998] 1. User Input

[0999] A user types "I have a headache" into a smartphone app and presses the "send" button.

[1000] 2. Data Collection

[1001] The device collects data from the wearable device, including heart rate (80 bpm), blood pressure (130 / 85), and sleep patterns (6 hours of light sleep), and sends it to a server.

[1002] 3. Data Analysis

[1003] The server passes the received data to the analysis module, where the AI ​​analyzes it and determines that there is a high possibility of a migraine.

[1004] 4. Collaboration with medical institutions

[1005] The server searches for the nearest neurologist and prompts the user, "Would you like to make an appointment with a neurologist?"

[1006] The user sees the notification and taps "Yes" to confirm the booking.

[1007] 5. Reservation execution and notification

[1008] The server sends the appointment to the medical institution and receives the appointment confirmation.

[1009] The server sends the reservation details (date, time, location) to the notification module, which notifies the user.

[1010] A push notification will appear on the user's smartphone saying, "Reservation completed. Date and time: XX / XX / XX XX time, Location: XX Hospital."

[1011] In this way, users can quickly check their health status and seek medical attention at the appropriate medical institution.

[1012] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1013] Step 1: User Input

[1014] Specific explanation: The user enters their symptoms into a smartphone device and sends them to the system.

[1015] Operation and data processing: The user enters "I have a headache" into the text box of the app and presses the "Send" button. This input information is sent from the smartphone to the server via an HTTP POST request.

[1016] Input: Text data of symptoms entered by the user (e.g., "My head hurts")

[1017] Output: Text data of symptoms received by the server

[1018] Step 2: Data collection

[1019] Specific description: Biometric data is collected from a wearable device and sent to a server.

[1020] Operation and data processing: The device connects to the wearable device via Bluetooth and acquires data such as heart rate, blood pressure, and sleep patterns. The acquired data is then sent to the server via an HTTP POST request.

[1021] Input: Biometric data obtained from wearable devices (e.g., heart rate 80 bpm, blood pressure 130 / 85, sleep patterns)

[1022] Output: Biometric data received by the server

[1023] Step 3: Data analysis

[1024] Specifically: A server analyzes health data and identifies a probable health condition.

[1025] Operation and data processing: The server passes the received biometric data to the analysis module, where the AI ​​model analyzes it by comparing it with a database of past cases. The AI ​​analyzes the data and identifies a probable health condition.

[1026] Input: Biometric data and case database stored on the server

[1027] Output: Text data of the estimated health condition (e.g., "High probability of migraine")

[1028] Step 4: Collaboration with medical institutions

[1029] Specific explanation: Based on the analysis results, an appropriate medical institution is searched for and notified to the user.

[1030] Operation and data processing: Based on the data analysis results, the server searches the database for relevant medical institutions. It selects the most suitable medical institution from the search results and sends a notification to the user asking, "Would you like to make an appointment with a neurologist?"

[1031] Input: Data analysis results and medical institution database

[1032] Output: Notification data sent to the user

[1033] Step 5: User response

[1034] Specific Description: The user confirms the notification and confirms the reservation.

[1035] Operation and data processing: The user checks the notification on their smartphone and taps "Yes." This response is sent to the server, and the reservation is confirmed.

[1036] Input: User response data (e.g. "Yes")

[1037] Output: The response data received by the server

[1038] Step 6: Reservation execution and notification

[1039] Specific details: Sends appointment details to the medical institution and notifies the user.

[1040] Operation and data processing: The server sends the user's appointment information to the medical institution and receives the appointment confirmation. Once confirmed, the server passes the appointment details to the notification module and sends a push notification to the user.

[1041] Input: Reservation information and medical institution confirmation data

[1042] Output: Push notification data sent to the user (e.g., "Your reservation has been completed. Date and time: ____ month ____ day ____ time, location: ____ Hospital")

[1043] In this way, the system performs specific actions and processes data at each step, allowing users to efficiently manage their health.

[1044] (Application example 1)

[1045] 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."

[1046] In modern society, it is important for users to be able to quickly check their health status and access appropriate medical institutions. There is a particular need for systems that can respond quickly and accurately when symptoms suddenly appear or when users are unsure which medical institution to choose. Conventional methods require users to search for medical information online and make their own judgments when selecting a medical institution, which is time- and knowledge-intensive. Therefore, there is a need for a system that can automatically analyze the user's condition based on their symptoms and suggest and book an appropriate medical institution.

[1047] 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.

[1048] In this invention, the server includes means for receiving symptom input from the user, means for collecting health data from the wearable device, means for analyzing the collected data and estimating possible medical conditions, means for searching for an appropriate medical institution and making a reservation, means for notifying the user of the diagnosis results and reservation information, means for analyzing the medical condition using AI based on the symptoms entered by the user and the health data collected from the wearable device, and means for sending a notification confirming a reservation at an appropriate medical institution based on the analysis results, thereby enabling the user to receive prompt and appropriate medical treatment for their symptoms.

[1049] "Symptom input" refers to users inputting their own physical condition and symptoms into a digital device.

[1050] "Health Data" refers to information about a user's health, such as heart rate, blood pressure, and sleep patterns, obtained from wearable devices or other sensors.

[1051] "Analysis" refers to the process of using AI and databases to identify possible medical conditions based on collected health data and symptom input.

[1052] "Disease prediction" refers to the prediction of possible diseases or health problems obtained as a result of the analysis.

[1053] "Medical institution search" refers to searching for a medical facility that can provide appropriate examination and treatment based on an estimated medical condition.

[1054] "Reservation" refers to the procedure by which a user reserves a date and time for an appointment at a medical institution.

[1055] "Notification of diagnosis results" refers to informing the user of the estimated condition and reservation information obtained through analysis.

[1056] "Analyzing data using AI" refers to using artificial intelligence technology to analyze collected health data and symptom inputs to predict or identify medical conditions.

[1057] A "case database" is a database that compiles previously recorded case data and is a collection of information used to collate new symptoms and health data.

[1058] A "wearable device" is an electronic device that can be worn by a user and has the ability to collect health data such as heart rate, blood pressure, and sleep patterns.

[1059] "Bluetooth" is a short-range wireless communication technology and refers to a standard used to wirelessly exchange data between electronic devices.

[1060] "Wi-Fi" is one of the wireless LAN standards and refers to the technology for connecting to the Internet and exchanging data wirelessly.

[1061] The system for implementing this invention quickly evaluates the user's health condition and automatically schedules an appointment with an appropriate medical institution. Below, we will explain in detail how each means works and how the overall flow proceeds.

[1062] Hardware and Software Configuration

[1063] Hardware

[1064] Smartphone: A device for user input and health data collection.

[1065] Wearable devices: Devices used to collect health data such as heart rate, blood pressure, and sleep patterns (e.g., Apple Watch, Fitbit).

[1066] Server: A central device that handles analytics and database management.

[1067] software

[1068] AI analysis module: A module that analyzes health data and predicts disease conditions using machine learning libraries such as TensorFlow and PyTorch.

[1069] Data collection application: An application for transferring health data from a wearable device to a smartphone and sending it to a server.

[1070] Medical institution search and reservation system: A system that uses medical APIs to search for appropriate medical institutions and make reservations.

[1071] Notification system: A system that uses push notifications to send users diagnostic results and appointment information.

[1072] Explanation of each method

[1073] Symptom input method

[1074] Users enter their symptoms in text form via their smartphone, and this information is used for subsequent analysis. For example, they might enter a symptom such as "I have a headache."

[1075] Health data collection methods

[1076] The smartphone uses Bluetooth or Wi-Fi to collect health data such as heart rate, blood pressure, and sleep patterns from the wearable device, which is then sent to a server for analysis.

[1077] Analysis means

[1078] The server uses AI to analyze the symptom input and health data it receives. The AI ​​analysis module uses TensorFlow and PyTorch to compare the data with a database of past cases and estimate the condition. For example, it analyzes data such as heart rate (80 bpm), blood pressure (130 / 85), and sleep patterns (six hours of light sleep) to determine a "high possibility of migraine."

[1079] Medical institution search and reservation method

[1080] Based on the analysis results, the server searches for an appropriate medical institution. Using a medical API, it lists the nearest medical institutions and sends the user a reservation confirmation notification for the appropriate medical institution. The user confirms the notification and selects "Yes" to confirm the reservation.

[1081] Diagnostic result notification method

[1082] Once the reservation is confirmed, the server sends the reservation details (date, time, location) to the user via push notification, and the user can check the reservation details on their smartphone.

[1083] Specific examples

[1084] 1. The user enters the symptom "headache" into their smartphone.

[1085] 2. The smartphone collects heart rate (80 bpm), blood pressure (130 / 85), and sleep pattern (6 hours of light sleep) from the wearable device and sends them to the server.

[1086] 3. The server analyzes the data received using an AI analysis module (TensorFlow, PyTorch) and determines that there is a high possibility of a migraine.

[1087] 4. Search for the nearest neurologist through a healthcare API and send a push notification to the user asking, "Would you like to make an appointment with a neurologist?"

[1088] 5. The user confirms the notification and selects "Yes." The server sends the appointment to the medical institution and receives a confirmation of the appointment.

[1089] 6. Reservation details (date, time, location) will be pushed to the user's smartphone.

[1090] Prompt Sentence Examples

[1091] When a user inputs the symptom "headache," analyze the heart rate (80 bpm), blood pressure (130 / 85), and sleep pattern (6 hours of light sleep) collected from the wearable device to deduce possible medical conditions, find the most appropriate medical institution, and ask the user to confirm an appointment.

[1092] This invention allows users to quickly and appropriately assess their health status and receive the necessary medical services.

[1093] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1094] Step 1:

[1095] The user launches the smartphone application and inputs their symptoms (e.g., "I have a headache"). The input symptom data is stored within the application and used in the next data collection step. Input is in text format and can be completed intuitively by the user.

[1096] Step 2:

[1097] The device (smartphone) collects health data from the user's wearable device (e.g., heart rate, blood pressure, sleep patterns) via Bluetooth or Wi-Fi. The collected data is stored within the application and then sent to a server. The input is sensor data, and the output is the collected biometric data.

[1098] Step 3:

[1099] The device organizes the collected health data and sends it to the server along with the user's symptom data via an HTTP POST request. The data is sent to the server in JSON format and prepared for processing by the analysis module. The input is the organized health data and symptom data pair, and the output is the data sent to the server.

[1100] Step 4:

[1101] The server passes the received data to the AI ​​analysis module, which uses TensorFlow or PyTorch to analyze the data and compare it with a database of past cases to estimate the condition. The AI ​​model then predicts the condition based on the generative AI model. The input is the user's health data and symptom data, and the output is a predicted condition.

[1102] Step 5:

[1103] The server uses a medical API to search for an appropriate medical institution based on the prediction results obtained from the AI ​​analysis module. Based on the analysis results, the most suitable medical institutions are listed, and from there the most suitable medical institution is selected based on criteria such as location, rating, and available hours. The input is the predicted condition result, and the output is information about the most suitable medical institution.

[1104] Step 6:

[1105] The server sends the user a reservation confirmation notification based on the information of the selected medical institution. This notification is sent to the user's smartphone as a push notification. The user can check the notification and choose whether to confirm the reservation. The input is medical institution information, and the output is a notification to the user.

[1106] Step 7:

[1107] If the user checks the notification and selects "Yes," the server sends the reservation to the medical institution and receives a reservation confirmation. This confirms the reservation. The input is the user's reservation confirmation, and the output is a reservation completion notification.

[1108] Step 8:

[1109] The server sends a push notification to the user's smartphone with appointment details (date, time, location). The user receives the notification and can check the appointment schedule. The input is appointment confirmation information from the medical institution, and the output is a notification of appointment details to the user.

[1110] Prompt Sentence Examples

[1111] When a user inputs the symptom "headache," analyze the heart rate (80 bpm), blood pressure (130 / 85), and sleep pattern (6 hours of light sleep) collected from the wearable device to deduce possible medical conditions, find the most appropriate medical institution, and ask the user to confirm an appointment.

[1112] 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.

[1113] This system receives symptom input from the user, collects health data from a wearable device, and uses AI-based data analysis to predict possible illnesses. It also uses an emotion engine to recognize the user's emotions and customizes the selection of medical institutions and notification content accordingly. The system searches for appropriate medical institutions, confirms appointments with the user, and notifies the user of diagnosis results and reservation information.

[1114] User Input

[1115] The system begins by the user entering their symptoms into their smartphone, for example, "I have a headache," and pressing the "send" button to send the symptoms to the system.

[1116] User: Type "I have a headache" and press the "Send" button.

[1117] Data collection

[1118] The wearable device collects the user's daily health data, including heart rate, blood pressure, sleep patterns, etc. The collected data is sent from the device to a server.

[1119] Device: Obtains heart rate, blood pressure, and sleep pattern data from a wearable device via Bluetooth.

[1120] Device: Health data and user symptom input are sent to the server via an HTTP POST request.

[1121] Data analysis and emotion recognition

[1122] The server passes the received data to the analysis module, which uses AI to analyze the data and estimate possible medical conditions. The emotion engine then uses the user's input symptoms and collected health data to recognize the user's emotions. For example, if the analysis module determines that there is a high possibility of a migraine, the emotion engine recognizes that the user is in an anxious state.

[1123] Server: Passes the received data to the analysis module.

[1124] Server: The AI ​​analyzes the health data and symptom input it receives, compares it with past cases, and determines that there is a high possibility of a migraine.

[1125] Server: The emotion engine analyzes the data and recognizes that the user is in an "anxious state."

[1126] Collaboration and optimization with medical institutions

[1127] Based on the analysis results and the emotion engine's recognition results, the server searches for an appropriate medical institution. Based on the search results, the server sends a notification to the user requesting them to confirm their appointment. For example, if the emotion engine detects the user's anxiety, it will prioritize medical institutions that can respond quickly.

[1128] Server: Lists relevant medical institutions from a database and selects the most suitable one.

[1129] Server: Sends a message to the user asking "Would you like to make an appointment with a neurologist?"

[1130] Server: When a user is in an "anxious state," the server prioritizes the selection of a medical institution that can respond quickly.

[1131] User reservation confirmation

[1132] The user checks the notification on their device and confirms the appointment with the medical institution. For example, the notification content is customized according to the user's emotion, and displays, "You have made an appointment with a medical institution that can respond quickly."

[1133] User: Check the notification and tap "Yes" to confirm the reservation.

[1134] User: Receives a customized notification that "Your appointment has been scheduled with a fast-acting provider."

[1135] Schedule execution and notification

[1136] The server sends the user's appointment information to the medical institution, then receives the appointment confirmation from the medical institution, and once the confirmation is received, the server sends the appointment details to the notification module and notifies the user.

[1137] Server: Sends appointment information to the selected neurology department (using HTTP requests or a dedicated API).

[1138] Server: Receives the reservation confirmation response from the medical institution.

[1139] Server: Sends the reservation details (date, time, location) to the notification module.

[1140] Device: The user will receive a push notification saying, "Reservation completed. Date and time: XX / XX / XX XX time, Location: XX Hospital."

[1141] Device: Provides customized lighting and messages based on the user's emotions.

[1142] Specific examples

[1143] 1. The user types "I have a headache" into their smartphone and sends it.

[1144] 2. The smartphone collects data from the wearable device, including heart rate (80 bpm), blood pressure (130 / 85), and sleep patterns (6 hours of light sleep), and sends it to the server.

[1145] 3. The server passes the received data to the analysis module, where the AI ​​analyzes it. The AI ​​determines that there is a high possibility of a migraine, and the emotion engine recognizes the user's anxiety.

[1146] 4. The server searches for the nearest neurology clinic and selects a medical institution that can respond quickly based on the recognition results of the emotion engine. The server then notifies the user, "Would you like to make an appointment with a neurology clinic?"

[1147] 5. The user confirms the notification and taps "Yes" to confirm the appointment. The notification then displays, "You have made an appointment with a medical institution that can respond promptly."

[1148] 6. The server sends the appointment to the medical institution and receives a confirmation of the appointment.

[1149] 7. The server sends the reservation details to the notification module, and the user is notified via push notification that "Reservation completed. Date and time: XX / XX / XX, Location: XX Hospital."

[1150] 8. The user's smartphone will display customized details and messages to alleviate anxiety.

[1151] In this way, the present invention allows users to quickly check their health status and seek medical advice from the most appropriate medical institution, taking into account their emotional state.

[1152] The processing flow will be explained below.

[1153] Step 1:

[1154] The user enters the symptom "headache" into their smartphone and presses the "send" button.

[1155] User: Type "I have a headache" and press the "Send" button.

[1156] Step 2:

[1157] The device collects health data from the wearable device, including heart rate, blood pressure, and sleep patterns, and transmits the collected data to a server.

[1158] Device: Heart rate (80 bpm), blood pressure (130 / 85), and sleep pattern (6 hours of light sleep) data are collected from the wearable device via Bluetooth.

[1159] Device: Health data and user symptom input are sent to the server via an HTTP POST request.

[1160] Step 3:

[1161] The server passes the received data to an analysis module, which uses AI to analyze the data and estimate possible medical conditions.

[1162] Server: Passes the received data to the analysis module.

[1163] Server: The AI ​​analyzes the health data and symptom input it receives, compares it with past cases, and determines that there is a high possibility of a migraine.

[1164] Step 4:

[1165] The emotion engine recognizes the user's emotional state based on the user's symptoms and collected health data. For example, it can recognize that the user is feeling anxious based on their symptoms and health data.

[1166] Server: The emotion engine analyzes the data and determines that the user is in an "anxious state."

[1167] Step 5:

[1168] Based on the analysis results and the emotion engine's recognition results, the server searches for an appropriate medical institution. For example, if the user is feeling anxious, it will prioritize medical institutions that can respond quickly.

[1169] Server: Lists relevant medical institutions from a database and selects the most suitable one.

[1170] Server: Based on the information of the selected medical institution, the server sends the user a message asking, "Would you like to make an appointment with a neurologist?"

[1171] Server: If a user is in an "anxious state," prioritize medical institutions that can respond quickly.

[1172] Step 6:

[1173] The user checks the notification on their device and confirms the appointment with the medical institution. For example, the notification content is customized according to the user's emotion, and displays, "You have made an appointment with a medical institution that can respond quickly."

[1174] User: Check the notification and tap "Yes" to confirm the reservation.

[1175] On the device: Display a customized notification to the user saying, "Your appointment has been made with a fast-acting provider."

[1176] Step 7:

[1177] The server sends the user's reservation information to the medical institution, and then receives reservation confirmation from the medical institution.

[1178] Server: Sends appointment information to the selected neurology department (using HTTP requests or a dedicated API).

[1179] Server: Receives the reservation confirmation response from the medical institution.

[1180] Step 8:

[1181] The server sends the reservation details (date, time, location) to the notification module. Finally, the device notifies the user of this information. For example, the user may receive a push notification saying, "Reservation completed. Date and time: XX / XX / XX, location: XX Hospital." Additionally, a message to alleviate anxiety may also be displayed.

[1182] Server: Sends reservation details such as "Reservation completed. Date and time: XX / XX / XX, Location: XX Hospital" to the notification module.

[1183] Device: The user will receive a push notification saying, "Reservation completed. Date and time: XX / XX / XX XX time, Location: XX Hospital."

[1184] Device: Depending on the user's emotions, customized messages such as "Don't worry, we'll respond quickly" are also displayed.

[1185] Example 2

[1186] 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."

[1187] In modern society, users need to quickly and accurately understand their health status and seek medical treatment at the appropriate medical institution. However, current systems select medical institutions without taking into account the user's emotional state, which can increase anxiety and stress. In addition, it is difficult for users to manually collect and analyze health data from wearable devices, making it difficult to complete the process of seeking medical treatment immediately.

[1188] 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 means for receiving symptom input from the user, means for collecting health data from the wearable device, means for analyzing the collected data and estimating possible medical conditions, means for recognizing the user's emotional state using an emotion engine, means for searching for and making an appointment with an appropriate medical institution based on the recognition result, and means for notifying the user of the diagnosis result and appointment information. This allows the user to quickly check their own health condition and receive a medical examination at the most appropriate medical institution taking their emotional state into consideration.

[1189] "User" refers to an individual who uses the System to manage their own health status and input symptoms.

[1190] "Symptom input" refers to the act of a user sending their symptoms of poor health or illness to the system using a device such as a smartphone or computer.

[1191] "Wearable devices" refer to devices worn by users on a daily basis that measure and record physical condition data, and specifically include smart watches, heart rate monitors, blood pressure monitors, etc.

[1192] "Health data" refers to data about a user's physical condition collected by wearable devices, including, for example, heart rate, blood pressure, and sleep patterns.

[1193] "Analysis Module" refers to software or algorithms used to analyze collected data and infer possible pathologies.

[1194] An "emotion engine" refers to software or algorithms that analyze user-entered symptoms and collected health data to recognize a user's emotional state.

[1195] "Medical institution" refers to a facility such as a hospital or clinic where users can receive medical examinations or treatment.

[1196] "Appointment confirmation" refers to the act of notifying a user that an appointment with a medical institution has been confirmed.

[1197] "Push notification" refers to a technology that instantly sends information to a device and notifies the user.

[1198] "Diagnosis result" refers to information about the medical condition estimated as a result of analysis performed by AI.

[1199] This system receives symptom input from the user, collects health data from a wearable device, and uses AI to analyze the data to predict the patient's condition. It also uses an emotion engine to recognize the user's emotions, and customizes the selection of medical institutions and notification content accordingly. The system searches for appropriate medical institutions, confirms appointments with the user, and notifies the user of diagnosis results and reservation information.

[1200] Users begin using the system by entering their symptoms into their smartphone. For example, they enter a specific symptom such as "headache," and then press the "send" button to send the information to the server.

[1201] The device collects health data from wearable devices using Bluetooth or Wi-Fi communication, including heart rate, blood pressure, and sleep patterns, and sends this data to the server via HTTP POST requests.

[1202] The server passes the received health data and symptom information to the analysis module. The analysis module uses AI to analyze the data and compare it with a database of past cases to estimate possible medical conditions. For example, it may diagnose "high probability of migraine." Furthermore, an emotion engine uses this data to recognize the user's emotional state (e.g., anxiety).

[1203] Based on the analysis results and the user's emotional state, the server searches for an appropriate medical institution. Taking into account information such as the user's location and the medical institution's response speed, it prioritizes medical institutions that can respond quickly. As a result, the server sends the user a confirmation message such as "Would you like to make an appointment with a neurologist?"

[1204] Once the user confirms the notification and confirms the appointment, the server sends the appointment information to the medical institution and receives a reservation confirmation response. If the appointment is confirmed, the server sends the appointment details (date, time, location, etc.) to the notification module and sends a push notification to the device stating, "Appointment completed. Date and time: XX / XX / XX, XX time, Location: XX Hospital." A customized message generated by the emotion engine is also displayed on the device, providing information to reduce anxiety.

[1205] For example, when a user types "I have a headache" into their smartphone and sends it, the smartphone sends health data collected from the wearable device, such as heart rate (80 bpm), blood pressure (130 / 85), and sleep pattern (6 hours of light sleep), to a server. The server analyzes this data and determines that there is a high possibility of a migraine, and the emotion engine recognizes the user's anxiety. The server searches for the nearest neurologist, selects a medical institution that can respond quickly, and notifies the user. When the user confirms an appointment, the server sends the appointment to the medical institution and notifies them of detailed information once the appointment is confirmed.

[1206] An example of a prompt for a generative AI model is, "Please tell me the steps to design a system that analyzes a user's symptoms and health data collected from a wearable device and selects the most appropriate medical institution." Such prompts can be used to extract information needed for system design.

[1207] In this way, the present invention allows users to quickly check their health status and seek appropriate medical attention, taking into account their emotional state.

[1208] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1209] Step 1:

[1210] Users enter their symptoms into a smartphone application.

[1211] User: Type "I have a headache" and press the "Send" button.

[1212] Specific action: Enter the words "I have a headache" into the text input field on your smartphone and tap the send button.

[1213] Input: User's symptoms (e.g. headache)

[1214] Output: Symptom data is sent to the server.

[1215] Step 2:

[1216] The terminal collects health data from wearable devices.

[1217] Device: Using Bluetooth or Wi-Fi communication, data on heart rate, blood pressure, and sleep patterns is collected from wearable devices.

[1218] Specific operation: By pressing the "Get health data" button, communication with the wearable device begins and data is collected.

[1219] Input: User's wearable device

[1220] Output: Health data (heart rate, blood pressure, sleep patterns) is sent to the device.

[1221] Step 3:

[1222] The device sends the collected health and symptom data to a server.

[1223] Device: Sends health data and symptom input to the server via an HTTP POST request.

[1224] Specific operation: The collected health and symptom data is converted into JSON format and sent to the specified endpoint on the server via an HTTP POST request.

[1225] Input: Health data, symptom data

[1226] Output: The server receives the health data and symptom data.

[1227] Step 4:

[1228] The server passes the received data to the analysis module.

[1229] Server: Passes health data and symptom data to the analysis module and prepares it for analysis.

[1230] Specific operation: Convert the received data into an appropriate format and input it into the analysis module.

[1231] Input: Received data (health data, symptom data)

[1232] Output: Data ready for analysis

[1233] Step 5:

[1234] The server uses an analysis module to analyze the data and estimate the disease state.

[1235] Server: Uses AI to analyze health data and symptom input, and compares it with a database of past cases to estimate the condition.

[1236] Specific operation: Pass data to the AI ​​model, perform pattern matching and collation, and obtain inference results.

[1237] Input: Data ready for analysis

[1238] Output: Estimated medical condition (e.g., high probability of migraine)

[1239] Step 6:

[1240] The server uses an emotion engine to recognize the user's emotional state.

[1241] Server: Passes the analysis results and symptom data to the emotion engine to recognize the user's emotional state.

[1242] Specific behavior: The emotion engine analyzes the data and classifies the emotional state.

[1243] Input: Analysis results, symptom data

[1244] Output: User's emotional state (e.g., anxious state)

[1245] Step 7:

[1246] The server searches for an appropriate medical institution taking into account the user's emotional state.

[1247] Server: Searches a medical institution database based on emotional state and location information to select the most suitable medical institution.

[1248] What it does: Scores and prioritizes medical facility listings based on user sentiment and location.

[1249] Input: Emotional state, location information

[1250] Output: Information on the best medical institution

[1251] Step 8:

[1252] The server sends the user a medical appointment confirmation notification.

[1253] Server: Sends a message to the user asking "Would you like to schedule an appointment with a neurologist?"

[1254] On the device: A push notification will be sent to the user to confirm.

[1255] Specific operation: A confirmation message is sent to the user's device via the notification API, allowing the user to confirm.

[1256] Input: Information on the most suitable medical institution

[1257] Output: A confirmation message is displayed on the user's terminal.

[1258] Step 9:

[1259] The user confirms the notification and confirms the reservation.

[1260] User: Check the notification and tap "Yes" to confirm the reservation.

[1261] Specific actions: Check the push notification and tap the displayed button.

[1262] Input:Confirmation message

[1263] Output: Reservation confirmation response

[1264] Step 10:

[1265] The server sends the appointment information to the medical facility and receives confirmation.

[1266] Server: Sends reservation information to the selected medical institution and receives a reservation confirmation response.

[1267] Specific operation: Access the medical institution's dedicated API, send the reservation information, and then receive a reservation confirmation response.

[1268] Input: Reservation confirmation response

[1269] Output: Appointment confirmation response from the medical institution

[1270] Step 11:

[1271] The server sends the reservation details to the notification module, which notifies the user.

[1272] Server: Sends the reservation details (date, time, location) to the notification module.

[1273] Device: A push notification will be sent stating, "Reservation completed. Date and time: XX / XX / XX XX time, Location: XX Hospital."

[1274] Specific operation: Send the reservation details to the user terminal via the notification module.

[1275] Input: Appointment confirmation response from medical institution

[1276] Output: Notification of reservation details to user

[1277] (Application example 2)

[1278] 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."

[1279] The challenge in autonomous vehicles is to quickly and accurately check the health status of drivers and passengers and provide optimal route guidance to medical institutions that users need. There is also a need for a means to take into account the user's emotional state and provide appropriate advice to reduce stress. Current systems have difficulty making appointments with medical institutions that correspond to individual health conditions or providing prompt diagnoses, making it difficult to ensure users' peace of mind.

[1280] 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.

[1281] In this invention, the server includes: means for receiving symptom input from the user; means for collecting health data from the wearable device; means for analyzing the collected data and estimating possible medical conditions; means for searching for an appropriate medical institution and making an appointment; means for notifying the user of diagnosis results and appointment information; means for collecting surrounding information from sensors inside the autonomous vehicle; means for calculating the optimal route to the medical institution based on the collected health data and location information; and means for recognizing the user's emotional state and customizing the notification content. This allows the user's health condition to be grasped in real time even inside the autonomous vehicle, and the user to be quickly guided to the most appropriate medical institution. Furthermore, appropriate messages based on the user's emotions can be provided to provide a sense of security.

[1282] "Means for receiving symptom input from the user" refers to an interface that allows the user to input symptoms related to their physical condition or health via a smartphone or other device.

[1283] "Means for collecting health data from wearable devices" refers to technology that collects physiological data such as heart rate, blood pressure, and sleep patterns from health management devices worn by users (e.g., smartwatches and fitness trackers).

[1284] "Means for analyzing collected data and inferring possible medical conditions" refers to algorithms and software that use AI to analyze collected physiological data and symptom input from the user, and infer possible medical conditions the user may be suffering from.

[1285] "Means for searching for an appropriate medical institution and making an appointment" refers to the process of selecting the most appropriate medical institution from a database based on the user's current location and estimated medical condition, and making an appointment for an examination.

[1286] "Means of notifying users of diagnosis results and reservation information" refers to methods of transmitting analyzed diagnosis results and information about the medical institution for which an appointment has been made to the user's device via push notification, email, SMS, etc.

[1287] "Means for collecting surrounding information from sensors within an autonomous vehicle" refers to technology that acquires information about the surrounding environment using sensors such as cameras, lidars, and radars installed in autonomous vehicles.

[1288] The "means for calculating the optimal route to a medical institution based on collected health data and location information" is an algorithm that uses collected health data and current location information to calculate the most efficient and safe route to the desired medical institution.

[1289] "Means for recognizing the user's emotional state and customizing notification content" refers to technology that infers the user's emotional state based on their input and physiological data, and then appropriately adjusts the notification content based on the results, providing the user with a sense of security.

[1290] The present invention provides a system that allows a user to check their health condition while in an autonomous vehicle and quickly access a medical institution. Specific embodiments for carrying out the present invention will be described below.

[1291] System Overview

[1292] It uses a smartphone application that allows users to input and check their health status inside the autonomous vehicle, as well as sensors and wearable devices installed in the autonomous vehicle. The system consists of the following main components:

[1293] 1. Symptom input method

[1294] Users enter their symptoms through a smartphone application, for example, "I have a headache," and press the "send" button to send the symptoms to the system.

[1295] 2. Health data collection methods

[1296] Health data, including heart rate, blood pressure, and sleep patterns, is collected from sensors and wearable devices inside the autonomous vehicle and transmitted to a smartphone via Bluetooth or Wi-Fi.

[1297] 3. Data Analysis Methods

[1298] Based on the health data and symptom input received by the server, the data is analyzed using AI to predict possible illnesses, which are also compared with a database of past cases.

[1299] 4. Emotion recognition means

[1300] The server also uses an emotion engine to recognize the user's emotional state, which is inferred from health data and symptom input.

[1301] 5. Medical institution search and reservation methods

[1302] Based on the analysis results and emotion recognition results, the server searches for an appropriate medical institution and makes an appointment. Taking into account surrounding information obtained from sensors inside the autonomous vehicle, the server calculates the optimal route to the medical institution.

[1303] 6. Means of notification

[1304] It notifies users of diagnosis results and medical appointment information, and the content of notifications is customized according to the user's emotional state.

[1305] Specific examples

[1306] 1. Cases where users input their symptoms

[1307] The user types "I have a headache" into their smartphone and sends it.

[1308] Sensors inside the self-driving vehicle capture the user's current location.

[1309] The smartphone collects heart rate (80 bpm), blood pressure (130 / 85), and sleep patterns (6 hours of light sleep) from the wearable device and sends them to a server.

[1310] 2. Data analysis and emotion recognition process

[1311] The server passes the received data to the analysis module, where the AI ​​analyzes it and determines that there is a high possibility of a migraine.

[1312] The emotion engine recognizes the user's anxiety.

[1313] 3. Searching for and booking medical appointments

[1314] The server searches for the nearest neurology department and selects a medical institution that can respond quickly.

[1315] The user is prompted, "Would you like to schedule an appointment with a neurologist?"

[1316] The user confirms the notification and taps "Yes" to confirm the appointment. The message "You have made an appointment with a medical institution that can respond promptly" is displayed.

[1317] 4. Notification Details

[1318] The server sends the appointment to the medical institution and receives the appointment confirmation.

[1319] The server sends the reservation details to the notification module, notifying the user that "Reservation completed. Date and time: XX / XX / XX, time: XX, location: XX Hospital."

[1320] It also displays customized messages based on the user's emotions to reduce anxiety.

[1321] Prompt Sentence Examples

[1322] "I'm thinking about developing a health check and optimal route application for self-driving vehicles. This app will allow drivers and passengers to input their own health status, and will collect health data from sensors in the vehicle and wearable devices worn by the user. Based on the collected data and input information, AI will make a diagnosis and suggest the optimal route to safely guide the user to the nearest medical institution. It will also take into account the user's emotional state and display warnings and calming messages to reassure them."

[1323] In this way, the system of the present invention can grasp the user's health condition in real time and provide prompt and appropriate guidance to medical institutions. Furthermore, it also takes into consideration the user's emotional state, improving their sense of security.

[1324] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1325] Step 1:

[1326] A user opens a smartphone application, enters their symptoms (e.g., "I have a headache"), and presses the send button. This sends the entered symptom data from the device to the server. The input is string data, and the output is sent to the server as an HTTP POST request.

[1327] Step 2:

[1328] The terminal collects health data from a wearable device. Specifically, a smartphone acquires heart rate, blood pressure, and sleep pattern data from the wearable device via Bluetooth or Wi-Fi and sends this data to a server. The input is physiological data from the wearable device, and the output is an HTTP POST request to the server.

[1329] Step 3:

[1330] The server passes the received symptom data and health data to the analysis module. Specifically, the data analysis algorithm uses AI to analyze this data and infer possible medical conditions. The input is text data and physiological data, and the output is an inferred medical condition (e.g., "High probability of migraine").

[1331] Step 4:

[1332] The server uses an emotion engine to recognize the user's emotional state. Based on the medical condition data and health data passed from the analysis module, the emotion engine estimates whether the user is anxious or unstable. The inputs are the analysis results and physiological data, and the output is the emotional state (e.g., "anxiety").

[1333] Step 5:

[1334] The server searches the database for appropriate medical institutions based on the analysis results and emotional state, and selects the most suitable one from the listed medical institutions. Specifically, it takes into account the user's current location information and prioritizes medical institutions that can respond quickly. The inputs are the user's emotional state, analysis results, and current location information, and the output is information about the selected medical institution.

[1335] Step 6:

[1336] The server sends the user a confirmation notification of the medical institution's appointment. Specifically, along with information about the selected medical institution, it sends a confirmation message such as "Do you want to make an appointment with a neurology specialist?" via push notification or email. The input is the information about the selected medical institution, and the output is a notification sent to the user's device.

[1337] Step 7:

[1338] The user confirms the notification on the device and taps "Yes" to confirm the reservation. This action causes the device to send the reservation confirmation data back to the server. The input is the user's selected data, and the output is an HTTP POST request to the server.

[1339] Step 8:

[1340] The server sends the reservation information to the medical institution and receives a reservation confirmation response. Specifically, the reservation information is transmitted to the medical institution using an HTTP request or a dedicated API, and a confirmation response is received from the medical institution. The input is the reservation confirmation data, and the output is the reservation confirmation response from the medical institution.

[1341] Step 9:

[1342] The server sends the reservation details (date, time, location) to the notification module and notifies the user. Specifically, it sends a push notification saying, "Reservation completed. Date and time: XX / XX / XX, XX time, Location: XX Hospital." The input is the reservation confirmation response from the medical institution, and the output is a push notification sent to the user's device.

[1343] Step 10:

[1344] The server customizes the notification content according to the user's emotions and adds a message to give a sense of security. For example, it sends a message such as, "We have made an appointment with a medical institution that can respond quickly. Please rest assured." The input is the user's emotional state, and the output is the customized notification message.

[1345] 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.

[1346] 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.

[1347] 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.

[1348] [Fourth embodiment]

[1349] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1350] 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.

[1351] 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).

[1352] 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.

[1353] 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.

[1354] 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).

[1355] 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. 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.

[1356] 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.

[1357] 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.

[1358] 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.

[1359] 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.

[1360] 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.

[1361] 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."

[1362] This system receives symptom input from the user, collects health data from a wearable device, and uses AI-based data analysis to predict possible illnesses. Furthermore, the system searches for appropriate medical institutions, confirms appointments with the user, and notifies the user of diagnosis results and appointment information.

[1363] User Input

[1364] The system begins by the user entering their symptoms into their smartphone, for example, "I have a headache," and pressing the "send" button to send the symptoms to the system.

[1365] User: Type "I have a headache" and press the "Send" button.

[1366] Data collection

[1367] The wearable device collects the user's daily health data, including heart rate, blood pressure, sleep patterns, etc. The collected data is sent from the device to a server.

[1368] Device: Wearable device collects heart rate, blood pressure, and sleep pattern data via Bluetooth.

[1369] Terminal: Sends collected data to the server via HTTP POST request.

[1370] Data analysis

[1371] The server passes the received data to an analysis module, which uses AI to analyze the data and predict possible medical conditions. For example, the AI ​​analyzes heart rate, blood pressure, and sleep patterns and determines that a person has a high probability of having a migraine.

[1372] Server: Passes the received data to the analysis module.

[1373] Server: Performs AI-based data analysis to identify possible medical conditions.

[1374] Collaboration with medical institutions

[1375] Based on the analysis results, the server searches for an appropriate medical institution. Based on the search results, the server sends a notification to the user requesting them to confirm an appointment with the medical institution. Once the user confirms the appointment, the server sends the appointment to the medical institution.

[1376] Server: Lists relevant medical institutions and selects the most suitable one.

[1377] Server: Prompts user, "Would you like to schedule an appointment with a neurologist?"

[1378] User: Check the notification and tap "Yes" to confirm the reservation.

[1379] Schedule execution and notification

[1380] The server sends the user's appointment to the medical institution and receives the appointment confirmation. Once confirmed, the server sends the appointment details to the notification module and notifies the user.

[1381] Server: Sends appointments to medical institutions and receives appointment confirmations.

[1382] Server: Sends the reservation details (date, time, location) to the notification module.

[1383] Device: Sends a push notification to the user saying, "Reservation completed. Date and time: XX / XX / XX, XX time, Location: XX Hospital."

[1384] Specific examples

[1385] 1. The user types "I have a headache" into their smartphone and sends it.

[1386] 2. The smartphone collects data from the wearable device, including heart rate (80 bpm), blood pressure (130 / 85), and sleep patterns (6 hours of light sleep), and sends it to the server.

[1387] 3. The server passes the received data to the analysis module, where the AI ​​analyzes it. The AI ​​determines that there is a high possibility of a migraine.

[1388] 4. The server searches for the nearest neurologist and notifies the user, "Would you like to make an appointment with a neurologist?"

[1389] 5. The user confirms the notification and taps "Yes" to confirm the reservation.

[1390] 6. The server sends the appointment to the medical institution and receives a confirmation of the appointment.

[1391] 7. The server sends the reservation details (date, time, location) to the notification module and notifies the user.

[1392] 8. A push notification will appear on the user's smartphone saying, "Reservation completed. Date and time: XX / XX / XX XX o'clock, Location: XX Hospital."

[1393] In this way, the present invention allows users to quickly check their health status and seek medical attention at an appropriate medical institution.

[1394] The processing flow will be explained below.

[1395] Step 1:

[1396] The user enters the symptom "headache" into their smartphone and presses the "send" button.

[1397] User: Type "I have a headache" and press the "Send" button.

[1398] Step 2:

[1399] The device collects health data from the wearable device, including heart rate, blood pressure, and sleep patterns, and transmits the collected data to a server.

[1400] Device: Heart rate (80 bpm), blood pressure (130 / 85), and sleep pattern (6 hours of light sleep) data are collected from the wearable device via Bluetooth.

[1401] Device: Health data and user symptom input are sent to the server via an HTTP POST request.

[1402] Step 3:

[1403] The server passes the received data to the analysis module, which then uses AI to analyze the data and estimate possible medical conditions.

[1404] Server: Passes the received data to the analysis module.

[1405] Server: The AI ​​analyzes the health data and symptom input it receives, compares it with past cases, and determines that there is a high possibility of a migraine.

[1406] Step 4:

[1407] The server searches for an appropriate medical institution based on the estimated medical condition, and then sends the user a notification requesting them to confirm an appointment.

[1408] Server: Lists relevant medical institutions such as internal medicine and neurology departments from a database and selects the nearest medical institution.

[1409] Server: Notifies the user of the information about the selected medical institution and sends a message asking, "Would you like to make an appointment with a neurologist?"

[1410] Step 5:

[1411] The user checks the notification on the device and confirms the medical appointment.

[1412] User: Check the notification and tap "Yes" to confirm the reservation.

[1413] Step 6:

[1414] The server sends the user's reservation information to the medical institution, and then receives reservation confirmation from the medical institution.

[1415] Server: Sends appointment information to the selected neurology department (using HTTP requests or a dedicated API).

[1416] Server: Receives the reservation confirmation response from the medical institution.

[1417] Step 7:

[1418] The server sends the reservation details (date, time, location) to the notification module, and finally the device notifies the user of the information.

[1419] Server: Sends reservation details such as "Reservation completed. Date and time: XX / XX / XX, Location: XX Hospital" to the notification module.

[1420] Device: The user will receive a push notification saying, "Reservation completed. Date and time: XX / XX / XX XX time, Location: XX Hospital."

[1421] Example 1

[1422] 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."

[1423] In modern society, there is a demand for systems that allow users to quickly understand their health status and receive treatment at the appropriate medical institution. However, it is extremely difficult for users to find a medical institution that is suitable for their symptoms and smoothly schedule an appointment. There is also a lack of systems that effectively utilize daily health data to realize preventive medicine. There is a need for a system that solves these problems and allows users to efficiently manage their health and receive appropriate medical care.

[1424] 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.

[1425] In this invention, the server includes means for receiving symptom input from the user, means for collecting biometric data from the wearable device, means for analyzing the collected data and identifying an estimated health condition, means for searching for an appropriate medical institution and making an appointment, and means for notifying the user of the diagnosis results and appointment information, thereby enabling the user to quickly check their own health condition and receive a medical examination at an appropriate medical institution.

[1426] "Means for receiving symptom input from the user" refers to a device or interface that acquires information about physical ailments and symptoms entered by the user and transmits it to the system.

[1427] "Means for collecting biometric data from wearable devices" refers to devices or methods that have the ability to obtain physical data such as heart rate, blood pressure, and sleep patterns from equipment worn by the user.

[1428] "Means for analyzing collected data and identifying estimated health status" refers to algorithms or systems that analyze collected biometric data and determine and identify health status from the results.

[1429] "Means for searching for and making appointments with appropriate medical institutions" refers to functions and systems that allow users to select the most appropriate medical institution based on their health condition and symptoms and make appointments for consultations.

[1430] "Means for notifying users of diagnostic results and appointment information" refers to systems or devices with notification functions for informing users of analysis results and appointment information with medical institutions.

[1431] "Means of using AI to analyze data and compare it with a database of past cases to estimate health status" refers to a system that uses artificial intelligence technology to analyze collected data and compare it with past case data to predict health status.

[1432] "Means for collecting data from a wearable device worn by a user via wireless communication" refers to a method or system for collecting data from a wearable device worn by a user using wireless communication technologies such as Bluetooth or Wi-Fi.

[1433] This invention is a system that receives symptom input from a user, collects biometric data from a wearable device, and estimates health status through data analysis using AI. This section describes the specific hardware and software configurations required to realize this system.

[1434] First, users use their smartphones to input their symptoms. A dedicated application is installed on the smartphone, and users enter their symptoms through this application and press the "send" button to send the symptoms to the system.

[1435] Next, wearable devices such as smartwatches and fitness trackers can be used. These devices connect to smartphones via Bluetooth and collect biometric data such as heart rate, blood pressure, sleep patterns, etc. The smartphone terminal acquires this data and sends it to a server using an HTTP POST request.

[1436] The server passes the received data to the analysis module, where an AI model analyzes the data. This AI model is a generative AI model that compares the data with a database of past cases to estimate the patient's health condition. The resulting estimate may be, for example, "high possibility of migraine."

[1437] Based on the analysis results, the server searches for an appropriate medical institution. It lists relevant medical institutions from a database of medical institutions and selects the most suitable one based on the user's location and symptoms. After selecting the institution, the server sends the user a notification asking, "Do you want to make an appointment with a neurologist?" The user confirms the notification and taps "Yes" to confirm the appointment.

[1438] The server sends the reservation details to the medical institution and receives a reservation confirmation. Once confirmation is received, the server passes the reservation details to the notification module and sends a push notification to the user. For example, the user's smartphone will display a message saying, "Reservation completed. Date and time: XX / XX / XX, Location: XX Hospital."

[1439] Specific examples

[1440] 1. User Input

[1441] A user types "I have a headache" into a smartphone app and presses the "send" button.

[1442] 2. Data Collection

[1443] The device collects data from the wearable device, including heart rate (80 bpm), blood pressure (130 / 85), and sleep patterns (6 hours of light sleep), and sends it to a server.

[1444] 3. Data Analysis

[1445] The server passes the received data to the analysis module, where the AI ​​analyzes it and determines that there is a high possibility of a migraine.

[1446] 4. Collaboration with medical institutions

[1447] The server searches for the nearest neurologist and prompts the user, "Would you like to make an appointment with a neurologist?"

[1448] The user sees the notification and taps "Yes" to confirm the booking.

[1449] 5. Reservation execution and notification

[1450] The server sends the appointment to the medical institution and receives the appointment confirmation.

[1451] The server sends the reservation details (date, time, location) to the notification module, which notifies the user.

[1452] A push notification will appear on the user's smartphone saying, "Reservation completed. Date and time: XX / XX / XX XX time, Location: XX Hospital."

[1453] In this way, users can quickly check their health status and seek medical attention at the appropriate medical institution.

[1454] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1455] Step 1: User Input

[1456] Specific explanation: The user enters their symptoms into a smartphone device and sends them to the system.

[1457] Operation and data processing: The user enters "I have a headache" into the text box of the app and presses the "Send" button. This input information is sent from the smartphone to the server via an HTTP POST request.

[1458] Input: Text data of symptoms entered by the user (e.g., "My head hurts")

[1459] Output: Text data of symptoms received by the server

[1460] Step 2: Data collection

[1461] Specific description: Biometric data is collected from a wearable device and sent to a server.

[1462] Operation and data processing: The device connects to the wearable device via Bluetooth and acquires data such as heart rate, blood pressure, and sleep patterns. The acquired data is then sent to the server via an HTTP POST request.

[1463] Input: Biometric data obtained from wearable devices (e.g., heart rate 80 bpm, blood pressure 130 / 85, sleep patterns)

[1464] Output: Biometric data received by the server

[1465] Step 3: Data analysis

[1466] Specifically: A server analyzes health data and identifies a probable health condition.

[1467] Operation and data processing: The server passes the received biometric data to the analysis module, where the AI ​​model analyzes it by comparing it with a database of past cases. The AI ​​analyzes the data and identifies a probable health condition.

[1468] Input: Biometric data and case database stored on the server

[1469] Output: Text data of the estimated health condition (e.g., "High probability of migraine")

[1470] Step 4: Collaboration with medical institutions

[1471] Specific explanation: Based on the analysis results, an appropriate medical institution is searched for and notified to the user.

[1472] Operation and data processing: Based on the data analysis results, the server searches the database for relevant medical institutions. It selects the most suitable medical institution from the search results and sends a notification to the user asking, "Would you like to make an appointment with a neurologist?"

[1473] Input: Data analysis results and medical institution database

[1474] Output: Notification data sent to the user

[1475] Step 5: User response

[1476] Specific Description: The user confirms the notification and confirms the reservation.

[1477] Operation and data processing: The user checks the notification on their smartphone and taps "Yes." This response is sent to the server, and the reservation is confirmed.

[1478] Input: User response data (e.g. "Yes")

[1479] Output: The response data received by the server

[1480] Step 6: Reservation execution and notification

[1481] Specific details: Sends appointment details to the medical institution and notifies the user.

[1482] Operation and data processing: The server sends the user's appointment information to the medical institution and receives the appointment confirmation. Once confirmed, the server passes the appointment details to the notification module and sends a push notification to the user.

[1483] Input: Reservation information and medical institution confirmation data

[1484] Output: Push notification data sent to the user (e.g., "Your reservation has been completed. Date and time: ____ month ____ day ____ time, location: ____ Hospital")

[1485] In this way, the system performs specific actions and processes data at each step, allowing users to efficiently manage their health.

[1486] (Application example 1)

[1487] 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."

[1488] In modern society, it is important for users to be able to quickly check their health status and access appropriate medical institutions. There is a particular need for systems that can respond quickly and accurately when symptoms suddenly appear or when users are unsure which medical institution to choose. Conventional methods require users to search for medical information online and make their own judgments when selecting a medical institution, which is time- and knowledge-intensive. Therefore, there is a need for a system that can automatically analyze the user's condition based on their symptoms and suggest and book an appropriate medical institution.

[1489] 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.

[1490] In this invention, the server includes means for receiving symptom input from the user, means for collecting health data from the wearable device, means for analyzing the collected data and estimating possible medical conditions, means for searching for an appropriate medical institution and making a reservation, means for notifying the user of the diagnosis results and reservation information, means for analyzing the medical condition using AI based on the symptoms entered by the user and the health data collected from the wearable device, and means for sending a notification confirming a reservation at an appropriate medical institution based on the analysis results, thereby enabling the user to receive prompt and appropriate medical treatment for their symptoms.

[1491] "Symptom input" refers to users inputting their own physical condition and symptoms into a digital device.

[1492] "Health Data" refers to information about a user's health, such as heart rate, blood pressure, and sleep patterns, obtained from wearable devices or other sensors.

[1493] "Analysis" refers to the process of using AI and databases to identify possible medical conditions based on collected health data and symptom input.

[1494] "Disease prediction" refers to the prediction of possible diseases or health problems obtained as a result of the analysis.

[1495] "Medical institution search" refers to searching for a medical facility that can provide appropriate examination and treatment based on an estimated medical condition.

[1496] "Reservation" refers to the procedure by which a user reserves a date and time for an appointment at a medical institution.

[1497] "Notification of diagnosis results" refers to informing the user of the estimated condition and reservation information obtained through analysis.

[1498] "Analyzing data using AI" refers to using artificial intelligence technology to analyze collected health data and symptom inputs to predict or identify medical conditions.

[1499] A "case database" is a database that compiles previously recorded case data and is a collection of information used to collate new symptoms and health data.

[1500] A "wearable device" is an electronic device that can be worn by a user and has the ability to collect health data such as heart rate, blood pressure, and sleep patterns.

[1501] "Bluetooth" is a short-range wireless communication technology and refers to a standard used to wirelessly exchange data between electronic devices.

[1502] "Wi-Fi" is one of the wireless LAN standards and refers to the technology for connecting to the Internet and exchanging data wirelessly.

[1503] The system for implementing this invention quickly evaluates the user's health condition and automatically schedules an appointment with an appropriate medical institution. Below, we will explain in detail how each means works and how the overall flow proceeds.

[1504] Hardware and Software Configuration

[1505] Hardware

[1506] Smartphone: A device for user input and health data collection.

[1507] Wearable devices: Devices used to collect health data such as heart rate, blood pressure, and sleep patterns (e.g., Apple Watch, Fitbit).

[1508] Server: A central device that handles analytics and database management.

[1509] software

[1510] AI analysis module: A module that analyzes health data and predicts disease conditions using machine learning libraries such as TensorFlow and PyTorch.

[1511] Data collection application: An application for transferring health data from a wearable device to a smartphone and sending it to a server.

[1512] Medical institution search and reservation system: A system that uses medical APIs to search for appropriate medical institutions and make reservations.

[1513] Notification system: A system that uses push notifications to send users diagnostic results and appointment information.

[1514] Explanation of each method

[1515] Symptom input method

[1516] Users enter their symptoms in text form via their smartphone, and this information is used for subsequent analysis. For example, they might enter a symptom such as "I have a headache."

[1517] Health data collection methods

[1518] The smartphone uses Bluetooth or Wi-Fi to collect health data such as heart rate, blood pressure, and sleep patterns from the wearable device, which is then sent to a server for analysis.

[1519] Analysis means

[1520] The server uses AI to analyze the symptom input and health data it receives. The AI ​​analysis module uses TensorFlow and PyTorch to compare the data with a database of past cases and estimate the condition. For example, it analyzes data such as heart rate (80 bpm), blood pressure (130 / 85), and sleep patterns (six hours of light sleep) to determine a "high possibility of migraine."

[1521] Medical institution search and reservation method

[1522] Based on the analysis results, the server searches for an appropriate medical institution. Using a medical API, it lists the nearest medical institutions and sends the user a reservation confirmation notification for the appropriate medical institution. The user confirms the notification and selects "Yes" to confirm the reservation.

[1523] Diagnostic result notification method

[1524] Once the reservation is confirmed, the server sends the reservation details (date, time, location) to the user via push notification, and the user can check the reservation details on their smartphone.

[1525] Specific examples

[1526] 1. The user enters the symptom "headache" into their smartphone.

[1527] 2. The smartphone collects heart rate (80 bpm), blood pressure (130 / 85), and sleep pattern (6 hours of light sleep) from the wearable device and sends them to the server.

[1528] 3. The server analyzes the data received using an AI analysis module (TensorFlow, PyTorch) and determines that there is a high possibility of a migraine.

[1529] 4. Search for the nearest neurologist through a healthcare API and send a push notification to the user asking, "Would you like to make an appointment with a neurologist?"

[1530] 5. The user confirms the notification and selects "Yes." The server sends the appointment to the medical institution and receives a confirmation of the appointment.

[1531] 6. Reservation details (date, time, location) will be pushed to the user's smartphone.

[1532] Prompt Sentence Examples

[1533] When a user inputs the symptom "headache," analyze the heart rate (80 bpm), blood pressure (130 / 85), and sleep pattern (6 hours of light sleep) collected from the wearable device to deduce possible medical conditions, find the most appropriate medical institution, and ask the user to confirm an appointment.

[1534] This invention allows users to quickly and appropriately assess their health status and receive the necessary medical services.

[1535] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1536] Step 1:

[1537] The user launches the smartphone application and inputs their symptoms (e.g., "I have a headache"). The input symptom data is stored within the application and used in the next data collection step. Input is in text format and can be completed intuitively by the user.

[1538] Step 2:

[1539] The device (smartphone) collects health data from the user's wearable device (e.g., heart rate, blood pressure, sleep patterns) via Bluetooth or Wi-Fi. The collected data is stored within the application and then sent to a server. The input is sensor data, and the output is the collected biometric data.

[1540] Step 3:

[1541] The device organizes the collected health data and sends it to the server along with the user's symptom data via an HTTP POST request. The data is sent to the server in JSON format and prepared for processing by the analysis module. The input is the organized health data and symptom data pair, and the output is the data sent to the server.

[1542] Step 4:

[1543] The server passes the received data to the AI ​​analysis module, which uses TensorFlow or PyTorch to analyze the data and compare it with a database of past cases to estimate the condition. The AI ​​model then predicts the condition based on the generative AI model. The input is the user's health data and symptom data, and the output is a predicted condition.

[1544] Step 5:

[1545] The server uses a medical API to search for an appropriate medical institution based on the prediction results obtained from the AI ​​analysis module. Based on the analysis results, the most suitable medical institutions are listed, and from there the most suitable medical institution is selected based on criteria such as location, rating, and available hours. The input is the predicted condition result, and the output is information about the most suitable medical institution.

[1546] Step 6:

[1547] The server sends the user a reservation confirmation notification based on the information of the selected medical institution. This notification is sent to the user's smartphone as a push notification. The user can check the notification and choose whether to confirm the reservation. The input is medical institution information, and the output is a notification to the user.

[1548] Step 7:

[1549] If the user checks the notification and selects "Yes," the server sends the reservation to the medical institution and receives a reservation confirmation. This confirms the reservation. The input is the user's reservation confirmation, and the output is a reservation completion notification.

[1550] Step 8:

[1551] The server sends a push notification to the user's smartphone with appointment details (date, time, location). The user receives the notification and can check the appointment schedule. The input is appointment confirmation information from the medical institution, and the output is a notification of appointment details to the user.

[1552] Prompt Sentence Examples

[1553] When a user inputs the symptom "headache," analyze the heart rate (80 bpm), blood pressure (130 / 85), and sleep pattern (6 hours of light sleep) collected from the wearable device to deduce possible medical conditions, find the most appropriate medical institution, and ask the user to confirm an appointment.

[1554] 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.

[1555] This system receives symptom input from the user, collects health data from a wearable device, and uses AI-based data analysis to predict possible illnesses. It also uses an emotion engine to recognize the user's emotions and customizes the selection of medical institutions and notification content accordingly. The system searches for appropriate medical institutions, confirms appointments with the user, and notifies the user of diagnosis results and reservation information.

[1556] User Input

[1557] The system begins by the user entering their symptoms into their smartphone, for example, "I have a headache," and pressing the "send" button to send the symptoms to the system.

[1558] User: Type "I have a headache" and press the "Send" button.

[1559] Data collection

[1560] The wearable device collects the user's daily health data, including heart rate, blood pressure, sleep patterns, etc. The collected data is sent from the device to a server.

[1561] Device: Obtains heart rate, blood pressure, and sleep pattern data from a wearable device via Bluetooth.

[1562] Device: Health data and user symptom input are sent to the server via an HTTP POST request.

[1563] Data analysis and emotion recognition

[1564] The server passes the received data to the analysis module, which uses AI to analyze the data and estimate possible medical conditions. The emotion engine then uses the user's input symptoms and collected health data to recognize the user's emotions. For example, if the analysis module determines that there is a high possibility of a migraine, the emotion engine recognizes that the user is in an anxious state.

[1565] Server: Passes the received data to the analysis module.

[1566] Server: The AI ​​analyzes the health data and symptom input it receives, compares it with past cases, and determines that there is a high possibility of a migraine.

[1567] Server: The emotion engine analyzes the data and recognizes that the user is in an "anxious state."

[1568] Collaboration and optimization with medical institutions

[1569] Based on the analysis results and the emotion engine's recognition results, the server searches for an appropriate medical institution. Based on the search results, the server sends a notification to the user requesting them to confirm their appointment. For example, if the emotion engine detects the user's anxiety, it will prioritize medical institutions that can respond quickly.

[1570] Server: Lists relevant medical institutions from a database and selects the most suitable one.

[1571] Server: Sends a message to the user asking "Would you like to make an appointment with a neurologist?"

[1572] Server: When a user is in an "anxious state," the server prioritizes the selection of a medical institution that can respond quickly.

[1573] User reservation confirmation

[1574] The user checks the notification on their device and confirms the appointment with the medical institution. For example, the notification content is customized according to the user's emotion, and displays, "You have made an appointment with a medical institution that can respond quickly."

[1575] User: Check the notification and tap "Yes" to confirm the reservation.

[1576] User: Receives a customized notification that "Your appointment has been scheduled with a fast-acting provider."

[1577] Schedule execution and notification

[1578] The server sends the user's appointment information to the medical institution, then receives the appointment confirmation from the medical institution, and once the confirmation is received, the server sends the appointment details to the notification module and notifies the user.

[1579] Server: Sends appointment information to the selected neurology department (using HTTP requests or a dedicated API).

[1580] Server: Receives the reservation confirmation response from the medical institution.

[1581] Server: Sends the reservation details (date, time, location) to the notification module.

[1582] Device: The user will receive a push notification saying, "Reservation completed. Date and time: XX / XX / XX XX time, Location: XX Hospital."

[1583] Device: Provides customized lighting and messages based on the user's emotions.

[1584] Specific examples

[1585] 1. The user types "I have a headache" into their smartphone and sends it.

[1586] 2. The smartphone collects data from the wearable device, including heart rate (80 bpm), blood pressure (130 / 85), and sleep patterns (6 hours of light sleep), and sends it to the server.

[1587] 3. The server passes the received data to the analysis module, where the AI ​​analyzes it. The AI ​​determines that there is a high possibility of a migraine, and the emotion engine recognizes the user's anxiety.

[1588] 4. The server searches for the nearest neurology clinic and selects a medical institution that can respond quickly based on the recognition results of the emotion engine. The server then notifies the user, "Would you like to make an appointment with a neurology clinic?"

[1589] 5. The user confirms the notification and taps "Yes" to confirm the appointment. The notification then displays, "You have made an appointment with a medical institution that can respond promptly."

[1590] 6. The server sends the appointment to the medical institution and receives a confirmation of the appointment.

[1591] 7. The server sends the reservation details to the notification module, and the user is notified via push notification that "Reservation completed. Date and time: XX / XX / XX, Location: XX Hospital."

[1592] 8. The user's smartphone will display customized details and messages to alleviate anxiety.

[1593] In this way, the present invention allows users to quickly check their health status and seek medical advice from the most appropriate medical institution, taking into account their emotional state.

[1594] The processing flow will be explained below.

[1595] Step 1:

[1596] The user enters the symptom "headache" into their smartphone and presses the "send" button.

[1597] User: Type "I have a headache" and press the "Send" button.

[1598] Step 2:

[1599] The device collects health data from the wearable device, including heart rate, blood pressure, and sleep patterns, and transmits the collected data to a server.

[1600] Device: Heart rate (80 bpm), blood pressure (130 / 85), and sleep pattern (6 hours of light sleep) data are collected from the wearable device via Bluetooth.

[1601] Device: Health data and user symptom input are sent to the server via an HTTP POST request.

[1602] Step 3:

[1603] The server passes the received data to an analysis module, which uses AI to analyze the data and estimate possible medical conditions.

[1604] Server: Passes the received data to the analysis module.

[1605] Server: The AI ​​analyzes the health data and symptom input it receives, compares it with past cases, and determines that there is a high possibility of a migraine.

[1606] Step 4:

[1607] The emotion engine recognizes the user's emotional state based on the user's symptoms and collected health data. For example, it can recognize that the user is feeling anxious based on their symptoms and health data.

[1608] Server: The emotion engine analyzes the data and determines that the user is in an "anxious state."

[1609] Step 5:

[1610] Based on the analysis results and the emotion engine's recognition results, the server searches for an appropriate medical institution. For example, if the user is feeling anxious, it will prioritize medical institutions that can respond quickly.

[1611] Server: Lists relevant medical institutions from a database and selects the most suitable one.

[1612] Server: Based on the information of the selected medical institution, the server sends the user a message asking, "Would you like to make an appointment with a neurologist?"

[1613] Server: If a user is in an "anxious state," prioritize medical institutions that can respond quickly.

[1614] Step 6:

[1615] The user checks the notification on their device and confirms the appointment with the medical institution. For example, the notification content is customized according to the user's emotion, and displays, "You have made an appointment with a medical institution that can respond quickly."

[1616] User: Check the notification and tap "Yes" to confirm the reservation.

[1617] On the device: Display a customized notification to the user saying, "Your appointment has been made with a fast-acting provider."

[1618] Step 7:

[1619] The server sends the user's reservation information to the medical institution, and then receives reservation confirmation from the medical institution.

[1620] Server: Sends appointment information to the selected neurology department (using HTTP requests or a dedicated API).

[1621] Server: Receives the reservation confirmation response from the medical institution.

[1622] Step 8:

[1623] The server sends the reservation details (date, time, location) to the notification module. Finally, the device notifies the user of this information. For example, the user may receive a push notification saying, "Reservation completed. Date and time: XX / XX / XX, location: XX Hospital." Additionally, a message to alleviate anxiety may also be displayed.

[1624] Server: Sends reservation details such as "Reservation completed. Date and time: XX / XX / XX, Location: XX Hospital" to the notification module.

[1625] Device: The user will receive a push notification saying, "Reservation completed. Date and time: XX / XX / XX XX time, Location: XX Hospital."

[1626] Device: Depending on the user's emotions, customized messages such as "Don't worry, we'll respond quickly" are also displayed.

[1627] Example 2

[1628] 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."

[1629] In modern society, users need to quickly and accurately understand their health status and seek medical treatment at the appropriate medical institution. However, current systems select medical institutions without taking into account the user's emotional state, which can increase anxiety and stress. In addition, it is difficult for users to manually collect and analyze health data from wearable devices, making it difficult to complete the process of seeking medical treatment immediately.

[1630] 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 means for receiving symptom input from the user, means for collecting health data from the wearable device, means for analyzing the collected data and estimating possible medical conditions, means for recognizing the user's emotional state using an emotion engine, means for searching for and making an appointment with an appropriate medical institution based on the recognition result, and means for notifying the user of the diagnosis result and appointment information. This allows the user to quickly check their own health condition and receive a medical examination at the most appropriate medical institution taking their emotional state into consideration.

[1631] "User" refers to an individual who uses the System to manage their own health status and input symptoms.

[1632] "Symptom input" refers to the act of a user sending their symptoms of poor health or illness to the system using a device such as a smartphone or computer.

[1633] "Wearable devices" refer to devices worn by users on a daily basis that measure and record physical condition data, and specifically include smart watches, heart rate monitors, blood pressure monitors, etc.

[1634] "Health data" refers to data about a user's physical condition collected by wearable devices, including, for example, heart rate, blood pressure, and sleep patterns.

[1635] "Analysis Module" refers to software or algorithms used to analyze collected data and infer possible pathologies.

[1636] An "emotion engine" refers to software or algorithms that analyze user-entered symptoms and collected health data to recognize a user's emotional state.

[1637] "Medical institution" refers to a facility such as a hospital or clinic where users can receive medical examinations or treatment.

[1638] "Appointment confirmation" refers to the act of notifying a user that an appointment with a medical institution has been confirmed.

[1639] "Push notification" refers to a technology that instantly sends information to a device and notifies the user.

[1640] "Diagnosis result" refers to information about the medical condition estimated as a result of analysis performed by AI.

[1641] This system receives symptom input from the user, collects health data from a wearable device, and uses AI to analyze the data to predict the patient's condition. It also uses an emotion engine to recognize the user's emotions, and customizes the selection of medical institutions and notification content accordingly. The system searches for appropriate medical institutions, confirms appointments with the user, and notifies the user of diagnosis results and reservation information.

[1642] Users begin using the system by entering their symptoms into their smartphone. For example, they enter a specific symptom such as "headache," and then press the "send" button to send the information to the server.

[1643] The device collects health data from wearable devices using Bluetooth or Wi-Fi communication, including heart rate, blood pressure, and sleep patterns, and sends this data to the server via HTTP POST requests.

[1644] The server passes the received health data and symptom information to the analysis module. The analysis module uses AI to analyze the data and compare it with a database of past cases to estimate possible medical conditions. For example, it may diagnose "high probability of migraine." Furthermore, an emotion engine uses this data to recognize the user's emotional state (e.g., anxiety).

[1645] Based on the analysis results and the user's emotional state, the server searches for an appropriate medical institution. Taking into account information such as the user's location and the medical institution's response speed, it prioritizes medical institutions that can respond quickly. As a result, the server sends the user a confirmation message such as "Would you like to make an appointment with a neurologist?"

[1646] Once the user confirms the notification and confirms the appointment, the server sends the appointment information to the medical institution and receives a reservation confirmation response. If the appointment is confirmed, the server sends the appointment details (date, time, location, etc.) to the notification module and sends a push notification to the device stating, "Appointment completed. Date and time: XX / XX / XX, XX time, Location: XX Hospital." A customized message generated by the emotion engine is also displayed on the device, providing information to reduce anxiety.

[1647] For example, when a user types "I have a headache" into their smartphone and sends it, the smartphone sends health data collected from the wearable device, such as heart rate (80 bpm), blood pressure (130 / 85), and sleep pattern (6 hours of light sleep), to a server. The server analyzes this data and determines that there is a high possibility of a migraine, and the emotion engine recognizes the user's anxiety. The server searches for the nearest neurologist, selects a medical institution that can respond quickly, and notifies the user. When the user confirms an appointment, the server sends the appointment to the medical institution and notifies them of detailed information once the appointment is confirmed.

[1648] An example of a prompt for a generative AI model is, "Please tell me the steps to design a system that analyzes a user's symptoms and health data collected from a wearable device and selects the most appropriate medical institution." Such prompts can be used to extract information needed for system design.

[1649] In this way, the present invention allows users to quickly check their health status and seek appropriate medical attention, taking into account their emotional state.

[1650] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1651] Step 1:

[1652] Users enter their symptoms into a smartphone application.

[1653] User: Type "I have a headache" and press the "Send" button.

[1654] Specific action: Enter the words "I have a headache" into the text input field on your smartphone and tap the send button.

[1655] Input: User's symptoms (e.g. headache)

[1656] Output: Symptom data is sent to the server.

[1657] Step 2:

[1658] The terminal collects health data from wearable devices.

[1659] Device: Using Bluetooth or Wi-Fi communication, data on heart rate, blood pressure, and sleep patterns is collected from wearable devices.

[1660] Specific operation: By pressing the "Get health data" button, communication with the wearable device begins and data is collected.

[1661] Input: User's wearable device

[1662] Output: Health data (heart rate, blood pressure, sleep patterns) is sent to the device.

[1663] Step 3:

[1664] The device sends the collected health and symptom data to a server.

[1665] Device: Sends health data and symptom input to the server via an HTTP POST request.

[1666] Specific operation: The collected health and symptom data is converted into JSON format and sent to the specified endpoint on the server via an HTTP POST request.

[1667] Input: Health data, symptom data

[1668] Output: The server receives the health data and symptom data.

[1669] Step 4:

[1670] The server passes the received data to the analysis module.

[1671] Server: Passes health data and symptom data to the analysis module and prepares it for analysis.

[1672] Specific operation: Convert the received data into an appropriate format and input it into the analysis module.

[1673] Input: Received data (health data, symptom data)

[1674] Output: Data ready for analysis

[1675] Step 5:

[1676] The server uses an analysis module to analyze the data and estimate the disease state.

[1677] Server: Uses AI to analyze health data and symptom input, and compares it with a database of past cases to estimate the condition.

[1678] Specific operation: Pass data to the AI ​​model, perform pattern matching and collation, and obtain inference results.

[1679] Input: Data ready for analysis

[1680] Output: Estimated medical condition (e.g., high probability of migraine)

[1681] Step 6:

[1682] The server uses an emotion engine to recognize the user's emotional state.

[1683] Server: Passes the analysis results and symptom data to the emotion engine to recognize the user's emotional state.

[1684] Specific behavior: The emotion engine analyzes the data and classifies the emotional state.

[1685] Input: Analysis results, symptom data

[1686] Output: User's emotional state (e.g., anxious state)

[1687] Step 7:

[1688] The server searches for an appropriate medical institution taking into account the user's emotional state.

[1689] Server: Searches a medical institution database based on emotional state and location information to select the most suitable medical institution.

[1690] What it does: Scores and prioritizes medical facility listings based on user sentiment and location.

[1691] Input: Emotional state, location information

[1692] Output: Information on the best medical institution

[1693] Step 8:

[1694] The server sends the user a medical appointment confirmation notification.

[1695] Server: Sends a message to the user asking "Would you like to schedule an appointment with a neurologist?"

[1696] On the device: A push notification will be sent to the user to confirm.

[1697] Specific operation: A confirmation message is sent to the user's device via the notification API, allowing the user to confirm.

[1698] Input: Information on the most suitable medical institution

[1699] Output: A confirmation message is displayed on the user's terminal.

[1700] Step 9:

[1701] The user confirms the notification and confirms the reservation.

[1702] User: Check the notification and tap "Yes" to confirm the reservation.

[1703] Specific actions: Check the push notification and tap the displayed button.

[1704] Input:Confirmation message

[1705] Output: Reservation confirmation response

[1706] Step 10:

[1707] The server sends the appointment information to the medical facility and receives confirmation.

[1708] Server: Sends reservation information to the selected medical institution and receives a reservation confirmation response.

[1709] Specific operation: Access the medical institution's dedicated API, send the reservation information, and then receive a reservation confirmation response.

[1710] Input: Reservation confirmation response

[1711] Output: Appointment confirmation response from the medical institution

[1712] Step 11:

[1713] The server sends the reservation details to the notification module, which notifies the user.

[1714] Server: Sends the reservation details (date, time, location) to the notification module.

[1715] Device: A push notification will be sent stating, "Reservation completed. Date and time: XX / XX / XX XX time, Location: XX Hospital."

[1716] Specific operation: Send the reservation details to the user terminal via the notification module.

[1717] Input: Appointment confirmation response from medical institution

[1718] Output: Notification of reservation details to user

[1719] (Application example 2)

[1720] 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."

[1721] The challenge in autonomous vehicles is to quickly and accurately check the health status of drivers and passengers and provide optimal route guidance to medical institutions that users need. There is also a need for a means to take into account the user's emotional state and provide appropriate advice to reduce stress. Current systems have difficulty making appointments with medical institutions that correspond to individual health conditions or providing prompt diagnoses, making it difficult to ensure users' peace of mind.

[1722] 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.

[1723] In this invention, the server includes: means for receiving symptom input from the user; means for collecting health data from the wearable device; means for analyzing the collected data and estimating possible medical conditions; means for searching for an appropriate medical institution and making an appointment; means for notifying the user of diagnosis results and appointment information; means for collecting surrounding information from sensors inside the autonomous vehicle; means for calculating the optimal route to the medical institution based on the collected health data and location information; and means for recognizing the user's emotional state and customizing the notification content. This allows the user's health condition to be grasped in real time even inside the autonomous vehicle, and the user to be quickly guided to the most appropriate medical institution. Furthermore, appropriate messages based on the user's emotions can be provided to provide a sense of security.

[1724] "Means for receiving symptom input from the user" refers to an interface that allows the user to input symptoms related to their physical condition or health via a smartphone or other device.

[1725] "Means for collecting health data from wearable devices" refers to technology that collects physiological data such as heart rate, blood pressure, and sleep patterns from health management devices worn by users (e.g., smartwatches and fitness trackers).

[1726] "Means for analyzing collected data and inferring possible medical conditions" refers to algorithms and software that use AI to analyze collected physiological data and symptom input from the user, and infer possible medical conditions the user may be suffering from.

[1727] "Means for searching for an appropriate medical institution and making an appointment" refers to the process of selecting the most appropriate medical institution from a database based on the user's current location and estimated medical condition, and making an appointment for an examination.

[1728] "Means of notifying users of diagnosis results and reservation information" refers to methods of transmitting analyzed diagnosis results and information about the medical institution for which an appointment has been made to the user's device via push notification, email, SMS, etc.

[1729] "Means for collecting surrounding information from sensors within an autonomous vehicle" refers to technology that acquires information about the surrounding environment using sensors such as cameras, lidars, and radars installed in autonomous vehicles.

[1730] The "means for calculating the optimal route to a medical institution based on collected health data and location information" is an algorithm that uses collected health data and current location information to calculate the most efficient and safe route to the desired medical institution.

[1731] "Means for recognizing the user's emotional state and customizing notification content" refers to technology that infers the user's emotional state based on their input and physiological data, and then appropriately adjusts the notification content based on the results, providing the user with a sense of security.

[1732] The present invention provides a system that allows a user to check their health condition while in an autonomous vehicle and quickly access a medical institution. Specific embodiments for carrying out the present invention will be described below.

[1733] System Overview

[1734] It uses a smartphone application that allows users to input and check their health status inside the autonomous vehicle, as well as sensors and wearable devices installed in the autonomous vehicle. The system consists of the following main components:

[1735] 1. Symptom input method

[1736] Users enter their symptoms through a smartphone application, for example, "I have a headache," and press the "send" button to send the symptoms to the system.

[1737] 2. Health data collection methods

[1738] Health data, including heart rate, blood pressure, and sleep patterns, is collected from sensors and wearable devices inside the autonomous vehicle and transmitted to a smartphone via Bluetooth or Wi-Fi.

[1739] 3. Data Analysis Methods

[1740] Based on the health data and symptom input received by the server, the data is analyzed using AI to predict possible illnesses, which are also compared with a database of past cases.

[1741] 4. Emotion recognition means

[1742] The server also uses an emotion engine to recognize the user's emotional state, which is inferred from health data and symptom input.

[1743] 5. Medical institution search and reservation methods

[1744] Based on the analysis results and emotion recognition results, the server searches for an appropriate medical institution and makes an appointment. Taking into account surrounding information obtained from sensors inside the autonomous vehicle, the server calculates the optimal route to the medical institution.

[1745] 6. Means of notification

[1746] It notifies users of diagnosis results and medical appointment information, and the content of notifications is customized according to the user's emotional state.

[1747] Specific examples

[1748] 1. Cases where users input their symptoms

[1749] The user types "I have a headache" into their smartphone and sends it.

[1750] Sensors inside the self-driving vehicle capture the user's current location.

[1751] The smartphone collects heart rate (80 bpm), blood pressure (130 / 85), and sleep patterns (6 hours of light sleep) from the wearable device and sends them to a server.

[1752] 2. Data analysis and emotion recognition process

[1753] The server passes the received data to the analysis module, where the AI ​​analyzes it and determines that there is a high possibility of a migraine.

[1754] The emotion engine recognizes the user's anxiety.

[1755] 3. Searching for and booking medical appointments

[1756] The server searches for the nearest neurology department and selects a medical institution that can respond quickly.

[1757] The user is prompted, "Would you like to schedule an appointment with a neurologist?"

[1758] The user confirms the notification and taps "Yes" to confirm the appointment. The message "You have made an appointment with a medical institution that can respond promptly" is displayed.

[1759] 4. Notification Details

[1760] The server sends the appointment to the medical institution and receives the appointment confirmation.

[1761] The server sends the reservation details to the notification module, notifying the user that "Reservation completed. Date and time: XX / XX / XX, time: XX, location: XX Hospital."

[1762] It also displays customized messages based on the user's emotions to reduce anxiety.

[1763] Prompt Sentence Examples

[1764] "I'm thinking about developing a health check and optimal route application for self-driving vehicles. This app will allow drivers and passengers to input their own health status, and will collect health data from sensors in the vehicle and wearable devices worn by the user. Based on the collected data and input information, AI will make a diagnosis and suggest the optimal route to safely guide the user to the nearest medical institution. It will also take into account the user's emotional state and display warnings and calming messages to reassure them."

[1765] In this way, the system of the present invention can grasp the user's health condition in real time and provide prompt and appropriate guidance to medical institutions. Furthermore, it also takes into consideration the user's emotional state, improving their sense of security.

[1766] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1767] Step 1:

[1768] A user opens a smartphone application, enters their symptoms (e.g., "I have a headache"), and presses the send button. This sends the entered symptom data from the device to the server. The input is string data, and the output is sent to the server as an HTTP POST request.

[1769] Step 2:

[1770] The terminal collects health data from a wearable device. Specifically, a smartphone acquires heart rate, blood pressure, and sleep pattern data from the wearable device via Bluetooth or Wi-Fi and sends this data to a server. The input is physiological data from the wearable device, and the output is an HTTP POST request to the server.

[1771] Step 3:

[1772] The server passes the received symptom data and health data to the analysis module. Specifically, the data analysis algorithm uses AI to analyze this data and infer possible medical conditions. The input is text data and physiological data, and the output is an inferred medical condition (e.g., "High probability of migraine").

[1773] Step 4:

[1774] The server uses an emotion engine to recognize the user's emotional state. Based on the medical condition data and health data passed from the analysis module, the emotion engine estimates whether the user is anxious or unstable. The inputs are the analysis results and physiological data, and the output is the emotional state (e.g., "anxiety").

[1775] Step 5:

[1776] The server searches the database for appropriate medical institutions based on the analysis results and emotional state, and selects the most suitable one from the listed medical institutions. Specifically, it takes into account the user's current location information and prioritizes medical institutions that can respond quickly. The inputs are the user's emotional state, analysis results, and current location information, and the output is information about the selected medical institution.

[1777] Step 6:

[1778] The server sends the user a confirmation notification of the medical institution's appointment. Specifically, along with information about the selected medical institution, it sends a confirmation message such as "Do you want to make an appointment with a neurology specialist?" via push notification or email. The input is the information about the selected medical institution, and the output is a notification sent to the user's device.

[1779] Step 7:

[1780] The user confirms the notification on the device and taps "Yes" to confirm the reservation. This action causes the device to send the reservation confirmation data back to the server. The input is the user's selected data, and the output is an HTTP POST request to the server.

[1781] Step 8:

[1782] The server sends the reservation information to the medical institution and receives a reservation confirmation response. Specifically, the reservation information is transmitted to the medical institution using an HTTP request or a dedicated API, and a confirmation response is received from the medical institution. The input is the reservation confirmation data, and the output is the reservation confirmation response from the medical institution.

[1783] Step 9:

[1784] The server sends the reservation details (date, time, location) to the notification module and notifies the user. Specifically, it sends a push notification saying, "Reservation completed. Date and time: XX / XX / XX, XX time, Location: XX Hospital." The input is the reservation confirmation response from the medical institution, and the output is a push notification sent to the user's device.

[1785] Step 10:

[1786] The server customizes the notification content according to the user's emotions and adds a message to give a sense of security. For example, it sends a message such as, "We have made an appointment with a medical institution that can respond quickly. Please rest assured." The input is the user's emotional state, and the output is the customized notification message.

[1787] 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.

[1788] 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.

[1789] 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 robot 414.

[1790] 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.

[1791] FIG. 9 illustrates 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 behaviors 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.

[1792] 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.

[1793] 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).

[1794] 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.

[1795] 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."

[1796] 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...

Claims

1. a means for receiving symptom input from a user; a means for collecting health data from a wearable device; A means of analyzing the collected data and estimating possible pathologies; A means to search for appropriate medical institutions and make appointments; a means for notifying the user of the diagnostic results and reservation information; A system including:

2. The system of claim 1 further comprising means for analyzing the data using AI and comparing it with a database of past cases to estimate the disease state.

3. The system of claim 1 , further comprising means for collecting data from a wearable device worn by a user via Bluetooth or Wi-Fi.

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