system

Abstract

Provide a system. 【Solution means】Means for generating an appropriate answer to a health-related question from a user using a generation AI model, Means for receiving and analyzing measurement data from medical devices, Means for sending a notification to a user or medical staff based on the analysis result, Means for collecting health information from a user and providing it to medical staff, Means for sending and displaying an instruction from a medical staff to a user, Means for generating a meal recipe based on a user's request, Means for relaying a medical consultation between a user and a doctor through a video call, Means for recording a medical consultation result and an instruction and sending it to a user A system including.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] The problem to be solved by the present invention is to provide appropriate and immediate medical support in areas where it is difficult to access medical staff or hospitals and in an aging society. It is necessary to provide an easy-to-use system for the elderly, residents in depopulated areas, and users who are not used to operating personal computers or smartphones, and to effectively carry out health management and medical support. In addition, in view of the increasing needs for telemedicine and home medical care, providing appropriate technical means is also an issue to be solved by the present invention.

Means for Solving the Problems

[0005] The present invention solves the above-mentioned problems with a system that includes means for generating appropriate answers to health-related questions from users using a generative AI model, means for receiving and analyzing measurement data from medical devices, means for sending notifications to users or medical staff based on the analysis results, means for collecting health information from users and providing it to medical staff, means for sending and displaying instructions from medical staff to the user, means for generating meal recipes based on user requests, means for relaying medical consultations between users and doctors via video calls, and means for recording and sending medical results and instructions to the user.

[0006] A "generative AI model" is an artificial intelligence technology that analyzes user input data and generates appropriate responses or information.

[0007] A "user" refers to an individual who uses the system, primarily those who receive health consultations and medical support.

[0008] A "medical device" is a device that measures health data and provides that data to a system.

[0009] "Measurement data" refers to numerical health information obtained by medical devices.

[0010] "Analysis results" refer to information obtained by generating AI models or systems by analyzing measurement data.

[0011] "Medical staff" refers to professionals such as doctors and nurses who provide medical support and instructions to users.

[0012] A "notification" is information or a warning sent from the system to the user or medical staff.

[0013] "Health information" refers to detailed data about a user's health status and physical condition that they provide to the system.

[0014] "Instruction" refers to advice or commands regarding treatments and actions that medical staff give to users.

[0015] "Diet recipe" refers to the method of cooking and list of ingredients for dishes created by an AI model generated based on the user's health condition and requests.

[0016] "Video call" is a communication method that uses the Internet to exchange video and audio in real time.

[0017] "Diagnosis result" refers to the diagnosis and treatment plan given by a doctor through video call consultations.

[0018] "Recording" is the act of storing important information such as diagnosis results and medical instructions in a database.

Brief Explanation of Drawings

[0019] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9]Shows an emotion map to which a plurality of emotions are mapped. [Figure 10] Shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0020] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0021] First, the terms used in the following description will be explained.

[0022] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), etc.

[0023] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0024] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0025] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0026] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0027] [First Embodiment]

[0028] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0029] As shown in Figure 1, the 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.

[0030] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0031] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0032] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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.

[0033] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0034] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0035] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0037] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0038] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0039] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0040] This invention is a last-mile medical support system that utilizes a generative AI model. This system functions through the coordinated efforts of the user, terminal, and server. The following details each component and its role.

[0041] Providing a health chatbot

[0042] Server: Uses a generative AI model to generate appropriate answers to health-related questions from users. The server analyzes the content of health consultations submitted by users and generates answers on the spot based on relevant medical knowledge.

[0043] Device: This device sends health-related question data entered by the user to the server and displays the server's responses to the user. Smartphones and PCs fall into this category.

[0044] User: Enters health-related questions from their device and receives answers from the server.

[0045] Specific example: If a user enters "I haven't been able to sleep lately," the server generates a response such as "You can try relaxing before bed and avoiding caffeine. If it persists, please consult a doctor," and displays it to the user via their device.

[0046] Monitoring of medical devices

[0047] Server: Analyzes measurement data transmitted from medical devices and checks for abnormalities. Sends notifications to users and medical staff as needed.

[0048] Terminal: Receives data from the medical device used by the user and sends it to the server.

[0049] User: Uses medical devices to measure their own health data and transmits the data through their device.

[0050] Specific example: A user sends blood pressure data measured using a blood pressure monitor from their device to a server. If the server detects high blood pressure, it notifies the user with the message, "Your blood pressure is high. Please consult a doctor."

[0051] Support for home healthcare

[0052] Server: Collects health information from users and provides it to medical staff. It also sends instructions from medical staff to users.

[0053] Terminal: Sends health information from the user to the server and displays instructions from medical staff on the server to the user.

[0054] User: Enter their health status and any changes in their physical condition into the device to receive support.

[0055] Specific example: When a user enters "My body temperature is high and I have a sore throat," the server analyzes the data and provides it to medical staff. The medical staff then advises, "Take paracetamol and drink plenty of fluids," and sends this information back to the user.

[0056] Recipe management

[0057] Server: Uses a generative AI model to generate meal recipes that match the user's request.

[0058] Terminal: Displays recipe information provided by the server to the user.

[0059] User: Enter a meal request and receive a generated recipe.

[0060] Specific example: When a user enters "Please tell me a recipe for a meal suitable for diabetes," the server generates a recipe for "Sautéed chicken breast and broccoli" and displays it to the user via their terminal.

[0061] Online medical consultation

[0062] Server: Relays the consultation between the user and the doctor via video call. Records the consultation results and medical instructions and sends them to the user.

[0063] Terminal: Connects the user's video call to the server and displays the medical results and instructions.

[0064] User: Makes an appointment for a medical consultation and conducts the consultation with a doctor via video call.

[0065] Specific example: A user makes an appointment for a medical consultation and starts a video call at the designated time. After the doctor conducts the examination and notifies the user of the diagnosis, such as "Please take the prescribed medication," this is displayed to the user via their device.

[0066] As described above, the system of the present invention effectively realizes last-mile medical support through the mutual cooperation of users, terminals, and servers, centered around a generated AI model. This system makes it possible to provide high-quality medical support even in areas with difficult access to medical care and in aging societies.

[0067] The following describes the processing flow.

[0068] Providing a health chatbot

[0069] Step 1:

[0070] User: Enter health-related questions into the device and submit.

[0071] Step 2:

[0072] Terminal: Sends the entered question data to the server.

[0073] Step 3:

[0074] Server: Receives question data and analyzes its content using a generative AI model.

[0075] Step 4:

[0076] Server: Generates an appropriate answer to the question and sends it to the terminal.

[0077] Step 5:

[0078] Terminal: Displays the generated response to the user.

[0079] Specific example:

[0080] User: Type "I haven't been able to sleep lately" into the device and send it.

[0081] Terminal: Sends question data to the server.

[0082] Server: Analyzes the question and generates a response such as, "You can try relaxing before bed and avoiding caffeine. If the problem persists, please consult a doctor," and sends it to the terminal.

[0083] Terminal: Displays the answer to the user.

[0084] Monitoring of medical devices

[0085] Step 1:

[0086] User: Uses medical devices to measure health data.

[0087] Step 2:

[0088] Terminal: Sends measured data to the server.

[0089] Step 3:

[0090] Server: Analyzes the received data and detects anomalies.

[0091] Step 4:

[0092] Server: If an anomaly is detected, it sends a notification to the user or medical staff.

[0093] Step 5:

[0094] Device: Displays notifications to the user.

[0095] Specific example:

[0096] User: Measure blood pressure with a blood pressure monitor.

[0097] Terminal: Sends measurement data to the server.

[0098] Server: Analyzes the data and, if high blood pressure is detected, generates a notification saying, "Your blood pressure is high. Please consult a doctor," and sends it to the terminal.

[0099] Device: Displays notifications to the user.

[0100] Support for home healthcare

[0101] Step 1:

[0102] User: Enters their health information and changes in their physical condition into the device and sends it.

[0103] Step 2:

[0104] Terminal: Sends health information to the server.

[0105] Step 3:

[0106] Server: Analyzes received data and provides it to medical staff.

[0107] Step 4:

[0108] Medical staff: Generate instructions based on the analysis results and input them into the server.

[0109] Step 5:

[0110] Server: Sends instructions from medical staff to the user's terminal.

[0111] Step 6:

[0112] Terminal: Displays instructions from medical staff to the user.

[0113] Specific example:

[0114] User: Enters "My body temperature is high and my throat hurts" into the device and sends it.

[0115] Terminal: Sends health information to the server.

[0116] Server: Analyzes the data and notifies medical staff of "elevated body temperature and sore throat."

[0117] Medical staff: Enter the instruction, "Take paracetamol and drink plenty of fluids."

[0118] Server: Sends instructions to the terminal.

[0119] Terminal: Displays instructions to the user.

[0120] Recipe management

[0121] Step 1:

[0122] User: Enter and submit a meal request on the terminal.

[0123] Step 2:

[0124] Terminal: Sends a request to the server.

[0125] Step 3:

[0126] Server: Uses a generative AI model to analyze requests and generate appropriate recipes.

[0127] Step 4:

[0128] Server: Sends the generated recipe to the terminal.

[0129] Step 5:

[0130] Terminal: Displays the recipe to the user.

[0131] Specific example:

[0132] User: Type "Please share some recipes suitable for people with diabetes" and submit.

[0133] Terminal: Sends a request to the server.

[0134] Server: Uses a generative AI model to generate "Sautéed Chicken Breast and Broccoli" and sends it to the terminal.

[0135] Terminal: Displays the recipe to the user.

[0136] Online medical consultation

[0137] Step 1:

[0138] User: Make an appointment for a medical consultation using the terminal.

[0139] Step 2:

[0140] Terminal: Sends reservation information to the server.

[0141] Step 3:

[0142] Server: Sets up a video call between the doctor and the user.

[0143] Step 4:

[0144] User: Start a video call from your device at the scheduled time.

[0145] Step 5:

[0146] Server: Records medical results and instructions, and sends them to the user's terminal.

[0147] Step 6:

[0148] Terminal: Displays medical results and instructions to the user.

[0149] Specific example:

[0150] User: Make an appointment for a medical consultation using the terminal.

[0151] Terminal: Sends reservation information to the server.

[0152] Server: Set up a video call with the doctor.

[0153] User: Start the video call at the scheduled time.

[0154] Server: Records the medical results as "You need rest, please take the prescribed medication" and sends it to the terminal.

[0155] Terminal: Displays the medical results to the user.

[0156] (Example 1)

[0157] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0158] In modern society, medical resources and equipment are limited, and providing appropriate medical support is particularly difficult in areas with limited access to healthcare and in aging societies. Furthermore, the lack of adequate means for users to monitor their health on a daily basis and receive prompt professional medical support when needed can increase the risk of health problems.

[0159] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0160] In this invention, the server includes means for generating appropriate answers to health-related questions from users using a generative AI model; means for receiving and analyzing measurement data from medical devices; means for sending notifications to users or medical professionals based on the analysis results; means for collecting health information from users and providing it to medical professionals; means for sending and displaying instructions from medical professionals to users; means for generating meal plan proposals based on user requests; means for relaying medical consultations between users and doctors via online communication; and means for recording and sending medical results and instructions to users. This makes it possible to provide high-quality medical support even in areas with limited access to medical care and in aging societies.

[0161] A "generative AI model" is an artificial intelligence model that analyzes user input data and generates appropriate responses or results based on that data.

[0162] A "health-related question" is an inquiry that a user submits to seek information or advice about their health condition or symptoms.

[0163] An "appropriate answer" is accurate and useful information based on medical knowledge, provided by a generative AI model in response to a user's question.

[0164] A "medical device" is a device used by a user to measure their own health indicators, and examples include blood pressure monitors and thermometers.

[0165] "Measurement data" refers to data on health indicators obtained using medical devices.

[0166] "Analysis" is the process of analyzing received data to identify important information and outliers.

[0167] A "notification" is a message sent to inform a user or medical professional of analysis results or important information.

[0168] "Health information" refers to comprehensive health data that includes information about a user's health status, symptoms, and daily life.

[0169] A "medical professional" is a person with specialized knowledge in the medical field, such as a doctor or nurse.

[0170] "Instructions" refer to guidance provided by healthcare professionals to users to prompt them to take specific actions regarding health management and treatment.

[0171] A "meal plan" is a nutritionally balanced meal plan provided by an AI model that generates data based on the user's health status and requests.

[0172] "Online communication" refers to a communication method that involves sending and receiving data via a network such as the internet.

[0173] "Medical treatment" refers to a series of medical actions and activities performed by a doctor on a user.

[0174] "Medical outcome" refers to medical conclusions or findings obtained through medical treatment.

[0175] "Record keeping" refers to the process of saving medical results and instructions in a digital format so that they can be reviewed later.

[0176] This invention is a last-mile medical support system that utilizes a generative AI model, in which the user, terminal, and server elements work together. The following details each component and its role.

[0177] Providing a health chatbot

[0178] Server: Uses a generative AI model to generate appropriate answers to health-related questions from users. Specifically, the server analyzes the content of health consultations sent by users and generates answers using the generative AI model "GPT-3 (registered trademark)". For example, if a user enters "I've been having trouble sleeping lately," the server generates an answer such as "You can try relaxing before bed and avoiding caffeine. If it persists, please consult a doctor," and sends this to the terminal.

[0179] Device: This device sends health-related question data entered by the user to the server and displays the server's responses to the user. Smartphones and PCs fall into this category.

[0180] User: Enters health-related questions from their device and receives answers from the server.

[0181] Monitoring of medical devices

[0182] Server: Analyzes measurement data transmitted from medical devices and checks for abnormalities. Sends notifications to users and medical staff as needed. For example, if it receives blood pressure monitor data and uses the generated AI model "anomaly detection algorithm" to detect high blood pressure, it will send a notification saying, "Your blood pressure is high. Please consult a doctor."

[0183] Terminal: Receives data from medical devices used by the user and transmits it to the server. Wireless communication technologies such as Bluetooth are also used.

[0184] User: Uses medical devices to measure their own health data and transmits that data to a server via their device.

[0185] Support for home healthcare

[0186] Server: Collects health information from users and provides it to medical staff. It also sends instructions from medical staff to users. For example, if a user enters "My temperature is high and I have a sore throat," the server provides this to the medical staff. Then, if the medical staff instructs "Take paracetamol and drink plenty of fluids," the server sends this to the user.

[0187] Terminal: Sends health information from the user to the server and displays instructions from medical staff on the server to the user.

[0188] User: Enter their health status and any changes in their physical condition into the device to receive support.

[0189] Recipe management

[0190] Server: Uses a generative AI model to generate meal recipes that match the user's request. For example, using the generative AI model "Cookpad AI," in response to a user's request, "Please tell me a meal recipe suitable for diabetes," it generates a recipe such as "Sautéed chicken breast and broccoli."

[0191] Terminal: Displays recipe information provided by the server to the user.

[0192] User: Enter a meal request and receive a generated recipe.

[0193] Online medical consultation

[0194] Server: Relays the consultation between the user and the doctor via video call. Records the consultation results and medical instructions and sends them to the user. For example, it uses WebRTC technology to conduct video calls, saves the consultation details to a document management system, and sends medical instructions such as "Please take the specified medication" to the user.

[0195] Terminal: Connects the user's video call to the server and displays the medical results and instructions.

[0196] User: Makes an appointment for a medical consultation and has a consultation with a doctor via video call at the designated time.

[0197] Examples of specific cases and prompt statements

[0198] For example, if a user enters "I haven't been able to sleep lately," the server generates a response such as "You can try relaxing before bed and avoiding caffeine. If it persists, please consult a doctor," and displays it to the user via their device.

[0199] Example input prompts for a generative AI model:

[0200] User input: I can't sleep lately

[0201] Example response from the generated AI model: Possible measures include relaxing before bed and avoiding caffeine. If symptoms persist, consult a doctor.

[0202] As described above, this system, centered around a generative AI model, enables effective last-mile medical support through the mutual cooperation of users, terminals, and servers. This system makes it possible to provide high-quality medical support even in areas with limited access to healthcare and in aging societies.

[0203] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0204] Providing a health chatbot

[0205] Step 1:

[0206] User: Enter a health-related question into the terminal. For example, enter "I've been having trouble sleeping lately."

[0207] Input: Text-based questions about health

[0208] Output: Send instructions on the terminal

[0209] Step 2:

[0210] Terminal: Sends the entered question data to the server. HTTPS is used as the communication protocol.

[0211] Input: Question data entered by the user

[0212] Data processing: Question data is converted into HTTP request format.

[0213] Output: HTTP request sent to the server

[0214] Step 3:

[0215] Server: Inputs question data into the AI ​​model, analyzes it, and generates appropriate answers. It uses "GPT-3" to generate answers to user questions.

[0216] Input: Question data in HTTP request format

[0217] Data processing: Text analysis and response generation using generative AI models.

[0218] Output: Response data generated in JSON format

[0219] Step 4:

[0220] Server: Sends the generated response to the terminal.

[0221] Input: Response data in JSON format

[0222] Data processing: The response data is converted into an HTTP response format.

[0223] Output: HTTP response sent to the terminal

[0224] Step 5:

[0225] Terminal: Displays received responses to the user. Responses are displayed on the terminal screen.

[0226] Input: Response data in HTTP response format

[0227] Data processing: Response data is converted into a format that users can understand.

[0228] Output: The answer displayed on the device screen

[0229] Monitoring of medical devices

[0230] Step 1:

[0231] User: Measuring data using medical devices. For example, measuring blood pressure using a blood pressure monitor.

[0232] Input: Blood pressure measuring device

[0233] Output: Measurement data

[0234] Step 2:

[0235] Medical devices: Transmit measurement data to a terminal. Wireless communication such as Bluetooth is used.

[0236] Input: Measurement data

[0237] Data processing: Measurement data is converted to a wireless communication format.

[0238] Output: Measurement data sent to the terminal

[0239] Step 3:

[0240] Terminal: Sends measurement data to the server. The HTTPS protocol is used again.

[0241] Input: Measurement data received via wireless communication

[0242] Data processing: Convert measurement data into HTTP request format.

[0243] Output: HTTP request sent to the server

[0244] Step 4:

[0245] Server: Analyzes data and checks for anomalies. It uses a generative AI model, "Anomaly Detection Algorithm," to analyze the data.

[0246] Input: Measurement data in HTTP request format

[0247] Data processing: Analysis of measurement data and anomaly detection.

[0248] Output: Notification data in case of an anomaly

[0249] Step 5:

[0250] Server: If an abnormality occurs, it sends a notification to the user or medical staff. A notification such as "Your blood pressure is high. Please consult a doctor" is generated.

[0251] Input: Data after anomaly detection

[0252] Data processing: Convert notification content into HTTP response format.

[0253] Output: Notification data sent to the terminal

[0254] Step 6:

[0255] Terminal: Displays received notifications to the user. The notification will appear on the user's terminal screen.

[0256] Input: Notification data in HTTP response format

[0257] Data processing: Convert notification data into a format that users can understand.

[0258] Output: Notification displayed on the device screen

[0259] Support for home healthcare

[0260] Step 1:

[0261] User: Enter health information into the terminal. For example, enter "My temperature is high and I have a sore throat."

[0262] Input: Health information

[0263] Output: Health information data

[0264] Step 2:

[0265] Terminal: Sends health information to the server. The HTTPS protocol is used.

[0266] Input: Entered health information data

[0267] Data processing: Convert health information data into HTTP request format.

[0268] Output: HTTP request sent to the server

[0269] Step 3:

[0270] Server: Provides health information to medical staff. The information is displayed on a dashboard for medical staff.

[0271] Input: Health information data in HTTP request format

[0272] Data processing: Convert health information to a display format.

[0273] Output: Display on the medical staff dashboard

[0274] Step 4:

[0275] Medical staff: Enter instructions for the user into the server. Enter the instruction, "Take paracetamol and drink plenty of fluids."

[0276] Input: Instructions from medical staff

[0277] Output: Instruction data

[0278] Step 5:

[0279] Server: Sends instructions from medical staff to terminals. This also uses the HTTPS protocol.

[0280] Input: Medical staff instruction data

[0281] Data processing: Convert the instruction data into an HTTP response format.

[0282] Output: Data to be sent to the terminal

[0283] Step 6:

[0284] Terminal: Display instructions from medical staff to the user. Pop-up notifications, etc. are used.

[0285] Input: Instruction data in the form of an HTTP response

[0286] Data processing: Convert the instruction data into a form that the user can understand

[0287] Output: Instructions displayed on the terminal screen

[0288] Recipe management

[0289] Step 1:

[0290] User: Enter a request related to diet into the terminal. For example, enter "Please teach me a recipe for a diet suitable for diabetes".

[0291] Input: Request related to diet

[0292] Output: Request data

[0293] Step 2:

[0294] Terminal: Send the request to the server.

[0295] Input: Request data from the user

[0296] <s Data processing: Convert the request data into the form of an HTTP request

[0297] Output: HTTP request sent to the server

[0298] Step 3:

[0299] Server: Generate recipes using a generative AI model. Use the generative AI model "Cookpad AI".

[0300] Input: Request data in the form of an HTTP request

[0301] Data calculation: Analyze the request data and generate recipes

[0302] Output: Generated recipe data in JSON format

[0303] Step 4:

[0304] Server: Send the generated recipe to the terminal.

[0305] Input: Recipe data in JSON format

[0306] Data processing: Convert the recipe data into the form of an HTTP response

[0307] Output: HTTP response sent to the terminal

[0308] Step 5:

[0309] Terminal: Display the recipe to the user. The ingredients and cooking steps of the recipe are displayed in a list format on the screen.

[0310] Input: Recipe data in the form of an HTTP response

[0311] Data processing: Convert the recipe data into a form that the user can understand

[0312] Output: Recipe displayed on the terminal screen

[0313] Online medical consultation

[0314] Step 1:

[0315] User: Make an appointment for a medical consultation via a terminal. Enter the date, time, and symptoms into the appointment system.

[0316] Input: Appointment data

[0317] Output: Reservation Request

[0318] Step 2:

[0319] Server: Notifies doctors of appointment information. This information is displayed on the doctor's dashboard.

[0320] Input: Reservation Request

[0321] Data processing: Convert appointment information to a format suitable for doctors.

[0322] Output: Display on the physician's dashboard

[0323] Step 3:

[0324] User: Start a video call on your device at the specified time. A WebRTC video conference will be set up.

[0325] Input: Call Initiation Request

[0326] Output: Start video call

[0327] Step 4:

[0328] Server: Connects users and doctors via video call.

[0329] Input: Video call request

[0330] Data processing: Call connection settings

[0331] Output: Video call connection between user and doctor

[0332] Step 5:

[0333] Doctor: Conducts medical examinations and records the results and instructions on the server. For example, they might enter an instruction such as, "Please take the prescribed medication."

[0334] Input: Medical results and instruction data

[0335] Output: Recorded data

[0336] Step 6:

[0337] Server: Sends medical results and instructions to the terminal.

[0338] Input: Recorded data

[0339] Data processing: Convert medical results and instructions into HTTP response format.

[0340] Output: HTTP response sent to the terminal

[0341] Step 7:

[0342] Terminal: Displays medical results and instructions to the user. Text notifications are displayed on the screen.

[0343] Input: Clinical results and instruction data in HTTP response format

[0344] Data processing: Converting medical results and instructions into a format that users can understand.

[0345] Output: Medical results and instructions displayed on the terminal screen.

[0346] (Application Example 1)

[0347] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0348] In modern society, providing appropriate meal plans tailored to users' health conditions and facilitating on-the-spot ordering and delivery is challenging. Furthermore, regularly tracking users' health data and continuously adjusting meal plans accordingly is also difficult. There is a need to develop a system that solves these problems and provides high-quality medical support based on users' health conditions.

[0349] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0350] In this invention, the server includes means for generating appropriate answers to health-related questions from the user using a generative AI model; means for receiving and analyzing measurement data from medical devices; means for sending notifications to the user or medical staff based on the analysis results; means for collecting health information from the user and providing it to medical staff; means for sending and displaying instructions from medical staff to the user; means for generating meal recipes based on the user's request; means for relaying consultations between the user and a doctor via video call; means for proposing a meal plan based on the user's health condition and ordering and delivering it on the spot; and means for regularly tracking the user's health data and adjusting the meal plan accordingly. This enables the provision of meal plans tailored to the user's health condition and on-the-spot ordering and delivery, as well as continuous tracking of health data and appropriate adjustment of the meal plan based on that data.

[0351] A "generative AI model" is an algorithm or system that uses artificial intelligence to generate appropriate answers or suggestions based on user input data.

[0352] "Means" refers to methods, devices, or software programs designed to achieve a specific function or role.

[0353] A "user" is an individual or organization that uses the system to receive health-related consultations or medical support.

[0354] "Health-related questions" are inquiries or consultations that users enter into the system regarding their health status and physical condition.

[0355] "Medical devices" are devices and equipment used for medical purposes, and include the measurement and recording of health data.

[0356] "Measurement data" refers to numerical information and measurement results obtained using medical devices.

[0357] "Analysis results" refer to diagnostic information and suggestions obtained from measurement data using generated AI models or other technical means.

[0358] "Health information" refers to various data and records related to the user's health status and physical condition.

[0359] "Medical staff" refers to individuals with specialized medical knowledge who are responsible for providing medical care and guidance to users.

[0360] "Instructions" refer to specific actions or procedures that medical staff provide to users.

[0361] A "meal recipe" is a document that lists ingredients and cooking methods suitable for a specific health condition.

[0362] "Video calling" is a technology that allows for real-time conversations using video and audio over the internet.

[0363] "Medical results" refer to diagnostic information and medical instructions obtained during medical consultations conducted via video call or similar means.

[0364] A "meal plan" is a set of meal suggestions or plans tailored to the user's health condition.

[0365] "Means of ordering and delivery" refers to the process and system for users to order food through a system and have it delivered.

[0366] "Health data tracking" refers to the act of regularly recording and monitoring health information collected from users.

[0367] "Adjusting meal plans" refers to the process of continuously modifying and optimizing meal plans to suit the user based on tracked health data.

[0368] This invention is a last-mile medical support system that utilizes a generative AI model, in which the user, terminal, and server elements work together in coordination. The following details each component and its role.

[0369] Providing a health chatbot

[0370] Server: Uses a generative AI model to generate appropriate answers to health-related questions from users. The server analyzes the content of health consultations submitted by users and generates answers on the spot based on relevant medical knowledge.

[0371] Device: This device sends health-related question data entered by the user to the server and displays the server's responses to the user. Smartphones and PCs fall into this category.

[0372] User: Enters health-related questions from their device and receives answers from the server.

[0373] Specific example: If a user enters "I haven't been able to sleep lately," the server generates a response such as "You can try relaxing before bed and avoiding caffeine. If it persists, please consult a doctor," and displays it to the user via their device.

[0374] Monitoring of medical devices

[0375] Server: Analyzes measurement data transmitted from medical devices and checks for abnormalities. Sends notifications to users and medical staff as needed.

[0376] Terminal: Receives data from the medical device used by the user and sends it to the server.

[0377] User: Uses medical devices to measure their own health data and transmits the data through their device.

[0378] Specific example: A user sends blood pressure data measured using a blood pressure monitor from their device to a server. If the server detects high blood pressure, it notifies the user with the message, "Your blood pressure is high. Please consult a doctor."

[0379] Support for home healthcare

[0380] Server: Collects health information from users and provides it to medical staff. It also sends instructions from medical staff to users.

[0381] Terminal: Sends health information from the user to the server and displays instructions from medical staff on the server to the user.

[0382] User: Enter their health status and any changes in their physical condition into the device to receive support.

[0383] Specific example: When a user enters "My body temperature is high and I have a sore throat," the server analyzes the data and provides it to medical staff. The medical staff then advises, "Take paracetamol and drink plenty of fluids," and sends this information back to the user.

[0384] Recipe management

[0385] Server: Uses a generative AI model to generate meal recipes that match the user's request.

[0386] Terminal: Displays recipe information provided by the server to the user.

[0387] User: Enter a meal request and receive a generated recipe.

[0388] Specific example: When a user enters "Please tell me a recipe for a meal suitable for diabetes," the server generates a recipe for "Sautéed chicken breast and broccoli" and displays it to the user via their terminal.

[0389] Online medical consultation

[0390] Server: Relays the consultation between the user and the doctor via video call. Records the consultation results and medical instructions and sends them to the user.

[0391] Terminal: Connects the user's video call to the server and displays the medical results and instructions.

[0392] User: Makes an appointment for a medical consultation and conducts the consultation with a doctor via video call.

[0393] Specific example: A user makes an appointment for a medical consultation and starts a video call at the designated time. After the doctor conducts the examination and notifies the user of the diagnosis, such as "Please take the prescribed medication," this is displayed to the user via their device.

[0394] Meal plan suggestions, ordering, and delivery.

[0395] Server: Proposes meal plans based on the user's health status, takes orders on the spot, and arranges delivery. It uses a generative AI model to analyze health data and generate meal suggestions. It also regularly tracks the user's health data and adjusts the meal plan accordingly.

[0396] Terminal: Sends user-entered health data and meal requests to the server, and displays suggestions and order confirmations from the server.

[0397] User: Enters their health status, receives a meal plan tailored to their condition from the server, and proceeds with ordering and delivery.

[0398] Specific example: If a diabetic user enters into the app, "My recent blood sugar level is 150, and I'd like some dietary advice," the server will suggest, "A low-carbohydrate diet is recommended to control blood sugar levels," and provide a suitable recipe (e.g., chicken breast and broccoli). The user can then order delivery.

[0399] Example prompt:

[0400] "My blood sugar level is currently 150. Please suggest a suitable diet for people with diabetes."

[0401] This system allows users to efficiently manage a series of processes, from health consultations and medical device data transmission to home healthcare support, recipe management, online consultations, and meal plan suggestions, ordering, and delivery. This enables high-quality medical support and appropriate nutritional management.

[0402] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0403] Step 1:

[0404] The user enters their health consultation into the terminal. The user uses the terminal to input specific questions or concerns about their health. For example, they might enter, "I've been having trouble sleeping lately." The input data is saved on the terminal.

[0405] Step 2:

[0406] The terminal sends the input data to the server. The entered health consultation content is converted into a data format and sent to the server using the HTTPS protocol. The server prepares to analyze the received data.

[0407] Step 3:

[0408] The server uses a generative AI model to generate appropriate answers to questions. The server analyzes the received health consultation data and inputs it into the generative AI model. The generative AI model generates answers based on relevant medical knowledge and returns them to the server.

[0409] Step 4:

[0410] The server sends the generated response to the terminal. The AI ​​model that generates the response sends the data, including the response, to the terminal. The terminal converts the received response data into a display format and displays it to the user.

[0411] Step 5:

[0412] The user uses a health device and inputs the measurement data into the terminal. For example, they might use a blood pressure monitor to measure their blood pressure and input that numerical data into the terminal.

[0413] Step 6:

[0414] The terminal sends measurement data to the server. The input measurement data is converted to a data format and sent to the server using the HTTPS protocol. The server prepares to analyze the received data.

[0415] Step 7:

[0416] The server analyzes the measurement data and checks for any abnormalities. The server analyzes the received measurement data and compares it to reference values. For example, if blood pressure is high, it will be determined to be "hypertension."

[0417] Step 8:

[0418] If the server detects an anomaly, it will send a notification to the user or medical staff. Based on the analysis results, if an anomaly is detected, a notification message will be created and sent to the user or medical staff.

[0419] Step 9:

[0420] The user enters a request regarding meals into the device. For example, they might enter, "Please provide recipes suitable for meals for people with diabetes." The entered data is saved on the device.

[0421] Step 10:

[0422] The terminal sends the input data to the server. The entered meal request details are converted into a data format and sent to the server using the HTTPS protocol. The server prepares to analyze the received data.

[0423] Step 11:

[0424] The server generates suitable meal recipes using a generative AI model. The server analyzes the received request data and inputs it into the generative AI model. The generative AI model generates suitable meal recipes and returns them to the server.

[0425] Step 12:

[0426] The server sends the generated recipe to the terminal. The AI ​​model sends data containing the meal recipe to the terminal. The terminal converts the received recipe data into a display format and displays it to the user.

[0427] Step 13:

[0428] Users order meal recipes and request delivery. They review the displayed recipes, place their orders on the spot via their terminal, and request delivery.

[0429] Step 14:

[0430] The server periodically tracks the user's health data and adjusts the meal plan accordingly. The server regularly collects the user's health data, generates an optimal meal plan using a generative AI model, and adjusts it as needed.

[0431] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0432] This invention is a last-mile medical support system that utilizes a generative AI model and an emotion engine. This system functions through the coordinated efforts of the user, terminal, server, and emotion engine. The following details each component and its role.

[0433] Providing a health chatbot

[0434] Server: Uses a generative AI model to generate appropriate answers to health-related questions from users. By combining it with an emotion engine, it recognizes the user's emotional state from the input data and generates answers corresponding to that emotional state.

[0435] Device: This device sends health-related question data entered by the user to the server and displays the server's responses to the user. Smartphones and PCs fall into this category.

[0436] User: Enters health-related questions from their device and receives answers from the server.

[0437] Specific example: If a user enters "I haven't been able to sleep lately" in an anxious tone, the server uses an emotion engine to recognize the user's anxiety and generates an emotionally sensitive response such as, "You can try relaxing before bed or avoiding caffeine. If your anxiety persists, please consult a doctor," which is then displayed to the user via their device.

[0438] Monitoring of medical devices

[0439] Server: Analyzes measurement data transmitted from medical devices and checks for abnormalities. It also analyzes the user's emotional state using an emotion engine and sends notifications to the user and medical staff as needed.

[0440] Terminal: Receives data from the medical device used by the user and sends it to the server.

[0441] User: Uses medical devices to measure their own health data and transmits the data through their device.

[0442] Specific example: A user sends blood pressure data measured using a blood pressure monitor from their device to a server. If the server detects high blood pressure and the emotion engine recognizes the user's anxiety level, it notifies the user with the message, "Your blood pressure is high. Please consult a doctor. If you are feeling anxious, try some relaxation techniques."

[0443] Support for home healthcare

[0444] Server: Collects health information from users and analyzes their emotional state using an emotion engine. Provides health information and emotional state data to medical staff and sends instructions from medical staff to the user.

[0445] Terminal: Sends health information from the user to the server and displays instructions from medical staff on the server to the user.

[0446] User: Enter their health status and any changes in their physical condition into the device to receive support.

[0447] Specific example: If a user enters "My body temperature is high and my throat hurts," and the emotion engine recognizes the user's anxiety, the server analyzes the data and notifies medical staff of "high body temperature, sore throat, and anxiety." The medical staff then instructs the user to "take paracetamol and drink plenty of fluids. Please contact us if your anxiety persists," and sends this to the user.

[0448] Recipe management

[0449] Server: Uses a generative AI model to generate meal recipes that match the user's request. It can also use an emotion engine to suggest recipes that take the user's emotional state into consideration.

[0450] Terminal: Displays recipe information provided by the server to the user.

[0451] User: Enter a meal request and receive a generated recipe.

[0452] Specific example: If a user types "Please tell me a recipe for a meal suitable for diabetes," and the emotion engine recognizes the user's stress, the server generates a recipe suggesting "Sautéed chicken breast and broccoli with relaxing herbal tea" and displays it to the user via their device.

[0453] Online medical consultation

[0454] Server: Relays the user's consultation with the doctor via video call. Records the consultation results and medical instructions, and analyzes and records the user's emotional state using an emotion engine.

[0455] Terminal: Connects the user's video call to the server and displays the medical results and instructions.

[0456] User: Makes an appointment for a medical consultation and conducts the consultation with a doctor via video call.

[0457] Specific example: A user makes an appointment for a medical consultation and starts a video call at the designated time. The doctor conducts the consultation, and if the emotion engine recognizes the user's anxiety, the doctor notifies the user of the consultation result, such as "Please take the prescribed medication and take time to relax." The device displays an emotionally sensitive message to the user along with the consultation result.

[0458] As described above, the system of the present invention, centered on a generative AI model and an emotion engine, enables the user, terminal, and server to cooperate with each other to effectively realize last-mile medical support. This system makes it possible to provide high-quality and emotionally sensitive medical support even in areas with limited access to medical care and in aging societies.

[0459] The following describes the processing flow.

[0460] Providing a health chatbot

[0461] Step 1:

[0462] User: Enter health-related questions into the device and submit.

[0463] Step 2:

[0464] Terminal: Sends the entered question data to the server.

[0465] Step 3:

[0466] Server: Receives question data and analyzes the user's emotional state using an emotion engine.

[0467] Step 4:

[0468] Server: Considers emotional states and generates appropriate responses using a generative AI model.

[0469] Step 5:

[0470] Server: Sends the generated response to the terminal.

[0471] Step 6:

[0472] Terminal: Displays the generated response to the user.

[0473] Specific example: If a user enters "I haven't been able to sleep lately" and indicates feelings of anxiety, the server generates a response such as "Try some relaxation techniques. If it persists, consult a doctor" and displays it to the user via their device.

[0474] Monitoring of medical devices

[0475] Step 1:

[0476] User: Uses medical devices to measure health data.

[0477] Step 2:

[0478] Terminal: Sends measured data to the server.

[0479] Step 3:

[0480] Server: Receives and analyzes measurement data.

[0481] Step 4:

[0482] Server: Analyzes the user's emotional state using an emotion engine.

[0483] Step 5:

[0484] Server: If an anomaly is detected, it generates a notification taking into account the emotional state.

[0485] Step 6:

[0486] Server: Sends a notification to the device.

[0487] Step 7:

[0488] Device: Displays notifications to the user.

[0489] Specific example: If a user measures their blood pressure with a blood pressure monitor and an abnormal reading is detected, the server generates a notification saying, "Your blood pressure is high. If you are worried, try some relaxation techniques. If it persists, consult a doctor," and displays it to the user via their device.

[0490] Support for home healthcare

[0491] Step 1:

[0492] User: Enter and submit information about their health status and any changes in their physical condition on the device.

[0493] Step 2:

[0494] Terminal: Sends health information to the server.

[0495] Step 3:

[0496] Server: Analyzes the received data.

[0497] Step 4:

[0498] Server: Analyzes the user's emotional state using an emotion engine.

[0499] Step 5:

[0500] Server: Provides health information and emotional status to medical staff.

[0501] Step 6:

[0502] Medical staff: Generate instructions based on health information and emotional state, and input them into the server.

[0503] Step 7:

[0504] Server: Sends instructions from medical staff to the user's terminal.

[0505] Step 8:

[0506] Terminal: Displays instructions from medical staff to the user.

[0507] Specific example: If a user enters "My temperature is high and I have a sore throat," and the emotion engine recognizes this as anxiety, the server analyzes the data and notifies medical staff. The medical staff then instructs the user to "Take paracetamol and drink plenty of fluids. Contact us if you have any concerns," and sends this to the user, who then sees it displayed on their device.

[0508] Recipe management

[0509] Step 1:

[0510] User: Enter and submit a meal request on the terminal.

[0511] Step 2:

[0512] Terminal: Sends a request to the server.

[0513] Step 3:

[0514] Server: Analyzes the user's emotional state using an emotion engine.

[0515] Step 4:

[0516] Server: Generates appropriate recipes using a generative AI model.

[0517] Step 5:

[0518] Server: Sends the generated recipe to the terminal.

[0519] Step 6:

[0520] Terminal: Displays the recipe to the user.

[0521] Specific example: If a user types "Please tell me a recipe for a meal suitable for diabetes," and the emotion engine recognizes the user's stress, the server will generate a recipe suggesting "Sautéed chicken breast and broccoli with relaxing herbal tea" and display it to the user via their device.

[0522] Online medical consultation

[0523] Step 1:

[0524] User: Make an appointment for a medical consultation using the terminal.

[0525] Step 2:

[0526] Terminal: Sends reservation information to the server.

[0527] Step 3:

[0528] Server: Sets up a video call between the doctor and the user.

[0529] Step 4:

[0530] User: Start a video call from your device at the scheduled time.

[0531] Step 5:

[0532] Server: Records medical results and instructions. Also analyzes the user's emotional state using an emotion engine.

[0533] Step 6:

[0534] Server: Sends medical results and instructions to the user's terminal.

[0535] Step 7:

[0536] Terminal: Displays medical results and instructions to the user.

[0537] Specific example: A user makes an appointment for a medical consultation and starts a video call at the designated time. The doctor conducts the consultation, and if the emotion engine recognizes the user's anxiety, the doctor notifies the user of the consultation result, such as "Please take the prescribed medication and take time to relax." The device displays an emotionally sensitive message to the user along with the consultation result.

[0538] As described above, the system of the present invention, centered on a generative AI model and an emotion engine, enables the user, terminal, and server to cooperate with each other to effectively and emotionally considerately provide last-mile medical support. This system makes it possible to provide high-quality medical support even in areas with limited access to medical care and in aging societies.

[0539] (Example 2)

[0540] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0541] Conventional medical support systems often provide information without considering the user's emotional state, leading to problems such as users' anxiety and stress not being alleviated. Furthermore, data analysis from medical devices also fails to consider emotional states, sometimes resulting in inappropriate advice and notifications to users and medical staff. Therefore, there is a need for a medical support system that takes emotional considerations into account.

[0542] The identification processing performed 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 generating appropriate answers to health-related questions from the user using a generation AI model, means for analyzing the emotional state using an emotion engine based on the user's input data and generating corresponding answers, and means for receiving and analyzing measurement data from medical devices. This enables not only the provision of information according to the user's health condition but also responses that take emotions into consideration.

[0543] A "generative AI model" is a type of artificial intelligence that automatically generates appropriate answers and suggestions based on user input data.

[0544] An "emotion engine" is a program that analyzes a user's emotional state based on their input data and actions, and then takes appropriate action based on that analysis.

[0545] "Health-related questions" are questions that users ask about their own health status or symptoms.

[0546] A "medical device" refers to a device or apparatus used to measure a user's health data. Examples include blood pressure monitors and thermometers.

[0547] "Measurement data" refers to numerical data about a user's health obtained using medical devices.

[0548] "Analysis" is the process of detecting and evaluating outliers and trends based on acquired data.

[0549] A "notification" is information that the system communicates to the user or medical staff regarding anomalies or advice.

[0550] "Health information" refers to data about the user's health status and symptoms that they enter into the system.

[0551] "Medical staff" refers to medical professionals who use the system to support users' health.

[0552] "Instructions" refer to the methods of action and advice provided by medical staff to the user.

[0553] A "meal recipe" refers to a list of cooking methods and ingredients suggested based on the user's requests and health condition.

[0554] A "video call" is a method of communication between a remote user and a doctor in real time using audio and video.

[0555] "Medical results" refer to the diagnostic information and treatment plan of a user obtained by a doctor through video calls or other means.

[0556] This invention is a last-mile medical support system in which the user, terminal, server, and emotion engine work together. By utilizing a generative AI model and emotion engine, this system can provide appropriate advice and notifications based on the user's health information.

[0557] Providing a health chatbot

[0558] Server: Uses a generative AI model to generate appropriate answers to health-related questions from users. By combining it with an emotion engine, it analyzes the user's emotional state from the input data and generates answers that correspond to that emotional state.

[0559] Terminal: The terminal sends health-related question data entered by the user to the server and displays the server's responses to the user. Specific hardware used includes smartphones and PCs.

[0560] User: Enters health-related questions from their device and receives answers from the server. This allows the user to receive specific advice tailored to their health condition.

[0561] Specific example: If a user enters "I haven't been able to sleep lately" in an anxious tone, the server uses an emotion engine to analyze the user's anxiety and generates an emotionally sensitive response such as, "You can try relaxing before bed or avoiding caffeine. If your anxiety persists, please consult a doctor," which is then displayed to the user via their device.

[0562] Monitoring of medical devices

[0563] Server: Analyzes measurement data transmitted from medical devices and checks for abnormalities. It also analyzes the user's emotional state using an emotion engine and sends notifications to the user and medical staff as needed.

[0564] Terminal: Receives data from the medical device used by the user and sends it to the server.

[0565] User: Uses medical devices to measure their own health data and transmits it through a terminal. This allows users to monitor their health status in real time.

[0566] Specific example: A user sends blood pressure data measured using a blood pressure monitor from their device to a server. If the server detects high blood pressure and the emotion engine further analyzes the user's anxiety level, it will notify the user with the message, "Your blood pressure is high. Please consult a doctor. If you are feeling anxious, try some relaxation techniques."

[0567] Support for home healthcare

[0568] Server: Collects health information from users and analyzes their emotional state using an emotion engine. Provides health information and emotional state data to medical staff and sends instructions from medical staff to the user.

[0569] Terminal: Sends health information from the user to the server and displays instructions from medical staff on the server to the user.

[0570] User: Enters their health status and changes in physical condition into the device to receive support. This allows users to receive detailed health information and instructions from the comfort of their homes.

[0571] Specific example: A user enters "My body temperature is high and my throat hurts," and the emotion engine analyzes the user's anxiety. The server then analyzes the data and notifies medical staff of "high body temperature, sore throat, and anxiety." The medical staff then instructs the user to "take paracetamol and drink plenty of fluids. Contact us if your anxiety persists," and sends this information to the user.

[0572] Recipe management

[0573] Server: Uses a generative AI model to generate meal recipes that match the user's request. It can also use an emotion engine to suggest recipes that take the user's emotional state into consideration.

[0574] Terminal: Displays recipe information provided by the server to the user.

[0575] User: Enters a request regarding meals and receives a generated recipe. This allows users to receive recipes tailored to their health condition.

[0576] Specific example: If a user enters "Please tell me a recipe for a meal suitable for diabetes," and the emotion engine analyzes the user's stress level, the server generates a recipe suggesting "Sautéed chicken breast and broccoli with relaxing herbal tea," which is then displayed to the user via their device.

[0577] Online medical consultation

[0578] Server: Relays the user's consultation with the doctor via video call. Records the consultation results and medical instructions, and analyzes and records the user's emotional state using an emotion engine.

[0579] Terminal: Connects the user's video call to the server and displays the medical results and instructions.

[0580] User: Schedules an appointment and conducts a consultation with a doctor via video call. This allows users to receive medical advice and emotional support from the comfort of their homes.

[0581] Specific example: A user makes an appointment for a medical consultation and starts a video call at the designated time. The doctor conducts the consultation, and if the emotion engine analyzes the user's anxiety, the doctor notifies the user of the consultation result, such as "Please take the prescribed medication and take time to relax." The device displays an emotionally sensitive message to the user along with the consultation result.

[0582] Examples of prompts include "I haven't been able to sleep lately," "Please give me some recipes for meals suitable for people with diabetes," and "My body temperature is high and I have a sore throat."

[0583] As described above, the system of the present invention utilizes a generative AI model and an emotion engine, enabling the user, terminal, and server to cooperate with each other to provide high-quality and emotionally sensitive last-mile medical support. This system makes it possible to provide convenient and reliable medical support to users even in areas with limited access to medical care and in aging societies.

[0584] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0585] Providing a health chatbot

[0586] Step 1:

[0587] The user enters health-related questions into the device.

[0588] Input: Health-related questions (e.g., "I've been having trouble sleeping lately")

[0589] Output: Input question data

[0590] Step 2:

[0591] The terminal sends the user's question data to the server.

[0592] Input: User's question data

[0593] Output: Question data sent to the server

[0594] Step 3:

[0595] The server receives the question data and the emotion engine analyzes the emotional state.

[0596] Input: Question data received by the server

[0597] Data processing: Analyze the user's emotional state (e.g., anxiety) based on the questionnaire data.

[0598] Output: Analyzed emotional state

[0599] Step 4:

[0600] The server uses a generation AI model to generate responses that correspond to the emotional state.

[0601] Input: Analyzed emotional state, questionnaire data

[0602] Data processing: Generative AI model generates appropriate answers.

[0603] Output: Generated response (Example: "You can try relaxing before bed and avoiding caffeine. If your anxiety persists, consult a doctor.")

[0604] Step 5:

[0605] The server sends the response to the terminal.

[0606] Input: Generated answer

[0607] Output: Response sent from server to terminal

[0608] Step 6:

[0609] The device displays the answer to the user.

[0610] Input: Response sent from the server

[0611] Output: Answer displayed on the terminal

[0612] Monitoring of medical devices

[0613] Step 1:

[0614] The user acquires measurement data using a medical device.

[0615] Input: Measurement using medical devices (e.g., blood pressure measurement)

[0616] Output: Measured data (e.g., blood pressure)

[0617] Step 2:

[0618] The terminal receives measurement data and sends it to the server.

[0619] Input: Data measured by the user

[0620] Output: Measurement data sent to the server

[0621] Step 3:

[0622] The server receives the measurement data and performs analysis.

[0623] Input: Measurement data received by the server

[0624] Data processing: Detection of abnormal values ​​based on measurement data (e.g., high blood pressure).

[0625] Output: Analysis results (e.g., hypertension)

[0626] Step 4:

[0627] The server uses an emotion engine to analyze the user's emotional state.

[0628] Input: Analysis results (e.g., hypertension), user sentiment data

[0629] Data processing: The emotion engine analyzes the user's anxiety state.

[0630] Output: Analyzed emotional state (e.g., anxiety)

[0631] Step 5:

[0632] The server generates notifications based on the abnormality and emotional state, and sends them to the terminal.

[0633] Input: Analysis results and emotional state

[0634] Data processing: Generate appropriate notifications

[0635] Output: Generated notification (Example: "Your blood pressure is high. Please consult a doctor. If you are feeling anxious, try some relaxation techniques.")

[0636] Step 6:

[0637] The device displays a notification to the user.

[0638] Input: Notification sent from the server

[0639] Output: Notification displayed on the terminal

[0640] Support for home healthcare

[0641] Step 1:

[0642] The user enters their health status into the device.

[0643] Input: Enter your health status (e.g., "My temperature is high and I have a sore throat.")

[0644] Output: Input health information

[0645] Step 2:

[0646] The device sends health information to the server.

[0647] Input: Health information entered by the user

[0648] Output: Health information sent to the server

[0649] Step 3:

[0650] The server analyzes health information and emotional state.

[0651] Input: Health information received by the server

[0652] Data processing: Analysis of emotional states using an emotion engine.

[0653] Output: Analyzed health information and emotional state

[0654] Step 4:

[0655] The server sends a notification to the medical staff.

[0656] Input: Analyzed health information and emotional state

[0657] Output: Notification sent to medical staff

[0658] Step 5:

[0659] Medical staff send instructions to the server.

[0660] Input: Instructions created by medical staff

[0661] Output: Instructions sent to the server

[0662] Step 6:

[0663] The server sends instructions to the terminal from the medical staff.

[0664] Input: Instructions received from medical staff

[0665] Output: Instructions sent to the terminal

[0666] Step 7:

[0667] The device displays instructions to the user.

[0668] Input: Instructions sent from the server

[0669] Output: Instructions displayed on the terminal

[0670] Recipe management

[0671] Step 1:

[0672] The user enters their meal requests into the terminal.

[0673] Input: Request regarding meals (e.g., "Please provide recipes suitable for meals for people with diabetes")

[0674] Output: Input Request

[0675] Step 2:

[0676] The terminal sends a request to the server.

[0677] Input: User-entered request

[0678] Output: Request sent to the server

[0679] Step 3:

[0680] The server analyzes the request and the emotional state.

[0681] Input: Request received by the server

[0682] Data processing: Analysis of emotional states using an emotion engine.

[0683] Output: Analyzed requests and sentiment states

[0684] Step 4:

[0685] The server uses an AI model to generate recipes in response to requests.

[0686] Input: Analyzed request and emotional state

[0687] Data processing: Generative AI model generates appropriate recipes.

[0688] Output: Generated recipe (Example: "Sautéed chicken breast and broccoli with relaxing herbal tea")

[0689] Step 5:

[0690] The server sends the generated recipe to the terminal.

[0691] Input: Generated recipe

[0692] Output: Recipe sent to the terminal

[0693] Step 6:

[0694] The device displays the recipe to the user.

[0695] Input: Recipe sent from the server

[0696] Output: Recipe displayed on the terminal

[0697] Online medical consultation

[0698] Step 1:

[0699] The user makes an appointment for a medical consultation.

[0700] Input: Appointment information for medical consultations

[0701] Output: Reservation confirmation

[0702] Step 2:

[0703] The user initiates a video call at the specified time.

[0704] Input: Start a video call

[0705] Output: Video call connection status

[0706] Step 3:

[0707] The server relays the medical consultation via video call.

[0708] Input: Video call data between user and doctor

[0709] Output: Relayed video call data

[0710] Step 4:

[0711] A doctor provides medical treatment and records the results on a server.

[0712] Input: Medical results

[0713] Output: Recorded medical results

[0714] Step 5:

[0715] The server uses an emotion engine to analyze the user's emotional state.

[0716] Input: Medical results, user sentiment data

[0717] Data processing: The emotion engine analyzes the user's anxiety state.

[0718] Output: Analyzed emotional state

[0719] Step 6:

[0720] The server generates a notification for the user based on the medical results and emotional state.

[0721] Input: Medical results and emotional state

[0722] Data processing: Generated notifications

[0723] Output: Appropriate notification (e.g., "Take the prescribed medication and allow yourself time to relax.")

[0724] Step 7:

[0725] The device displays a notification to the user.

[0726] Input: Notification sent from the server

[0727] Output: Notification displayed on the terminal

[0728] (Application Example 2)

[0729] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0730] In modern healthcare support, providing appropriate health management and medical support is a challenge, especially in remote areas and regions with limited access to medical care. Furthermore, there is a lack of flexible medical advice based on the user's emotional state, and a system is needed that allows for continuous and individualized communication between medical staff and patients.

[0731] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0732] In this invention, the server includes means for generating appropriate answers to health-related questions from the user using a generative AI model, means for analyzing the user's emotional state using an emotion analysis engine and generating medical results and advice corresponding to that emotional state, and means for receiving and analyzing measurement data from medical devices. This enables appropriate and individualized medical support remotely based on the user's health and emotional state.

[0733] A "generative AI model" is an artificial intelligence model that generates appropriate answers and advice based on user input data.

[0734] An "emotion analysis engine" is a system that analyzes a user's emotional state and provides information based on those emotions.

[0735] A "medical device" is a device that measures a user's health condition, and includes, for example, blood pressure monitors and electrocardiographs.

[0736] "Health information" refers to data about a user's physical condition and symptoms.

[0737] A "video call" is a communication method that uses audio and video to connect users in different locations with doctors in real time.

[0738] "Medical results" refer to the diagnosis and treatment determined by a doctor based on the user's health condition and symptoms.

[0739] "Analysis results" refer to conclusions and information obtained after analyzing data using generative AI models or sentiment analysis engines.

[0740] "Medical staff" refers to healthcare professionals such as doctors and nurses, who are responsible for managing and treating the user's health.

[0741] A "notification" is a message that conveys analysis results or doctor's instructions to the user or medical staff.

[0742] A "recipe" is a suggestion of how to prepare a meal or a menu, generated based on the user's health condition and requests.

[0743] "Emotional information" refers to data and analysis results related to the user's emotional state.

[0744] This invention is a last-mile medical support system that utilizes a generative AI model and an emotion analysis engine, aiming to support users' health management and medical treatment. The specific embodiments for implementing this invention are described in detail below.

[0745] System Configuration

[0746] 1. User terminal

[0747] User terminals consist of smartphones, tablets, and other devices. They transmit user input and data from medical devices to a server, and display responses and notifications from the server to the user. The terminals are linked to a cloud database to store and manage health information and medical results.

[0748] 2. Server

[0749] The server implements the following main functions:

[0750] Generative AI models (e.g., OpenAI®, GPT-4®) are used to generate appropriate answers to user questions.

[0751] An emotion analysis engine (e.g., Affectiva AI) is used to analyze the user's emotional state and generate medical results and advice based on those emotions.

[0752] It analyzes data transmitted from medical devices to detect abnormal values ​​and symptoms.

[0753] Send notifications to users and medical staff.

[0754] We use video conferencing services (e.g., Twilio) to facilitate online medical consultations between users and doctors.

[0755] 3. Management of health information

[0756] The server analyzes health information collected from users (physical condition, symptoms, and measurement data from medical devices) and provides it to medical staff. Based on the analysis results, the medical staff issues treatment instructions and sends them to the user.

[0757] Specific example

[0758] User login and health information entry

[0759] After the user launches the application and logs in, they enter their current physical condition or symptoms. For example, they might enter, "I've been having trouble sleeping lately."

[0760] Data transmission and analysis

[0761] The entered information is sent to a cloud server and analyzed by a generative AI model and an emotion analysis engine. The emotion analysis engine recognizes the user's anxiety state, and the generative AI model generates advice such as, "To relax before going to bed, try drinking some warm tea or doing some light stretching. We also recommend consulting a doctor."

[0762] Starting a video call

[0763] Users schedule video calls within the application and have real-time consultations with doctors at the designated time. An emotion analysis engine analyzes the user's facial expressions during the video call and notifies the doctor of the user's emotional state.

[0764] Providing medical results and advice

[0765] After the consultation is complete, the AI ​​model sends the user the consultation results and advice it has generated. For example, it might say, "Take the prescribed medication and make time to relax."

[0766] Example of a prompt

[0767] User input: "Lately, I've been having trouble sleeping a lot, and it's making me feel stressed."

[0768] AI prompt: "If the user is experiencing sleep deprivation and stress, please provide specific advice on how to relax."

[0769] Thus, the system of the present invention enables the remote provision of advanced, personalized medical support based on the user's health and emotional state. Furthermore, by combining a generative AI model with an emotion analysis engine, medical treatment and advice that take the user's emotions into consideration can be realized.

[0770] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0771] Step 1:

[0772] The user launches the application and logs in. The application receives the user ID and password as input and performs authentication through database matching in conjunction with the authentication system. The output confirms the login status. If the login is successful, the application retrieves the user's basic information from the server and displays it on the terminal.

[0773] Step 2:

[0774] The user enters their current physical condition and symptoms. A text input field is provided for this input, allowing the user to enter health information and symptom data, such as "I've been having trouble sleeping lately." The device receives this data, converts it to a data format, and sends it to a cloud server. The input information is saved on the server as output.

[0775] Step 3:

[0776] The server analyzes the health information it receives. The input is health information submitted by the user, which is then fed into a generative AI model. The generative AI model generates appropriate answers to user questions, and the results are saved as output. In parallel, an emotion analysis engine is used to analyze the user's emotional state, and the results are also saved.

[0777] Step 4:

[0778] The server sends the analysis results to the user's terminal. The input consists of the analysis results from the generative AI model and the emotion analysis engine, which are then combined and structured into a message. The output is the analysis results sent to the user's terminal, displaying health advice and suggestions for addressing anxiety.

[0779] Step 5:

[0780] The user schedules a video call. The input includes the date and time of the video call and the selection of a doctor, and this information is sent to the server. The server stores the reservation information and displays a reservation confirmation message on the user's terminal as output.

[0781] Step 6:

[0782] The video call starts at the scheduled time. As input, the server receives a signal to begin the video call and activates the video call service. As output, an environment is provided where the user and doctor can communicate in real time. Simultaneously, an emotion analysis engine analyzes the user's facial expressions and sends them to the server.

[0783] Step 7:

[0784] The server provides doctors with the results of emotion analysis during video calls. As input, the user's facial expression data obtained during the video call is analyzed by the emotion analysis engine. The server sends the results to the doctor's terminal, and the emotional state is displayed to the doctor as output, which is used as reference information for medical treatment.

[0785] Step 8:

[0786] After the video call ends, the system generates and provides the user with a medical assessment and advice. Inputs, including the doctor's assessment and advice generated by the AI ​​model, are collected on the server. Outputs, this information is sent to the user's terminal and displayed within the application. This data is also stored in a database.

[0787] Step 9:

[0788] The system continuously monitors users' health information and emotional states. Health data and emotional analysis data are collected periodically from users as input. The server analyzes this data, and if an anomaly is detected, it promptly sends notifications to the user and medical staff.

[0789] These steps enable effective and personalized medical support based on the user's health and emotional state.

[0790] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0791] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0792] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0793] [Second Embodiment]

[0794] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0795] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0796] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0797] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0798] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0799] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0800] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0801] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0802] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0803] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0804] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0805] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0806] This invention is a last-mile medical support system that utilizes a generative AI model. This system functions through the coordinated efforts of the user, terminal, and server. The following details each component and its role.

[0807] Providing a health chatbot

[0808] Server: Uses a generative AI model to generate appropriate answers to health-related questions from users. The server analyzes the content of health consultations submitted by users and generates answers on the spot based on relevant medical knowledge.

[0809] Device: This device sends health-related question data entered by the user to the server and displays the server's responses to the user. Smartphones and PCs fall into this category.

[0810] User: Enters health-related questions from their device and receives answers from the server.

[0811] Specific example: If a user enters "I haven't been able to sleep lately," the server generates a response such as "You can try relaxing before bed and avoiding caffeine. If it persists, please consult a doctor," and displays it to the user via their device.

[0812] Monitoring of medical devices

[0813] Server: Analyzes measurement data transmitted from medical devices and checks for abnormalities. Sends notifications to users and medical staff as needed.

[0814] Terminal: Receives data from the medical device used by the user and sends it to the server.

[0815] User: Uses medical devices to measure their own health data and transmits the data through their device.

[0816] Specific example: A user sends blood pressure data measured using a blood pressure monitor from their device to a server. If the server detects high blood pressure, it notifies the user with the message, "Your blood pressure is high. Please consult a doctor."

[0817] Support for home healthcare

[0818] Server: Collects health information from users and provides it to medical staff. It also sends instructions from medical staff to users.

[0819] Terminal: Sends health information from the user to the server and displays instructions from medical staff on the server to the user.

[0820] User: Enter their health status and any changes in their physical condition into the device to receive support.

[0821] Specific example: When a user enters "My body temperature is high and I have a sore throat," the server analyzes the data and provides it to medical staff. The medical staff then advises, "Take paracetamol and drink plenty of fluids," and sends this information back to the user.

[0822] Recipe management

[0823] Server: Uses a generative AI model to generate meal recipes that match the user's request.

[0824] Terminal: Displays recipe information provided by the server to the user.

[0825] User: Enter a meal request and receive a generated recipe.

[0826] Specific example: When a user enters "Please tell me a recipe for a meal suitable for diabetes," the server generates a recipe for "Sautéed chicken breast and broccoli" and displays it to the user via their terminal.

[0827] Online medical consultation

[0828] Server: Relays the consultation between the user and the doctor via video call. Records the consultation results and medical instructions and sends them to the user.

[0829] Terminal: Connects the user's video call to the server and displays the medical results and instructions.

[0830] User: Makes an appointment for a medical consultation and conducts the consultation with a doctor via video call.

[0831] Specific example: A user makes an appointment for a medical consultation and starts a video call at the designated time. After the doctor conducts the examination and notifies the user of the diagnosis, such as "Please take the prescribed medication," this is displayed to the user via their device.

[0832] As described above, the system of the present invention effectively realizes last-mile medical support through the mutual cooperation of users, terminals, and servers, centered around a generated AI model. This system makes it possible to provide high-quality medical support even in areas with difficult access to medical care and in aging societies.

[0833] The following describes the processing flow.

[0834] Providing a health chatbot

[0835] Step 1:

[0836] User: Enter health-related questions into the device and submit.

[0837] Step 2:

[0838] Terminal: Sends the entered question data to the server.

[0839] Step 3:

[0840] Server: Receives question data and analyzes its content using a generative AI model.

[0841] Step 4:

[0842] Server: Generates an appropriate answer to the question and sends it to the terminal.

[0843] Step 5:

[0844] Terminal: Displays the generated response to the user.

[0845] Specific example:

[0846] User: Type "I haven't been able to sleep lately" into the device and send it.

[0847] Terminal: Sends question data to the server.

[0848] Server: Analyzes the question and generates a response such as, "You can try relaxing before bed and avoiding caffeine. If the problem persists, please consult a doctor," and sends it to the terminal.

[0849] Terminal: Displays the answer to the user.

[0850] Monitoring of medical devices

[0851] Step 1:

[0852] User: Uses medical devices to measure health data.

[0853] Step 2:

[0854] Terminal: Sends measured data to the server.

[0855] Step 3:

[0856] Server: Analyzes the received data and detects anomalies.

[0857] Step 4:

[0858] Server: If an anomaly is detected, it sends a notification to the user or medical staff.

[0859] Step 5:

[0860] Device: Displays notifications to the user.

[0861] Specific example:

[0862] User: Measure blood pressure with a blood pressure monitor.

[0863] Terminal: Sends measurement data to the server.

[0864] Server: Analyzes the data and, if high blood pressure is detected, generates a notification saying, "Your blood pressure is high. Please consult a doctor," and sends it to the terminal.

[0865] Device: Displays notifications to the user.

[0866] Support for home healthcare

[0867] Step 1:

[0868] User: Enters their health information and changes in their physical condition into the device and sends it.

[0869] Step 2:

[0870] Terminal: Sends health information to the server.

[0871] Step 3:

[0872] Server: Analyzes received data and provides it to medical staff.

[0873] Step 4:

[0874] Medical staff: Generate instructions based on the analysis results and input them into the server.

[0875] Step 5:

[0876] Server: Sends instructions from medical staff to the user's terminal.

[0877] Step 6:

[0878] Terminal: Displays instructions from medical staff to the user.

[0879] Specific example:

[0880] User: Enters "My body temperature is high and my throat hurts" into the device and sends it.

[0881] Terminal: Sends health information to the server.

[0882] Server: Analyzes the data and notifies medical staff of "elevated body temperature and sore throat."

[0883] Medical staff: Enter the instruction, "Take paracetamol and drink plenty of fluids."

[0884] Server: Sends instructions to the terminal.

[0885] Terminal: Displays instructions to the user.

[0886] Recipe management

[0887] Step 1:

[0888] User: Enter and submit a meal request on the terminal.

[0889] Step 2:

[0890] Terminal: Sends a request to the server.

[0891] Step 3:

[0892] Server: Uses a generative AI model to analyze requests and generate appropriate recipes.

[0893] Step 4:

[0894] Server: Sends the generated recipe to the terminal.

[0895] Step 5:

[0896] Terminal: Displays the recipe to the user.

[0897] Specific example:

[0898] User: Type "Please share some recipes suitable for people with diabetes" and submit.

[0899] Terminal: Sends a request to the server.

[0900] Server: Uses a generative AI model to generate "Sautéed Chicken Breast and Broccoli" and sends it to the terminal.

[0901] Terminal: Displays the recipe to the user.

[0902] Online medical consultation

[0903] Step 1:

[0904] User: Make an appointment for a medical consultation using the terminal.

[0905] Step 2:

[0906] Terminal: Sends reservation information to the server.

[0907] Step 3:

[0908] Server: Sets up a video call between the doctor and the user.

[0909] Step 4:

[0910] User: Start a video call from your device at the scheduled time.

[0911] Step 5:

[0912] Server: Records medical results and instructions, and sends them to the user's terminal.

[0913] Step 6:

[0914] Terminal: Displays medical results and instructions to the user.

[0915] Specific example:

[0916] User: Make an appointment for a medical consultation using the terminal.

[0917] Terminal: Sends reservation information to the server.

[0918] Server: Set up a video call with the doctor.

[0919] User: Start the video call at the scheduled time.

[0920] Server: Records the medical results as "You need rest, please take the prescribed medication" and sends it to the terminal.

[0921] Terminal: Displays the medical results to the user.

[0922] (Example 1)

[0923] Next, we will describe Example 1. 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."

[0924] In modern society, medical resources and equipment are limited, and providing appropriate medical support is particularly difficult in areas with limited access to healthcare and in aging societies. Furthermore, the lack of adequate means for users to monitor their health on a daily basis and receive prompt professional medical support when needed can increase the risk of health problems.

[0925] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0926] In this invention, the server includes means for generating appropriate answers to health-related questions from users using a generative AI model; means for receiving and analyzing measurement data from medical devices; means for sending notifications to users or medical professionals based on the analysis results; means for collecting health information from users and providing it to medical professionals; means for sending and displaying instructions from medical professionals to users; means for generating meal plan proposals based on user requests; means for relaying medical consultations between users and doctors via online communication; and means for recording and sending medical results and instructions to users. This makes it possible to provide high-quality medical support even in areas with limited access to medical care and in aging societies.

[0927] A "generative AI model" is an artificial intelligence model that analyzes user input data and generates appropriate responses or results based on that data.

[0928] A "health-related question" is an inquiry that a user submits to seek information or advice about their health condition or symptoms.

[0929] An "appropriate answer" is accurate and useful information based on medical knowledge, provided by a generative AI model in response to a user's question.

[0930] A "medical device" is a device used by a user to measure their own health indicators, and examples include blood pressure monitors and thermometers.

[0931] "Measurement data" refers to data on health indicators obtained using medical devices.

[0932] "Analysis" is the process of analyzing received data to identify important information and outliers.

[0933] A "notification" is a message sent to inform a user or medical professional of analysis results or important information.

[0934] "Health information" refers to comprehensive health data that includes information about a user's health status, symptoms, and daily life.

[0935] A "medical professional" is a person with specialized knowledge in the medical field, such as a doctor or nurse.

[0936] "Instructions" refer to guidance provided by healthcare professionals to users to prompt them to take specific actions regarding health management and treatment.

[0937] A "meal plan" is a nutritionally balanced meal plan provided by an AI model that generates data based on the user's health status and requests.

[0938] "Online communication" refers to a communication method that involves sending and receiving data via a network such as the internet.

[0939] "Medical treatment" refers to a series of medical actions and activities performed by a doctor on a user.

[0940] "Medical outcome" refers to medical conclusions or findings obtained through medical treatment.

[0941] "Record keeping" refers to the process of saving medical results and instructions in a digital format so that they can be reviewed later.

[0942] This invention is a last-mile medical support system that utilizes a generative AI model, in which the user, terminal, and server elements work together. The following details each component and its role.

[0943] Providing a health chatbot

[0944] Server: Uses a generative AI model to generate appropriate answers to health-related questions from users. Specifically, the server analyzes the content of health consultations sent by users and generates answers using the generative AI model "GPT-3". For example, if a user enters "I've been having trouble sleeping lately," the server generates an answer such as "You can try relaxing before bed and avoiding caffeine. If it persists, please consult a doctor," and sends this to the terminal.

[0945] Device: This device sends health-related question data entered by the user to the server and displays the server's responses to the user. Smartphones and PCs fall into this category.

[0946] User: Enters health-related questions from their device and receives answers from the server.

[0947] Monitoring of medical devices

[0948] Server: Analyzes measurement data transmitted from medical devices and checks for abnormalities. Sends notifications to users and medical staff as needed. For example, if it receives blood pressure monitor data and uses the generated AI model "anomaly detection algorithm" to detect high blood pressure, it will send a notification saying, "Your blood pressure is high. Please consult a doctor."

[0949] Terminal: Receives data from medical devices used by the user and transmits it to the server. Wireless communication technologies such as Bluetooth are also used.

[0950] User: Uses medical devices to measure their own health data and transmits that data to a server via their device.

[0951] Support for home healthcare

[0952] Server: Collects health information from users and provides it to medical staff. It also sends instructions from medical staff to users. For example, if a user enters "My temperature is high and I have a sore throat," the server provides this to the medical staff. Then, if the medical staff instructs "Take paracetamol and drink plenty of fluids," the server sends this to the user.

[0953] Terminal: Sends health information from the user to the server and displays instructions from medical staff on the server to the user.

[0954] User: Enter their health status and any changes in their physical condition into the device to receive support.

[0955] Recipe management

[0956] Server: Uses a generative AI model to generate meal recipes that match the user's request. For example, using the generative AI model "Cookpad AI," in response to a user's request, "Please tell me a meal recipe suitable for diabetes," it generates a recipe such as "Sautéed chicken breast and broccoli."

[0957] Terminal: Displays recipe information provided by the server to the user.

[0958] User: Enter a meal request and receive a generated recipe.

[0959] Online medical consultation

[0960] Server: Relays the consultation between the user and the doctor via video call. Records the consultation results and medical instructions and sends them to the user. For example, it uses WebRTC technology to conduct video calls, saves the consultation details to a document management system, and sends medical instructions such as "Please take the specified medication" to the user.

[0961] Terminal: Connects the user's video call to the server and displays the medical results and instructions.

[0962] User: Makes an appointment for a medical consultation and has a consultation with a doctor via video call at the designated time.

[0963] Examples of specific cases and prompt statements

[0964] For example, if a user enters "I haven't been able to sleep lately," the server generates a response such as "You can try relaxing before bed and avoiding caffeine. If it persists, please consult a doctor," and displays it to the user via their device.

[0965] Example input prompts for a generative AI model:

[0966] User input: I can't sleep lately

[0967] Example response from the generated AI model: Possible measures include relaxing before bed and avoiding caffeine. If symptoms persist, consult a doctor.

[0968] As described above, this system, centered around a generative AI model, enables effective last-mile medical support through the mutual cooperation of users, terminals, and servers. This system makes it possible to provide high-quality medical support even in areas with limited access to healthcare and in aging societies.

[0969] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0970] Providing a health chatbot

[0971] Step 1:

[0972] User: Enter a health-related question into the terminal. For example, enter "I've been having trouble sleeping lately."

[0973] Input: Text-based questions about health

[0974] Output: Send instructions on the terminal

[0975] Step 2:

[0976] Terminal: Sends the entered question data to the server. HTTPS is used as the communication protocol.

[0977] Input: Question data entered by the user

[0978] Data processing: Question data is converted into HTTP request format.

[0979] Output: HTTP request sent to the server

[0980] Step 3:

[0981] Server: Inputs question data into the AI ​​model, analyzes it, and generates appropriate answers. It uses "GPT-3" to generate answers to user questions.

[0982] Input: Question data in HTTP request format

[0983] Data processing: Text analysis and response generation using generative AI models.

[0984] Output: Response data generated in JSON format

[0985] Step 4:

[0986] Server: Sends the generated response to the terminal.

[0987] Input: Response data in JSON format

[0988] Data processing: The response data is converted into an HTTP response format.

[0989] Output: HTTP response sent to the terminal

[0990] Step 5:

[0991] Terminal: Displays received responses to the user. Responses are displayed on the terminal screen.

[0992] Input: Response data in HTTP response format

[0993] Data processing: Response data is converted into a format that users can understand.

[0994] Output: The answer displayed on the device screen

[0995] Monitoring of medical devices

[0996] Step 1:

[0997] User: Measuring data using medical devices. For example, measuring blood pressure using a blood pressure monitor.

[0998] Input: Blood pressure measuring device

[0999] Output: Measurement data

[1000] Step 2:

[1001] Medical devices: Transmit measurement data to a terminal. Wireless communication such as Bluetooth is used.

[1002] Input: Measurement data

[1003] Data processing: Measurement data is converted to a wireless communication format.

[1004] Output: Measurement data sent to the terminal

[1005] Step 3:

[1006] Terminal: Sends measurement data to the server. The HTTPS protocol is used again.

[1007] Input: Measurement data received via wireless communication

[1008] Data processing: Convert measurement data into HTTP request format.

[1009] Output: HTTP request sent to the server

[1010] Step 4:

[1011] Server: Analyzes data and checks for anomalies. It uses a generative AI model, "Anomaly Detection Algorithm," to analyze the data.

[1012] Input: Measurement data in HTTP request format

[1013] Data processing: Analysis of measurement data and anomaly detection.

[1014] Output: Notification data in case of an anomaly

[1015] Step 5:

[1016] Server: If an abnormality occurs, it sends a notification to the user or medical staff. A notification such as "Your blood pressure is high. Please consult a doctor" is generated.

[1017] Input: Data after anomaly detection

[1018] Data processing: Convert notification content into HTTP response format.

[1019] Output: Notification data sent to the terminal

[1020] Step 6:

[1021] Terminal: Displays received notifications to the user. The notification will appear on the user's terminal screen.

[1022] Input: Notification data in HTTP response format

[1023] Data processing: Convert notification data into a format that users can understand.

[1024] Output: Notification displayed on the device screen

[1025] Support for home healthcare

[1026] Step 1:

[1027] User: Enter health information into the terminal. For example, enter "My temperature is high and I have a sore throat."

[1028] Input: Health information

[1029] Output: Health information data

[1030] Step 2:

[1031] Terminal: Sends health information to the server. The HTTPS protocol is used.

[1032] Input: Entered health information data

[1033] Data processing: Convert health information data into HTTP request format.

[1034] Output: HTTP request sent to the server

[1035] Step 3:

[1036] Server: Provides health information to medical staff. The information is displayed on a dashboard for medical staff.

[1037] Input: Health information data in HTTP request format

[1038] Data processing: Convert health information to a display format.

[1039] Output: Display on the medical staff dashboard

[1040] Step 4:

[1041] Medical staff: Enter instructions for the user into the server. Enter the instruction, "Take paracetamol and drink plenty of fluids."

[1042] Input: Instructions from medical staff

[1043] Output: Instruction data

[1044] Step 5:

[1045] Server: Sends instructions from medical staff to terminals. This also uses the HTTPS protocol.

[1046] Input: Medical staff instruction data

[1047] Data processing: Convert the instruction data into an HTTP response format.

[1048] Output: Data to send to the terminal

[1049] Step 6:

[1050] Terminal: Displays instructions from medical staff to the user. Pop-up notifications are used, etc.

[1051] Input: Instruction data in HTTP response format

[1052] Data processing: Convert the instruction data into a format that the user can understand.

[1053] Output: Instructions displayed on the device screen

[1054] Recipe management

[1055] Step 1:

[1056] User: Enter a request regarding meals into the terminal. For example, enter "Please provide recipes suitable for meals for people with diabetes."

[1057] Input: Meal request

[1058] Output: Request data

[1059] Step 2:

[1060] Terminal: Sends a request to the server.

[1061] Input: User request data

[1062] Data processing: Convert request data into HTTP request format.

[1063] Output: HTTP request sent to the server

[1064] Step 3:

[1065] Server: Generates recipes using a generative AI model. The generative AI model used is "Cookpad AI".

[1066] Input: Request data in HTTP request format

[1067] Data processing: Parsing request data and generating recipes.

[1068] Output: Recipe data generated in JSON format

[1069] Step 4:

[1070] Server: Sends the generated recipe to the terminal.

[1071] Input: Recipe data in JSON format

[1072] Data processing: Convert recipe data into HTTP response format.

[1073] Output: HTTP response sent to the terminal

[1074] Step 5:

[1075] Terminal: Displays recipes to the user. Recipe ingredients and cooking instructions are displayed on the screen in a list format.

[1076] Input: Recipe data in HTTP response format

[1077] Data processing: Convert recipe data into a format that users can understand.

[1078] Output: Recipe displayed on the terminal screen

[1079] Online medical consultation

[1080] Step 1:

[1081] User: Make an appointment for a medical consultation via a terminal. Enter the date, time, and symptoms into the appointment system.

[1082] Input: Appointment data

[1083] Output: Reservation Request

[1084] Step 2:

[1085] Server: Notifies doctors of appointment information. This information is displayed on the doctor's dashboard.

[1086] Input: Reservation Request

[1087] Data processing: Convert appointment information to a format suitable for doctors.

[1088] Output: Display on the physician's dashboard

[1089] Step 3:

[1090] User: Start a video call on your device at the specified time. A WebRTC video conference will be set up.

[1091] Input: Call Initiation Request

[1092] Output: Start video call

[1093] Step 4:

[1094] Server: Connects users and doctors via video call.

[1095] Input: Video call request

[1096] Data processing: Call connection settings

[1097] Output: Video call connection between user and doctor

[1098] Step 5:

[1099] Doctor: Conducts medical examinations and records the results and instructions on the server. For example, they might enter an instruction such as, "Please take the prescribed medication."

[1100] Input: Medical results and instruction data

[1101] Output: Recorded data

[1102] Step 6:

[1103] Server: Sends medical results and instructions to the terminal.

[1104] Input: Recorded data

[1105] Data processing: Convert medical results and instructions into HTTP response format.

[1106] Output: HTTP response sent to the terminal

[1107] Step 7:

[1108] Terminal: Displays medical results and instructions to the user. Text notifications are displayed on the screen.

[1109] Input: Clinical results and instruction data in HTTP response format

[1110] Data processing: Converting medical results and instructions into a format that users can understand.

[1111] Output: Medical results and instructions displayed on the terminal screen.

[1112] (Application Example 1)

[1113] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[1114] In modern society, providing appropriate meal plans tailored to users' health conditions and facilitating on-the-spot ordering and delivery is challenging. Furthermore, regularly tracking users' health data and continuously adjusting meal plans accordingly is also difficult. There is a need to develop a system that solves these problems and provides high-quality medical support based on users' health conditions.

[1115] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1116] In this invention, the server includes means for generating appropriate answers to health-related questions from the user using a generative AI model; means for receiving and analyzing measurement data from medical devices; means for sending notifications to the user or medical staff based on the analysis results; means for collecting health information from the user and providing it to medical staff; means for sending and displaying instructions from medical staff to the user; means for generating meal recipes based on the user's request; means for relaying consultations between the user and a doctor via video call; means for proposing a meal plan based on the user's health condition and ordering and delivering it on the spot; and means for regularly tracking the user's health data and adjusting the meal plan accordingly. This enables the provision of meal plans tailored to the user's health condition and on-the-spot ordering and delivery, as well as continuous tracking of health data and appropriate adjustment of the meal plan based on that data.

[1117] A "generative AI model" is an algorithm or system that uses artificial intelligence to generate appropriate answers or suggestions based on user input data.

[1118] "Means" refers to methods, devices, or software programs designed to achieve a specific function or role.

[1119] A "user" is an individual or organization that uses the system to receive health-related consultations or medical support.

[1120] "Health-related questions" are inquiries or consultations that users enter into the system regarding their health status and physical condition.

[1121] "Medical devices" are devices and equipment used for medical purposes, and include the measurement and recording of health data.

[1122] "Measurement data" refers to numerical information and measurement results obtained using medical devices.

[1123] "Analysis results" refer to diagnostic information and suggestions obtained from measurement data using generated AI models or other technical means.

[1124] "Health information" refers to various data and records related to the user's health status and physical condition.

[1125] "Medical staff" refers to individuals with specialized medical knowledge who are responsible for providing medical care and guidance to users.

[1126] "Instructions" refer to specific actions or procedures that medical staff provide to users.

[1127] A "meal recipe" is a document that lists ingredients and cooking methods suitable for a specific health condition.

[1128] "Video calling" is a technology that allows for real-time conversations using video and audio over the internet.

[1129] "Medical results" refer to diagnostic information and medical instructions obtained during medical consultations conducted via video call or similar means.

[1130] A "meal plan" is a set of meal suggestions or plans tailored to the user's health condition.

[1131] "Means of ordering and delivery" refers to the process and system for users to order food through a system and have it delivered.

[1132] "Health data tracking" refers to the act of regularly recording and monitoring health information collected from users.

[1133] "Adjusting meal plans" refers to the process of continuously modifying and optimizing meal plans to suit the user based on tracked health data.

[1134] This invention is a last-mile medical support system that utilizes a generative AI model, in which the user, terminal, and server elements work together in coordination. The following details each component and its role.

[1135] Providing a health chatbot

[1136] Server: Uses a generative AI model to generate appropriate answers to health-related questions from users. The server analyzes the content of health consultations submitted by users and generates answers on the spot based on relevant medical knowledge.

[1137] Device: This device sends health-related question data entered by the user to the server and displays the server's responses to the user. Smartphones and PCs fall into this category.

[1138] User: Enters health-related questions from their device and receives answers from the server.

[1139] Specific example: If a user enters "I haven't been able to sleep lately," the server generates a response such as "You can try relaxing before bed and avoiding caffeine. If it persists, please consult a doctor," and displays it to the user via their device.

[1140] Monitoring of medical devices

[1141] Server: Analyzes measurement data transmitted from medical devices and checks for abnormalities. Sends notifications to users and medical staff as needed.

[1142] Terminal: Receives data from the medical device used by the user and sends it to the server.

[1143] User: Uses medical devices to measure their own health data and transmits the data through their device.

[1144] Specific example: A user sends blood pressure data measured using a blood pressure monitor from their device to a server. If the server detects high blood pressure, it notifies the user with the message, "Your blood pressure is high. Please consult a doctor."

[1145] Support for home healthcare

[1146] Server: Collects health information from users and provides it to medical staff. It also sends instructions from medical staff to users.

[1147] Terminal: Sends health information from the user to the server and displays instructions from medical staff on the server to the user.

[1148] User: Enter their health status and any changes in their physical condition into the device to receive support.

[1149] Specific example: When a user enters "My body temperature is high and I have a sore throat," the server analyzes the data and provides it to medical staff. The medical staff then advises, "Take paracetamol and drink plenty of fluids," and sends this information back to the user.

[1150] Recipe management

[1151] Server: Uses a generative AI model to generate meal recipes that match the user's request.

[1152] Terminal: Displays recipe information provided by the server to the user.

[1153] User: Enter a meal request and receive a generated recipe.

[1154] Specific example: When a user enters "Please tell me a recipe for a meal suitable for diabetes," the server generates a recipe for "Sautéed chicken breast and broccoli" and displays it to the user via their terminal.

[1155] Online medical consultation

[1156] Server: Relays the consultation between the user and the doctor via video call. Records the consultation results and medical instructions and sends them to the user.

[1157] Terminal: Connects the user's video call to the server and displays the medical results and instructions.

[1158] User: Makes an appointment for a medical consultation and conducts the consultation with a doctor via video call.

[1159] Specific example: A user makes an appointment for a medical consultation and starts a video call at the designated time. After the doctor conducts the examination and notifies the user of the diagnosis, such as "Please take the prescribed medication," this is displayed to the user via their device.

[1160] Meal plan suggestions, ordering, and delivery.

[1161] Server: Proposes meal plans based on the user's health status, takes orders on the spot, and arranges delivery. It uses a generative AI model to analyze health data and generate meal suggestions. It also regularly tracks the user's health data and adjusts the meal plan accordingly.

[1162] Terminal: Sends user-entered health data and meal requests to the server, and displays suggestions and order confirmations from the server.

[1163] User: Enters their health status, receives a meal plan tailored to their condition from the server, and proceeds with ordering and delivery.

[1164] Specific example: If a diabetic user enters into the app, "My recent blood sugar level is 150, and I'd like some dietary advice," the server will suggest, "A low-carbohydrate diet is recommended to control blood sugar levels," and provide a suitable recipe (e.g., chicken breast and broccoli). The user can then order delivery.

[1165] Example prompt:

[1166] "My blood sugar level is currently 150. Please suggest a suitable diet for people with diabetes."

[1167] This system allows users to efficiently manage a series of processes, from health consultations and medical device data transmission to home healthcare support, recipe management, online consultations, and meal plan suggestions, ordering, and delivery. This enables high-quality medical support and appropriate nutritional management.

[1168] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1169] Step 1:

[1170] The user enters their health consultation into the terminal. The user uses the terminal to input specific questions or concerns about their health. For example, they might enter, "I've been having trouble sleeping lately." The input data is saved on the terminal.

[1171] Step 2:

[1172] The terminal sends the input data to the server. The entered health consultation content is converted into a data format and sent to the server using the HTTPS protocol. The server prepares to analyze the received data.

[1173] Step 3:

[1174] The server uses a generative AI model to generate appropriate answers to questions. The server analyzes the received health consultation data and inputs it into the generative AI model. The generative AI model generates answers based on relevant medical knowledge and returns them to the server.

[1175] Step 4:

[1176] The server sends the generated response to the terminal. The AI ​​model that generates the response sends the data, including the response, to the terminal. The terminal converts the received response data into a display format and displays it to the user.

[1177] Step 5:

[1178] The user uses a health device and inputs the measurement data into the terminal. For example, they might use a blood pressure monitor to measure their blood pressure and input that numerical data into the terminal.

[1179] Step 6:

[1180] The terminal sends measurement data to the server. The input measurement data is converted to a data format and sent to the server using the HTTPS protocol. The server prepares to analyze the received data.

[1181] Step 7:

[1182] The server analyzes the measurement data and checks for any abnormalities. The server analyzes the received measurement data and compares it to reference values. For example, if blood pressure is high, it will be determined to be "hypertension."

[1183] Step 8:

[1184] If the server detects an anomaly, it will send a notification to the user or medical staff. Based on the analysis results, if an anomaly is detected, a notification message will be created and sent to the user or medical staff.

[1185] Step 9:

[1186] The user enters a request regarding meals into the device. For example, they might enter, "Please provide recipes suitable for meals for people with diabetes." The entered data is saved on the device.

[1187] Step 10:

[1188] The terminal sends the input data to the server. The entered meal request details are converted into a data format and sent to the server using the HTTPS protocol. The server prepares to analyze the received data.

[1189] Step 11:

[1190] The server generates suitable meal recipes using a generative AI model. The server analyzes the received request data and inputs it into the generative AI model. The generative AI model generates suitable meal recipes and returns them to the server.

[1191] Step 12:

[1192] The server sends the generated recipe to the terminal. The AI ​​model sends data containing the meal recipe to the terminal. The terminal converts the received recipe data into a display format and displays it to the user.

[1193] Step 13:

[1194] Users order meal recipes and request delivery. They review the displayed recipes, place their orders on the spot via their terminal, and request delivery.

[1195] Step 14:

[1196] The server periodically tracks the user's health data and adjusts the meal plan accordingly. The server regularly collects the user's health data, generates an optimal meal plan using a generative AI model, and adjusts it as needed.

[1197] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1198] This invention is a last-mile medical support system that utilizes a generative AI model and an emotion engine. This system functions through the coordinated efforts of the user, terminal, server, and emotion engine. The following details each component and its role.

[1199] Providing a health chatbot

[1200] Server: Uses a generative AI model to generate appropriate answers to health-related questions from users. By combining it with an emotion engine, it recognizes the user's emotional state from the input data and generates answers corresponding to that emotional state.

[1201] Device: This device sends health-related question data entered by the user to the server and displays the server's responses to the user. Smartphones and PCs fall into this category.

[1202] User: Enters health-related questions from their device and receives answers from the server.

[1203] Specific example: If a user enters "I haven't been able to sleep lately" in an anxious tone, the server uses an emotion engine to recognize the user's anxiety and generates an emotionally sensitive response such as, "You can try relaxing before bed or avoiding caffeine. If your anxiety persists, please consult a doctor," which is then displayed to the user via their device.

[1204] Monitoring of medical devices

[1205] Server: Analyzes measurement data transmitted from medical devices and checks for abnormalities. It also analyzes the user's emotional state using an emotion engine and sends notifications to the user and medical staff as needed.

[1206] Terminal: Receives data from the medical device used by the user and sends it to the server.

[1207] User: Uses medical devices to measure their own health data and transmits the data through their device.

[1208] Specific example: A user sends blood pressure data measured using a blood pressure monitor from their device to a server. If the server detects high blood pressure and the emotion engine recognizes the user's anxiety level, it notifies the user with the message, "Your blood pressure is high. Please consult a doctor. If you are feeling anxious, try some relaxation techniques."

[1209] Support for home healthcare

[1210] Server: Collects health information from users and analyzes their emotional state using an emotion engine. Provides health information and emotional state data to medical staff and sends instructions from medical staff to the user.

[1211] Terminal: Sends health information from the user to the server and displays instructions from medical staff on the server to the user.

[1212] User: Enter their health status and any changes in their physical condition into the device to receive support.

[1213] Specific example: If a user enters "My body temperature is high and my throat hurts," and the emotion engine recognizes the user's anxiety, the server analyzes the data and notifies medical staff of "high body temperature, sore throat, and anxiety." The medical staff then instructs the user to "take paracetamol and drink plenty of fluids. Please contact us if your anxiety persists," and sends this to the user.

[1214] Recipe management

[1215] Server: Uses a generative AI model to generate meal recipes that match the user's request. It can also use an emotion engine to suggest recipes that take the user's emotional state into consideration.

[1216] Terminal: Displays recipe information provided by the server to the user.

[1217] User: Enter a meal request and receive a generated recipe.

[1218] Specific example: If a user types "Please tell me a recipe for a meal suitable for diabetes," and the emotion engine recognizes the user's stress, the server generates a recipe suggesting "Sautéed chicken breast and broccoli with relaxing herbal tea" and displays it to the user via their device.

[1219] Online medical consultation

[1220] Server: Relays the user's consultation with the doctor via video call. Records the consultation results and medical instructions, and analyzes and records the user's emotional state using an emotion engine.

[1221] Terminal: Connects the user's video call to the server and displays the medical results and instructions.

[1222] User: Makes an appointment for a medical consultation and conducts the consultation with a doctor via video call.

[1223] Specific example: A user makes an appointment for a medical consultation and starts a video call at the designated time. The doctor conducts the consultation, and if the emotion engine recognizes the user's anxiety, the doctor notifies the user of the consultation result, such as "Please take the prescribed medication and take time to relax." The device displays an emotionally sensitive message to the user along with the consultation result.

[1224] As described above, the system of the present invention, centered on a generative AI model and an emotion engine, enables the user, terminal, and server to cooperate with each other to effectively realize last-mile medical support. This system makes it possible to provide high-quality and emotionally sensitive medical support even in areas with limited access to medical care and in aging societies.

[1225] The following describes the processing flow.

[1226] Providing a health chatbot

[1227] Step 1:

[1228] User: Enter health-related questions into the device and submit.

[1229] Step 2:

[1230] Terminal: Sends the entered question data to the server.

[1231] Step 3:

[1232] Server: Receives question data and analyzes the user's emotional state using an emotion engine.

[1233] Step 4:

[1234] Server: Considers emotional states and generates appropriate responses using a generative AI model.

[1235] Step 5:

[1236] Server: Sends the generated response to the terminal.

[1237] Step 6:

[1238] Terminal: Displays the generated response to the user.

[1239] Specific example: If a user enters "I haven't been able to sleep lately" and indicates feelings of anxiety, the server generates a response such as "Try some relaxation techniques. If it persists, consult a doctor" and displays it to the user via their device.

[1240] Monitoring of medical devices

[1241] Step 1:

[1242] User: Uses medical devices to measure health data.

[1243] Step 2:

[1244] Terminal: Sends measured data to the server.

[1245] Step 3:

[1246] Server: Receives and analyzes measurement data.

[1247] Step 4:

[1248] Server: Analyzes the user's emotional state using an emotion engine.

[1249] Step 5:

[1250] Server: If an anomaly is detected, it generates a notification taking into account the emotional state.

[1251] Step 6:

[1252] Server: Sends a notification to the device.

[1253] Step 7:

[1254] Device: Displays notifications to the user.

[1255] Specific example: If a user measures their blood pressure with a blood pressure monitor and an abnormal reading is detected, the server generates a notification saying, "Your blood pressure is high. If you are worried, try some relaxation techniques. If it persists, consult a doctor," and displays it to the user via their device.

[1256] Support for home healthcare

[1257] Step 1:

[1258] User: Enter and submit information about their health status and any changes in their physical condition on the device.

[1259] Step 2:

[1260] Terminal: Sends health information to the server.

[1261] Step 3:

[1262] Server: Analyzes the received data.

[1263] Step 4:

[1264] Server: Analyzes the user's emotional state using an emotion engine.

[1265] Step 5:

[1266] Server: Provides health information and emotional status to medical staff.

[1267] Step 6:

[1268] Medical staff: Generate instructions based on health information and emotional state, and input them into the server.

[1269] Step 7:

[1270] Server: Sends instructions from medical staff to the user's terminal.

[1271] Step 8:

[1272] Terminal: Displays instructions from medical staff to the user.

[1273] Specific example: If a user enters "My temperature is high and I have a sore throat," and the emotion engine recognizes this as anxiety, the server analyzes the data and notifies medical staff. The medical staff then instructs the user to "Take paracetamol and drink plenty of fluids. Contact us if you have any concerns," and sends this to the user, who then sees it displayed on their device.

[1274] Recipe management

[1275] Step 1:

[1276] User: Enter and submit a meal request on the terminal.

[1277] Step 2:

[1278] Terminal: Sends a request to the server.

[1279] Step 3:

[1280] Server: Analyzes the user's emotional state using an emotion engine.

[1281] Step 4:

[1282] Server: Generates appropriate recipes using a generative AI model.

[1283] Step 5:

[1284] Server: Sends the generated recipe to the terminal.

[1285] Step 6:

[1286] Terminal: Displays the recipe to the user.

[1287] Specific example: If a user types "Please tell me a recipe for a meal suitable for diabetes," and the emotion engine recognizes the user's stress, the server will generate a recipe suggesting "Sautéed chicken breast and broccoli with relaxing herbal tea" and display it to the user via their device.

[1288] Online medical consultation

[1289] Step 1:

[1290] User: Make an appointment for a medical consultation using the terminal.

[1291] Step 2:

[1292] Terminal: Sends reservation information to the server.

[1293] Step 3:

[1294] Server: Sets up a video call between the doctor and the user.

[1295] Step 4:

[1296] User: Start a video call from your device at the scheduled time.

[1297] Step 5:

[1298] Server: Records medical results and instructions. Also analyzes the user's emotional state using an emotion engine.

[1299] Step 6:

[1300] Server: Sends medical results and instructions to the user's terminal.

[1301] Step 7:

[1302] Terminal: Displays medical results and instructions to the user.

[1303] Specific example: A user makes an appointment for a medical consultation and starts a video call at the designated time. The doctor conducts the consultation, and if the emotion engine recognizes the user's anxiety, the doctor notifies the user of the consultation result, such as "Please take the prescribed medication and take time to relax." The device displays an emotionally sensitive message to the user along with the consultation result.

[1304] As described above, the system of the present invention, centered on a generative AI model and an emotion engine, enables the user, terminal, and server to cooperate with each other to effectively and emotionally considerately provide last-mile medical support. This system makes it possible to provide high-quality medical support even in areas with limited access to medical care and in aging societies.

[1305] (Example 2)

[1306] Next, we will describe Example 2. 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".

[1307] Conventional medical support systems often provide information without considering the user's emotional state, leading to problems such as users' anxiety and stress not being alleviated. Furthermore, data analysis from medical devices also fails to consider emotional states, sometimes resulting in inappropriate advice and notifications to users and medical staff. Therefore, there is a need for a medical support system that takes emotional considerations into account.

[1308] The identification processing performed 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 generating appropriate answers to health-related questions from the user using a generation AI model, means for analyzing the emotional state using an emotion engine based on the user's input data and generating corresponding answers, and means for receiving and analyzing measurement data from medical devices. This enables not only the provision of information according to the user's health condition but also responses that take emotions into consideration.

[1309] A "generative AI model" is a type of artificial intelligence that automatically generates appropriate answers and suggestions based on user input data.

[1310] An "emotion engine" is a program that analyzes a user's emotional state based on their input data and actions, and then takes appropriate action based on that analysis.

[1311] "Health-related questions" are questions that users ask about their own health status or symptoms.

[1312] A "medical device" refers to a device or apparatus used to measure a user's health data. Examples include blood pressure monitors and thermometers.

[1313] "Measurement data" refers to numerical data about a user's health obtained using medical devices.

[1314] "Analysis" is the process of detecting and evaluating outliers and trends based on acquired data.

[1315] A "notification" is information that the system communicates to the user or medical staff regarding anomalies or advice.

[1316] "Health information" refers to data about the user's health status and symptoms that they enter into the system.

[1317] "Medical staff" refers to medical professionals who use the system to support users' health.

[1318] "Instructions" refer to the methods of action and advice provided by medical staff to the user.

[1319] A "meal recipe" refers to a list of cooking methods and ingredients suggested based on the user's requests and health condition.

[1320] A "video call" is a method of communication between a remote user and a doctor in real time using audio and video.

[1321] "Medical results" refer to the diagnostic information and treatment plan of a user obtained by a doctor through video calls or other means.

[1322] This invention is a last-mile medical support system in which the user, terminal, server, and emotion engine work together. By utilizing a generative AI model and emotion engine, this system can provide appropriate advice and notifications based on the user's health information.

[1323] Providing a health chatbot

[1324] Server: Uses a generative AI model to generate appropriate answers to health-related questions from users. By combining it with an emotion engine, it analyzes the user's emotional state from the input data and generates answers that correspond to that emotional state.

[1325] Terminal: The terminal sends health-related question data entered by the user to the server and displays the server's responses to the user. Specific hardware used includes smartphones and PCs.

[1326] User: Enters health-related questions from their device and receives answers from the server. This allows the user to receive specific advice tailored to their health condition.

[1327] Specific example: If a user enters "I haven't been able to sleep lately" in an anxious tone, the server uses an emotion engine to analyze the user's anxiety and generates an emotionally sensitive response such as, "You can try relaxing before bed or avoiding caffeine. If your anxiety persists, please consult a doctor," which is then displayed to the user via their device.

[1328] Monitoring of medical devices

[1329] Server: Analyzes measurement data transmitted from medical devices and checks for abnormalities. It also analyzes the user's emotional state using an emotion engine and sends notifications to the user and medical staff as needed.

[1330] Terminal: Receives data from the medical device used by the user and sends it to the server.

[1331] User: Uses medical devices to measure their own health data and transmits it through a terminal. This allows users to monitor their health status in real time.

[1332] Specific example: A user sends blood pressure data measured using a blood pressure monitor from their device to a server. If the server detects high blood pressure and the emotion engine further analyzes the user's anxiety level, it will notify the user with the message, "Your blood pressure is high. Please consult a doctor. If you are feeling anxious, try some relaxation techniques."

[1333] Support for home healthcare

[1334] Server: Collects health information from users and analyzes their emotional state using an emotion engine. Provides health information and emotional state data to medical staff and sends instructions from medical staff to the user.

[1335] Terminal: Sends health information from the user to the server and displays instructions from medical staff on the server to the user.

[1336] User: Enters their health status and changes in physical condition into the device to receive support. This allows users to receive detailed health information and instructions from the comfort of their homes.

[1337] Specific example: A user enters "My body temperature is high and my throat hurts," and the emotion engine analyzes the user's anxiety. The server then analyzes the data and notifies medical staff of "high body temperature, sore throat, and anxiety." The medical staff then instructs the user to "take paracetamol and drink plenty of fluids. Contact us if your anxiety persists," and sends this information to the user.

[1338] Recipe management

[1339] Server: Uses a generative AI model to generate meal recipes that match the user's request. It can also use an emotion engine to suggest recipes that take the user's emotional state into consideration.

[1340] Terminal: Displays recipe information provided by the server to the user.

[1341] User: Enters a request regarding meals and receives a generated recipe. This allows users to receive recipes tailored to their health condition.

[1342] Specific example: If a user enters "Please tell me a recipe for a meal suitable for diabetes," and the emotion engine analyzes the user's stress level, the server generates a recipe suggesting "Sautéed chicken breast and broccoli with relaxing herbal tea," which is then displayed to the user via their device.

[1343] Online medical consultation

[1344] Server: Relays the user's consultation with the doctor via video call. Records the consultation results and medical instructions, and analyzes and records the user's emotional state using an emotion engine.

[1345] Terminal: Connects the user's video call to the server and displays the medical results and instructions.

[1346] User: Schedules an appointment and conducts a consultation with a doctor via video call. This allows users to receive medical advice and emotional support from the comfort of their homes.

[1347] Specific example: A user makes an appointment for a medical consultation and starts a video call at the designated time. The doctor conducts the consultation, and if the emotion engine analyzes the user's anxiety, the doctor notifies the user of the consultation result, such as "Please take the prescribed medication and take time to relax." The device displays an emotionally sensitive message to the user along with the consultation result.

[1348] Examples of prompts include "I haven't been able to sleep lately," "Please give me some recipes for meals suitable for people with diabetes," and "My body temperature is high and I have a sore throat."

[1349] As described above, the system of the present invention utilizes a generative AI model and an emotion engine, enabling the user, terminal, and server to cooperate with each other to provide high-quality and emotionally sensitive last-mile medical support. This system makes it possible to provide convenient and reliable medical support to users even in areas with limited access to medical care and in aging societies.

[1350] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1351] Providing a health chatbot

[1352] Step 1:

[1353] The user enters health-related questions into the device.

[1354] Input: Health-related questions (e.g., "I've been having trouble sleeping lately")

[1355] Output: Input question data

[1356] Step 2:

[1357] The terminal sends the user's question data to the server.

[1358] Input: User's question data

[1359] Output: Question data sent to the server

[1360] Step 3:

[1361] The server receives the question data and the emotion engine analyzes the emotional state.

[1362] Input: Question data received by the server

[1363] Data processing: Analyze the user's emotional state (e.g., anxiety) based on the questionnaire data.

[1364] Output: Analyzed emotional state

[1365] Step 4:

[1366] The server uses a generation AI model to generate responses that correspond to the emotional state.

[1367] Input: Analyzed emotional state, questionnaire data

[1368] Data processing: Generative AI model generates appropriate answers.

[1369] Output: Generated response (Example: "You can try relaxing before bed and avoiding caffeine. If your anxiety persists, consult a doctor.")

[1370] Step 5:

[1371] The server sends the response to the terminal.

[1372] Input: Generated answer

[1373] Output: Response sent from server to terminal

[1374] Step 6:

[1375] The device displays the answer to the user.

[1376] Input: Response sent from the server

[1377] Output: Answer displayed on the terminal

[1378] Monitoring of medical devices

[1379] Step 1:

[1380] The user acquires measurement data using a medical device.

[1381] Input: Measurement using medical devices (e.g., blood pressure measurement)

[1382] Output: Measured data (e.g., blood pressure)

[1383] Step 2:

[1384] The terminal receives measurement data and sends it to the server.

[1385] Input: Data measured by the user

[1386] Output: Measurement data sent to the server

[1387] Step 3:

[1388] The server receives the measurement data and performs analysis.

[1389] Input: Measurement data received by the server

[1390] Data processing: Detection of abnormal values ​​based on measurement data (e.g., high blood pressure).

[1391] Output: Analysis results (e.g., hypertension)

[1392] Step 4:

[1393] The server uses an emotion engine to analyze the user's emotional state.

[1394] Input: Analysis results (e.g., hypertension), user sentiment data

[1395] Data processing: The emotion engine analyzes the user's anxiety state.

[1396] Output: Analyzed emotional state (e.g., anxiety)

[1397] Step 5:

[1398] The server generates notifications based on the abnormality and emotional state, and sends them to the terminal.

[1399] Input: Analysis results and emotional state

[1400] Data processing: Generate appropriate notifications

[1401] Output: Generated notification (Example: "Your blood pressure is high. Please consult a doctor. If you are feeling anxious, try some relaxation techniques.")

[1402] Step 6:

[1403] The device displays a notification to the user.

[1404] Input: Notification sent from the server

[1405] Output: Notification displayed on the terminal

[1406] Support for home healthcare

[1407] Step 1:

[1408] The user enters their health status into the device.

[1409] Input: Enter your health status (e.g., "My temperature is high and I have a sore throat.")

[1410] Output: Input health information

[1411] Step 2:

[1412] The device sends health information to the server.

[1413] Input: Health information entered by the user

[1414] Output: Health information sent to the server

[1415] Step 3:

[1416] The server analyzes health information and emotional state.

[1417] Input: Health information received by the server

[1418] Data processing: Analysis of emotional states using an emotion engine.

[1419] Output: Analyzed health information and emotional state

[1420] Step 4:

[1421] The server sends a notification to the medical staff.

[1422] Input: Analyzed health information and emotional state

[1423] Output: Notification sent to medical staff

[1424] Step 5:

[1425] Medical staff send instructions to the server.

[1426] Input: Instructions created by medical staff

[1427] Output: Instructions sent to the server

[1428] Step 6:

[1429] The server sends instructions to the terminal from the medical staff.

[1430] Input: Instructions received from medical staff

[1431] Output: Instructions sent to the terminal

[1432] Step 7:

[1433] The device displays instructions to the user.

[1434] Input: Instructions sent from the server

[1435] Output: Instructions displayed on the terminal

[1436] Recipe management

[1437] Step 1:

[1438] The user enters their meal requests into the terminal.

[1439] Input: Request regarding meals (e.g., "Please provide recipes suitable for meals for people with diabetes")

[1440] Output: Input Request

[1441] Step 2:

[1442] The terminal sends a request to the server.

[1443] Input: User-entered request

[1444] Output: Request sent to the server

[1445] Step 3:

[1446] The server analyzes the request and the emotional state.

[1447] Input: Request received by the server

[1448] Data processing: Analysis of emotional states using an emotion engine.

[1449] Output: Analyzed requests and sentiment states

[1450] Step 4:

[1451] The server uses an AI model to generate recipes in response to requests.

[1452] Input: Analyzed request and emotional state

[1453] Data processing: Generative AI model generates appropriate recipes.

[1454] Output: Generated recipe (Example: "Sautéed chicken breast and broccoli with relaxing herbal tea")

[1455] Step 5:

[1456] The server sends the generated recipe to the terminal.

[1457] Input: Generated recipe

[1458] Output: Recipe sent to the terminal

[1459] Step 6:

[1460] The device displays the recipe to the user.

[1461] Input: Recipe sent from the server

[1462] Output: Recipe displayed on the terminal

[1463] Online medical consultation

[1464] Step 1:

[1465] The user makes an appointment for a medical consultation.

[1466] Input: Appointment information for medical consultations

[1467] Output: Reservation confirmation

[1468] Step 2:

[1469] The user initiates a video call at the specified time.

[1470] Input: Start a video call

[1471] Output: Video call connection status

[1472] Step 3:

[1473] The server relays the medical consultation via video call.

[1474] Input: Video call data between user and doctor

[1475] Output: Relayed video call data

[1476] Step 4:

[1477] A doctor provides medical treatment and records the results on a server.

[1478] Input: Medical results

[1479] Output: Recorded medical results

[1480] Step 5:

[1481] The server uses an emotion engine to analyze the user's emotional state.

[1482] Input: Medical results, user sentiment data

[1483] Data processing: The emotion engine analyzes the user's anxiety state.

[1484] Output: Analyzed emotional state

[1485] Step 6:

[1486] The server generates a notification for the user based on the medical results and emotional state.

[1487] Input: Medical results and emotional state

[1488] Data processing: Generated notifications

[1489] Output: Appropriate notification (e.g., "Take the prescribed medication and allow yourself time to relax.")

[1490] Step 7:

[1491] The device displays a notification to the user.

[1492] Input: Notification sent from the server

[1493] Output: Notification displayed on the terminal

[1494] (Application Example 2)

[1495] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[1496] In modern healthcare support, providing appropriate health management and medical support is a challenge, especially in remote areas and regions with limited access to medical care. Furthermore, there is a lack of flexible medical advice based on the user's emotional state, and a system is needed that allows for continuous and individualized communication between medical staff and patients.

[1497] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1498] In this invention, the server includes means for generating appropriate answers to health-related questions from the user using a generative AI model, means for analyzing the user's emotional state using an emotion analysis engine and generating medical results and advice corresponding to that emotional state, and means for receiving and analyzing measurement data from medical devices. This enables appropriate and individualized medical support remotely based on the user's health and emotional state.

[1499] A "generative AI model" is an artificial intelligence model that generates appropriate answers and advice based on user input data.

[1500] An "emotion analysis engine" is a system that analyzes a user's emotional state and provides information based on those emotions.

[1501] A "medical device" is a device that measures a user's health condition, and includes, for example, blood pressure monitors and electrocardiographs.

[1502] "Health information" refers to data about a user's physical condition and symptoms.

[1503] A "video call" is a communication method that uses audio and video to connect users in different locations with doctors in real time.

[1504] "Medical results" refer to the diagnosis and treatment determined by a doctor based on the user's health condition and symptoms.

[1505] "Analysis results" refer to conclusions and information obtained after analyzing data using generative AI models or sentiment analysis engines.

[1506] "Medical staff" refers to healthcare professionals such as doctors and nurses, who are responsible for managing and treating the user's health.

[1507] A "notification" is a message that conveys analysis results or doctor's instructions to the user or medical staff.

[1508] A "recipe" is a suggestion of how to prepare a meal or a menu, generated based on the user's health condition and requests.

[1509] "Emotional information" refers to data and analysis results related to the user's emotional state.

[1510] This invention is a last-mile medical support system that utilizes a generative AI model and an emotion analysis engine, aiming to support users' health management and medical treatment. The specific embodiments for implementing this invention are described in detail below.

[1511] System Configuration

[1512] 1. User terminal

[1513] User terminals consist of smartphones, tablets, and other devices. They transmit user input and data from medical devices to a server, and display responses and notifications from the server to the user. The terminals are linked to a cloud database to store and manage health information and medical results.

[1514] 2. Server

[1515] The server implements the following main functions:

[1516] Generative AI models (e.g., OpenAI GPT-4) are used to generate appropriate answers to user questions.

[1517] An emotion analysis engine (e.g., Affectiva AI) is used to analyze the user's emotional state and generate medical results and advice based on those emotions.

[1518] It analyzes data transmitted from medical devices to detect abnormal values ​​and symptoms.

[1519] Send notifications to users and medical staff.

[1520] We use video conferencing services (e.g., Twilio) to facilitate online medical consultations between users and doctors.

[1521] 3. Management of health information

[1522] The server analyzes health information collected from users (physical condition, symptoms, and measurement data from medical devices) and provides it to medical staff. Based on the analysis results, the medical staff issues treatment instructions and sends them to the user.

[1523] Specific example

[1524] User login and health information entry

[1525] After the user launches the application and logs in, they enter their current physical condition or symptoms. For example, they might enter, "I've been having trouble sleeping lately."

[1526] Data transmission and analysis

[1527] The entered information is sent to a cloud server and analyzed by a generative AI model and an emotion analysis engine. The emotion analysis engine recognizes the user's anxiety state, and the generative AI model generates advice such as, "To relax before going to bed, try drinking some warm tea or doing some light stretching. We also recommend consulting a doctor."

[1528] Starting a video call

[1529] Users schedule video calls within the application and have real-time consultations with doctors at the designated time. An emotion analysis engine analyzes the user's facial expressions during the video call and notifies the doctor of the user's emotional state.

[1530] Providing medical results and advice

[1531] After the consultation is complete, the AI ​​model sends the user the consultation results and advice it has generated. For example, it might say, "Take the prescribed medication and make time to relax."

[1532] Example of a prompt

[1533] User input: "Lately, I've been having trouble sleeping a lot, and it's making me feel stressed."

[1534] AI prompt: "If the user is experiencing sleep deprivation and stress, please provide specific advice on how to relax."

[1535] Thus, the system of the present invention enables the remote provision of advanced, personalized medical support based on the user's health and emotional state. Furthermore, by combining a generative AI model with an emotion analysis engine, medical treatment and advice that take the user's emotions into consideration can be realized.

[1536] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1537] Step 1:

[1538] The user launches the application and logs in. The application receives the user ID and password as input and performs authentication through database matching in conjunction with the authentication system. The output confirms the login status. If the login is successful, the application retrieves the user's basic information from the server and displays it on the terminal.

[1539] Step 2:

[1540] The user enters their current physical condition and symptoms. A text input field is provided for this input, allowing the user to enter health information and symptom data, such as "I've been having trouble sleeping lately." The device receives this data, converts it to a data format, and sends it to a cloud server. The input information is saved on the server as output.

[1541] Step 3:

[1542] The server analyzes the health information it receives. The input is health information submitted by the user, which is then fed into a generative AI model. The generative AI model generates appropriate answers to user questions, and the results are saved as output. In parallel, an emotion analysis engine is used to analyze the user's emotional state, and the results are also saved.

[1543] Step 4:

[1544] The server sends the analysis results to the user's terminal. The input consists of the analysis results from the generative AI model and the emotion analysis engine, which are then combined and structured into a message. The output is the analysis results sent to the user's terminal, displaying health advice and suggestions for addressing anxiety.

[1545] Step 5:

[1546] The user schedules a video call. The input includes the date and time of the video call and the selection of a doctor, and this information is sent to the server. The server stores the reservation information and displays a reservation confirmation message on the user's terminal as output.

[1547] Step 6:

[1548] The video call starts at the scheduled time. As input, the server receives a signal to begin the video call and activates the video call service. As output, an environment is provided where the user and doctor can communicate in real time. Simultaneously, an emotion analysis engine analyzes the user's facial expressions and sends them to the server.

[1549] Step 7:

[1550] The server provides doctors with the results of emotion analysis during video calls. As input, the user's facial expression data obtained during the video call is analyzed by the emotion analysis engine. The server sends the results to the doctor's terminal, and the emotional state is displayed to the doctor as output, which is used as reference information for medical treatment.

[1551] Step 8:

[1552] After the video call ends, the system generates and provides the user with a medical assessment and advice. Inputs, including the doctor's assessment and advice generated by the AI ​​model, are collected on the server. Outputs, this information is sent to the user's terminal and displayed within the application. This data is also stored in a database.

[1553] Step 9:

[1554] The system continuously monitors users' health information and emotional states. Health data and emotional analysis data are collected periodically from users as input. The server analyzes this data, and if an anomaly is detected, it promptly sends notifications to the user and medical staff.

[1555] These steps enable effective and personalized medical support based on the user's health and emotional state.

[1556] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1557] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1558] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[1559] [Third Embodiment]

[1560] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[1561] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1562] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1563] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[1564] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1565] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1566] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1567] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1568] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[1569] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1570] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1571] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[1572] This invention is a last-mile medical support system that utilizes a generative AI model. This system functions through the coordinated efforts of the user, terminal, and server. The following details each component and its role.

[1573] Providing a health chatbot

[1574] Server: Uses a generative AI model to generate appropriate answers to health-related questions from users. The server analyzes the content of health consultations submitted by users and generates answers on the spot based on relevant medical knowledge.

[1575] Device: This device sends health-related question data entered by the user to the server and displays the server's responses to the user. Smartphones and PCs fall into this category.

[1576] User: Enters health-related questions from their device and receives answers from the server.

[1577] Specific example: If a user enters "I haven't been able to sleep lately," the server generates a response such as "You can try relaxing before bed and avoiding caffeine. If it persists, please consult a doctor," and displays it to the user via their device.

[1578] Monitoring of medical devices

[1579] Server: Analyzes measurement data transmitted from medical devices and checks for abnormalities. Sends notifications to users and medical staff as needed.

[1580] Terminal: Receives data from the medical device used by the user and sends it to the server.

[1581] User: Uses medical devices to measure their own health data and transmits the data through their device.

[1582] Specific example: A user sends blood pressure data measured using a blood pressure monitor from their device to a server. If the server detects high blood pressure, it notifies the user with the message, "Your blood pressure is high. Please consult a doctor."

[1583] Support for home healthcare

[1584] Server: Collects health information from users and provides it to medical staff. It also sends instructions from medical staff to users.

[1585] Terminal: Sends health information from the user to the server and displays instructions from medical staff on the server to the user.

[1586] User: Enter their health status and any changes in their physical condition into the device to receive support.

[1587] Specific example: When a user enters "My body temperature is high and I have a sore throat," the server analyzes the data and provides it to medical staff. The medical staff then advises, "Take paracetamol and drink plenty of fluids," and sends this information back to the user.

[1588] Recipe management

[1589] Server: Uses a generative AI model to generate meal recipes that match the user's request.

[1590] Terminal: Displays recipe information provided by the server to the user.

[1591] User: Enter a meal request and receive a generated recipe.

[1592] Specific example: When a user enters "Please tell me a recipe for a meal suitable for diabetes," the server generates a recipe for "Sautéed chicken breast and broccoli" and displays it to the user via their terminal.

[1593] Online medical consultation

[1594] Server: Relays the consultation between the user and the doctor via video call. Records the consultation results and medical instructions and sends them to the user.

[1595] Terminal: Connects the user's video call to the server and displays the medical results and instructions.

[1596] User: Makes an appointment for a medical consultation and conducts the consultation with a doctor via video call.

[1597] Specific example: A user makes an appointment for a medical consultation and starts a video call at the designated time. After the doctor conducts the examination and notifies the user of the diagnosis, such as "Please take the prescribed medication," this is displayed to the user via their device.

[1598] As described above, the system of the present invention effectively realizes last-mile medical support through the mutual cooperation of users, terminals, and servers, centered around a generated AI model. This system makes it possible to provide high-quality medical support even in areas with difficult access to medical care and in aging societies.

[1599] The following describes the processing flow.

[1600] Providing a health chatbot

[1601] Step 1:

[1602] User: Enter health-related questions into the device and submit.

[1603] Step 2:

[1604] Terminal: Sends the entered question data to the server.

[1605] Step 3:

[1606] Server: Receives question data and analyzes its content using a generative AI model.

[1607] Step 4:

[1608] Server: Generates an appropriate answer to the question and sends it to the terminal.

[1609] Step 5:

[1610] Terminal: Displays the generated response to the user.

[1611] Specific example:

[1612] User: Type "I haven't been able to sleep lately" into the device and send it.

[1613] Terminal: Sends question data to the server.

[1614] Server: Analyzes the question and generates a response such as, "You can try relaxing before bed and avoiding caffeine. If the problem persists, please consult a doctor," and sends it to the terminal.

[1615] Terminal: Displays the answer to the user.

[1616] Monitoring of medical devices

[1617] Step 1:

[1618] User: Uses medical devices to measure health data.

[1619] Step 2:

[1620] Terminal: Sends measured data to the server.

[1621] Step 3:

[1622] Server: Analyzes the received data and detects anomalies.

[1623] Step 4:

[1624] Server: If an anomaly is detected, it sends a notification to the user or medical staff.

[1625] Step 5:

[1626] Device: Displays notifications to the user.

[1627] Specific example:

[1628] User: Measure blood pressure with a blood pressure monitor.

[1629] Terminal: Sends measurement data to the server.

[1630] Server: Analyzes the data and, if high blood pressure is detected, generates a notification saying, "Your blood pressure is high. Please consult a doctor," and sends it to the terminal.

[1631] Device: Displays notifications to the user.

[1632] Support for home healthcare

[1633] Step 1:

[1634] User: Enters their health information and changes in their physical condition into the device and sends it.

[1635] Step 2:

[1636] Terminal: Sends health information to the server.

[1637] Step 3:

[1638] Server: Analyzes received data and provides it to medical staff.

[1639] Step 4:

[1640] Medical staff: Generate instructions based on the analysis results and input them into the server.

[1641] Step 5:

[1642] Server: Sends instructions from medical staff to the user's terminal.

[1643] Step 6:

[1644] Terminal: Displays instructions from medical staff to the user.

[1645] Specific example:

[1646] User: Enters "My body temperature is high and my throat hurts" into the device and sends it.

[1647] Terminal: Sends health information to the server.

[1648] Server: Analyzes the data and notifies medical staff of "elevated body temperature and sore throat."

[1649] Medical staff: Enter the instruction, "Take paracetamol and drink plenty of fluids."

[1650] Server: Sends instructions to the terminal.

[1651] Terminal: Displays instructions to the user.

[1652] Recipe management

[1653] Step 1:

[1654] User: Enter and submit a meal request on the terminal.

[1655] Step 2:

[1656] Terminal: Sends a request to the server.

[1657] Step 3:

[1658] Server: Uses a generative AI model to analyze requests and generate appropriate recipes.

[1659] Step 4:

[1660] Server: Sends the generated recipe to the terminal.

[1661] Step 5:

[1662] Terminal: Displays the recipe to the user.

[1663] Specific example:

[1664] User: Type "Please share some recipes suitable for people with diabetes" and submit.

[1665] Terminal: Sends a request to the server.

[1666] Server: Uses a generative AI model to generate "Sautéed Chicken Breast and Broccoli" and sends it to the terminal.

[1667] Terminal: Displays the recipe to the user.

[1668] Online medical consultation

[1669] Step 1:

[1670] User: Make an appointment for a medical consultation using the terminal.

[1671] Step 2:

[1672] Terminal: Sends reservation information to the server.

[1673] Step 3:

[1674] Server: Sets up a video call between the doctor and the user.

[1675] Step 4:

[1676] User: Start a video call from your device at the scheduled time.

[1677] Step 5:

[1678] Server: Records medical results and instructions, and sends them to the user's terminal.

[1679] Step 6:

[1680] Terminal: Displays medical results and instructions to the user.

[1681] Specific example:

[1682] User: Make an appointment for a medical consultation using the terminal.

[1683] Terminal: Sends reservation information to the server.

[1684] Server: Set up a video call with the doctor.

[1685] User: Start the video call at the scheduled time.

[1686] Server: Records the medical results as "You need rest, please take the prescribed medication" and sends it to the terminal.

[1687] Terminal: Displays the medical results to the user.

[1688] (Example 1)

[1689] Next, we will describe Example 1. 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."

[1690] In modern society, medical resources and equipment are limited, and providing appropriate medical support is particularly difficult in areas with limited access to healthcare and in aging societies. Furthermore, the lack of adequate means for users to monitor their health on a daily basis and receive prompt professional medical support when needed can increase the risk of health problems.

[1691] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1692] In this invention, the server includes means for generating appropriate answers to health-related questions from users using a generative AI model; means for receiving and analyzing measurement data from medical devices; means for sending notifications to users or medical professionals based on the analysis results; means for collecting health information from users and providing it to medical professionals; means for sending and displaying instructions from medical professionals to users; means for generating meal plan proposals based on user requests; means for relaying medical consultations between users and doctors via online communication; and means for recording and sending medical results and instructions to users. This makes it possible to provide high-quality medical support even in areas with limited access to medical care and in aging societies.

[1693] A "generative AI model" is an artificial intelligence model that analyzes user input data and generates appropriate responses or results based on that data.

[1694] A "health-related question" is an inquiry that a user submits to seek information or advice about their health condition or symptoms.

[1695] An "appropriate answer" is accurate and useful information based on medical knowledge, provided by a generative AI model in response to a user's question.

[1696] A "medical device" is a device used by a user to measure their own health indicators, and examples include blood pressure monitors and thermometers.

[1697] "Measurement data" refers to data on health indicators obtained using medical devices.

[1698] "Analysis" is the process of analyzing received data to identify important information and outliers.

[1699] A "notification" is a message sent to inform a user or medical professional of analysis results or important information.

[1700] "Health information" refers to comprehensive health data that includes information about a user's health status, symptoms, and daily life.

[1701] A "medical professional" is a person with specialized knowledge in the medical field, such as a doctor or nurse.

[1702] "Instructions" refer to guidance provided by healthcare professionals to users to prompt them to take specific actions regarding health management and treatment.

[1703] A "meal plan" is a nutritionally balanced meal plan provided by an AI model that generates data based on the user's health status and requests.

[1704] "Online communication" refers to a communication method that involves sending and receiving data via a network such as the internet.

[1705] "Medical treatment" refers to a series of medical actions and activities performed by a doctor on a user.

[1706] "Medical outcome" refers to medical conclusions or findings obtained through medical treatment.

[1707] "Record keeping" refers to the process of saving medical results and instructions in a digital format so that they can be reviewed later.

[1708] This invention is a last-mile medical support system that utilizes a generative AI model, in which the user, terminal, and server elements work together. The following details each component and its role.

[1709] Providing a health chatbot

[1710] Server: Uses a generative AI model to generate appropriate answers to health-related questions from users. Specifically, the server analyzes the content of health consultations sent by users and generates answers using the generative AI model "GPT-3". For example, if a user enters "I've been having trouble sleeping lately," the server generates an answer such as "You can try relaxing before bed and avoiding caffeine. If it persists, please consult a doctor," and sends this to the terminal.

[1711] Device: This device sends health-related question data entered by the user to the server and displays the server's responses to the user. Smartphones and PCs fall into this category.

[1712] User: Enters health-related questions from their device and receives answers from the server.

[1713] Monitoring of medical devices

[1714] Server: Analyzes measurement data transmitted from medical devices and checks for abnormalities. Sends notifications to users and medical staff as needed. For example, if it receives blood pressure monitor data and uses the generated AI model "anomaly detection algorithm" to detect high blood pressure, it will send a notification saying, "Your blood pressure is high. Please consult a doctor."

[1715] Terminal: Receives data from medical devices used by the user and transmits it to the server. Wireless communication technologies such as Bluetooth are also used.

[1716] User: Uses medical devices to measure their own health data and transmits that data to a server via their device.

[1717] Support for home healthcare

[1718] Server: Collects health information from users and provides it to medical staff. It also sends instructions from medical staff to users. For example, if a user enters "My temperature is high and I have a sore throat," the server provides this to the medical staff. Then, if the medical staff instructs "Take paracetamol and drink plenty of fluids," the server sends this to the user.

[1719] Terminal: Sends health information from the user to the server and displays instructions from medical staff on the server to the user.

[1720] User: Enter their health status and any changes in their physical condition into the device to receive support.

[1721] Recipe management

[1722] Server: Uses a generative AI model to generate meal recipes that match the user's request. For example, using the generative AI model "Cookpad AI," in response to a user's request, "Please tell me a meal recipe suitable for diabetes," it generates a recipe such as "Sautéed chicken breast and broccoli."

[1723] Terminal: Displays recipe information provided by the server to the user.

[1724] User: Enter a meal request and receive a generated recipe.

[1725] Online medical consultation

[1726] Server: Relays the consultation between the user and the doctor via video call. Records the consultation results and medical instructions and sends them to the user. For example, it uses WebRTC technology to conduct video calls, saves the consultation details to a document management system, and sends medical instructions such as "Please take the specified medication" to the user.

[1727] Terminal: Connects the user's video call to the server and displays the medical results and instructions.

[1728] User: Makes an appointment for a medical consultation and has a consultation with a doctor via video call at the designated time.

[1729] Examples of specific cases and prompt statements

[1730] For example, if a user enters "I haven't been able to sleep lately," the server generates a response such as "You can try relaxing before bed and avoiding caffeine. If it persists, please consult a doctor," and displays it to the user via their device.

[1731] Example input prompts for a generative AI model:

[1732] User input: I can't sleep lately

[1733] Example response from the generated AI model: Possible measures include relaxing before bed and avoiding caffeine. If symptoms persist, consult a doctor.

[1734] As described above, this system, centered around a generative AI model, enables effective last-mile medical support through the mutual cooperation of users, terminals, and servers. This system makes it possible to provide high-quality medical support even in areas with limited access to healthcare and in aging societies.

[1735] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1736] Providing a health chatbot

[1737] Step 1:

[1738] User: Enter a health-related question into the terminal. For example, enter "I've been having trouble sleeping lately."

[1739] Input: Text-based questions about health

[1740] Output: Send instructions on the terminal

[1741] Step 2:

[1742] Terminal: Sends the entered question data to the server. HTTPS is used as the communication protocol.

[1743] Input: Question data entered by the user

[1744] Data processing: Question data is converted into HTTP request format.

[1745] Output: HTTP request sent to the server

[1746] Step 3:

[1747] Server: Inputs question data into the AI ​​model, analyzes it, and generates appropriate answers. It uses "GPT-3" to generate answers to user questions.

[1748] Input: Question data in HTTP request format

[1749] Data processing: Text analysis and response generation using generative AI models.

[1750] Output: Response data generated in JSON format

[1751] Step 4:

[1752] Server: Sends the generated response to the terminal.

[1753] Input: Response data in JSON format

[1754] Data processing: The response data is converted into an HTTP response format.

[1755] Output: HTTP response sent to the terminal

[1756] Step 5:

[1757] Terminal: Displays received responses to the user. Responses are displayed on the terminal screen.

[1758] Input: Response data in HTTP response format

[1759] Data processing: Response data is converted into a format that users can understand.

[1760] Output: The answer displayed on the device screen

[1761] Monitoring of medical devices

[1762] Step 1:

[1763] User: Measuring data using medical devices. For example, measuring blood pressure using a blood pressure monitor.

[1764] Input: Blood pressure measuring device

[1765] Output: Measurement data

[1766] Step 2:

[1767] Medical devices: Transmit measurement data to a terminal. Wireless communication such as Bluetooth is used.

[1768] Input: Measurement data

[1769] Data processing: Measurement data is converted to a wireless communication format.

[1770] Output: Measurement data sent to the terminal

[1771] Step 3:

[1772] Terminal: Sends measurement data to the server. The HTTPS protocol is used again.

[1773] Input: Measurement data received via wireless communication

[1774] Data processing: Convert measurement data into HTTP request format.

[1775] Output: HTTP request sent to the server

[1776] Step 4:

[1777] Server: Analyzes data and checks for anomalies. It uses a generative AI model, "Anomaly Detection Algorithm," to analyze the data.

[1778] Input: Measurement data in HTTP request format

[1779] Data processing: Analysis of measurement data and anomaly detection.

[1780] Output: Notification data in case of an anomaly

[1781] Step 5:

[1782] Server: If an abnormality occurs, it sends a notification to the user or medical staff. A notification such as "Your blood pressure is high. Please consult a doctor" is generated.

[1783] Input: Data after anomaly detection

[1784] Data processing: Convert notification content into HTTP response format.

[1785] Output: Notification data sent to the terminal

[1786] Step 6:

[1787] Terminal: Displays received notifications to the user. The notification will appear on the user's terminal screen.

[1788] Input: Notification data in HTTP response format

[1789] Data processing: Convert notification data into a format that users can understand.

[1790] Output: Notification displayed on the device screen

[1791] Support for home healthcare

[1792] Step 1:

[1793] User: Enter health information into the terminal. For example, enter "My temperature is high and I have a sore throat."

[1794] Input: Health information

[1795] Output: Health information data

[1796] Step 2:

[1797] Terminal: Sends health information to the server. The HTTPS protocol is used.

[1798] Input: Entered health information data

[1799] Data processing: Convert health information data into HTTP request format.

[1800] Output: HTTP request sent to the server

[1801] Step 3:

[1802] Server: Provides health information to medical staff. The information is displayed on a dashboard for medical staff.

[1803] Input: Health information data in HTTP request format

[1804] Data processing: Convert health information to a display format.

[1805] Output: Display on the medical staff dashboard

[1806] Step 4:

[1807] Medical staff: Enter instructions for the user into the server. Enter the instruction, "Take paracetamol and drink plenty of fluids."

[1808] Input: Instructions from medical staff

[1809] Output: Instruction data

[1810] Step 5:

[1811] Server: Sends instructions from medical staff to terminals. This also uses the HTTPS protocol.

[1812] Input: Medical staff instruction data

[1813] Data processing: Convert the instruction data into an HTTP response format.

[1814] Output: Data to send to the terminal

[1815] Step 6:

[1816] Terminal: Displays instructions from medical staff to the user. Pop-up notifications are used, etc.

[1817] Input: Instruction data in HTTP response format

[1818] Data processing: Convert the instruction data into a format that the user can understand.

[1819] Output: Instructions displayed on the device screen

[1820] Recipe management

[1821] Step 1:

[1822] User: Enter a request regarding meals into the terminal. For example, enter "Please provide recipes suitable for meals for people with diabetes."

[1823] Input: Meal request

[1824] Output: Request data

[1825] Step 2:

[1826] Terminal: Sends a request to the server.

[1827] Input: User request data

[1828] Data processing: Convert request data into HTTP request format.

[1829] Output: HTTP request sent to the server

[1830] Step 3:

[1831] Server: Generates recipes using a generative AI model. The generative AI model used is "Cookpad AI".

[1832] Input: Request data in HTTP request format

[1833] Data processing: Parsing request data and generating recipes.

[1834] Output: Recipe data generated in JSON format

[1835] Step 4:

[1836] Server: Sends the generated recipe to the terminal.

[1837] Input: Recipe data in JSON format

[1838] Data processing: Convert recipe data into HTTP response format.

[1839] Output: HTTP response sent to the terminal

[1840] Step 5:

[1841] Terminal: Displays recipes to the user. Recipe ingredients and cooking instructions are displayed on the screen in a list format.

[1842] Input: Recipe data in HTTP response format

[1843] Data processing: Convert recipe data into a format that users can understand.

[1844] Output: Recipe displayed on the terminal screen

[1845] Online medical consultation

[1846] Step 1:

[1847] User: Make an appointment for a medical consultation via a terminal. Enter the date, time, and symptoms into the appointment system.

[1848] Input: Appointment data

[1849] Output: Reservation Request

[1850] Step 2:

[1851] Server: Notifies doctors of appointment information. This information is displayed on the doctor's dashboard.

[1852] Input: Reservation Request

[1853] Data processing: Convert appointment information to a format suitable for doctors.

[1854] Output: Display on the physician's dashboard

[1855] Step 3:

[1856] User: Start a video call on your device at the specified time. A WebRTC video conference will be set up.

[1857] Input: Call Initiation Request

[1858] Output: Start video call

[1859] Step 4:

[1860] Server: Connects users and doctors via video call.

[1861] Input: Video call request

[1862] Data processing: Call connection settings

[1863] Output: Video call connection between user and doctor

[1864] Step 5:

[1865] Doctor: Conducts medical examinations and records the results and instructions on the server. For example, they might enter an instruction such as, "Please take the prescribed medication."

[1866] Input: Medical results and instruction data

[1867] Output: Recorded data

[1868] Step 6:

[1869] Server: Sends medical results and instructions to the terminal.

[1870] Input: Recorded data

[1871] Data processing: Convert medical results and instructions into HTTP response format.

[1872] Output: HTTP response sent to the terminal

[1873] Step 7:

[1874] Terminal: Displays medical results and instructions to the user. Text notifications are displayed on the screen.

[1875] Input: Clinical results and instruction data in HTTP response format

[1876] Data processing: Converting medical results and instructions into a format that users can understand.

[1877] Output: Medical results and instructions displayed on the terminal screen.

[1878] (Application Example 1)

[1879] Next, we will explain Application Example 1. In the following explanation, 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."

[1880] In modern society, providing appropriate meal plans tailored to users' health conditions and facilitating on-the-spot ordering and delivery is challenging. Furthermore, regularly tracking users' health data and continuously adjusting meal plans accordingly is also difficult. There is a need to develop a system that solves these problems and provides high-quality medical support based on users' health conditions.

[1881] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1882] In this invention, the server includes means for generating appropriate answers to health-related questions from the user using a generative AI model; means for receiving and analyzing measurement data from medical devices; means for sending notifications to the user or medical staff based on the analysis results; means for collecting health information from the user and providing it to medical staff; means for sending and displaying instructions from medical staff to the user; means for generating meal recipes based on the user's request; means for relaying consultations between the user and a doctor via video call; means for proposing a meal plan based on the user's health condition and ordering and delivering it on the spot; and means for regularly tracking the user's health data and adjusting the meal plan accordingly. This enables the provision of meal plans tailored to the user's health condition and on-the-spot ordering and delivery, as well as continuous tracking of health data and appropriate adjustment of the meal plan based on that data.

[1883] A "generative AI model" is an algorithm or system that uses artificial intelligence to generate appropriate answers or suggestions based on user input data.

[1884] "Means" refers to methods, devices, or software programs designed to achieve a specific function or role.

[1885] A "user" is an individual or organization that uses the system to receive health-related consultations or medical support.

[1886] "Health-related questions" are inquiries or consultations that users enter into the system regarding their health status and physical condition.

[1887] "Medical devices" are devices and equipment used for medical purposes, and include the measurement and recording of health data.

[1888] "Measurement data" refers to numerical information and measurement results obtained using medical devices.

[1889] "Analysis results" refer to diagnostic information and suggestions obtained from measurement data using generated AI models or other technical means.

[1890] "Health information" refers to various data and records related to the user's health status and physical condition.

[1891] "Medical staff" refers to individuals with specialized medical knowledge who are responsible for providing medical care and guidance to users.

[1892] "Instructions" refer to specific actions or procedures that medical staff provide to users.

[1893] A "meal recipe" is a document that lists ingredients and cooking methods suitable for a specific health condition.

[1894] "Video calling" is a technology that allows for real-time conversations using video and audio over the internet.

[1895] "Medical results" refer to diagnostic information and medical instructions obtained during medical consultations conducted via video call or similar means.

[1896] A "meal plan" is a set of meal suggestions or plans tailored to the user's health condition.

[1897] "Means of ordering and delivery" refers to the process and system for users to order food through a system and have it delivered.

[1898] "Health data tracking" refers to the act of regularly recording and monitoring health information collected from users.

[1899] "Adjusting meal plans" refers to the process of continuously modifying and optimizing meal plans to suit the user based on tracked health data.

[1900] This invention is a last-mile medical support system that utilizes a generative AI model, in which the user, terminal, and server elements work together in coordination. The following details each component and its role.

[1901] Providing a health chatbot

[1902] Server: Uses a generative AI model to generate appropriate answers to health-related questions from users. The server analyzes the content of health consultations submitted by users and generates answers on the spot based on relevant medical knowledge.

[1903] Device: This device sends health-related question data entered by the user to the server and displays the server's responses to the user. Smartphones and PCs fall into this category.

[1904] User: Enters health-related questions from their device and receives answers from the server.

[1905] Specific example: If a user enters "I haven't been able to sleep lately," the server generates a response such as "You can try relaxing before bed and avoiding caffeine. If it persists, please consult a doctor," and displays it to the user via their device.

[1906] Monitoring of medical devices

[1907] Server: Analyzes measurement data transmitted from medical devices and checks for abnormalities. Sends notifications to users and medical staff as needed.

[1908] Terminal: Receives data from the medical device used by the user and sends it to the server.

[1909] User: Uses medical devices to measure their own health data and transmits the data through their device.

[1910] Specific example: A user sends blood pressure data measured using a blood pressure monitor from their device to a server. If the server detects high blood pressure, it notifies the user with the message, "Your blood pressure is high. Please consult a doctor."

[1911] Support for home healthcare

[1912] Server: Collects health information from users and provides it to medical staff. It also sends instructions from medical staff to users.

[1913] Terminal: Sends health information from the user to the server and displays instructions from medical staff on the server to the user.

[1914] User: Enter their health status and any changes in their physical condition into the device to receive support.

[1915] Specific example: When a user enters "My body temperature is high and I have a sore throat," the server analyzes the data and provides it to medical staff. The medical staff then advises, "Take paracetamol and drink plenty of fluids," and sends this information back to the user.

[1916] Recipe management

[1917] Server: Uses a generative AI model to generate meal recipes that match the user's request.

[1918] Terminal: Displays recipe information provided by the server to the user.

[1919] User: Enter a meal request and receive a generated recipe.

[1920] Specific example: When a user enters "Please tell me a recipe for a meal suitable for diabetes," the server generates a recipe for "Sautéed chicken breast and broccoli" and displays it to the user via their terminal.

[1921] Online medical consultation

[1922] Server: Relays the consultation between the user and the doctor via video call. Records the consultation results and medical instructions and sends them to the user.

[1923] Terminal: Connects the user's video call to the server and displays the medical results and instructions.

[1924] User: Makes an appointment for a medical consultation and conducts the consultation with a doctor via video call.

[1925] Specific example: A user makes an appointment for a medical consultation and starts a video call at the designated time. After the doctor conducts the examination and notifies the user of the diagnosis, such as "Please take the prescribed medication," this is displayed to the user via their device.

[1926] Meal plan suggestions, ordering, and delivery.

[1927] Server: Proposes meal plans based on the user's health status, takes orders on the spot, and arranges delivery. It uses a generative AI model to analyze health data and generate meal suggestions. It also regularly tracks the user's health data and adjusts the meal plan accordingly.

[1928] Terminal: Sends user-entered health data and meal requests to the server, and displays suggestions and order confirmations from the server.

[1929] User: Enters their health status, receives a meal plan tailored to their condition from the server, and proceeds with ordering and delivery.

[1930] Specific example: If a diabetic user enters into the app, "My recent blood sugar level is 150, and I'd like some dietary advice," the server will suggest, "A low-carbohydrate diet is recommended to control blood sugar levels," and provide a suitable recipe (e.g., chicken breast and broccoli). The user can then order delivery.

[1931] Example prompt:

[1932] "My blood sugar level is currently 150. Please suggest a suitable diet for people with diabetes."

[1933] This system allows users to efficiently manage a series of processes, from health consultations and medical device data transmission to home healthcare support, recipe management, online consultations, and meal plan suggestions, ordering, and delivery. This enables high-quality medical support and appropriate nutritional management.

[1934] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1935] Step 1:

[1936] The user enters their health consultation into the terminal. The user uses the terminal to input specific questions or concerns about their health. For example, they might enter, "I've been having trouble sleeping lately." The input data is saved on the terminal.

[1937] Step 2:

[1938] The terminal sends the input data to the server. The entered health consultation content is converted into a data format and sent to the server using the HTTPS protocol. The server prepares to analyze the received data.

[1939] Step 3:

[1940] The server uses a generative AI model to generate appropriate answers to questions. The server analyzes the received health consultation data and inputs it into the generative AI model. The generative AI model generates answers based on relevant medical knowledge and returns them to the server.

[1941] Step 4:

[1942] The server sends the generated response to the terminal. The AI ​​model that generates the response sends the data, including the response, to the terminal. The terminal converts the received response data into a display format and displays it to the user.

[1943] Step 5:

[1944] The user uses a health device and inputs the measurement data into the terminal. For example, they might use a blood pressure monitor to measure their blood pressure and input that numerical data into the terminal.

[1945] Step 6:

[1946] The terminal sends measurement data to the server. The input measurement data is converted to a data format and sent to the server using the HTTPS protocol. The server prepares to analyze the received data.

[1947] Step 7:

[1948] The server analyzes the measurement data and checks for any abnormalities. The server analyzes the received measurement data and compares it to reference values. For example, if blood pressure is high, it will be determined to be "hypertension."

[1949] Step 8:

[1950] If the server detects an anomaly, it will send a notification to the user or medical staff. Based on the analysis results, if an anomaly is detected, a notification message will be created and sent to the user or medical staff.

[1951] Step 9:

[1952] The user enters a request regarding meals into the device. For example, they might enter, "Please provide recipes suitable for meals for people with diabetes." The entered data is saved on the device.

[1953] Step 10:

[1954] The terminal sends the input data to the server. The entered meal request details are converted into a data format and sent to the server using the HTTPS protocol. The server prepares to analyze the received data.

[1955] Step 11:

[1956] The server generates suitable meal recipes using a generative AI model. The server analyzes the received request data and inputs it into the generative AI model. The generative AI model generates suitable meal recipes and returns them to the server.

[1957] Step 12:

[1958] The server sends the generated recipe to the terminal. The AI ​​model sends data containing the meal recipe to the terminal. The terminal converts the received recipe data into a display format and displays it to the user.

[1959] Step 13:

[1960] Users order meal recipes and request delivery. They review the displayed recipes, place their orders on the spot via their terminal, and request delivery.

[1961] Step 14:

[1962] The server periodically tracks the user's health data and adjusts the meal plan accordingly. The server regularly collects the user's health data, generates an optimal meal plan using a generative AI model, and adjusts it as needed.

[1963] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1964] This invention is a last-mile medical support system that utilizes a generative AI model and an emotion engine. This system functions through the coordinated efforts of the user, terminal, server, and emotion engine. The following details each component and its role.

[1965] Providing a health chatbot

[1966] Server: Uses a generative AI model to generate appropriate answers to health-related questions from users. By combining it with an emotion engine, it recognizes the user's emotional state from the input data and generates answers corresponding to that emotional state.

[1967] Device: This device sends health-related question data entered by the user to the server and displays the server's responses to the user. Smartphones and PCs fall into this category.

[1968] User: Enters health-related questions from their device and receives answers from the server.

[1969] Specific example: If a user enters "I haven't been able to sleep lately" in an anxious tone, the server uses an emotion engine to recognize the user's anxiety and generates an emotionally sensitive response such as, "You can try relaxing before bed or avoiding caffeine. If your anxiety persists, please consult a doctor," which is then displayed to the user via their device.

[1970] Monitoring of medical devices

[1971] Server: Analyzes measurement data transmitted from medical devices and checks for abnormalities. It also analyzes the user's emotional state using an emotion engine and sends notifications to the user and medical staff as needed.

[1972] Terminal: Receives data from the medical device used by the user and sends it to the server.

[1973] User: Uses medical devices to measure their own health data and transmits the data through their device.

[1974] Specific example: A user sends blood pressure data measured using a blood pressure monitor from their device to a server. If the server detects high blood pressure and the emotion engine recognizes the user's anxiety level, it notifies the user with the message, "Your blood pressure is high. Please consult a doctor. If you are feeling anxious, try some relaxation techniques."

[1975] Support for home healthcare

[1976] Server: Collects health information from users and analyzes their emotional state using an emotion engine. Provides health information and emotional state data to medical staff and sends instructions from medical staff to the user.

[1977] Terminal: Sends health information from the user to the server and displays instructions from medical staff on the server to the user.

[1978] User: Enter their health status and any changes in their physical condition into the device to receive support.

[1979] Specific example: If a user enters "My body temperature is high and my throat hurts," and the emotion engine recognizes the user's anxiety, the server analyzes the data and notifies medical staff of "high body temperature, sore throat, and anxiety." The medical staff then instructs the user to "take paracetamol and drink plenty of fluids. Please contact us if your anxiety persists," and sends this to the user.

[1980] Recipe management

[1981] Server: Uses a generative AI model to generate meal recipes that match the user's request. It can also use an emotion engine to suggest recipes that take the user's emotional state into consideration.

[1982] Terminal: Displays recipe information provided by the server to the user.

[1983] User: Enter a meal request and receive a generated recipe.

[1984] Specific example: If a user types "Please tell me a recipe for a meal suitable for diabetes," and the emotion engine recognizes the user's stress, the server generates a recipe suggesting "Sautéed chicken breast and broccoli with relaxing herbal tea" and displays it to the user via their device.

[1985] Online medical consultation

[1986] Server: Relays the user's consultation with the doctor via video call. Records the consultation results and medical instructions, and analyzes and records the user's emotional state using an emotion engine.

[1987] Terminal: Connects the user's video call to the server and displays the medical results and instructions.

[1988] User: Makes an appointment for a medical consultation and conducts the consultation with a doctor via video call.

[1989] Specific example: A user makes an appointment for a medical consultation and starts a video call at the designated time. The doctor conducts the consultation, and if the emotion engine recognizes the user's anxiety, the doctor notifies the user of the consultation result, such as "Please take the prescribed medication and take time to relax." The device displays an emotionally sensitive message to the user along with the consultation result.

[1990] As described above, the system of the present invention, centered on a generative AI model and an emotion engine, enables the user, terminal, and server to cooperate with each other to effectively realize last-mile medical support. This system makes it possible to provide high-quality and emotionally sensitive medical support even in areas with limited access to medical care and in aging societies.

[1991] The following describes the processing flow.

[1992] Providing a health chatbot

[1993] Step 1:

[1994] User: Enter health-related questions into the device and submit.

[1995] Step 2:

[1996] Terminal: Sends the entered question data to the server.

[1997] Step 3:

[1998] Server: Receives question data and analyzes the user's emotional state using an emotion engine.

[1999] Step 4:

[2000] Server: Considers emotional states and generates appropriate responses using a generative AI model.

[2001] Step 5:

[2002] Server: Sends the generated response to the terminal.

[2003] Step 6:

[2004] Terminal: Displays the generated response to the user.

[2005] Specific example: If a user enters "I haven't been able to sleep lately" and indicates feelings of anxiety, the server generates a response such as "Try some relaxation techniques. If it persists, consult a doctor" and displays it to the user via their device.

[2006] Monitoring of medical devices

[2007] Step 1:

[2008] User: Uses medical devices to measure health data.

[2009] Step 2:

[2010] Terminal: Sends measured data to the server.

[2011] Step 3:

[2012] Server: Receives and analyzes measurement data.

[2013] Step 4:

[2014] Server: Analyzes the user's emotional state using an emotion engine.

[2015] Step 5:

[2016] Server: If an anomaly is detected, it generates a notification taking into account the emotional state.

[2017] Step 6:

[2018] Server: Sends a notification to the device.

[2019] Step 7:

[2020] Device: Displays notifications to the user.

[2021] Specific example: If a user measures their blood pressure with a blood pressure monitor and an abnormal reading is detected, the server generates a notification saying, "Your blood pressure is high. If you are worried, try some relaxation techniques. If it persists, consult a doctor," and displays it to the user via their device.

[2022] Support for home healthcare

[2023] Step 1:

[2024] User: Enter and submit information about their health status and any changes in their physical condition on the device.

[2025] Step 2:

[2026] Terminal: Sends health information to the server.

[2027] Step 3:

[2028] Server: Analyzes the received data.

[2029] Step 4:

[2030] Server: Analyzes the user's emotional state using an emotion engine.

[2031] Step 5:

[2032] Server: Provides health information and emotional status to medical staff.

[2033] Step 6:

[2034] Medical staff: Generate instructions based on health information and emotional state, and input them into the server.

[2035] Step 7:

[2036] Server: Sends instructions from medical staff to the user's terminal.

[2037] Step 8:

[2038] Terminal: Displays instructions from medical staff to the user.

[2039] Specific example: If a user enters "My temperature is high and I have a sore throat," and the emotion engine recognizes this as anxiety, the server analyzes the data and notifies medical staff. The medical staff then instructs the user to "Take paracetamol and drink plenty of fluids. Contact us if you have any concerns," and sends this to the user, who then sees it displayed on their device.

[2040] Recipe management

[2041] Step 1:

[2042] User: Enter and submit a meal request on the terminal.

[2043] Step 2:

[2044] Terminal: Sends a request to the server.

[2045] Step 3:

[2046] Server: Analyzes the user's emotional state using an emotion engine.

[2047] Step 4:

[2048] Server: Generates appropriate recipes using a generative AI model.

[2049] Step 5:

[2050] Server: Sends the generated recipe to the terminal.

[2051] Step 6:

[2052] Terminal: Displays the recipe to the user.

[2053] Specific example: If a user types "Please tell me a recipe for a meal suitable for diabetes," and the emotion engine recognizes the user's stress, the server will generate a recipe suggesting "Sautéed chicken breast and broccoli with relaxing herbal tea" and display it to the user via their device.

[2054] Online medical consultation

[2055] Step 1:

[2056] User: Make an appointment for a medical consultation using the terminal.

[2057] Step 2:

[2058] Terminal: Sends reservation information to the server.

[2059] Step 3:

[2060] Server: Sets up a video call between the doctor and the user.

[2061] Step 4:

[2062] User: Start a video call from your device at the scheduled time.

[2063] Step 5:

[2064] Server: Records medical results and instructions. Also analyzes the user's emotional state using an emotion engine.

[2065] Step 6:

[2066] Server: Sends medical results and instructions to the user's terminal.

[2067] Step 7:

[2068] Terminal: Displays medical results and instructions to the user.

[2069] Specific example: A user makes an appointment for a medical consultation and starts a video call at the designated time. The doctor conducts the consultation, and if the emotion engine recognizes the user's anxiety, the doctor notifies the user of the consultation result, such as "Please take the prescribed medication and take time to relax." The device displays an emotionally sensitive message to the user along with the consultation result.

[2070] As described above, the system of the present invention, centered on a generative AI model and an emotion engine, enables the user, terminal, and server to cooperate with each other to effectively and emotionally considerately provide last-mile medical support. This system makes it possible to provide high-quality medical support even in areas with limited access to medical care and in aging societies.

[2071] (Example 2)

[2072] Next, we will describe Example 2. 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."

[2073] Conventional medical support systems often provide information without considering the user's emotional state, leading to problems such as users' anxiety and stress not being alleviated. Furthermore, data analysis from medical devices also fails to consider emotional states, sometimes resulting in inappropriate advice and notifications to users and medical staff. Therefore, there is a need for a medical support system that takes emotional considerations into account.

[2074] The identification processing performed 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 generating appropriate answers to health-related questions from the user using a generation AI model, means for analyzing the emotional state using an emotion engine based on the user's input data and generating corresponding answers, and means for receiving and analyzing measurement data from medical devices. This enables not only the provision of information according to the user's health condition but also responses that take emotions into consideration.

[2075] A "generative AI model" is a type of artificial intelligence that automatically generates appropriate answers and suggestions based on user input data.

[2076] An "emotion engine" is a program that analyzes a user's emotional state based on their input data and actions, and then takes appropriate action based on that analysis.

[2077] "Health-related questions" are questions that users ask about their own health status or symptoms.

[2078] A "medical device" refers to a device or apparatus used to measure a user's health data. Examples include blood pressure monitors and thermometers.

[2079] "Measurement data" refers to numerical data about a user's health obtained using medical devices.

[2080] "Analysis" is the process of detecting and evaluating outliers and trends based on acquired data.

[2081] A "notification" is information that the system communicates to the user or medical staff regarding anomalies or advice.

[2082] "Health information" refers to data about the user's health status and symptoms that they enter into the system.

[2083] "Medical staff" refers to medical professionals who use the system to support users' health.

[2084] "Instructions" refer to the methods of action and advice provided by medical staff to the user.

[2085] A "meal recipe" refers to a list of cooking methods and ingredients suggested based on the user's requests and health condition.

[2086] A "video call" is a method of communication between a remote user and a doctor in real time using audio and video.

[2087] "Medical results" refer to the diagnostic information and treatment plan of a user obtained by a doctor through video calls or other means.

[2088] This invention is a last-mile medical support system in which the user, terminal, server, and emotion engine work together. By utilizing a generative AI model and emotion engine, this system can provide appropriate advice and notifications based on the user's health information.

[2089] Providing a health chatbot

[2090] Server: Uses a generative AI model to generate appropriate answers to health-related questions from users. By combining it with an emotion engine, it analyzes the user's emotional state from the input data and generates answers that correspond to that emotional state.

[2091] Terminal: The terminal sends health-related question data entered by the user to the server and displays the server's responses to the user. Specific hardware used includes smartphones and PCs.

[2092] User: Enters health-related questions from their device and receives answers from the server. This allows the user to receive specific advice tailored to their health condition.

[2093] Specific example: If a user enters "I haven't been able to sleep lately" in an anxious tone, the server uses an emotion engine to analyze the user's anxiety and generates an emotionally sensitive response such as, "You can try relaxing before bed or avoiding caffeine. If your anxiety persists, please consult a doctor," which is then displayed to the user via their device.

[2094] Monitoring of medical devices

[2095] Server: Analyzes measurement data transmitted from medical devices and checks for abnormalities. It also analyzes the user's emotional state using an emotion engine and sends notifications to the user and medical staff as needed.

[2096] Terminal: Receives data from the medical device used by the user and sends it to the server.

[2097] User: Uses medical devices to measure their own health data and transmits it through a terminal. This allows users to monitor their health status in real time.

[2098] Specific example: A user sends blood pressure data measured using a blood pressure monitor from their device to a server. If the server detects high blood pressure and the emotion engine further analyzes the user's anxiety level, it will notify the user with the message, "Your blood pressure is high. Please consult a doctor. If you are feeling anxious, try some relaxation techniques."

[2099] Support for home healthcare

[2100] Server: Collects health information from users and analyzes their emotional state using an emotion engine. Provides health information and emotional state data to medical staff and sends instructions from medical staff to the user.

[2101] Terminal: Sends health information from the user to the server and displays instructions from medical staff on the server to the user.

[2102] User: Enters their health status and changes in physical condition into the device to receive support. This allows users to receive detailed health information and instructions from the comfort of their homes.

[2103] Specific example: A user enters "My body temperature is high and my throat hurts," and the emotion engine analyzes the user's anxiety. The server then analyzes the data and notifies medical staff of "high body temperature, sore throat, and anxiety." The medical staff then instructs the user to "take paracetamol and drink plenty of fluids. Contact us if your anxiety persists," and sends this information to the user.

[2104] Recipe management

[2105] Server: Uses a generative AI model to generate meal recipes that match the user's request. It can also use an emotion engine to suggest recipes that take the user's emotional state into consideration.

[2106] Terminal: Displays recipe information provided by the server to the user.

[2107] User: Enters a request regarding meals and receives a generated recipe. This allows users to receive recipes tailored to their health condition.

[2108] Specific example: If a user enters "Please tell me a recipe for a meal suitable for diabetes," and the emotion engine analyzes the user's stress level, the server generates a recipe suggesting "Sautéed chicken breast and broccoli with relaxing herbal tea," which is then displayed to the user via their device.

[2109] Online medical consultation

[2110] Server: Relays the user's consultation with the doctor via video call. Records the consultation results and medical instructions, and analyzes and records the user's emotional state using an emotion engine.

[2111] Terminal: Connects the user's video call to the server and displays the medical results and instructions.

[2112] User: Schedules an appointment and conducts a consultation with a doctor via video call. This allows users to receive medical advice and emotional support from the comfort of their homes.

[2113] Specific example: A user makes an appointment for a medical consultation and starts a video call at the designated time. The doctor conducts the consultation, and if the emotion engine analyzes the user's anxiety, the doctor notifies the user of the consultation result, such as "Please take the prescribed medication and take time to relax." The device displays an emotionally sensitive message to the user along with the consultation result.

[2114] Examples of prompts include "I haven't been able to sleep lately," "Please give me some recipes for meals suitable for people with diabetes," and "My body temperature is high and I have a sore throat."

[2115] As described above, the system of the present invention utilizes a generative AI model and an emotion engine, enabling the user, terminal, and server to cooperate with each other to provide high-quality and emotionally sensitive last-mile medical support. This system makes it possible to provide convenient and reliable medical support to users even in areas with limited access to medical care and in aging societies.

[2116] The flow of the specific processing in Example 2 will be explained using Figure 13.

[2117] Providing a health chatbot

[2118] Step 1:

[2119] The user enters health-related questions into the device.

[2120] Input: Health-related questions (e.g., "I've been having trouble sleeping lately")

[2121] Output: Input question data

[2122] Step 2:

[2123] The terminal sends the user's question data to the server.

[2124] Input: User's question data

[2125] Output: Question data sent to the server

[2126] Step 3:

[2127] The server receives the question data and the emotion engine analyzes the emotional state.

[2128] Input: Question data received by the server

[2129] Data processing: Analyze the user's emotional state (e.g., anxiety) based on the questionnaire data.

[2130] Output: Analyzed emotional state

[2131] Step 4:

[2132] The server uses a generation AI model to generate responses that correspond to the emotional state.

[2133] Input: Analyzed emotional state, questionnaire data

[2134] Data processing: Generative AI model generates appropriate answers.

[2135] Output: Generated response (Example: "You can try relaxing before bed and avoiding caffeine. If your anxiety persists, consult a doctor.")

[2136] Step 5:

[2137] The server sends the response to the terminal.

[2138] Input: Generated answer

[2139] Output: Response sent from server to terminal

[2140] Step 6:

[2141] The device displays the answer to the user.

[2142] Input: Response sent from the server

[2143] Output: Answer displayed on the terminal

[2144] Monitoring of medical devices

[2145] Step 1:

[2146] The user acquires measurement data using a medical device.

[2147] Input: Measurement using medical devices (e.g., blood pressure measurement)

[2148] Output: Measured data (e.g., blood pressure)

[2149] Step 2:

[2150] The terminal receives measurement data and sends it to the server.

[2151] Input: Data measured by the user

[2152] Output: Measurement data sent to the server

[2153] Step 3:

[2154] The server receives the measurement data and performs analysis.

[2155] Input: Measurement data received by the server

[2156] Data processing: Detection of abnormal values ​​based on measurement data (e.g., high blood pressure).

[2157] Output: Analysis results (e.g., hypertension)

[2158] Step 4:

[2159] The server uses an emotion engine to analyze the user's emotional state.

[2160] Input: Analysis results (e.g., hypertension), user sentiment data

[2161] Data processing: The emotion engine analyzes the user's anxiety state.

[2162] Output: Analyzed emotional state (e.g., anxiety)

[2163] Step 5:

[2164] The server generates notifications based on the abnormality and emotional state, and sends them to the terminal.

[2165] Input: Analysis results and emotional state

[2166] Data processing: Generate appropriate notifications

[2167] Output: Generated notification (Example: "Your blood pressure is high. Please consult a doctor. If you are feeling anxious, try some relaxation techniques.")

[2168] Step 6:

[2169] The device displays a notification to the user.

[2170] Input: Notification sent from the server

[2171] Output: Notification displayed on the terminal

[2172] Support for home healthcare

[2173] Step 1:

[2174] The user enters their health status into the device.

[2175] Input: Enter your health status (e.g., "My temperature is high and I have a sore throat.")

[2176] Output: Input health information

[2177] Step 2:

[2178] The device sends health information to the server.

[2179] Input: Health information entered by the user

[2180] Output: Health information sent to the server

[2181] Step 3:

[2182] The server analyzes health information and emotional state.

[2183] Input: Health information received by the server

[2184] Data processing: Analysis of emotional states using an emotion engine.

[2185] Output: Analyzed health information and emotional state

[2186] Step 4:

[2187] The server sends a notification to the medical staff.

[2188] Input: Analyzed health information and emotional state

[2189] Output: Notification sent to medical staff

[2190] Step 5:

[2191] Medical staff send instructions to the server.

[2192] Input: Instructions created by medical staff

[2193] Output: Instructions sent to the server

[2194] Step 6:

[2195] The server sends instructions to the terminal from the medical staff.

[2196] Input: Instructions received from medical staff

[2197] Output: Instructions sent to the terminal

[2198] Step 7:

[2199] The device displays instructions to the user.

[2200] Input: Instructions sent from the server

[2201] Output: Instructions displayed on the terminal

[2202] Recipe management

[2203] Step 1:

[2204] The user enters their meal requests into the terminal.

[2205] Input: Request regarding meals (e.g., "Please provide recipes suitable for meals for people with diabetes")

[2206] Output: Input Request

[2207] Step 2:

[2208] The terminal sends a request to the server.

[2209] Input: User-entered request

[2210] Output: Request sent to the server

[2211] Step 3:

[2212] The server analyzes the request and the emotional state.

[2213] Input: Request received by the server

[2214] Data processing: Analysis of emotional states using an emotion engine.

[2215] Output: Analyzed requests and sentiment states

[2216] Step 4:

[2217] The server uses an AI model to generate recipes in response to requests.

[2218] Input: Analyzed request and emotional state

[2219] Data processing: Generative AI model generates appropriate recipes.

[2220] Output: Generated recipe (Example: "Sautéed chicken breast and broccoli with relaxing herbal tea")

[2221] Step 5:

[2222] The server sends the generated recipe to the terminal.

[2223] Input: Generated recipe

[2224] Output: Recipe sent to the terminal

[2225] Step 6:

[2226] The device displays the recipe to the user.

[2227] Input: Recipe sent from the server

[2228] Output: Recipe displayed on the terminal

[2229] Online medical consultation

[2230] Step 1:

[2231] The user makes an appointment for a medical consultation.

[2232] Input: Appointment information for medical consultations

[2233] Output: Reservation confirmation

[2234] Step 2:

[2235] The user initiates a video call at the specified time.

[2236] Input: Start a video call

[2237] Output: Video call connection status

[2238] Step 3:

[2239] The server relays the medical consultation via video call.

[2240] Input: Video call data between user and doctor

[2241] Output: Relayed video call data

[2242] Step 4:

[2243] A doctor provides medical treatment and records the results on a server.

[2244] Input: Medical results

[2245] Output: Recorded medical results

[2246] Step 5:

[2247] The server uses an emotion engine to analyze the user's emotional state.

[2248] Input: Medical results, user sentiment data

[2249] Data processing: The emotion engine analyzes the user's anxiety state.

[2250] Output: Analyzed emotional state

[2251] Step 6:

[2252] The server generates a notification for the user based on the medical results and emotional state.

[2253] Input: Medical results and emotional state

[2254] Data processing: Generated notifications

[2255] Output: Appropriate notification (e.g., "Take the prescribed medication and allow yourself time to relax.")

[2256] Step 7:

[2257] The device displays a notification to the user.

[2258] Input: Notification sent from the server

[2259] Output: Notification displayed on the terminal

[2260] (Application Example 2)

[2261] Next, we will explain application example 2. In the following explanation, 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."

[2262] In modern healthcare support, providing appropriate health management and medical support is a challenge, especially in remote areas and regions with limited access to medical care. Furthermore, there is a lack of flexible medical advice based on the user's emotional state, and a system is needed that allows for continuous and individualized communication between medical staff and patients.

[2263] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[2264] In this invention, the server includes means for generating appropriate answers to health-related questions from the user using a generative AI model, means for analyzing the user's emotional state using an emotion analysis engine and generating medical results and advice corresponding to that emotional state, and means for receiving and analyzing measurement data from medical devices. This enables appropriate and individualized medical support remotely based on the user's health and emotional state.

[2265] A "generative AI model" is an artificial intelligence model that generates appropriate answers and advice based on user input data.

[2266] An "emotion analysis engine" is a system that analyzes a user's emotional state and provides information based on those emotions.

[2267] A "medical device" is a device that measures a user's health condition, and includes, for example, blood pressure monitors and electrocardiographs.

[2268] "Health information" refers to data about a user's physical condition and symptoms.

[2269] A "video call" is a communication method that uses audio and video to connect users in different locations with doctors in real time.

[2270] "Medical results" refer to the diagnosis and treatment determined by a doctor based on the user's health condition and symptoms.

[2271] "Analysis results" refer to conclusions and information obtained after analyzing data using generative AI models or sentiment analysis engines.

[2272] "Medical staff" refers to healthcare professionals such as doctors and nurses, who are responsible for managing and treating the user's health.

[2273] A "notification" is a message that conveys analysis results or doctor's instructions to the user or medical staff.

[2274] A "recipe" is a suggestion of how to prepare a meal or a menu, generated based on the user's health condition and requests.

[2275] "Emotional information" refers to data and analysis results related to the user's emotional state.

[2276] This invention is a last-mile medical support system that utilizes a generative AI model and an emotion analysis engine, aiming to support users' health management and medical treatment. The specific embodiments for implementing this invention are described in detail below.

[2277] System Configuration

[2278] 1. User terminal

[2279] User terminals consist of smartphones, tablets, and other devices. They transmit user input and data from medical devices to a server, and display responses and notifications from the server to the user. The terminals are linked to a cloud database to store and manage health information and medical results.

[2280] 2. Server

[2281] The server implements the following main functions:

[2282] Generative AI models (e.g., OpenAI GPT-4) are used to generate appropriate answers to user questions.

[2283] An emotion analysis engine (e.g., Affectiva AI) is used to analyze the user's emotional state and generate medical results and advice based on those emotions.

[2284] It analyzes data transmitted from medical devices to detect abnormal values ​​and symptoms.

[2285] Send notifications to users and medical staff.

[2286] We use video conferencing services (e.g., Twilio) to facilitate online medical consultations between users and doctors.

[2287] 3. Management of health information

[2288] The server analyzes health information collected from users (physical condition, symptoms, and measurement data from medical devices) and provides it to medical staff. Based on the analysis results, the medical staff issues treatment instructions and sends them to the user.

[2289] Specific example

[2290] User login and health information entry

[2291] After the user launches the application and logs in, they enter their current physical condition or symptoms. For example, they might enter, "I've been having trouble sleeping lately."

[2292] Data transmission and analysis

[2293] The entered information is sent to a cloud server and analyzed by a generative AI model and an emotion analysis engine. The emotion analysis engine recognizes the user's anxiety state, and the generative AI model generates advice such as, "To relax before going to bed, try drinking some warm tea or doing some light stretching. We also recommend consulting a doctor."

[2294] Starting a video call

[2295] Users schedule video calls within the application and have real-time consultations with doctors at the designated time. An emotion analysis engine analyzes the user's facial expressions during the video call and notifies the doctor of the user's emotional state.

[2296] Providing medical results and advice

[2297] After the consultation is complete, the AI ​​model sends the user the consultation results and advice it has generated. For example, it might say, "Take the prescribed medication and make time to relax."

[2298] Example of a prompt

[2299] User input: "Lately, I've been having trouble sleeping a lot, and it's making me feel stressed."

[2300] AI prompt: "If the user is experiencing sleep deprivation and stress, please provide specific advice on how to relax."

[2301] Thus, the system of the present invention enables the remote provision of advanced, personalized medical support based on the user's health and emotional state. Furthermore, by combining a generative AI model with an emotion analysis engine, medical treatment and advice that take the user's emotions into consideration can be realized.

[2302] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[2303] Step 1:

[2304] The user launches the application and logs in. The application receives the user ID and password as input and performs authentication through database matching in conjunction with the authentication system. The output confirms the login status. If the login is successful, the application retrieves the user's basic information from the server and displays it on the terminal.

[2305] Step 2:

[2306] The user enters their current physical condition and symptoms. A text input field is provided for this input, allowing the user to enter health information and symptom data, such as "I've been having trouble sleeping lately." The device receives this data, converts it to a data format, and sends it to a cloud server. The input information is saved on the server as output.

[2307] Step 3:

[2308] The server analyzes the health information it receives. The input is health information submitted by the user, which is then fed into a generative AI model. The generative AI model generates appropriate answers to user questions, and the results are saved as output. In parallel, an emotion analysis engine is used to analyze the user's emotional state, and the results are also saved.

[2309] Step 4:

[2310] The server sends the analysis results to the user's terminal. The input consists of the analysis results from the generative AI model and the emotion analysis engine, which are then combined and structured into a message. The output is the analysis results sent to the user's terminal, displaying health advice and suggestions for addressing anxiety.

[2311] Step 5:

[2312] The user schedules a video call. The input includes the date and time of the video call and the selection of a doctor, and this information is sent to the server. The server stores the reservation information and displays a reservation confirmation message on the user's terminal as output.

[2313] Step 6:

[2314] The video call starts at the scheduled time. As input, the server receives a signal to begin the video call and activates the video call service. As output, an environment is provided where the user and doctor can communicate in real time. Simultaneously, an emotion analysis engine analyzes the user's facial expressions and sends them to the server.

[2315] Step 7:

[2316] The server provides doctors with the results of emotion analysis during video calls. As input, the user's facial expression data obtained during the video call is analyzed by the emotion analysis engine. The server sends the results to the doctor's terminal, and the emotional state is displayed to the doctor as output, which is used as reference information for medical treatment.

[2317] Step 8:

[2318] After the video call ends, the system generates and provides the user with a medical assessment and advice. Inputs, including the doctor's assessment and advice generated by the AI ​​model, are collected on the server. Outputs, this information is sent to the user's terminal and displayed within the application. This data is also stored in a database.

[2319] Step 9:

[2320] The system continuously monitors users' health information and emotional states. Health data and emotional analysis data are collected periodically from users as input. The server analyzes this data, and if an anomaly is detected, it promptly sends notifications to the user and medical staff.

[2321] These steps enable effective and personalized medical support based on the user's health and emotional state.

[2322] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[2323] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2324] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[2325] [Fourth Embodiment]

[2326] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[2327] As shown in Figure 7, the 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.

[2328] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[2329] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[2330] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[2331] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[2332] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[2333] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[2334] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[2335] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[2336] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[2337] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[2338] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[2339] This invention is a last-mile medical support system that utilizes a generative AI model. This system functions through the coordinated efforts of the user, terminal, and server. The following details each component and its role.

[2340] Providing a health chatbot

[2341] Server: Uses a generative AI model to generate appropriate answers to health-related questions from users. The server analyzes the content of health consultations submitted by users and generates answers on the spot based on relevant medical knowledge.

[2342] Device: This device sends health-related question data entered by the user to the server and displays the server's responses to the user. Smartphones and PCs fall into this category.

[2343] User: Enters health-related questions from their device and receives answers from the server.

[2344] Specific example: If a user enters "I haven't been able to sleep lately," the server generates a response such as "You can try relaxing before bed and avoiding caffeine. If it persists, please consult a doctor," and displays it to the user via their device.

[2345] Monitoring of medical devices

[2346] Server: Analyzes measurement data transmitted from medical devices and checks for abnormalities. Sends notifications to users and medical staff as needed.

[2347] Terminal: Receives data from the medical device used by the user and sends it to the server.

[2348] User: Uses medical devices to measure their own health data and transmits the data through their device.

[2349] Specific example: A user sends blood pressure data measured using a blood pressure monitor from their device to a server. If the server detects high blood pressure, it notifies the user with the message, "Your blood pressure is high. Please consult a doctor."

[2350] Support for home healthcare

[2351] Server: Collects health information from users and provides it to medical staff. It also sends instructions from medical staff to users.

[2352] Terminal: Sends health information from the user to the server and displays instructions from medical staff on the server to the user.

[2353] User: Enter their health status and any changes in their physical condition into the device to receive support.

[2354] Specific example: When a user enters "My body temperature is high and I have a sore throat," the server analyzes the data and provides it to medical staff. The medical staff then advises, "Take paracetamol and drink plenty of fluids," and sends this information back to the user.

[2355] Recipe management

[2356] Server: Uses a generative AI model to generate meal recipes that match the user's request.

[2357] Terminal: Displays recipe information provided by the server to the user.

[2358] User: Enter a meal request and receive a generated recipe.

[2359] Specific example: When a user enters "Please tell me a recipe for a meal suitable for diabetes," the server generates a recipe for "Sautéed chicken breast and broccoli" and displays it to the user via their terminal.

[2360] Online medical consultation

[2361] Server: Relays the consultation between the user and the doctor via video call. Records the consultation results and medical instructions and sends them to the user.

[2362] Terminal: Connects the user's video call to the server and displays the medical results and instructions.

[2363] User: Makes an appointment for a medical consultation and conducts the consultation with a doctor via video call.

[2364] Specific example: A user makes an appointment for a medical consultation and starts a video call at the designated time. After the doctor conducts the examination and notifies the user of the diagnosis, such as "Please take the prescribed medication," this information is displayed to the user via their device.

[2365] As described above, the system of the present invention effectively realizes last-mile medical support through the mutual cooperation of users, terminals, and servers, centered around a generated AI model. This system makes it possible to provide high-quality medical support even in areas with difficult access to medical care and in aging societies.

[2366] The following describes the processing flow.

[2367] Providing a health chatbot

[2368] Step 1:

[2369] User: Enter health-related questions into the device and submit.

[2370] Step 2:

[2371] Terminal: Sends the entered question data to the server.

[2372] Step 3:

[2373] Server: Receives question data and analyzes its content using a generative AI model.

[2374] Step 4:

[2375] Server: Generates an appropriate answer to the question and sends it to the terminal.

[2376] Step 5:

[2377] Terminal: Displays the generated response to the user.

[2378] Specific example:

[2379] User: Type "I haven't been able to sleep lately" into the device and send it.

[2380] Terminal: Sends question data to the server.

[2381] Server: Analyzes the question and generates a response such as, "You can try relaxing before bed and avoiding caffeine. If the problem persists, please consult a doctor," and sends it to the terminal.

[2382] Terminal: Displays the answer to the user.

[2383] Monitoring of medical devices

[2384] Step 1:

[2385] User: Uses medical devices to measure health data.

[2386] Step 2:

[2387] Terminal: Sends measured data to the server.

[2388] Step 3:

[2389] Server: Analyzes the received data and detects anomalies.

[2390] Step 4:

[2391] Server: If an anomaly is detected, it sends a notification to the user or medical staff.

[2392] Step 5:

[2393] Device: Displays notifications to the user.

[2394] Specific example:

[2395] User: Measure blood pressure with a blood pressure monitor.

[2396] Terminal: Sends measurement data to the server.

[2397] Server: Analyzes the data and, if high blood pressure is detected, generates a notification saying, "Your blood pressure is high. Please consult a doctor," and sends it to the terminal.

[2398] Device: Displays notifications to the user.

[2399] Support for home healthcare

[2400] Step 1:

[2401] User: Enters their health information and changes in their physical condition into the device and sends it.

[2402] Step 2:

[2403] Terminal: Sends health information to the server.

[2404] Step 3:

[2405] Server: Analyzes received data and provides it to medical staff.

[2406] Step 4:

[2407] Medical staff: Generate instructions based on the analysis results and input them into the server.

[2408] Step 5:

[2409] Server: Sends instructions from medical staff to the user's terminal.

[2410] Step 6:

[2411] Terminal: Displays instructions from medical staff to the user.

[2412] Specific example:

[2413] User: Enters "My body temperature is high and my throat hurts" into the device and sends it.

[2414] Terminal: Sends health information to the server.

[2415] Server: Analyzes the data and notifies medical staff of "elevated body temperature and sore throat."

[2416] Medical staff: Enter the instruction, "Take paracetamol and drink plenty of fluids."

[2417] Server: Sends instructions to the terminal.

[2418] Terminal: Displays instructions to the user.

[2419] Recipe management

[2420] Step 1:

[2421] User: Enter and submit a meal request on the terminal.

[2422] Step 2:

[2423] Terminal: Sends a request to the server.

[2424] Step 3:

[2425] Server: Uses a generative AI model to analyze requests and generate appropriate recipes.

[2426] Step 4:

[2427] Server: Sends the generated recipe to the terminal.

[2428] Step 5:

[2429] Terminal: Displays the recipe to the user.

[2430] Specific example:

[2431] User: Type "Please share some recipes suitable for people with diabetes" and submit.

[2432] Terminal: Sends a request to the server.

[2433] Server: Uses a generative AI model to generate "Sautéed Chicken Breast and Broccoli" and sends it to the terminal.

[2434] Terminal: Displays the recipe to the user.

[2435] Online medical consultation

[2436] Step 1:

[2437] User: Make an appointment for a medical consultation using the terminal.

[2438] Step 2:

[2439] Terminal: Sends reservation information to the server.

[2440] Step 3:

[2441] Server: Sets up a video call between the doctor and the user.

[2442] Step 4:

[2443] User: Start a video call from your device at the scheduled time.

[2444] Step 5:

[2445] Server: Records medical results and instructions, and sends them to the user's terminal.

[2446] Step 6:

[2447] Terminal: Displays medical results and instructions to the user.

[2448] Specific example:

[2449] User: Make an appointment for a medical consultation using the terminal.

[2450] Terminal: Sends reservation information to the server.

[2451] Server: Set up a video call with the doctor.

[2452] User: Start the video call at the scheduled time.

[2453] Server: Records the medical results as "You need rest, please take the prescribed medication" and sends it to the terminal.

[2454] Terminal: Displays the medical results to the user.

[2455] (Example 1)

[2456] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[2457] In modern society, medical resources and equipment are limited, and providing appropriate medical support is particularly difficult in areas with limited access to healthcare and in aging societies. Furthermore, the lack of adequate means for users to monitor their health on a daily basis and receive prompt professional medical support when needed can increase the risk of health problems.

[2458] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[2459] In this invention, the server includes means for generating appropriate answers to health-related questions from users using a generative AI model; means for receiving and analyzing measurement data from medical devices; means for sending notifications to users or medical professionals based on the analysis results; means for collecting health information from users and providing it to medical professionals; means for sending and displaying instructions from medical professionals to users; means for generating meal plan proposals based on user requests; means for relaying medical consultations between users and doctors via online communication; and means for recording and sending medical results and instructions to users. This makes it possible to provide high-quality medical support even in areas with limited access to medical care and in aging societies.

[2460] A "generative AI model" is an artificial intelligence model that analyzes user input data and generates appropriate responses or results based on that data.

[2461] A "health-related question" is an inquiry that a user submits to seek information or advice about their health condition or symptoms.

[2462] An "appropriate answer" is accurate and useful information based on medical knowledge, provided by a generative AI model in response to a user's question.

[2463] A "medical device" is a device used by a user to measure their own health indicators, and examples include blood pressure monitors and thermometers.

[2464] "Measurement data" refers to data on health indicators obtained using medical devices.

[2465] "Analysis" is the process of analyzing received data to identify important information and outliers.

[2466] A "notification" is a message sent to inform a user or medical professional of analysis results or important information.

[2467] "Health information" refers to comprehensive health data that includes information about a user's health status, symptoms, and daily life.

[2468] A "medical professional" is a person with specialized knowledge in the medical field, such as a doctor or nurse.

[2469] "Instructions" refer to guidance provided by healthcare professionals to users to prompt them to take specific actions regarding health management and treatment.

[2470] A "meal plan" is a nutritionally balanced meal plan provided by an AI model that generates data based on the user's health status and requests.

[2471] "Online communication" refers to a communication method that involves sending and receiving data via a network such as the internet.

[2472] "Medical treatment" refers to a series of medical actions and activities performed by a doctor on a user.

[2473] "Medical outcome" refers to medical conclusions or findings obtained through medical treatment.

[2474] "Record keeping" refers to the process of saving medical results and instructions in a digital format so that they can be reviewed later.

[2475] This invention is a last-mile medical support system that utilizes a generative AI model, in which the user, terminal, and server elements work together. The following details each component and its role.

[2476] Providing a health chatbot

[2477] Server: Uses a generative AI model to generate appropriate answers to health-related questions from users. Specifically, the server analyzes the content of health consultations sent by users and generates answers using the generative AI model "GPT-3". For example, if a user enters "I've been having trouble sleeping lately," the server generates an answer such as "You can try relaxing before bed and avoiding caffeine. If it persists, please consult a doctor," and sends this to the terminal.

[2478] Device: This device sends health-related question data entered by the user to the server and displays the server's responses to the user. Smartphones and PCs fall into this category.

[2479] User: Enters health-related questions from their device and receives answers from the server.

[2480] Monitoring of medical devices

[2481] Server: Analyzes measurement data transmitted from medical devices and checks for abnormalities. Sends notifications to users and medical staff as needed. For example, if it receives blood pressure monitor data and uses the generated AI model "anomaly detection algorithm" to detect high blood pressure, it will send a notification saying, "Your blood pressure is high. Please consult a doctor."

[2482] Terminal: Receives data from medical devices used by the user and transmits it to the server. Wireless communication technologies such as Bluetooth are also used.

[2483] User: Uses medical devices to measure their own health data and transmits that data to a server via their device.

[2484] Support for home healthcare

[2485] Server: Collects health information from users and provides it to medical staff. It also sends instructions from medical staff to users. For example, if a user enters "My temperature is high and I have a sore throat," the server provides this to the medical staff. Then, if the medical staff instructs "Take paracetamol and drink plenty of fluids," the server sends this to the user.

[2486] Terminal: Sends health information from the user to the server and displays instructions from medical staff on the server to the user.

[2487] User: Enter their health status and any changes in their physical condition into the device to receive support.

[2488] Recipe management

[2489] Server: Uses a generative AI model to generate meal recipes that match the user's request. For example, using the generative AI model "Cookpad AI," in response to a user's request, "Please tell me a meal recipe suitable for diabetes," it generates a recipe such as "Sautéed chicken breast and broccoli."

[2490] Terminal: Displays recipe information provided by the server to the user.

[2491] User: Enter a meal request and receive a generated recipe.

[2492] Online medical consultation

[2493] Server: Relays the consultation between the user and the doctor via video call. Records the consultation results and medical instructions and sends them to the user. For example, it uses WebRTC technology to conduct video calls, saves the consultation details to a document management system, and sends medical instructions such as "Please take the specified medication" to the user.

[2494] Terminal: Connects the user's video call to the server and displays the medical results and instructions.

[2495] User: Makes an appointment for a medical consultation and has a consultation with a doctor via video call at the designated time.

[2496] Examples of specific cases and prompt statements

[2497] For example, if a user enters "I haven't been able to sleep lately," the server generates a response such as "You can try relaxing before bed and avoiding caffeine. If it persists, please consult a doctor," and displays it to the user via their device.

[2498] Example input prompts for a generative AI model:

[2499] User input: I can't sleep lately

[2500] Example response from the generated AI model: Possible measures include relaxing before bed and avoiding caffeine. If symptoms persist, consult a doctor.

[2501] As described above, this system, centered around a generative AI model, enables effective last-mile medical support through the mutual cooperation of users, terminals, and servers. This system makes it possible to provide high-quality medical support even in areas with limited access to healthcare and in aging societies.

[2502] The flow of the specific processing in Example 1 will be explained using Figure 11.

[2503] Providing a health chatbot

[2504] Step 1:

[2505] User: Enter a health-related question into the terminal. For example, enter "I've been having trouble sleeping lately."

[2506] Input: Text-based questions about health

[2507] Output: Send instructions on the terminal

[2508] Step 2:

[2509] Terminal: Sends the entered question data to the server. HTTPS is used as the communication protocol.

[2510] Input: Question data entered by the user

[2511] Data processing: Question data is converted into HTTP request format.

[2512] Output: HTTP request sent to the server

[2513] Step 3:

[2514] Server: Inputs question data into the AI ​​model, analyzes it, and generates appropriate answers. It uses "GPT-3" to generate answers to user questions.

[2515] Input: Question data in HTTP request format

[2516] Data processing: Text analysis and response generation using generative AI models.

[2517] Output: Response data generated in JSON format

[2518] Step 4:

[2519] Server: Sends the generated response to the terminal.

[2520] Input: Response data in JSON format

[2521] Data processing: The response data is converted into an HTTP response format.

[2522] Output: HTTP response sent to the terminal

[2523] Step 5:

[2524] Terminal: Displays received responses to the user. Responses are displayed on the terminal screen.

[2525] Input: Response data in HTTP response format

[2526] Data processing: Response data is converted into a format that users can understand.

[2527] Output: The answer displayed on the device screen

[2528] Monitoring of medical devices

[2529] Step 1:

[2530] User: Measuring data using medical devices. For example, measuring blood pressure using a blood pressure monitor.

[2531] Input: Blood pressure measuring device

[2532] Output: Measurement data

[2533] Step 2:

[2534] Medical devices: Transmit measurement data to a terminal. Wireless communication such as Bluetooth is used.

[2535] Input: Measurement data

[2536] Data processing: Measurement data is converted to a wireless communication format.

[2537] Output: Measurement data sent to the terminal

[2538] Step 3:

[2539] Terminal: Sends measurement data to the server. The HTTPS protocol is used again.

[2540] Input: Measurement data received via wireless communication

[2541] Data processing: Convert measurement data into HTTP request format.

[2542] Output: HTTP request sent to the server

[2543] Step 4:

[2544] Server: Analyzes data and checks for anomalies. It uses a generative AI model, "Anomaly Detection Algorithm," to analyze the data.

[2545] Input: Measurement data in HTTP request format

[2546] Data processing: Analysis of measurement data and anomaly detection.

[2547] Output: Notification data in case of an anomaly

[2548] Step 5:

[2549] Server: If an abnormality occurs, it sends a notification to the user or medical staff. A notification such as "Your blood pressure is high. Please consult a doctor" is generated.

[2550] Input: Data after anomaly detection

[2551] Data processing: Convert notification content into HTTP response format.

[2552] Output: Notification data sent to the terminal

[2553] Step 6:

[2554] Terminal: Displays received notifications to the user. The notification will appear on the user's terminal screen.

[2555] Input: Notification data in HTTP response format

[2556] Data processing: Convert notification data into a format that users can understand.

[2557] Output: Notification displayed on the device screen

[2558] Support for home healthcare

[2559] Step 1:

[2560] User: Enter health information into the terminal. For example, enter "My temperature is high and I have a sore throat."

[2561] Input: Health information

[2562] Output: Health information data

[2563] Step 2:

[2564] Terminal: Sends health information to the server. The HTTPS protocol is used.

[2565] Input: Entered health information data

[2566] Data processing: Convert health information data into HTTP request format.

[2567] Output: HTTP request sent to the server

[2568] Step 3:

[2569] Server: Provides health information to medical staff. The information is displayed on a dashboard for medical staff.

[2570] Input: Health information data in HTTP request format

[2571] Data processing: Convert health information to a display format.

[2572] Output: Display on the medical staff dashboard

[2573] Step 4:

[2574] Medical staff: Enter instructions for the user into the server. Enter the instruction, "Take paracetamol and drink plenty of fluids."

[2575] Input: Instructions from medical staff

[2576] Output: Instruction data

[2577] Step 5:

[2578] Server: Sends instructions from medical staff to terminals. This also uses the HTTPS protocol.

[2579] Input: Medical staff instruction data

[2580] Data processing: Convert the instruction data into an HTTP response format.

[2581] Output: Data to send to the terminal

[2582] Step 6:

[2583] Terminal: Displays instructions from medical staff to the user. Pop-up notifications are used, etc.

[2584] Input: Instruction data in HTTP response format

[2585] Data processing: Convert the instruction data into a format that the user can understand.

[2586] Output: Instructions displayed on the device screen

[2587] Recipe management

[2588] Step 1:

[2589] User: Enter a request regarding meals into the terminal. For example, enter "Please provide recipes suitable for meals for people with diabetes."

[2590] Input: Meal request

[2591] Output: Request data

[2592] Step 2:

[2593] Terminal: Sends a request to the server.

[2594] Input: User request data

[2595] Data processing: Convert request data into HTTP request format.

[2596] Output: HTTP request sent to the server

[2597] Step 3:

[2598] Server: Generates recipes using a generative AI model. The generative AI model used is "Cookpad AI".

[2599] Input: Request data in HTTP request format

[2600] Data processing: Parsing request data and generating recipes.

[2601] Output: Recipe data generated in JSON format

[2602] Step 4:

[2603] Server: Sends the generated recipe to the terminal.

[2604] Input: Recipe data in JSON format

[2605] Data processing: Convert recipe data into HTTP response format.

[2606] Output: HTTP response sent to the terminal

[2607] Step 5:

[2608] Terminal: Displays recipes to the user. Recipe ingredients and cooking instructions are displayed on the screen in a list format.

[2609] Input: Recipe data in HTTP response format

[2610] Data processing: Convert recipe data into a format that users can understand.

[2611] Output: Recipe displayed on the terminal screen

[2612] Online medical consultation

[2613] Step 1:

[2614] User: Make an appointment for a medical consultation via a terminal. Enter the date, time, and symptoms into the appointment system.

[2615] Input: Appointment data

[2616] Output: Reservation Request

[2617] Step 2:

[2618] Server: Notifies doctors of appointment information. This information is displayed on the doctor's dashboard.

[2619] Input: Reservation Request

[2620] Data processing: Convert appointment information to a format suitable for doctors.

[2621] Output: Display on the physician's dashboard

[2622] Step 3:

[2623] User: Start a video call on your device at the specified time. A WebRTC video conference will be set up.

[2624] Input: Call Initiation Request

[2625] Output: Start video call

[2626] Step 4:

[2627] Server: Connects users and doctors via video call.

[2628] Input: Video call request

[2629] Data processing: Call connection settings

[2630] Output: Video call connection between user and doctor

[2631] Step 5:

[2632] Doctor: Conducts medical examinations and records the results and instructions on the server. For example, they might enter an instruction such as, "Please take the prescribed medication."

[2633] Input: Medical results and instruction data

[2634] Output: Recorded data

[2635] Step 6:

[2636] Server: Sends medical results and instructions to the terminal.

[2637] Input: Recorded data

[2638] Data processing: Convert medical results and instructions into HTTP response format.

[2639] Output: HTTP response sent to the terminal

[2640] Step 7:

[2641] Terminal: Displays medical results and instructions to the user. Text notifications are displayed on the screen.

[2642] Input: Clinical results and instruction data in HTTP response format

[2643] Data processing: Converting medical results and instructions into a format that users can und...

Claims

1. A means of generating appropriate answers to health-related questions from users using a generative AI model, A means of receiving and analyzing measurement data from medical devices, A means of sending notifications to users or medical staff based on the analysis results, A means of collecting health information from users and providing it to medical staff, A means of sending and displaying instructions from medical staff to the user, A means for generating meal recipes based on user requests, A means of relaying medical consultations between users and doctors via video calls, A means of recording medical results and instructions and sending them to the user. Includes system.

2. The system according to claim 1, which provides a health chatbot and generates responses to users' health inquiries.

3. The system according to claim 1, which monitors medical devices and sends a notification to the user or medical staff if an abnormal value is detected.

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