System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving patient-input information; means for generating a response based on the received input information using a generative AI model; means for receiving patient-physiological information from a healthcare device; and means for analyzing the received physiological information and providing results to the subject.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional medical consultation systems have the problem that it is difficult for patients with mobility issues, the elderly, people with disabilities, and patients with chronic diseases to receive appropriate medical consultation and counseling. In particular, in situations where access to medical institutions is limited, it is difficult for patients to explain their symptoms in detail and receive accurate feedback. This can result in worsening symptoms and inappropriate self-care.

[0005] Furthermore, while many health management devices and applications have the ability to collect biometric data, this data is often not utilized effectively and patients have difficulty interpreting it properly, which can lead to neglecting ongoing health management and widening health disparities.

[0006] The present invention aims to solve these problems, provide an environment in which even patients who have difficulty moving can receive medical support, and make health management easier. [Means for solving the problem]

[0007] The present invention is a system including a means for receiving patient input information, a means for generating a response based on the received input information using a generative AI model, a means for receiving the patient's biometric data from a health management device, and a means for analyzing the received biometric data and providing the results to the patient.

[0008] Specifically, when a patient seeks medical advice, the generative AI model generates responses in natural language through the chatbot. This lowers the barrier to medical consultations and provides an environment where patients can feel at ease. Furthermore, biometric data acquired from health management devices is analyzed on the server side, and the results are fed back to the patient, allowing them to accurately understand their own health status and manage their health appropriately.

[0009] This system will improve access to medical care, allowing patients who have difficulty moving around to receive medical support from their homes or nursing facilities. Furthermore, the effective use of health data will enable continuous health management, contributing to the elimination of health disparities.

[0010] "Patient" refers to anyone who requires medical consultation, including the elderly, disabled people, and those with chronic illnesses.

[0011] "Input information" refers to the text or questions that a patient enters into the system for medical or health consultation.

[0012] A "generative AI model" refers to a system that uses artificial intelligence technology to process natural language and generate appropriate responses based on input information.

[0013] "Health management device" refers to a device or application that collects and records a patient's biometric data (e.g., heart rate, blood pressure, activity level, etc.).

[0014] "Biometric data" refers to data that indicates the patient's health condition, and includes, for example, blood pressure, heart rate, body temperature, and activity level.

[0015] "Means for generating a response" refers to the process by which the generative AI model generates an appropriate response or advice based on the patient's input information.

[0016] The "means for analyzing and providing the results" refers to the process in which the server analyzes the biometric data received from the health management device and feeds back the analysis results to the patient in an easy-to-understand format.

[0017] "System" refers to an overall device or platform that combines the above means to provide medical support and health management assistance to patients. [Brief explanation of the drawings]

[0018] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8]FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0019] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0020] First, the terms used in the following description will be explained.

[0021] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0022] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0023] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0024] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0025] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0029] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0030] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0031] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0032] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0033] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0036] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0037] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0038] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0039] This invention is a remote medical consultation system that utilizes a chatbot with a generative AI model to provide medical support to patients with mobility issues. The system receives patient input, generates appropriate responses, analyzes biometric data collected from health management devices, and provides the results to the patient.

[0040] System configuration

[0041] This system mainly consists of the following parts:

[0042] 1. User Device:

[0043] A device used by patients to input medical consultations, such as a smartphone, tablet, or PC. The user terminal provides an interface for inputting medical consultation text and collecting data from health management devices.

[0044] 2. Server:

[0045] This refers to a processing device that uses a generative AI model to generate responses based on input information received from a user device, and analyzes biometric data from a health management device to provide results. The server includes a generative AI model, a data analysis engine, and a database.

[0046] 3. Health management equipment:

[0047] A health management device is a device that collects a patient's biological data, such as a fitness tracker, blood pressure monitor, or heart rate monitor. The health management device transmits the collected data to a server via a user terminal.

[0048] System Operation

[0049] The specific flow of operation of this system is shown below.

[0050] Medical consultation handling

[0051] 1. User Input:

[0052] The patient inputs a medical inquiry through the chat interface of the user terminal. For example, the patient may input, "I've been having frequent headaches recently."

[0053] 2. Data transmission:

[0054] The user terminal transmits this input information to the server.

[0055] 3. Response generation:

[0056] The server inputs the received input information into the generative AI model, which then uses natural language processing techniques to generate the optimal response. For example, it might generate a response like, "Possible causes of your headache include stress, lack of sleep, or other health conditions. Consult your doctor for a detailed diagnosis."

[0057] 4. Send response:

[0058] The server generates a response and sends it to the user terminal.

[0059] 5. User Verification:

[0060] The user terminal displays this response to the patient, who can review the generated response and re-enter any further questions.

[0061] Health data management

[0062] 1. Data Collection:

[0063] The health management device collects the patient's biometric data (e.g., activity level, heart rate, blood pressure, etc.).

[0064] 2. Data transmission:

[0065] The health management device transmits the collected data to the user terminal, which then transmits the data to the server.

[0066] 3. Data Analysis:

[0067] The server analyzes the received biometric data using a data analysis engine, and the analysis results are converted into natural language using a generative AI model to generate information in a format that is easy for the patient to understand.

[0068] 4. Send results:

[0069] The server transmits the analysis results to the user terminal.

[0070] 5. User Feedback:

[0071] The user device displays the analysis results to the patient. For example, the patient may receive feedback such as, "Your current blood pressure is 130 / 85 mmHg, which is within the normal range. Please continue to take appropriate health care measures."

[0072] Specific examples

[0073] As a concrete example, consider the following scenario.

[0074] Scenario 1: Headache consultation

[0075] A user types into a chat interface, "I've been having a lot of headaches lately."

[0076] The server uses a generative AI model to generate a response such as, "Possible causes of your headache include stress, lack of sleep, or other health conditions."

[0077] The user terminal displays this response to the user.

[0078] Scenario 2: Blood Pressure Management

[0079] The health management device measures the user's blood pressure to be 130 / 85 mmHg.

[0080] The data is transmitted to the server via the user terminal.

[0081] The server analyzes the data and generates feedback such as, "Your current blood pressure is within the normal range."

[0082] The user terminal displays this feedback to the user.

[0083] Through this series of processes, the system provides an environment where even patients who have difficulty moving around can receive medical consultations and health management with peace of mind.

[0084] The processing flow will be explained below.

[0085] Medical consultation handling

[0086] Step 1: User Input

[0087] The user inputs a medical consultation request via a chat interface on the user terminal. For example, the user may input "I've been having frequent headaches recently."

[0088] Step 2: Send data

[0089] The terminal transmits the text of the medical consultation sent by the user to the server.

[0090] Step 3: Response Generation

[0091] The server inputs the received medical consultation text into a generative AI model.

[0092] Step 4: Natural Language Processing

[0093] The generative AI model uses natural language processing techniques to analyze the input text and generate an optimal response, such as, "Your headache may be due to stress, lack of sleep, or other health conditions. Please consult your doctor for a detailed diagnosis."

[0094] Step 5: Send response

[0095] The server sends the generated response to the user terminal.

[0096] Step 6: User Verification

[0097] The terminal displays the received response to the user, who can then review the generated response and re-enter the question if necessary.

[0098] Health data management

[0099] Step 1: Data collection

[0100] The health management device collects biological data of the patient (e.g., activity level, heart rate, blood pressure, etc.).

[0101] Step 2: Send data

[0102] The health management device transmits the collected data to the user terminal, which then periodically transmits this data to the server.

[0103] Step 3: Receiving data

[0104] The server receives the biometric data transmitted from the user terminal.

[0105] Step 4: Data analysis

[0106] The server analyzes the received biometric data using a data analysis engine.

[0107] Step 5: Interpretation by generative AI models

[0108] The server uses a generative AI model to interpret the results obtained by the data analysis engine in natural language. For example, it generates feedback such as, "Your current blood pressure is 130 / 85 mmHg, which is within the normal range. Please continue to take appropriate measures to manage your health."

[0109] Step 6: Send results

[0110] The server transmits the generated feedback to the user terminal.

[0111] Step 7: User Feedback

[0112] The device displays the analysis results to the user, who then checks the feedback and initiates medical consultation if any abnormalities are detected.

[0113] These are the specific processing steps. This system allows patients to receive medical consultations and manage their health from the comfort of their own homes or nursing homes.

[0114] Example 1

[0115] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0116] In remote medical consultation systems, real-time assessment of health status and accurate feedback based on that assessment are essential for patients with mobility issues to receive appropriate medical support. However, existing systems lack the means to effectively integrate and analyze patient input information and biometric data from health management devices, and provide rapid and accurate feedback to users. Furthermore, the lack of smooth intercommunication makes it difficult to provide a user-friendly service. Therefore, there is a need for an effective system that can appropriately integrate input information and biometric data, generate responses in natural language, and provide accurate feedback.

[0117] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0118] In this invention, the server

[0119] means for generating a response based on the received input information using a generative AI model;

[0120] means for receiving patient biometric data from a health management device;

[0121] means for transmitting the biometric data to a server using a user terminal;

[0122] means for inputting the input information and biometric data as prompts to a generative AI model;

[0123] means for generating the response and analysis results in natural language and providing feedback to the patient;

[0124] means for analyzing biometric data from the health management device and detecting abnormal values;

[0125] a means for determining whether the level is within normal limits;

[0126] means for displaying said responses and feedback on a user terminal;

[0127] This allows for efficient and accurate analysis of patient input and biometric data, and provides appropriate responses and feedback in real time.

[0128] "Patient" refers to a person who receives medical consultation or health management services using the remote medical consultation system.

[0129] "Input information" refers to text data such as symptoms and questions that a patient provides to the system via a user terminal.

[0130] A "generative AI model" refers to artificial intelligence that generates responses in natural language based on input data.

[0131] "Response" refers to the answer or advice to a medical consultation that the generative AI model generates based on the patient's input information.

[0132] "Health management devices" are devices used to measure and collect patients' biometric data, and generally refer to devices such as fitness trackers, heart rate monitors, and blood pressure monitors.

[0133] "Biometric data" refers to data that indicates the patient's health condition, such as blood pressure, heart rate, and activity level, collected by a health management device.

[0134] The "analysis result" refers to information generated as a result of the server analyzing the biometric data received from the health management device.

[0135] "User terminal" refers to a device used by a patient to access the system, send input information, and receive feedback, such as a smartphone, tablet, or PC.

[0136] A "server" is the central processing unit of the system, which uses a generative AI model to generate responses based on input information, analyzes biometric data, and provides the results.

[0137] "Feedback" refers to information provided to the patient regarding the analysis results and generated responses.

[0138] This invention is a remote medical consultation system that utilizes a generative AI model to provide medical support to patients who have difficulty traveling. The main components of the system are a user terminal for patients to input medical consultation information, a server that processes the input information and biometric data, and a health management device that collects the patient's biometric data.

[0139] System configuration

[0140] User terminal

[0141] The user terminal is a device such as a smartphone, tablet, or PC that provides an interface for patients to input medical consultation information and transmit biometric data. The terminal communicates with the server via the Internet.

[0142] server

[0143] The server uses a generative AI model to generate responses based on input information received from the user device, analyzes biometric data received from the health management device, and provides the results to the patient. The server includes the following main components:

[0144] Generative AI models (e.g., natural language processing models such as GPT-3)

[0145] Data Analysis Engine

[0146] Database

[0147] health management device

[0148] Health management devices are devices for collecting patient biometric data, and examples include fitness trackers, blood pressure monitors, heart rate monitors, etc. The devices transmit the collected data to a server via a user terminal.

[0149] System Operation

[0150] Medical consultation handling

[0151] A patient inputs a medical inquiry through a chat interface on the user's device. For example, they might input, "I've been having frequent headaches lately." The user's device then sends this input information to the server. The server then inputs the received input information into a generative AI model and generates an optimal response using natural language processing technology. The generated response is then sent from the server to the user's device and displayed to the user. The user can review the generated response and re-enter any further questions they may have.

[0152] Health data management

[0153] The health management device collects the patient's biometric data (e.g., activity level, heart rate, blood pressure, etc.). The collected data is sent to the user's terminal, which then sends it to the server. The server analyzes the received biometric data using a data analysis engine and converts it into natural language using a generative AI model. The analysis results are sent from the server to the user's terminal and displayed to the user. For example, feedback such as "Your current blood pressure is 130 / 85 mmHg, which is within the normal range" is generated.

[0154] Specific examples

[0155] Scenario 1: Headache consultation

[0156] The patient types into the chat interface, "I've been having frequent headaches lately."

[0157] The user terminal transmits this input content to the server.

[0158] The server uses a generative AI model to generate a response such as, "Possible causes of your headache include stress, lack of sleep, or other health conditions."

[0159] The user terminal displays this response to the user.

[0160] Scenario 2: Blood Pressure Management

[0161] The health management device measures the user's blood pressure to be 130 / 85 mmHg.

[0162] The data is transmitted to the server via the user terminal.

[0163] The server analyzes the data and generates feedback such as, "Your current blood pressure is within the normal range."

[0164] The user terminal displays this feedback to the user.

[0165] This system allows patients with mobility issues to receive medical consultations and health management in real time. By utilizing generative AI models and data analysis engines, appropriate and prompt responses to patients can be provided, improving the quality of medical care.

[0166] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0167] Medical consultation handling

[0168] Step 1:

[0169] The user inputs a medical consultation request via a chat interface on the user terminal. The input is text data including "I've been having frequent headaches recently." This input data becomes the input for the next step.

[0170] Step 2:

[0171] The user device sends the entered consultation details to the server via the Internet. The text data is transferred to the server in JSON format. The input is the text data entered in step 1, and the output is the data sent to the server.

[0172] Step 3:

[0173] The server analyzes the received consultation content and inputs it as a prompt to the generative AI model. Here, the generative AI model (e.g., GPT-3) generates the optimal response in natural language based on the input data. The input is the received text data, and the output is a response in natural language.

[0174] Step 4:

[0175] The server then sends the generated response back to the user's device. This response is in text format and is displayed on the user's device. The input is the response data generated by the generative AI model, and the output is the data sent to the user's device.

[0176] Step 5:

[0177] The user terminal displays the received response on the chat interface. The user can then review it and decide whether they understand the content or need to ask again. The input is the response data sent from the server, and the output is the user's confirmation action.

[0178] Health data management

[0179] Step 1:

[0180] The health management device collects the user's biometric data (e.g., blood pressure and heart rate). The data is collected in real time and set to be sent periodically. The input is the user's biometric data, and the output is the accumulation of collected data.

[0181] Step 2:

[0182] The health management device transmits the collected biometric data to the user terminal, which then transmits the data to the server. The input is the collected biometric data, and the output is the data transmitted to the server.

[0183] Step 3:

[0184] The server analyzes the received biometric data using a data analysis engine. Specifically, it checks whether the data is within the normal range and detects abnormal values. The analysis results are then input back into the generative AI model, which generates feedback in natural language. The input is the received biometric data, and the output is the generated analysis results.

[0185] Step 4:

[0186] The server sends the analysis results to the user's device. Specific feedback includes, for example, "Your current blood pressure is 130 / 85 mmHg, which is within the normal range." The input is the analysis results, and the output is the data sent to the user's device.

[0187] Step 5:

[0188] The user terminal displays the received feedback. The user can check their health status and contact a medical institution if necessary. The input is the feedback data sent from the server, and the output is the user's confirmation action.

[0189] Through this series of steps, the system enables real-time health management and appropriate medical consultation for patients. The combination of generative AI models and a data analysis engine enables fast and accurate responses and feedback.

[0190] (Application example 1)

[0191] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0192] Conventional telemedicine systems allow patients to receive medical consultations even when travel is difficult, but they are limited in their ability to provide both medical consultations and health management in real time. Furthermore, text-based interfaces alone are insufficient to provide a clear understanding of the patient. This creates the challenge of making it difficult for patients to feel at ease. Furthermore, there is a lack of a way to provide the results of biometric data analysis in an intuitive, easy-to-understand format.

[0193] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0194] In this invention, the server includes a means for receiving patient input information, a means for generating a response based on the received input information using a generative AI model, a means for receiving the patient's biometric data from a health management device, a means for analyzing the received biometric data and providing the results to the patient, and a means for performing remote medical consultations and health management in real time using a virtual reality device. This allows patients to receive real-time medical consultations and health management in a virtual space. Providing real-time medical support can increase patients' sense of security and promote the efficiency and understanding of health management.

[0195] "Patient input information" refers to text data of medical consultation details and questions entered by the patient via a chat interface or the like.

[0196] A "generative AI model" is an algorithm or program that uses machine learning and natural language processing techniques to generate responses to input patient questions and data.

[0197] "Health management devices" are devices used to collect patient biometric data, including fitness trackers, blood pressure monitors, and heart rate monitors.

[0198] "Biometric data" refers to data that indicates the patient's physical condition, and includes information such as activity level, heart rate, and blood pressure.

[0199] A "virtual reality device" is a device that allows patients to experience images and information in a virtual space, and includes head-mounted displays.

[0200] "Telemedical consultation" is a service that allows patients to receive medical consultation using communication technology without having to go to a medical institution in person.

[0201] "Data analysis" is the process of analyzing collected biometric data and interpreting it as meaningful information.

[0202] "Real-time" refers to instantaneous data processing and response, with little or no time delay.

[0203] "Natural language processing technology" is a technology that enables computers to understand the natural language used by humans on a daily basis and generate appropriate responses.

[0204] MODE FOR CARRYING OUT THE INVENTION

[0205] This invention is a system that utilizes a chatbot with a generative AI model and a virtual reality device to provide real-time remote medical consultations and health management for patients who have difficulty moving around. The system features a means for receiving patient input information and generating responses based on that information, and a means for receiving and analyzing biometric data from a health management device and providing the results to the patient.

[0206] System configuration

[0207] 1. User Device:

[0208] A device for patients to input medical consultations, such as a smartphone, tablet, or personal computer, provides a chat interface and an interface for collecting data from health management devices.

[0209] 2. Server:

[0210] The system uses a generative AI model to generate responses based on input information received from the user's device, analyzes biometric data from the health management device, and provides results. The server includes the generative AI model, a data analysis engine, and a database. The server can use cloud services such as AWS EC2 instances.

[0211] 3. Health management equipment:

[0212] A device that collects a patient's biometric data, such as a fitness tracker, blood pressure monitor, or heart rate monitor. The health management device transmits the collected data to a server via the user's terminal.

[0213] 4. Virtual reality devices:

[0214] A head-mounted display (HMD) is a device that allows patients to experience medical consultations and health management in a virtual space. Examples include Oculus Rift, HTC Vive, and Sony PlayStation VR.

[0215] System Operation

[0216] Medical consultation handling

[0217] 1. Patient Entry:

[0218] The user inputs a medical inquiry through the HMD's chat interface, for example, "I've been having frequent headaches lately."

[0219] 2. Data transmission:

[0220] The user terminal and the HMD device transmit this input information to the server.

[0221] 3. Response generation:

[0222] The server then inputs the received input information into a generative AI model (e.g., OpenAI's GPT-4), which uses natural language processing techniques to generate the optimal response. For example, it generates a response such as, "Possible causes of your headache include stress, lack of sleep, or other health conditions."

[0223] 4. Response sending and display:

[0224] The server sends the generated response to the user terminal and the HMD device, which displays the response to the user.

[0225] Health data management

[0226] 1. Data Collection:

[0227] The health management device collects the patient's biometric data (e.g., activity level, heart rate, blood pressure, etc.).

[0228] 2. Data transmission:

[0229] The health management device transmits the collected data to the user terminal, which then transmits the data to the server.

[0230] 3. Data Analysis:

[0231] The server analyzes the received biometric data using a data analysis engine and converts it into natural language using a generative AI model. For example, it generates feedback such as, "Your current blood pressure is 130 / 85 mmHg, which is within the normal range."

[0232] 4. Send and display results:

[0233] The server transmits the analysis results to the user terminal and the HMD device, which displays this feedback to the user.

[0234] Specific examples

[0235] Scenario: Health consultation

[0236] 1. Patient entry:

[0237] A user types into a chat interface, "I've been having a lot of headaches lately."

[0238] 2. Generative AI model prompt:

[0239] I've been having frequent headaches lately. What could be the cause?

[0240] 3. Example of generated response:

[0241] "Headaches can be caused by stress, lack of sleep, or other health conditions."

[0242] Scenario: Blood Pressure Management

[0243] 1. Data Collection:

[0244] The health management device measures the patient's blood pressure to be 130 / 85 mmHg.

[0245] 2. Data Analysis:

[0246] The server analyzes the data and generates feedback such as, "Your current blood pressure is within the normal range."

[0247] This system allows patients to efficiently receive real-time medical consultations and health management in a virtual space.

[0248] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0249] Step 1:

[0250] The patient wears a virtual reality device (HMD) and inputs a medical consultation request into the chat interface. The patient inputs, "I've been having frequent headaches recently."

[0251] Step 2:

[0252] The user terminal and the HMD device receive this input information and send it to the server, and the user terminal transfers the input text data to the server.

[0253] Step 3:

[0254] The server inputs the received input information into a generative AI model (e.g., OpenAI's GPT-4). The server passes the prompt sentence "I've been having frequent headaches lately. What could be the cause?" to the generative AI model and obtains its response.

[0255] Step 4:

[0256] The generative AI model uses natural language processing techniques to generate appropriate responses based on the input information, such as "Possible causes of headaches include stress, lack of sleep, or other health conditions" based on the prompt.

[0257] Step 5:

[0258] The server sends the generated response to the user terminal and the HMD device. The server then transfers the text data obtained from the generated AI model to the user terminal and the HMD device.

[0259] Step 6:

[0260] The HMD device displays this response to the patient, who can then review the generated response and ask further questions.

[0261] Step 7:

[0262] The health management device collects the patient's biological data (e.g., activity level, heart rate, blood pressure, etc.). For example, the health management device measures "130 / 85 mmHg" using a blood pressure monitor.

[0263] Step 8:

[0264] The health management device transmits the collected data to the user terminal. The health management device transfers the measured data to the user terminal.

[0265] Step 9:

[0266] The user terminal transmits the biometric data to the server, and the user terminal transfers the data received from the health management device to the server.

[0267] Step 10:

[0268] The server analyzes the received biometric data using a data analysis engine. For example, the server analyzes blood pressure data and generates feedback such as, "Your current blood pressure is within the normal range."

[0269] Step 11:

[0270] The server converts the analysis results into natural language using a generative AI model, and inputs the analysis results into the generative AI model to obtain easy-to-understand feedback text.

[0271] Step 12:

[0272] The server transmits the generated feedback to the user terminal and the HMD device. The server transfers the feedback text to the user terminal and the HMD device.

[0273] Step 13:

[0274] The HMD device displays the analysis results and feedback to the patient, who can see the feedback, "Your current blood pressure is 130 / 85 mmHg, which is within the normal range."

[0275] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0276] This invention is a remote medical consultation system that utilizes a chatbot with a generative AI model and an emotion engine to provide medical support to patients with mobility issues. The system receives patient input, generates appropriate responses, analyzes biometric data collected from a health management device, and provides the results to the patient. The emotion engine also recognizes the patient's emotional state and adjusts responses and feedback based on this emotional information, enabling more personalized support.

[0277] System configuration

[0278] This system mainly consists of the following parts:

[0279] 1. User Device:

[0280] This refers to a device that patients use to input medical consultations, such as a smartphone, tablet, or PC. The user terminal provides an interface for inputting medical consultation text, collecting data from health management devices, and recognizing emotions.

[0281] 2. Server:

[0282] This refers to a processing device that uses a generative AI model and emotion engine to analyze input information and biometric data received from a user device and provides responses and feedback based on that. The server includes a generative AI model, emotion engine, data analysis engine, and database.

[0283] 3. Health management equipment:

[0284] A health management device is a device that collects a patient's biological data, such as a fitness tracker, blood pressure monitor, or heart rate monitor. The health management device transmits the collected data to a server via a user terminal.

[0285] System Operation

[0286] The specific flow of operation of this system is shown below.

[0287] Medical consultation handling

[0288] 1. User Input:

[0289] The patient inputs a medical inquiry through the chat interface of the user terminal. For example, the patient may input, "I've been having frequent headaches recently."

[0290] 2. Data transmission:

[0291] The terminal transmits the user's input information to the server.

[0292] 3. Emotion recognition:

[0293] The server inputs the received input information into an emotion engine to recognize the patient's emotional state (e.g., anxiety, tension, relief, etc.).

[0294] 4. Response Generation:

[0295] The server inputs the input information, along with the emotional information recognized by the emotion engine, into the generative AI model. The generative AI model then uses natural language processing technology to generate the optimal response. For example, it might generate a response such as, "Possible causes of your headache include stress, lack of sleep, or other health conditions. If you are concerned, consult your doctor."

[0296] 5. Send Response:

[0297] The server sends the generated response to the user terminal.

[0298] 6. User Verification:

[0299] The terminal displays the received response to the user, who can then review the generated response and re-enter the question if necessary.

[0300] Health data management

[0301] 1. Data Collection:

[0302] The health management device collects biological data of the patient (e.g., activity level, heart rate, blood pressure, etc.).

[0303] 2. Data transmission:

[0304] The health management device transmits the collected data to the user terminal, which then periodically transmits this data to the server.

[0305] 3. Data reception:

[0306] The server receives the biometric data transmitted from the user terminal.

[0307] 4. Data Analysis:

[0308] The server analyzes the received biometric data using a data analysis engine.

[0309] 5. Emotional reflection:

[0310] The server reflects the patient's emotional information recognized by the emotion engine in the analysis results and generates feedback, such as "Your current blood pressure is 130 / 85 mmHg, which is within the normal range. Please continue to take appropriate health care measures."

[0311] 6. Send results:

[0312] The server transmits the generated feedback to the user terminal.

[0313] 7. User Feedback:

[0314] The device displays the analysis results to the patient, who then checks the feedback and initiates medical consultation if any abnormalities are detected.

[0315] Specific examples

[0316] As a concrete example, consider the following scenario.

[0317] Scenario 1: Headache consultation

[0318] A user types into a chat interface, "I've been having a lot of headaches lately."

[0319] The server uses an emotion engine to recognize "anxiety" from the user's input.

[0320] The server uses a generative AI model to generate a response such as, "Possible causes of headaches include stress, lack of sleep, and other health conditions. If you are concerned, consult your doctor."

[0321] The user terminal displays this response to the user.

[0322] Scenario 2: Blood Pressure Management

[0323] The health management device measures the user's blood pressure to be 130 / 85 mmHg.

[0324] The data is transmitted to the server via the user terminal.

[0325] The server analyzes the data using a data analysis engine, and then recognizes the feeling of "security" using an emotion engine.

[0326] Generate feedback such as "Your current blood pressure is 130 / 85 mmHg, which is within the normal range. Please continue to take appropriate care of your health."

[0327] The user terminal displays this feedback to the user.

[0328] Through this series of processes, the system provides an environment where even patients who have difficulty moving around can receive medical consultations and health management with peace of mind.

[0329] The processing flow will be explained below.

[0330] Medical consultation handling

[0331] Step 1: User Input

[0332] The user inputs a medical consultation request via a chat interface on the user terminal. For example, the user may input "I've been having frequent headaches recently."

[0333] Step 2: Send data

[0334] The terminal transmits the user's input information to the server.

[0335] Step 3: Emotion Recognition

[0336] The server inputs the received input information into an emotion engine to recognize the patient's emotional state (e.g., anxiety, tension, relief, etc.).

[0337] Step 4: Response Generation

[0338] The server inputs the input information, along with the emotions recognized by the emotion engine, into the generative AI model. The generative AI model then uses natural language processing techniques to generate the optimal response. For example, it might generate a response such as, "Possible causes of your headache include stress, lack of sleep, or other health conditions. If you are concerned, consult your doctor."

[0339] Step 5: Send response

[0340] The server sends the generated response to the user terminal.

[0341] Step 6: User Verification

[0342] The terminal displays the received response to the user, who can then review the generated response and re-enter the question if necessary.

[0343] Health data management

[0344] Step 1: Data collection

[0345] The health management device collects biological data of the patient (e.g., activity level, heart rate, blood pressure, etc.).

[0346] Step 2: Send data

[0347] The health management device transmits the collected data to the user terminal, which then periodically transmits this data to the server.

[0348] Step 3: Receiving data

[0349] The server receives the biometric data transmitted from the user terminal.

[0350] Step 4: Data analysis

[0351] The server analyzes the received biometric data using a data analysis engine.

[0352] Step 5: Emotional reflection

[0353] The server reflects the patient's emotional information recognized by the emotion engine in the analysis results and generates feedback, such as "Your current blood pressure is 130 / 85 mmHg, which is within the normal range. Please continue to take appropriate health care measures."

[0354] Step 6: Send results

[0355] The server transmits the generated feedback to the user terminal.

[0356] Step 7: User Feedback

[0357] The device displays the analysis results to the patient, who then checks the feedback and initiates medical consultation if any abnormalities are detected.

[0358] These are the specific processing steps. This system allows patients to receive medical consultations and manage their health from the comfort of their own homes or nursing homes.

[0359] Example 2

[0360] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0361] The present invention relates to a telemedicine system that allows patients with mobility issues to receive medical consultations and health management from the comfort of their own homes. Conventional telemedicine systems often provide mechanical responses without considering the patient's current emotional state, which can lead to unsatisfactory results for the patient. To solve this problem, a more personalized response that also takes the patient's emotional state into account is needed.

[0362] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0363] In this invention, the server includes means for receiving input information from a patient, means for generating a response based on the received input information using a generative AI model, means for receiving biometric data of the patient from a health management device, means for recognizing the emotional state of the patient using an emotion engine and reflecting the recognized emotional state in generating the response, and means for reflecting the emotional state of the patient in feedback generated based on the analyzed biometric data, thereby making it possible to provide personalized responses and feedback to patients that take emotions into consideration.

[0364] The "means for receiving patient input information" is a mechanism for transmitting text data and questions input by the patient via a user terminal to a server, and for the server to receive the information.

[0365] "Means for generating a response based on received input information using a generative AI model" refers to a mechanism for inputting received input information into a generative AI model and automatically generating an optimal response using natural language processing technology.

[0366] The "means for receiving patient biometric data from a health management device" is a mechanism by which the server receives biometric data sent from a health management device such as a fitness tracker or blood pressure monitor.

[0367] "Means for recognizing the emotional state of the patient using an emotion engine and reflecting the recognized emotional state in the generation of the response" refers to a mechanism for inputting received input information into an emotion engine, extracting the user's emotional state (e.g., anxiety, relief, etc.), and reflecting that emotional information in the response generation process via a generative AI model.

[0368] The "means for reflecting the patient's emotional state in the feedback generated based on the analyzed biometric data" is a mechanism for analyzing the received biometric data using a data analysis engine, integrating the patient's emotional state recognized by the emotion engine, and adjusting the feedback message based on the analysis results to provide to the patient.

[0369] This invention is a remote medical consultation system that utilizes a chatbot with a generative AI model and an emotion engine to provide medical support to patients with mobility issues. The system receives patient input and generates appropriate responses. It also has the ability to analyze biometric data collected from health management devices and provide the results to the patient. Furthermore, the emotion engine recognizes the patient's emotional state and adjusts responses and feedback based on this emotional information, thereby achieving more personalized support.

[0370] System configuration

[0371] This system mainly consists of the following parts:

[0372] 1. User Device:

[0373] This is a device for patients to input medical consultations, and can be a smartphone, tablet, PC, etc. The user terminal provides an interface for inputting medical consultation text, collecting data from health management devices, and recognizing emotions.

[0374] 2. Server:

[0375] This refers to a processing device that uses a generative AI model and emotion engine to analyze input information and biometric data received from a user device and generate responses and feedback based on that. The server includes a generative AI model, emotion engine, data analysis engine, and database.

[0376] 3. Health management equipment:

[0377] These are devices that collect patient biometric data, such as fitness trackers, blood pressure monitors, and heart rate monitors. Health management devices send the collected data to a server via a user terminal.

[0378] Medical consultation handling

[0379] Specific examples

[0380] 1. Receiving user input

[0381] A user inputs a medical consultation request via a chat interface on a smartphone or PC. For example, the user might input, "I've been having frequent headaches recently."

[0382] 2. Data Transmission

[0383] The terminal sends the user's input information to the server using the HTTPS protocol.

[0384] 3. Emotion Recognition

[0385] The server inputs the received text data into an emotion engine to recognize the user's emotional state, for example, extracting emotions such as anxiety or tension.

[0386] 4. Response Generation

[0387] The server inputs text data, including emotional information, into the generative AI model. The generative AI model uses natural language processing techniques to generate an appropriate response to the user's input. For example, it might generate a response like, "Possible causes of headaches include stress and lack of sleep. If you're concerned, consult a doctor."

[0388] 5. Sending the Response

[0389] The server sends the generated response to the user terminal.

[0390] 6. User Display

[0391] The terminal displays the received response to the user, who can then check the displayed response and re-enter the question if necessary.

[0392] Health data management

[0393] Specific examples

[0394] 1. Data Collection

[0395] Health monitoring devices (such as fitness trackers and blood pressure monitors) collect patient biometric data. For example, a blood pressure monitor measures a blood pressure of 130 / 85 mmHg.

[0396] 2. Data Transmission

[0397] Health management devices send collected biometric data to a user's device, which then periodically transmits the data to a server, often via Bluetooth or Wi-Fi.

[0398] 3. Data Reception

[0399] The server receives the biometric data sent from the user terminal and stores it in a database.

[0400] 4. Data Analysis

[0401] The server then analyzes the received biometric data using a data analysis engine, which uses statistical analysis and machine learning algorithms.

[0402] 5. Emotional reflection

[0403] The server reflects the user's emotional information recognized by the emotion engine in the analysis results and generates feedback, such as a message like "Your current blood pressure is 130 / 85 mmHg, which is within the normal range. Please continue to take care of your health."

[0404] 6. Send results

[0405] The server transmits the generated feedback to the user terminal.

[0406] 7. User Presentation

[0407] The device displays the feedback sent from the server to the user, who then checks the analysis results and begins medical consultation if any abnormalities are detected.

[0408] Prompt Sentence Examples

[0409] Prompt example 1: Medical consultation

[0410] When a patient types, "I've been having frequent headaches lately," use an emotion engine to recognize the emotion and a generative AI model to generate the optimal response.

[0411] Prompt example 2: Health data management

[0412] When the patient's blood pressure is measured at 130 / 85 mmHg and the emotion engine recognizes relief, generate appropriate feedback.

[0413] In this way, this system uses an emotion engine and generative AI model to provide advanced remote medical support to patients, creating an environment where even patients with difficulty traveling can receive medical consultations and manage their health with peace of mind.

[0414] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0415] Medical consultation handling

[0416] Step 1: User inputs medical consultation

[0417] A user inputs a medical consultation through a chat interface, for example, inputting the text "I've been having frequent headaches lately."

[0418] Input: User text input

[0419] Output: The text entered

[0420] Step 2: Send text data

[0421] The device sends the entered text data to the server using the HTTPS protocol.

[0422] Input: Text entered by the user

[0423] Output: Text data sent to the server

[0424] Step 3: Performing emotion recognition

[0425] The server inputs the received text data into an emotion engine to recognize the patient's emotional state, for example, by extracting emotions such as "anxiety" or "tension."

[0426] Input: Received text data

[0427] Output: Recognized emotion information (e.g., "anxiety")

[0428] Step 4: Generate a response

[0429] The server inputs text data based on the emotional information into the generative AI model, which then uses natural language processing technology to generate the optimal response. For example, the model might generate a response such as, "Possible causes of headaches include stress and lack of sleep. If you're concerned, consult a doctor."

[0430] Input: Text data and emotion information

[0431] Output: The generated response

[0432] Step 5: Sending a Response

[0433] The server sends the generated response to the user's terminal using the HTTPS protocol.

[0434] Input: The generated response

[0435] Output: Response sent to the user's device

[0436] Step 6: View the response

[0437] The terminal displays the received response to the user, who can then review the displayed response and re-enter the question if necessary.

[0438] Input: Response received from the server

[0439] Output: The response displayed to the user

[0440] Health data management

[0441] Step 1: Collect biometric data

[0442] Health management devices collect patient biometric data, for example, a blood pressure monitor measures "130 / 85 mmHg."

[0443] Input: Patient biometric data (e.g. blood pressure)

[0444] Output: Measured biometric data

[0445] Step 2: Sending data

[0446] The health management device transmits the collected biometric data to the user's terminal, which then transmits the data to a server via Bluetooth or Wi-Fi.

[0447] Input: Measured biometric data

[0448] Output: Biometric data sent to the server

[0449] Step 3: Receiving the data

[0450] The server receives the biometric data sent from the user terminal and stores it in a database.

[0451] Input: Data sent from the user's device

[0452] Output: Stored biometric data

[0453] Step 4: Analyze the data

[0454] The server then analyzes the received biometric data using a data analysis engine, which uses statistical analysis and machine learning algorithms.

[0455] Input: Stored biometric data

[0456] Output: Analysis results

[0457] Step 5: Reflecting emotional information

[0458] The server then incorporates the emotional information recognized by the emotion engine into the analysis results and generates final feedback, such as a message saying, "Your current blood pressure is 130 / 85 mmHg, which is within the normal range. Please continue to take care of your health."

[0459] Input: Analysis results and emotion information

[0460] Output: Final feedback message

[0461] Step 6: Sending the results

[0462] The server sends the generated feedback to the user terminal using the HTTPS protocol.

[0463] Input: Final feedback message

[0464] Output: Feedback message sent to the user's device

[0465] Step 7: View your feedback

[0466] The terminal displays the feedback sent from the server to the user, who can check the feedback and start another medical consultation if there is any abnormality.

[0467] Input: Feedback message received from the server

[0468] Output: Feedback displayed to the user

[0469] (Application example 2)

[0470] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0471] In today's world, patients with mobility issues have limited access to medical support, making it particularly difficult for those living in remote areas to receive appropriate medical services. Furthermore, traditional telemedicine systems typically provide responses and feedback without considering the patient's emotional state, making it difficult to provide personalized, optimal support. This can lead to a lack of reassurance and trust for patients, resulting in a decline in the quality of medical consultations.

[0472] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving patient input information, means for generating a response based on the received input information using a generative AI model, means for receiving the patient's biometric data from the health management device, means for recognizing the patient's emotional state using an emotion engine, and means for adjusting the response based on the recognized emotional state. This not only enables patients with limited mobility to receive medical consultations remotely, but also provides personalized responses through emotion recognition, thereby providing patients with an appropriate sense of security and trust.

[0473] "Patient input information" is text data entered by patients as questions or messages regarding medical consultations or health care.

[0474] A "generative AI model" is an artificial intelligence algorithm used to generate natural language responses based on large datasets.

[0475] A "health management device" is a device that measures and collects a patient's biometric data, such as a fitness tracker, blood pressure monitor, or heart rate monitor.

[0476] "Biometric data" refers to data that indicates physical indicators such as a patient's heart rate, blood pressure, and activity level.

[0477] The "emotion engine" is an artificial intelligence algorithm that analyzes the patient's input information and recognizes their emotional state (e.g., anxiety, relief, tension, etc.).

[0478] A "means for tailoring responses" is a system that customizes the responses generated based on the emotional state of an individual patient.

[0479] "Natural language processing technology" is an information processing technology for analyzing, understanding, and generating human language.

[0480] MODE FOR CARRYING OUT THE INVENTION

[0481] System configuration

[0482] This invention is a remote medical consultation system for providing medical support to patients with mobility issues. The system receives patient input, generates responses using a generative AI model, and uses an emotion engine to recognize the patient's emotional state and adjust responses based on that emotional information. The system primarily consists of the following hardware and software:

[0483] 1. User Device

[0484] Smartphones, tablets, computers, etc. are used.

[0485] It is an interface through which patients input their medical consultations, and is responsible for collecting biometric data from health management devices and sending it to the server.

[0486] 2. Generative AI Models

[0487] It is an algorithm that uses natural language processing technology to generate optimal responses based on the patient's input information received.

[0488] Model name example: "text-davinci-003"

[0489] 3. Emotion Engine

[0490] It includes algorithms for analyzing patient input and recognizing their emotional state.

[0491] Example of technology used: Emotion recognition model using deep learning

[0492] 4. Health management device

[0493] These are devices that measure a patient's biometric data, such as fitness trackers, blood pressure monitors, and heart rate monitors.

[0494] System Operation

[0495] Medical consultation handling

[0496] 1. A patient inputs a medical consultation through a user terminal. For example, the patient inputs, "I've been having frequent headaches recently."

[0497] 2. The device sends this input information to the server.

[0498] 3. The server inputs this input information into an emotion engine to recognize the patient's emotional state (e.g., anxiety).

[0499] 4. The server passes the recognized emotion information to a generative AI model to generate an optimal response.

[0500] Example generated response: "Possible causes of headaches include stress, lack of sleep, or other health conditions. If you are concerned, consult your doctor."

[0501] 5. The server sends the generated response to the user terminal, which displays it to the patient.

[0502] Health data management

[0503] 1. Collect patient biometric data (e.g., heart rate, blood pressure) using health monitoring devices.

[0504] 2. The user terminal periodically sends the collected data to the server.

[0505] 3. The server receives the biometric data and analyzes it using a data analysis engine.

[0506] 4. The generative AI model provides feedback based on the emotional information recognized by the emotion engine.

[0507] Example of generated feedback: "Your current blood pressure is 130 / 85 mmHg, which is within the normal range. Please continue to take good care of your health."

[0508] 5. The server sends this feedback to the user terminal, which displays the analysis results to the patient.

[0509] Prompt Sentence Examples

[0510] For medical consultations

[0511] User Input: I've been having frequent headaches lately.

[0512] Emotion: Anxiety

[0513] Appropriate response: Headaches can be caused by stress, lack of sleep, or other health conditions. If you're concerned, consult your doctor.

[0514] In the case of biometric data

[0515] Example of vital data: {"heart_rate": 75, "blood_pressure": "130 / 85"}

[0516] Health feedback: Health is normal.

[0517] In this way, an environment is provided where even patients who have difficulty moving around can receive medical consultations and health management remotely with peace of mind.

[0518] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0519] Program processing steps

[0520] Step 1:

[0521] A user inputs a medical consultation request using a smartphone. For example, the user might input, "I've been having frequent headaches recently." This input information is saved as text data on the device.

[0522] Input: User's medical consultation text

[0523] Output: Medical consultation in text data format

[0524] Step 2:

[0525] The terminal transmits the stored medical consultation text of the user to the server, where the terminal makes a request using internet communication.

[0526] Input: Medical consultation in text format

[0527] Output: Medical consultation data sent to the server

[0528] Step 3:

[0529] The server inputs the received medical consultation data into the emotion engine, which uses a deep learning model to analyze the patient's emotional state from the input text. For example, it can recognize an emotional state such as "anxiety."

[0530] Input: Medical consultation data

[0531] Output: Perceived emotional state (e.g., anxiety)

[0532] Step 4:

[0533] The server passes the emotional state to a generative AI model, which generates a response. The generative AI model uses natural language processing technology to generate the optimal response based on the medical consultation and emotional information. For example, it might generate text like, "Possible causes of your headache include stress, lack of sleep, or other health conditions. If you're concerned, consult a doctor."

[0534] Input: medical consultation data, recognized emotional state

[0535] Output: Generated response

[0536] Step 5:

[0537] The server sends the generated response to the user's terminal, which receives the response and displays it to the user.

[0538] Input: Generated response sentence

[0539] Output: Response text displayed on the terminal

[0540] Step 6:

[0541] The health management device is used to collect biometric data of the user, such as measuring heart rate and blood pressure, and this data is transmitted to the terminal.

[0542] Input: Biometric data from health management device

[0543] Output: Biometric data stored on the device

[0544] Step 7:

[0545] The device transmits the stored biometric data to a server, and the device is programmed to periodically transmit this data to the server.

[0546] Input: Stored biometric data

[0547] Output: Biometric data sent to the server

[0548] Step 8:

[0549] The server analyzes the received biometric data using a data analysis engine. For example, if the heart rate is over 100, it is determined to be a "high heart rate."

[0550] Input: Biometric data

[0551] Output: Analysis results (e.g. high heart rate)

[0552] Step 9:

[0553] The server reflects the patient's emotional information recognized by the emotion engine in the analysis results, and the generative AI model provides feedback, such as, "Your current blood pressure is 130 / 85 mmHg, which is within the normal range. Please continue to take appropriate health care measures."

[0554] Input: Analysis results, recognized emotional state

[0555] Output: Generated feedback statement

[0556] Step 10:

[0557] The server sends the generated feedback sentence to the user terminal, which displays the feedback to the user.

[0558] Input: Generated feedback sentence

[0559] Output: Feedback text displayed on the terminal

[0560] In this way, patients can receive medical consultations and health management remotely through each step of the system. The combination of generative AI models and emotion engines makes it possible to provide personalized responses and feedback.

[0561] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0562] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0563] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0564] [Second embodiment]

[0565] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0566] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0567] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0568] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0569] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0570] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0571] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0572] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0573] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0574] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0575] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0576] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0577] This invention is a remote medical consultation system that utilizes a chatbot with a generative AI model to provide medical support to patients with mobility issues. The system receives patient input, generates appropriate responses, analyzes biometric data collected from health management devices, and provides the results to the patient.

[0578] System configuration

[0579] This system mainly consists of the following parts:

[0580] 1. User Device:

[0581] A device used by patients to input medical consultations, such as a smartphone, tablet, or PC. The user terminal provides an interface for inputting medical consultation text and collecting data from health management devices.

[0582] 2. Server:

[0583] This refers to a processing device that uses a generative AI model to generate responses based on input information received from a user device, and analyzes biometric data from a health management device to provide results. The server includes a generative AI model, a data analysis engine, and a database.

[0584] 3. Health management equipment:

[0585] A health management device is a device that collects a patient's biological data, such as a fitness tracker, blood pressure monitor, or heart rate monitor. The health management device transmits the collected data to a server via a user terminal.

[0586] System Operation

[0587] The specific flow of operation of this system is shown below.

[0588] Medical consultation handling

[0589] 1. User Input:

[0590] The patient inputs a medical inquiry through the chat interface of the user terminal. For example, the patient may input, "I've been having frequent headaches recently."

[0591] 2. Data transmission:

[0592] The user terminal transmits this input information to the server.

[0593] 3. Response generation:

[0594] The server inputs the received input information into the generative AI model, which then uses natural language processing techniques to generate the optimal response. For example, it might generate a response like, "Possible causes of your headache include stress, lack of sleep, or other health conditions. Consult your doctor for a detailed diagnosis."

[0595] 4. Send response:

[0596] The server generates a response and sends it to the user terminal.

[0597] 5. User Verification:

[0598] The user terminal displays this response to the patient, who can review the generated response and re-enter any further questions.

[0599] Health data management

[0600] 1. Data Collection:

[0601] The health management device collects the patient's biometric data (e.g., activity level, heart rate, blood pressure, etc.).

[0602] 2. Data transmission:

[0603] The health management device transmits the collected data to the user terminal, which then transmits the data to the server.

[0604] 3. Data Analysis:

[0605] The server analyzes the received biometric data using a data analysis engine, and the analysis results are converted into natural language using a generative AI model to generate information in a format that is easy for the patient to understand.

[0606] 4. Send results:

[0607] The server transmits the analysis results to the user terminal.

[0608] 5. User Feedback:

[0609] The user device displays the analysis results to the patient. For example, the patient may receive feedback such as, "Your current blood pressure is 130 / 85 mmHg, which is within the normal range. Please continue to take appropriate health care measures."

[0610] Specific examples

[0611] As a concrete example, consider the following scenario.

[0612] Scenario 1: Headache consultation

[0613] A user types into a chat interface, "I've been having a lot of headaches lately."

[0614] The server uses a generative AI model to generate a response such as, "Possible causes of your headache include stress, lack of sleep, or other health conditions."

[0615] The user terminal displays this response to the user.

[0616] Scenario 2: Blood Pressure Management

[0617] The health management device measures the user's blood pressure to be 130 / 85 mmHg.

[0618] The data is transmitted to the server via the user terminal.

[0619] The server analyzes the data and generates feedback such as, "Your current blood pressure is within the normal range."

[0620] The user terminal displays this feedback to the user.

[0621] Through this series of processes, the system provides an environment where even patients who have difficulty moving around can receive medical consultations and health management with peace of mind.

[0622] The processing flow will be explained below.

[0623] Medical consultation handling

[0624] Step 1: User Input

[0625] The user inputs a medical consultation request via a chat interface on the user terminal. For example, the user may input "I've been having frequent headaches recently."

[0626] Step 2: Send data

[0627] The terminal transmits the text of the medical consultation sent by the user to the server.

[0628] Step 3: Response Generation

[0629] The server inputs the received medical consultation text into a generative AI model.

[0630] Step 4: Natural Language Processing

[0631] The generative AI model uses natural language processing techniques to analyze the input text and generate an optimal response, such as, "Your headache may be due to stress, lack of sleep, or other health conditions. Please consult your doctor for a detailed diagnosis."

[0632] Step 5: Send response

[0633] The server sends the generated response to the user terminal.

[0634] Step 6: User Verification

[0635] The terminal displays the received response to the user, who can then review the generated response and re-enter the question if necessary.

[0636] Health data management

[0637] Step 1: Data collection

[0638] The health management device collects biological data of the patient (e.g., activity level, heart rate, blood pressure, etc.).

[0639] Step 2: Send data

[0640] The health management device transmits the collected data to the user terminal, which then periodically transmits this data to the server.

[0641] Step 3: Receiving data

[0642] The server receives the biometric data transmitted from the user terminal.

[0643] Step 4: Data analysis

[0644] The server analyzes the received biometric data using a data analysis engine.

[0645] Step 5: Interpretation by generative AI models

[0646] The server uses a generative AI model to interpret the results obtained by the data analysis engine in natural language. For example, it generates feedback such as, "Your current blood pressure is 130 / 85 mmHg, which is within the normal range. Please continue to take appropriate measures to manage your health."

[0647] Step 6: Send results

[0648] The server transmits the generated feedback to the user terminal.

[0649] Step 7: User Feedback

[0650] The device displays the analysis results to the user, who then checks the feedback and initiates medical consultation if any abnormalities are detected.

[0651] These are the specific processing steps. This system allows patients to receive medical consultations and manage their health from the comfort of their own homes or nursing homes.

[0652] Example 1

[0653] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0654] In remote medical consultation systems, real-time assessment of health status and accurate feedback based on that assessment are essential for patients with mobility issues to receive appropriate medical support. However, existing systems lack the means to effectively integrate and analyze patient input information and biometric data from health management devices, and provide rapid and accurate feedback to users. Furthermore, the lack of smooth intercommunication makes it difficult to provide a user-friendly service. Therefore, there is a need for an effective system that can appropriately integrate input information and biometric data, generate responses in natural language, and provide accurate feedback.

[0655] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0656] In this invention, the server

[0657] means for generating a response based on the received input information using a generative AI model;

[0658] means for receiving patient biometric data from a health management device;

[0659] means for transmitting the biometric data to a server using a user terminal;

[0660] means for inputting the input information and biometric data as prompts to a generative AI model;

[0661] means for generating the response and analysis results in natural language and providing feedback to the patient;

[0662] means for analyzing biometric data from the health management device and detecting abnormal values;

[0663] a means for determining whether the level is within normal limits;

[0664] means for displaying said responses and feedback on a user terminal;

[0665] This allows for efficient and accurate analysis of patient input and biometric data, and provides appropriate responses and feedback in real time.

[0666] "Patient" refers to a person who receives medical consultation or health management services using the remote medical consultation system.

[0667] "Input information" refers to text data such as symptoms and questions that a patient provides to the system via a user terminal.

[0668] A "generative AI model" refers to artificial intelligence that generates responses in natural language based on input data.

[0669] "Response" refers to the answer or advice to a medical consultation that the generative AI model generates based on the patient's input information.

[0670] "Health management devices" are devices used to measure and collect patients' biometric data, and generally refer to devices such as fitness trackers, heart rate monitors, and blood pressure monitors.

[0671] "Biometric data" refers to data that indicates the patient's health condition, such as blood pressure, heart rate, and activity level, collected by a health management device.

[0672] The "analysis result" refers to information generated as a result of the server analyzing the biometric data received from the health management device.

[0673] "User terminal" refers to a device used by a patient to access the system, send input information, and receive feedback, such as a smartphone, tablet, or PC.

[0674] A "server" is the central processing unit of the system, which uses a generative AI model to generate responses based on input information, analyzes biometric data, and provides the results.

[0675] "Feedback" refers to information provided to the patient regarding the analysis results and generated responses.

[0676] This invention is a remote medical consultation system that utilizes a generative AI model to provide medical support to patients who have difficulty traveling. The main components of the system are a user terminal for patients to input medical consultation information, a server that processes the input information and biometric data, and a health management device that collects the patient's biometric data.

[0677] System configuration

[0678] User terminal

[0679] The user terminal is a device such as a smartphone, tablet, or PC that provides an interface for patients to input medical consultation information and transmit biometric data. The terminal communicates with the server via the Internet.

[0680] server

[0681] The server uses a generative AI model to generate responses based on input information received from the user device, analyzes biometric data received from the health management device, and provides the results to the patient. The server includes the following main components:

[0682] Generative AI models (e.g., natural language processing models such as GPT-3)

[0683] Data Analysis Engine

[0684] Database

[0685] health management device

[0686] Health management devices are devices for collecting patient biometric data, and examples include fitness trackers, blood pressure monitors, heart rate monitors, etc. The devices transmit the collected data to a server via a user terminal.

[0687] System Operation

[0688] Medical consultation handling

[0689] A patient inputs a medical inquiry through a chat interface on the user's device. For example, they might input, "I've been having frequent headaches lately." The user's device then sends this input information to the server. The server then inputs the received input information into a generative AI model and generates an optimal response using natural language processing technology. The generated response is then sent from the server to the user's device and displayed to the user. The user can review the generated response and re-enter any further questions they may have.

[0690] Health data management

[0691] The health management device collects the patient's biometric data (e.g., activity level, heart rate, blood pressure, etc.). The collected data is sent to the user's terminal, which then sends it to the server. The server analyzes the received biometric data using a data analysis engine and converts it into natural language using a generative AI model. The analysis results are sent from the server to the user's terminal and displayed to the user. For example, feedback such as "Your current blood pressure is 130 / 85 mmHg, which is within the normal range" is generated.

[0692] Specific examples

[0693] Scenario 1: Headache consultation

[0694] The patient types into the chat interface, "I've been having frequent headaches lately."

[0695] The user terminal transmits this input content to the server.

[0696] The server uses a generative AI model to generate a response such as, "Possible causes of your headache include stress, lack of sleep, or other health conditions."

[0697] The user terminal displays this response to the user.

[0698] Scenario 2: Blood Pressure Management

[0699] The health management device measures the user's blood pressure to be 130 / 85 mmHg.

[0700] The data is transmitted to the server via the user terminal.

[0701] The server analyzes the data and generates feedback such as, "Your current blood pressure is within the normal range."

[0702] The user terminal displays this feedback to the user.

[0703] This system allows patients with mobility issues to receive medical consultations and health management in real time. By utilizing generative AI models and data analysis engines, appropriate and prompt responses to patients can be provided, improving the quality of medical care.

[0704] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0705] Medical consultation handling

[0706] Step 1:

[0707] The user inputs a medical consultation request via a chat interface on the user terminal. The input is text data including "I've been having frequent headaches recently." This input data becomes the input for the next step.

[0708] Step 2:

[0709] The user device sends the entered consultation details to the server via the Internet. The text data is transferred to the server in JSON format. The input is the text data entered in step 1, and the output is the data sent to the server.

[0710] Step 3:

[0711] The server analyzes the received consultation content and inputs it as a prompt to the generative AI model. Here, the generative AI model (e.g., GPT-3) generates the optimal response in natural language based on the input data. The input is the received text data, and the output is a response in natural language.

[0712] Step 4:

[0713] The server then sends the generated response back to the user's device. This response is in text format and is displayed on the user's device. The input is the response data generated by the generative AI model, and the output is the data sent to the user's device.

[0714] Step 5:

[0715] The user terminal displays the received response on the chat interface. The user can then review it and decide whether they understand the content or need to ask again. The input is the response data sent from the server, and the output is the user's confirmation action.

[0716] Health data management

[0717] Step 1:

[0718] The health management device collects the user's biometric data (e.g., blood pressure and heart rate). The data is collected in real time and set to be sent periodically. The input is the user's biometric data, and the output is the accumulation of collected data.

[0719] Step 2:

[0720] The health management device transmits the collected biometric data to the user terminal, which then transmits the data to the server. The input is the collected biometric data, and the output is the data transmitted to the server.

[0721] Step 3:

[0722] The server analyzes the received biometric data using a data analysis engine. Specifically, it checks whether the data is within the normal range and detects abnormal values. The analysis results are then input back into the generative AI model, which generates feedback in natural language. The input is the received biometric data, and the output is the generated analysis results.

[0723] Step 4:

[0724] The server sends the analysis results to the user's device. Specific feedback includes, for example, "Your current blood pressure is 130 / 85 mmHg, which is within the normal range." The input is the analysis results, and the output is the data sent to the user's device.

[0725] Step 5:

[0726] The user terminal displays the received feedback. The user can check their health status and contact a medical institution if necessary. The input is the feedback data sent from the server, and the output is the user's confirmation action.

[0727] Through this series of steps, the system enables real-time health management and appropriate medical consultation for patients. The combination of generative AI models and a data analysis engine enables fast and accurate responses and feedback.

[0728] (Application example 1)

[0729] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0730] Conventional telemedicine systems allow patients to receive medical consultations even when travel is difficult, but they are limited in their ability to provide both medical consultations and health management in real time. Furthermore, text-based interfaces alone are insufficient to provide a clear understanding of the patient. This creates the challenge of making it difficult for patients to feel at ease. Furthermore, there is a lack of a way to provide the results of biometric data analysis in an intuitive, easy-to-understand format.

[0731] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0732] In this invention, the server includes a means for receiving patient input information, a means for generating a response based on the received input information using a generative AI model, a means for receiving the patient's biometric data from a health management device, a means for analyzing the received biometric data and providing the results to the patient, and a means for performing remote medical consultations and health management in real time using a virtual reality device. This allows patients to receive real-time medical consultations and health management in a virtual space. Providing real-time medical support can increase patients' sense of security and promote the efficiency and understanding of health management.

[0733] "Patient input information" refers to text data of medical consultation details and questions entered by the patient via a chat interface or the like.

[0734] A "generative AI model" is an algorithm or program that uses machine learning and natural language processing techniques to generate responses to input patient questions and data.

[0735] "Health management devices" are devices used to collect patient biometric data, including fitness trackers, blood pressure monitors, and heart rate monitors.

[0736] "Biometric data" refers to data that indicates the patient's physical condition, and includes information such as activity level, heart rate, and blood pressure.

[0737] A "virtual reality device" is a device that allows patients to experience images and information in a virtual space, and includes head-mounted displays.

[0738] "Telemedical consultation" is a service that allows patients to receive medical consultation using communication technology without having to go to a medical institution in person.

[0739] "Data analysis" is the process of analyzing collected biometric data and interpreting it as meaningful information.

[0740] "Real-time" refers to instantaneous data processing and response, with little or no time delay.

[0741] "Natural language processing technology" is a technology that enables computers to understand the natural language used by humans on a daily basis and generate appropriate responses.

[0742] MODE FOR CARRYING OUT THE INVENTION

[0743] This invention is a system that utilizes a chatbot with a generative AI model and a virtual reality device to provide real-time remote medical consultations and health management for patients who have difficulty moving around. The system features a means for receiving patient input information and generating responses based on that information, and a means for receiving and analyzing biometric data from a health management device and providing the results to the patient.

[0744] System configuration

[0745] 1. User Device:

[0746] A device for patients to input medical consultations, such as a smartphone, tablet, or personal computer, provides a chat interface and an interface for collecting data from health management devices.

[0747] 2. Server:

[0748] The system uses a generative AI model to generate responses based on input information received from the user's device, analyzes biometric data from the health management device, and provides results. The server includes the generative AI model, a data analysis engine, and a database. The server can use cloud services such as AWS EC2 instances.

[0749] 3. Health management equipment:

[0750] A device that collects a patient's biometric data, such as a fitness tracker, blood pressure monitor, or heart rate monitor. The health management device transmits the collected data to a server via the user's terminal.

[0751] 4. Virtual reality devices:

[0752] A head-mounted display (HMD) is a device that allows patients to experience medical consultations and health management in a virtual space. Examples include Oculus Rift, HTC Vive, and Sony PlayStation VR.

[0753] System Operation

[0754] Medical consultation handling

[0755] 1. Patient Entry:

[0756] The user inputs a medical inquiry through the HMD's chat interface, for example, "I've been having frequent headaches lately."

[0757] 2. Data transmission:

[0758] The user terminal and the HMD device transmit this input information to the server.

[0759] 3. Response generation:

[0760] The server then inputs the received input information into a generative AI model (e.g., OpenAI's GPT-4), which uses natural language processing techniques to generate the optimal response. For example, it generates a response such as, "Possible causes of your headache include stress, lack of sleep, or other health conditions."

[0761] 4. Response sending and display:

[0762] The server sends the generated response to the user terminal and the HMD device, which displays the response to the user.

[0763] Health data management

[0764] 1. Data Collection:

[0765] The health management device collects the patient's biometric data (e.g., activity level, heart rate, blood pressure, etc.).

[0766] 2. Data transmission:

[0767] The health management device transmits the collected data to the user terminal, which then transmits the data to the server.

[0768] 3. Data Analysis:

[0769] The server analyzes the received biometric data using a data analysis engine and converts it into natural language using a generative AI model. For example, it generates feedback such as, "Your current blood pressure is 130 / 85 mmHg, which is within the normal range."

[0770] 4. Send and display results:

[0771] The server transmits the analysis results to the user terminal and the HMD device, which displays this feedback to the user.

[0772] Specific examples

[0773] Scenario: Health consultation

[0774] 1. Patient entry:

[0775] A user types into a chat interface, "I've been having a lot of headaches lately."

[0776] 2. Generative AI model prompt:

[0777] I've been having frequent headaches lately. What could be the cause?

[0778] 3. Example of generated response:

[0779] "Headaches can be caused by stress, lack of sleep, or other health conditions."

[0780] Scenario: Blood Pressure Management

[0781] 1. Data Collection:

[0782] The health management device measures the patient's blood pressure to be 130 / 85 mmHg.

[0783] 2. Data Analysis:

[0784] The server analyzes the data and generates feedback such as, "Your current blood pressure is within the normal range."

[0785] This system allows patients to efficiently receive real-time medical consultations and health management in a virtual space.

[0786] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0787] Step 1:

[0788] The patient wears a virtual reality device (HMD) and inputs a medical consultation request into the chat interface. The patient inputs, "I've been having frequent headaches recently."

[0789] Step 2:

[0790] The user terminal and the HMD device receive this input information and send it to the server, and the user terminal transfers the input text data to the server.

[0791] Step 3:

[0792] The server inputs the received input information into a generative AI model (e.g., OpenAI's GPT-4). The server passes the prompt sentence "I've been having frequent headaches lately. What could be the cause?" to the generative AI model and obtains its response.

[0793] Step 4:

[0794] The generative AI model uses natural language processing techniques to generate appropriate responses based on the input information, such as "Possible causes of headaches include stress, lack of sleep, or other health conditions" based on the prompt.

[0795] Step 5:

[0796] The server sends the generated response to the user terminal and the HMD device. The server then transfers the text data obtained from the generated AI model to the user terminal and the HMD device.

[0797] Step 6:

[0798] The HMD device displays this response to the patient, who can then review the generated response and ask further questions.

[0799] Step 7:

[0800] The health management device collects the patient's biological data (e.g., activity level, heart rate, blood pressure, etc.). For example, the health management device measures "130 / 85 mmHg" using a blood pressure monitor.

[0801] Step 8:

[0802] The health management device transmits the collected data to the user terminal. The health management device transfers the measured data to the user terminal.

[0803] Step 9:

[0804] The user terminal transmits the biometric data to the server, and the user terminal transfers the data received from the health management device to the server.

[0805] Step 10:

[0806] The server analyzes the received biometric data using a data analysis engine. For example, the server analyzes blood pressure data and generates feedback such as, "Your current blood pressure is within the normal range."

[0807] Step 11:

[0808] The server converts the analysis results into natural language using a generative AI model, and inputs the analysis results into the generative AI model to obtain easy-to-understand feedback text.

[0809] Step 12:

[0810] The server transmits the generated feedback to the user terminal and the HMD device. The server transfers the feedback text to the user terminal and the HMD device.

[0811] Step 13:

[0812] The HMD device displays the analysis results and feedback to the patient, who can see the feedback, "Your current blood pressure is 130 / 85 mmHg, which is within the normal range."

[0813] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0814] This invention is a remote medical consultation system that utilizes a chatbot with a generative AI model and an emotion engine to provide medical support to patients with mobility issues. The system receives patient input, generates appropriate responses, analyzes biometric data collected from a health management device, and provides the results to the patient. The emotion engine also recognizes the patient's emotional state and adjusts responses and feedback based on this emotional information, enabling more personalized support.

[0815] System configuration

[0816] This system mainly consists of the following parts:

[0817] 1. User Device:

[0818] This refers to a device that patients use to input medical consultations, such as a smartphone, tablet, or PC. The user terminal provides an interface for inputting medical consultation text, collecting data from health management devices, and recognizing emotions.

[0819] 2. Server:

[0820] This refers to a processing device that uses a generative AI model and emotion engine to analyze input information and biometric data received from a user device and provides responses and feedback based on that. The server includes a generative AI model, emotion engine, data analysis engine, and database.

[0821] 3. Health management equipment:

[0822] A health management device is a device that collects a patient's biological data, such as a fitness tracker, blood pressure monitor, or heart rate monitor. The health management device transmits the collected data to a server via a user terminal.

[0823] System Operation

[0824] The specific flow of operation of this system is shown below.

[0825] Medical consultation handling

[0826] 1. User Input:

[0827] The patient inputs a medical inquiry through the chat interface of the user terminal. For example, the patient may input, "I've been having frequent headaches recently."

[0828] 2. Data transmission:

[0829] The terminal transmits the user's input information to the server.

[0830] 3. Emotion recognition:

[0831] The server inputs the received input information into an emotion engine to recognize the patient's emotional state (e.g., anxiety, tension, relief, etc.).

[0832] 4. Response Generation:

[0833] The server inputs the input information, along with the emotional information recognized by the emotion engine, into the generative AI model. The generative AI model then uses natural language processing technology to generate the optimal response. For example, it might generate a response such as, "Possible causes of your headache include stress, lack of sleep, or other health conditions. If you are concerned, consult your doctor."

[0834] 5. Send Response:

[0835] The server sends the generated response to the user terminal.

[0836] 6. User Verification:

[0837] The terminal displays the received response to the user, who can then review the generated response and re-enter the question if necessary.

[0838] Health data management

[0839] 1. Data Collection:

[0840] The health management device collects biological data of the patient (e.g., activity level, heart rate, blood pressure, etc.).

[0841] 2. Data transmission:

[0842] The health management device transmits the collected data to the user terminal, which then periodically transmits this data to the server.

[0843] 3. Data reception:

[0844] The server receives the biometric data transmitted from the user terminal.

[0845] 4. Data Analysis:

[0846] The server analyzes the received biometric data using a data analysis engine.

[0847] 5. Emotional reflection:

[0848] The server reflects the patient's emotional information recognized by the emotion engine in the analysis results and generates feedback, such as "Your current blood pressure is 130 / 85 mmHg, which is within the normal range. Please continue to take appropriate health care measures."

[0849] 6. Send results:

[0850] The server transmits the generated feedback to the user terminal.

[0851] 7. User Feedback:

[0852] The device displays the analysis results to the patient, who then checks the feedback and initiates medical consultation if any abnormalities are detected.

[0853] Specific examples

[0854] As a concrete example, consider the following scenario.

[0855] Scenario 1: Headache consultation

[0856] A user types into a chat interface, "I've been having a lot of headaches lately."

[0857] The server uses an emotion engine to recognize "anxiety" from the user's input.

[0858] The server uses a generative AI model to generate a response such as, "Possible causes of headaches include stress, lack of sleep, and other health conditions. If you are concerned, consult your doctor."

[0859] The user terminal displays this response to the user.

[0860] Scenario 2: Blood Pressure Management

[0861] The health management device measures the user's blood pressure to be 130 / 85 mmHg.

[0862] The data is transmitted to the server via the user terminal.

[0863] The server analyzes the data using a data analysis engine, and then recognizes the feeling of "security" using an emotion engine.

[0864] Generate feedback such as "Your current blood pressure is 130 / 85 mmHg, which is within the normal range. Please continue to take appropriate care of your health."

[0865] The user terminal displays this feedback to the user.

[0866] Through this series of processes, the system provides an environment where even patients who have difficulty moving around can receive medical consultations and health management with peace of mind.

[0867] The processing flow will be explained below.

[0868] Medical consultation handling

[0869] Step 1: User Input

[0870] The user inputs a medical consultation request via a chat interface on the user terminal. For example, the user may input "I've been having frequent headaches recently."

[0871] Step 2: Send data

[0872] The terminal transmits the user's input information to the server.

[0873] Step 3: Emotion Recognition

[0874] The server inputs the received input information into an emotion engine to recognize the patient's emotional state (e.g., anxiety, tension, relief, etc.).

[0875] Step 4: Response Generation

[0876] The server inputs the input information, along with the emotions recognized by the emotion engine, into the generative AI model. The generative AI model then uses natural language processing techniques to generate the optimal response. For example, it might generate a response such as, "Possible causes of your headache include stress, lack of sleep, or other health conditions. If you are concerned, consult your doctor."

[0877] Step 5: Send response

[0878] The server sends the generated response to the user terminal.

[0879] Step 6: User Verification

[0880] The terminal displays the received response to the user, who can then review the generated response and re-enter the question if necessary.

[0881] Health data management

[0882] Step 1: Data collection

[0883] The health management device collects biological data of the patient (e.g., activity level, heart rate, blood pressure, etc.).

[0884] Step 2: Send data

[0885] The health management device transmits the collected data to the user terminal, which then periodically transmits this data to the server.

[0886] Step 3: Receiving data

[0887] The server receives the biometric data transmitted from the user terminal.

[0888] Step 4: Data analysis

[0889] The server analyzes the received biometric data using a data analysis engine.

[0890] Step 5: Emotional reflection

[0891] The server reflects the patient's emotional information recognized by the emotion engine in the analysis results and generates feedback, such as "Your current blood pressure is 130 / 85 mmHg, which is within the normal range. Please continue to take appropriate health care measures."

[0892] Step 6: Send results

[0893] The server transmits the generated feedback to the user terminal.

[0894] Step 7: User Feedback

[0895] The device displays the analysis results to the patient, who then checks the feedback and initiates medical consultation if any abnormalities are detected.

[0896] These are the specific processing steps. This system allows patients to receive medical consultations and manage their health from the comfort of their own homes or nursing homes.

[0897] Example 2

[0898] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0899] The present invention relates to a telemedicine system that allows patients with mobility issues to receive medical consultations and health management from the comfort of their own homes. Conventional telemedicine systems often provide mechanical responses without considering the patient's current emotional state, which can lead to unsatisfactory results for the patient. To solve this problem, a more personalized response that also takes the patient's emotional state into account is needed.

[0900] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0901] In this invention, the server includes means for receiving input information from a patient, means for generating a response based on the received input information using a generative AI model, means for receiving biometric data of the patient from a health management device, means for recognizing the emotional state of the patient using an emotion engine and reflecting the recognized emotional state in generating the response, and means for reflecting the emotional state of the patient in feedback generated based on the analyzed biometric data, thereby making it possible to provide personalized responses and feedback to patients that take emotions into consideration.

[0902] The "means for receiving patient input information" is a mechanism for transmitting text data and questions input by the patient via a user terminal to a server, and for the server to receive the information.

[0903] "Means for generating a response based on received input information using a generative AI model" refers to a mechanism for inputting received input information into a generative AI model and automatically generating an optimal response using natural language processing technology.

[0904] The "means for receiving patient biometric data from a health management device" is a mechanism by which the server receives biometric data sent from a health management device such as a fitness tracker or blood pressure monitor.

[0905] "Means for recognizing the emotional state of the patient using an emotion engine and reflecting the recognized emotional state in the generation of the response" refers to a mechanism for inputting received input information into an emotion engine, extracting the user's emotional state (e.g., anxiety, relief, etc.), and reflecting that emotional information in the response generation process via a generative AI model.

[0906] The "means for reflecting the patient's emotional state in the feedback generated based on the analyzed biometric data" is a mechanism for analyzing the received biometric data using a data analysis engine, integrating the patient's emotional state recognized by the emotion engine, and adjusting the feedback message based on the analysis results to provide to the patient.

[0907] This invention is a remote medical consultation system that utilizes a chatbot with a generative AI model and an emotion engine to provide medical support to patients with mobility issues. The system receives patient input and generates appropriate responses. It also has the ability to analyze biometric data collected from health management devices and provide the results to the patient. Furthermore, the emotion engine recognizes the patient's emotional state and adjusts responses and feedback based on this emotional information, thereby achieving more personalized support.

[0908] System configuration

[0909] This system mainly consists of the following parts:

[0910] 1. User Device:

[0911] This is a device for patients to input medical consultations, and can be a smartphone, tablet, PC, etc. The user terminal provides an interface for inputting medical consultation text, collecting data from health management devices, and recognizing emotions.

[0912] 2. Server:

[0913] This refers to a processing device that uses a generative AI model and emotion engine to analyze input information and biometric data received from a user device and generate responses and feedback based on that. The server includes a generative AI model, emotion engine, data analysis engine, and database.

[0914] 3. Health management equipment:

[0915] These are devices that collect patient biometric data, such as fitness trackers, blood pressure monitors, and heart rate monitors. Health management devices send the collected data to a server via a user terminal.

[0916] Medical consultation handling

[0917] Specific examples

[0918] 1. Receiving user input

[0919] A user inputs a medical consultation request via a chat interface on a smartphone or PC. For example, the user might input, "I've been having frequent headaches recently."

[0920] 2. Data Transmission

[0921] The terminal sends the user's input information to the server using the HTTPS protocol.

[0922] 3. Emotion Recognition

[0923] The server inputs the received text data into an emotion engine to recognize the user's emotional state, for example, extracting emotions such as anxiety or tension.

[0924] 4. Response Generation

[0925] The server inputs text data, including emotional information, into the generative AI model. The generative AI model uses natural language processing techniques to generate an appropriate response to the user's input. For example, it might generate a response like, "Possible causes of headaches include stress and lack of sleep. If you're concerned, consult a doctor."

[0926] 5. Sending the Response

[0927] The server sends the generated response to the user terminal.

[0928] 6. User Display

[0929] The terminal displays the received response to the user, who can then check the displayed response and re-enter the question if necessary.

[0930] Health data management

[0931] Specific examples

[0932] 1. Data Collection

[0933] Health monitoring devices (such as fitness trackers and blood pressure monitors) collect patient biometric data. For example, a blood pressure monitor measures a blood pressure of 130 / 85 mmHg.

[0934] 2. Data Transmission

[0935] Health management devices send collected biometric data to a user's device, which then periodically transmits the data to a server, often via Bluetooth or Wi-Fi.

[0936] 3. Data Reception

[0937] The server receives the biometric data sent from the user terminal and stores it in a database.

[0938] 4. Data Analysis

[0939] The server then analyzes the received biometric data using a data analysis engine, which uses statistical analysis and machine learning algorithms.

[0940] 5. Emotional reflection

[0941] The server reflects the user's emotional information recognized by the emotion engine in the analysis results and generates feedback, such as a message like "Your current blood pressure is 130 / 85 mmHg, which is within the normal range. Please continue to take care of your health."

[0942] 6. Send results

[0943] The server transmits the generated feedback to the user terminal.

[0944] 7. User Presentation

[0945] The device displays the feedback sent from the server to the user, who then checks the analysis results and begins medical consultation if any abnormalities are detected.

[0946] Prompt Sentence Examples

[0947] Prompt example 1: Medical consultation

[0948] When a patient types, "I've been having frequent headaches lately," use an emotion engine to recognize the emotion and a generative AI model to generate the optimal response.

[0949] Prompt example 2: Health data management

[0950] When the patient's blood pressure is measured at 130 / 85 mmHg and the emotion engine recognizes relief, generate appropriate feedback.

[0951] In this way, this system uses an emotion engine and generative AI model to provide advanced remote medical support to patients, creating an environment where even patients with difficulty traveling can receive medical consultations and manage their health with peace of mind.

[0952] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0953] Medical consultation handling

[0954] Step 1: User inputs medical consultation

[0955] A user inputs a medical consultation through a chat interface, for example, inputting the text "I've been having frequent headaches lately."

[0956] Input: User text input

[0957] Output: The text entered

[0958] Step 2: Send text data

[0959] The device sends the entered text data to the server using the HTTPS protocol.

[0960] Input: Text entered by the user

[0961] Output: Text data sent to the server

[0962] Step 3: Performing emotion recognition

[0963] The server inputs the received text data into an emotion engine to recognize the patient's emotional state, for example, by extracting emotions such as "anxiety" or "tension."

[0964] Input: Received text data

[0965] Output: Recognized emotion information (e.g., "anxiety")

[0966] Step 4: Generate a response

[0967] The server inputs text data based on the emotional information into the generative AI model, which then uses natural language processing technology to generate the optimal response. For example, the model might generate a response such as, "Possible causes of headaches include stress and lack of sleep. If you're concerned, consult a doctor."

[0968] Input: Text data and emotion information

[0969] Output: The generated response

[0970] Step 5: Sending a Response

[0971] The server sends the generated response to the user's terminal using the HTTPS protocol.

[0972] Input: The generated response

[0973] Output: Response sent to the user's device

[0974] Step 6: View the response

[0975] The terminal displays the received response to the user, who can then review the displayed response and re-enter the question if necessary.

[0976] Input: Response received from the server

[0977] Output: The response displayed to the user

[0978] Health data management

[0979] Step 1: Collect biometric data

[0980] Health management devices collect patient biometric data, for example, a blood pressure monitor measures "130 / 85 mmHg."

[0981] Input: Patient biometric data (e.g. blood pressure)

[0982] Output: Measured biometric data

[0983] Step 2: Sending data

[0984] The health management device transmits the collected biometric data to the user's terminal, which then transmits the data to a server via Bluetooth or Wi-Fi.

[0985] Input: Measured biometric data

[0986] Output: Biometric data sent to the server

[0987] Step 3: Receiving the data

[0988] The server receives the biometric data sent from the user terminal and stores it in a database.

[0989] Input: Data sent from the user's device

[0990] Output: Stored biometric data

[0991] Step 4: Analyze the data

[0992] The server then analyzes the received biometric data using a data analysis engine, which uses statistical analysis and machine learning algorithms.

[0993] Input: Stored biometric data

[0994] Output: Analysis results

[0995] Step 5: Reflecting emotional information

[0996] The server then incorporates the emotional information recognized by the emotion engine into the analysis results and generates final feedback, such as a message saying, "Your current blood pressure is 130 / 85 mmHg, which is within the normal range. Please continue to take care of your health."

[0997] Input: Analysis results and emotion information

[0998] Output: Final feedback message

[0999] Step 6: Sending the results

[1000] The server sends the generated feedback to the user terminal using the HTTPS protocol.

[1001] Input: Final feedback message

[1002] Output: Feedback message sent to the user's device

[1003] Step 7: View your feedback

[1004] The terminal displays the feedback sent from the server to the user, who can check the feedback and start another medical consultation if there is any abnormality.

[1005] Input: Feedback message received from the server

[1006] Output: Feedback displayed to the user

[1007] (Application example 2)

[1008] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1009] In today's world, patients with mobility issues have limited access to medical support, making it particularly difficult for those living in remote areas to receive appropriate medical services. Furthermore, traditional telemedicine systems typically provide responses and feedback without considering the patient's emotional state, making it difficult to provide personalized, optimal support. This can lead to a lack of reassurance and trust for patients, resulting in a decline in the quality of medical consultations.

[1010] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving patient input information, means for generating a response based on the received input information using a generative AI model, means for receiving the patient's biometric data from the health management device, means for recognizing the patient's emotional state using an emotion engine, and means for adjusting the response based on the recognized emotional state. This not only enables patients with limited mobility to receive medical consultations remotely, but also provides personalized responses through emotion recognition, thereby providing patients with an appropriate sense of security and trust.

[1011] "Patient input information" is text data entered by patients as questions or messages regarding medical consultations or health care.

[1012] A "generative AI model" is an artificial intelligence algorithm used to generate natural language responses based on large datasets.

[1013] A "health management device" is a device that measures and collects a patient's biometric data, such as a fitness tracker, blood pressure monitor, or heart rate monitor.

[1014] "Biometric data" refers to data that indicates physical indicators such as a patient's heart rate, blood pressure, and activity level.

[1015] The "emotion engine" is an artificial intelligence algorithm that analyzes the patient's input information and recognizes their emotional state (e.g., anxiety, relief, tension, etc.).

[1016] A "means for tailoring responses" is a system that customizes the responses generated based on the emotional state of an individual patient.

[1017] "Natural language processing technology" is an information processing technology for analyzing, understanding, and generating human language.

[1018] MODE FOR CARRYING OUT THE INVENTION

[1019] System configuration

[1020] This invention is a remote medical consultation system for providing medical support to patients with mobility issues. The system receives patient input, generates responses using a generative AI model, and uses an emotion engine to recognize the patient's emotional state and adjust responses based on that emotional information. The system primarily consists of the following hardware and software:

[1021] 1. User Device

[1022] Smartphones, tablets, computers, etc. are used.

[1023] It is an interface through which patients input their medical consultations, and is responsible for collecting biometric data from health management devices and sending it to the server.

[1024] 2. Generative AI Models

[1025] It is an algorithm that uses natural language processing technology to generate optimal responses based on the patient's input information received.

[1026] Model name example: "text-davinci-003"

[1027] 3. Emotion Engine

[1028] It includes algorithms for analyzing patient input and recognizing their emotional state.

[1029] Example of technology used: Emotion recognition model using deep learning

[1030] 4. Health management device

[1031] These are devices that measure a patient's biometric data, such as fitness trackers, blood pressure monitors, and heart rate monitors.

[1032] System Operation

[1033] Medical consultation handling

[1034] 1. A patient inputs a medical consultation through a user terminal. For example, the patient inputs, "I've been having frequent headaches recently."

[1035] 2. The device sends this input information to the server.

[1036] 3. The server inputs this input information into an emotion engine to recognize the patient's emotional state (e.g., anxiety).

[1037] 4. The server passes the recognized emotion information to a generative AI model to generate an optimal response.

[1038] Example generated response: "Possible causes of headaches include stress, lack of sleep, or other health conditions. If you are concerned, consult your doctor."

[1039] 5. The server sends the generated response to the user terminal, which displays it to the patient.

[1040] Health data management

[1041] 1. Collect patient biometric data (e.g., heart rate, blood pressure) using health monitoring devices.

[1042] 2. The user terminal periodically sends the collected data to the server.

[1043] 3. The server receives the biometric data and analyzes it using a data analysis engine.

[1044] 4. The generative AI model provides feedback based on the emotional information recognized by the emotion engine.

[1045] Example of generated feedback: "Your current blood pressure is 130 / 85 mmHg, which is within the normal range. Please continue to take good care of your health."

[1046] 5. The server sends this feedback to the user terminal, which displays the analysis results to the patient.

[1047] Prompt Sentence Examples

[1048] For medical consultations

[1049] User Input: I've been having frequent headaches lately.

[1050] Emotion: Anxiety

[1051] Appropriate response: Headaches can be caused by stress, lack of sleep, or other health conditions. If you're concerned, consult your doctor.

[1052] In the case of biometric data

[1053] Example of vital data: {"heart_rate": 75, "blood_pressure": "130 / 85"}

[1054] Health feedback: Health is normal.

[1055] In this way, an environment is provided where even patients who have difficulty moving around can receive medical consultations and health management remotely with peace of mind.

[1056] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1057] Program processing steps

[1058] Step 1:

[1059] A user inputs a medical consultation request using a smartphone. For example, the user might input, "I've been having frequent headaches recently." This input information is saved as text data on the device.

[1060] Input: User's medical consultation text

[1061] Output: Medical consultation in text data format

[1062] Step 2:

[1063] The terminal transmits the stored medical consultation text of the user to the server, where the terminal makes a request using internet communication.

[1064] Input: Medical consultation in text format

[1065] Output: Medical consultation data sent to the server

[1066] Step 3:

[1067] The server inputs the received medical consultation data into the emotion engine, which uses a deep learning model to analyze the patient's emotional state from the input text. For example, it can recognize an emotional state such as "anxiety."

[1068] Input: Medical consultation data

[1069] Output: Perceived emotional state (e.g., anxiety)

[1070] Step 4:

[1071] The server passes the emotional state to a generative AI model, which generates a response. The generative AI model uses natural language processing technology to generate the optimal response based on the medical consultation and emotional information. For example, it might generate text like, "Possible causes of your headache include stress, lack of sleep, or other health conditions. If you're concerned, consult a doctor."

[1072] Input: medical consultation data, recognized emotional state

[1073] Output: Generated response

[1074] Step 5:

[1075] The server sends the generated response to the user's terminal, which receives the response and displays it to the user.

[1076] Input: Generated response sentence

[1077] Output: Response text displayed on the terminal

[1078] Step 6:

[1079] The health management device is used to collect biometric data of the user, such as measuring heart rate and blood pressure, and this data is transmitted to the terminal.

[1080] Input: Biometric data from health management device

[1081] Output: Biometric data stored on the device

[1082] Step 7:

[1083] The device transmits the stored biometric data to a server, and the device is programmed to periodically transmit this data to the server.

[1084] Input: Stored biometric data

[1085] Output: Biometric data sent to the server

[1086] Step 8:

[1087] The server analyzes the received biometric data using a data analysis engine. For example, if the heart rate is over 100, it is determined to be a "high heart rate."

[1088] Input: Biometric data

[1089] Output: Analysis results (e.g. high heart rate)

[1090] Step 9:

[1091] The server reflects the patient's emotional information recognized by the emotion engine in the analysis results, and the generative AI model provides feedback, such as, "Your current blood pressure is 130 / 85 mmHg, which is within the normal range. Please continue to take appropriate health care measures."

[1092] Input: Analysis results, recognized emotional state

[1093] Output: Generated feedback statement

[1094] Step 10:

[1095] The server sends the generated feedback sentence to the user terminal, which displays the feedback to the user.

[1096] Input: Generated feedback sentence

[1097] Output: Feedback text displayed on the terminal

[1098] In this way, patients can receive medical consultations and health management remotely through each step of the system. The combination of generative AI models and emotion engines makes it possible to provide personalized responses and feedback.

[1099] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1100] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1101] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1102] [Third embodiment]

[1103] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1104] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1105] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1106] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1107] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1109] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1110] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1111] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1113] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1114] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1115] This invention is a remote medical consultation system that utilizes a chatbot with a generative AI model to provide medical support to patients with mobility issues. The system receives patient input, generates appropriate responses, analyzes biometric data collected from health management devices, and provides the results to the patient.

[1116] System configuration

[1117] This system mainly consists of the following parts:

[1118] 1. User Device:

[1119] A device used by patients to input medical consultations, such as a smartphone, tablet, or PC. The user terminal provides an interface for inputting medical consultation text and collecting data from health management devices.

[1120] 2. Server:

[1121] This refers to a processing device that uses a generative AI model to generate responses based on input information received from a user device, and analyzes biometric data from a health management device to provide results. The server includes a generative AI model, a data analysis engine, and a database.

[1122] 3. Health management equipment:

[1123] A health management device is a device that collects a patient's biological data, such as a fitness tracker, blood pressure monitor, or heart rate monitor. The health management device transmits the collected data to a server via a user terminal.

[1124] System Operation

[1125] The specific flow of operation of this system is shown below.

[1126] Medical consultation handling

[1127] 1. User Input:

[1128] The patient inputs a medical inquiry through the chat interface of the user terminal. For example, the patient may input, "I've been having frequent headaches recently."

[1129] 2. Data transmission:

[1130] The user terminal transmits this input information to the server.

[1131] 3. Response generation:

[1132] The server inputs the received input information into the generative AI model, which then uses natural language processing techniques to generate the optimal response. For example, it might generate a response like, "Possible causes of your headache include stress, lack of sleep, or other health conditions. Consult your doctor for a detailed diagnosis."

[1133] 4. Send response:

[1134] The server generates a response and sends it to the user terminal.

[1135] 5. User Verification:

[1136] The user terminal displays this response to the patient, who can review the generated response and re-enter any further questions.

[1137] Health data management

[1138] 1. Data Collection:

[1139] The health management device collects the patient's biometric data (e.g., activity level, heart rate, blood pressure, etc.).

[1140] 2. Data transmission:

[1141] The health management device transmits the collected data to the user terminal, which then transmits the data to the server.

[1142] 3. Data Analysis:

[1143] The server analyzes the received biometric data using a data analysis engine, and the analysis results are converted into natural language using a generative AI model to generate information in a format that is easy for the patient to understand.

[1144] 4. Send results:

[1145] The server transmits the analysis results to the user terminal.

[1146] 5. User Feedback:

[1147] The user device displays the analysis results to the patient. For example, the patient may receive feedback such as, "Your current blood pressure is 130 / 85 mmHg, which is within the normal range. Please continue to take appropriate health care measures."

[1148] Specific examples

[1149] As a concrete example, consider the following scenario.

[1150] Scenario 1: Headache consultation

[1151] A user types into a chat interface, "I've been having a lot of headaches lately."

[1152] The server uses a generative AI model to generate a response such as, "Possible causes of your headache include stress, lack of sleep, or other health conditions."

[1153] The user terminal displays this response to the user.

[1154] Scenario 2: Blood Pressure Management

[1155] The health management device measures the user's blood pressure to be 130 / 85 mmHg.

[1156] The data is transmitted to the server via the user terminal.

[1157] The server analyzes the data and generates feedback such as, "Your current blood pressure is within the normal range."

[1158] The user terminal displays this feedback to the user.

[1159] Through this series of processes, the system provides an environment where even patients who have difficulty moving around can receive medical consultations and health management with peace of mind.

[1160] The processing flow will be explained below.

[1161] Medical consultation handling

[1162] Step 1: User Input

[1163] The user inputs a medical consultation request via a chat interface on the user terminal. For example, the user may input "I've been having frequent headaches recently."

[1164] Step 2: Send data

[1165] The terminal transmits the text of the medical consultation sent by the user to the server.

[1166] Step 3: Response Generation

[1167] The server inputs the received medical consultation text into a generative AI model.

[1168] Step 4: Natural Language Processing

[1169] The generative AI model uses natural language processing techniques to analyze the input text and generate an optimal response, such as, "Your headache may be due to stress, lack of sleep, or other health conditions. Please consult your doctor for a detailed diagnosis."

[1170] Step 5: Send response

[1171] The server sends the generated response to the user terminal.

[1172] Step 6: User Verification

[1173] The terminal displays the received response to the user, who can then review the generated response and re-enter the question if necessary.

[1174] Health data management

[1175] Step 1: Data collection

[1176] The health management device collects biological data of the patient (e.g., activity level, heart rate, blood pressure, etc.).

[1177] Step 2: Send data

[1178] The health management device transmits the collected data to the user terminal, which then periodically transmits this data to the server.

[1179] Step 3: Receiving data

[1180] The server receives the biometric data transmitted from the user terminal.

[1181] Step 4: Data analysis

[1182] The server analyzes the received biometric data using a data analysis engine.

[1183] Step 5: Interpretation by generative AI models

[1184] The server uses a generative AI model to interpret the results obtained by the data analysis engine in natural language. For example, it generates feedback such as, "Your current blood pressure is 130 / 85 mmHg, which is within the normal range. Please continue to take appropriate measures to manage your health."

[1185] Step 6: Send results

[1186] The server transmits the generated feedback to the user terminal.

[1187] Step 7: User Feedback

[1188] The device displays the analysis results to the user, who then checks the feedback and initiates medical consultation if any abnormalities are detected.

[1189] These are the specific processing steps. This system allows patients to receive medical consultations and manage their health from the comfort of their own homes or nursing homes.

[1190] Example 1

[1191] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1192] In remote medical consultation systems, real-time assessment of health status and accurate feedback based on that assessment are essential for patients with mobility issues to receive appropriate medical support. However, existing systems lack the means to effectively integrate and analyze patient input information and biometric data from health management devices, and provide rapid and accurate feedback to users. Furthermore, the lack of smooth intercommunication makes it difficult to provide a user-friendly service. Therefore, there is a need for an effective system that can appropriately integrate input information and biometric data, generate responses in natural language, and provide accurate feedback.

[1193] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1194] In this invention, the server

[1195] means for generating a response based on the received input information using a generative AI model;

[1196] means for receiving patient biometric data from a health management device;

[1197] means for transmitting the biometric data to a server using a user terminal;

[1198] means for inputting the input information and biometric data as prompts to a generative AI model;

[1199] means for generating the response and analysis results in natural language and providing feedback to the patient;

[1200] means for analyzing biometric data from the health management device and detecting abnormal values;

[1201] a means for determining whether the level is within normal limits;

[1202] means for displaying said responses and feedback on a user terminal;

[1203] This allows for efficient and accurate analysis of patient input and biometric data, and provides appropriate responses and feedback in real time.

[1204] "Patient" refers to a person who receives medical consultation or health management services using the remote medical consultation system.

[1205] "Input information" refers to text data such as symptoms and questions that a patient provides to the system via a user terminal.

[1206] A "generative AI model" refers to artificial intelligence that generates responses in natural language based on input data.

[1207] "Response" refers to the answer or advice to a medical consultation that the generative AI model generates based on the patient's input information.

[1208] "Health management devices" are devices used to measure and collect patients' biometric data, and generally refer to devices such as fitness trackers, heart rate monitors, and blood pressure monitors.

[1209] "Biometric data" refers to data that indicates the patient's health condition, such as blood pressure, heart rate, and activity level, collected by a health management device.

[1210] The "analysis result" refers to information generated as a result of the server analyzing the biometric data received from the health management device.

[1211] "User terminal" refers to a device used by a patient to access the system, send input information, and receive feedback, such as a smartphone, tablet, or PC.

[1212] A "server" is the central processing unit of the system, which uses a generative AI model to generate responses based on input information, analyzes biometric data, and provides the results.

[1213] "Feedback" refers to information provided to the patient regarding the analysis results and generated responses.

[1214] This invention is a remote medical consultation system that utilizes a generative AI model to provide medical support to patients who have difficulty traveling. The main components of the system are a user terminal for patients to input medical consultation information, a server that processes the input information and biometric data, and a health management device that collects the patient's biometric data.

[1215] System configuration

[1216] User terminal

[1217] The user terminal is a device such as a smartphone, tablet, or PC that provides an interface for patients to input medical consultation information and transmit biometric data. The terminal communicates with the server via the Internet.

[1218] server

[1219] The server uses a generative AI model to generate responses based on input information received from the user device, analyzes biometric data received from the health management device, and provides the results to the patient. The server includes the following main components:

[1220] Generative AI models (e.g., natural language processing models such as GPT-3)

[1221] Data Analysis Engine

[1222] Database

[1223] health management device

[1224] Health management devices are devices for collecting patient biometric data, and examples include fitness trackers, blood pressure monitors, heart rate monitors, etc. The devices transmit the collected data to a server via a user terminal.

[1225] System Operation

[1226] Medical consultation handling

[1227] A patient inputs a medical inquiry through a chat interface on the user's device. For example, they might input, "I've been having frequent headaches lately." The user's device then sends this input information to the server. The server then inputs the received input information into a generative AI model and generates an optimal response using natural language processing technology. The generated response is then sent from the server to the user's device and displayed to the user. The user can review the generated response and re-enter any further questions they may have.

[1228] Health data management

[1229] The health management device collects the patient's biometric data (e.g., activity level, heart rate, blood pressure, etc.). The collected data is sent to the user's terminal, which then sends it to the server. The server analyzes the received biometric data using a data analysis engine and converts it into natural language using a generative AI model. The analysis results are sent from the server to the user's terminal and displayed to the user. For example, feedback such as "Your current blood pressure is 130 / 85 mmHg, which is within the normal range" is generated.

[1230] Specific examples

[1231] Scenario 1: Headache consultation

[1232] The patient types into the chat interface, "I've been having frequent headaches lately."

[1233] The user terminal transmits this input content to the server.

[1234] The server uses a generative AI model to generate a response such as, "Possible causes of your headache include stress, lack of sleep, or other health conditions."

[1235] The user terminal displays this response to the user.

[1236] Scenario 2: Blood Pressure Management

[1237] The health management device measures the user's blood pressure to be 130 / 85 mmHg.

[1238] The data is transmitted to the server via the user terminal.

[1239] The server analyzes the data and generates feedback such as, "Your current blood pressure is within the normal range."

[1240] The user terminal displays this feedback to the user.

[1241] This system allows patients with mobility issues to receive medical consultations and health management in real time. By utilizing generative AI models and data analysis engines, appropriate and prompt responses to patients can be provided, improving the quality of medical care.

[1242] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1243] Medical consultation handling

[1244] Step 1:

[1245] The user inputs a medical consultation request via a chat interface on the user terminal. The input is text data including "I've been having frequent headaches recently." This input data becomes the input for the next step.

[1246] Step 2:

[1247] The user device sends the entered consultation details to the server via the Internet. The text data is transferred to the server in JSON format. The input is the text data entered in step 1, and the output is the data sent to the server.

[1248] Step 3:

[1249] The server analyzes the received consultation content and inputs it as a prompt to the generative AI model. Here, the generative AI model (e.g., GPT-3) generates the optimal response in natural language based on the input data. The input is the received text data, and the output is a response in natural language.

[1250] Step 4:

[1251] The server then sends the generated response back to the user's device. This response is in text format and is displayed on the user's device. The input is the response data generated by the generative AI model, and the output is the data sent to the user's device.

[1252] Step 5:

[1253] The user terminal displays the received response on the chat interface. The user can then review it and decide whether they understand the content or need to ask again. The input is the response data sent from the server, and the output is the user's confirmation action.

[1254] Health data management

[1255] Step 1:

[1256] The health management device collects the user's biometric data (e.g., blood pressure and heart rate). The data is collected in real time and set to be sent periodically. The input is the user's biometric data, and the output is the accumulation of collected data.

[1257] Step 2:

[1258] The health management device transmits the collected biometric data to the user terminal, which then transmits the data to the server. The input is the collected biometric data, and the output is the data transmitted to the server.

[1259] Step 3:

[1260] The server analyzes the received biometric data using a data analysis engine. Specifically, it checks whether the data is within the normal range and detects abnormal values. The analysis results are then input back into the generative AI model, which generates feedback in natural language. The input is the received biometric data, and the output is the generated analysis results.

[1261] Step 4:

[1262] The server sends the analysis results to the user's device. Specific feedback includes, for example, "Your current blood pressure is 130 / 85 mmHg, which is within the normal range." The input is the analysis results, and the output is the data sent to the user's device.

[1263] Step 5:

[1264] The user terminal displays the received feedback. The user can check their health status and contact a medical institution if necessary. The input is the feedback data sent from the server, and the output is the user's confirmation action.

[1265] Through this series of steps, the system enables real-time health management and appropriate medical consultation for patients. The combination of generative AI models and a data analysis engine enables fast and accurate responses and feedback.

[1266] (Application example 1)

[1267] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1268] Conventional telemedicine systems allow patients to receive medical consultations even when travel is difficult, but they are limited in their ability to provide both medical consultations and health management in real time. Furthermore, text-based interfaces alone are insufficient to provide a clear understanding of the patient. This creates the challenge of making it difficult for patients to feel at ease. Furthermore, there is a lack of a way to provide the results of biometric data analysis in an intuitive, easy-to-understand format.

[1269] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1270] In this invention, the server includes a means for receiving patient input information, a means for generating a response based on the received input information using a generative AI model, a means for receiving the patient's biometric data from a health management device, a means for analyzing the received biometric data and providing the results to the patient, and a means for performing remote medical consultations and health management in real time using a virtual reality device. This allows patients to receive real-time medical consultations and health management in a virtual space. Providing real-time medical support can increase patients' sense of security and promote the efficiency and understanding of health management.

[1271] "Patient input information" refers to text data of medical consultation details and questions entered by the patient via a chat interface or the like.

[1272] A "generative AI model" is an algorithm or program that uses machine learning and natural language processing techniques to generate responses to input patient questions and data.

[1273] "Health management devices" are devices used to collect patient biometric data, including fitness trackers, blood pressure monitors, and heart rate monitors.

[1274] "Biometric data" refers to data that indicates the patient's physical condition, and includes information such as activity level, heart rate, and blood pressure.

[1275] A "virtual reality device" is a device that allows patients to experience images and information in a virtual space, and includes head-mounted displays.

[1276] "Telemedical consultation" is a service that allows patients to receive medical consultation using communication technology without having to go to a medical institution in person.

[1277] "Data analysis" is the process of analyzing collected biometric data and interpreting it as meaningful information.

[1278] "Real-time" refers to instantaneous data processing and response, with little or no time delay.

[1279] "Natural language processing technology" is a technology that enables computers to understand the natural language used by humans on a daily basis and generate appropriate responses.

[1280] MODE FOR CARRYING OUT THE INVENTION

[1281] This invention is a system that utilizes a chatbot with a generative AI model and a virtual reality device to provide real-time remote medical consultations and health management for patients who have difficulty moving around. The system features a means for receiving patient input information and generating responses based on that information, and a means for receiving and analyzing biometric data from a health management device and providing the results to the patient.

[1282] System configuration

[1283] 1. User Device:

[1284] A device for patients to input medical consultations, such as a smartphone, tablet, or personal computer, provides a chat interface and an interface for collecting data from health management devices.

[1285] 2. Server:

[1286] The system uses a generative AI model to generate responses based on input information received from the user's device, analyzes biometric data from the health management device, and provides results. The server includes the generative AI model, a data analysis engine, and a database. The server can use cloud services such as AWS EC2 instances.

[1287] 3. Health management equipment:

[1288] A device that collects a patient's biometric data, such as a fitness tracker, blood pressure monitor, or heart rate monitor. The health management device transmits the collected data to a server via the user's terminal.

[1289] 4. Virtual reality devices:

[1290] A head-mounted display (HMD) is a device that allows patients to experience medical consultations and health management in a virtual space. Examples include Oculus Rift, HTC Vive, and Sony PlayStation VR.

[1291] System Operation

[1292] Medical consultation handling

[1293] 1. Patient Entry:

[1294] The user inputs a medical inquiry through the HMD's chat interface, for example, "I've been having frequent headaches lately."

[1295] 2. Data transmission:

[1296] The user terminal and the HMD device transmit this input information to the server.

[1297] 3. Response generation:

[1298] The server then inputs the received input information into a generative AI model (e.g., OpenAI's GPT-4), which uses natural language processing techniques to generate the optimal response. For example, it generates a response such as, "Possible causes of your headache include stress, lack of sleep, or other health conditions."

[1299] 4. Response sending and display:

[1300] The server sends the generated response to the user terminal and the HMD device, which displays the response to the user.

[1301] Health data management

[1302] 1. Data Collection:

[1303] The health management device collects the patient's biometric data (e.g., activity level, heart rate, blood pressure, etc.).

[1304] 2. Data transmission:

[1305] The health management device transmits the collected data to the user terminal, which then transmits the data to the server.

[1306] 3. Data Analysis:

[1307] The server analyzes the received biometric data using a data analysis engine and converts it into natural language using a generative AI model. For example, it generates feedback such as, "Your current blood pressure is 130 / 85 mmHg, which is within the normal range."

[1308] 4. Send and display results:

[1309] The server transmits the analysis results to the user terminal and the HMD device, which displays this feedback to the user.

[1310] Specific examples

[1311] Scenario: Health consultation

[1312] 1. Patient entry:

[1313] A user types into a chat interface, "I've been having a lot of headaches lately."

[1314] 2. Generative AI model prompt:

[1315] I've been having frequent headaches lately. What could be the cause?

[1316] 3. Example of generated response:

[1317] "Headaches can be caused by stress, lack of sleep, or other health conditions."

[1318] Scenario: Blood Pressure Management

[1319] 1. Data Collection:

[1320] The health management device measures the patient's blood pressure to be 130 / 85 mmHg.

[1321] 2. Data Analysis:

[1322] The server analyzes the data and generates feedback such as, "Your current blood pressure is within the normal range."

[1323] This system allows patients to efficiently receive real-time medical consultations and health management in a virtual space.

[1324] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1325] Step 1:

[1326] The patient wears a virtual reality device (HMD) and inputs a medical consultation request into the chat interface. The patient inputs, "I've been having frequent headaches recently."

[1327] Step 2:

[1328] The user terminal and the HMD device receive this input information and send it to the server, and the user terminal transfers the input text data to the server.

[1329] Step 3:

[1330] The server inputs the received input information into a generative AI model (e.g., OpenAI's GPT-4). The server passes the prompt sentence "I've been having frequent headaches lately. What could be the cause?" to the generative AI model and obtains its response.

[1331] Step 4:

[1332] The generative AI model uses natural language processing techniques to generate appropriate responses based on the input information, such as "Possible causes of headaches include stress, lack of sleep, or other health conditions" based on the prompt.

[1333] Step 5:

[1334] The server sends the generated response to the user terminal and the HMD device. The server then transfers the text data obtained from the generated AI model to the user terminal and the HMD device.

[1335] Step 6:

[1336] The HMD device displays this response to the patient, who can then review the generated response and ask further questions.

[1337] Step 7:

[1338] The health management device collects the patient's biological data (e.g., activity level, heart rate, blood pressure, etc.). For example, the health management device measures "130 / 85 mmHg" using a blood pressure monitor.

[1339] Step 8:

[1340] The health management device transmits the collected data to the user terminal. The health management device transfers the measured data to the user terminal.

[1341] Step 9:

[1342] The user terminal transmits the biometric data to the server, and the user terminal transfers the data received from the health management device to the server.

[1343] Step 10:

[1344] The server analyzes the received biometric data using a data analysis engine. For example, the server analyzes blood pressure data and generates feedback such as, "Your current blood pressure is within the normal range."

[1345] Step 11:

[1346] The server converts the analysis results into natural language using a generative AI model, and inputs the analysis results into the generative AI model to obtain easy-to-understand feedback text.

[1347] Step 12:

[1348] The server transmits the generated feedback to the user terminal and the HMD device. The server transfers the feedback text to the user terminal and the HMD device.

[1349] Step 13:

[1350] The HMD device displays the analysis results and feedback to the patient, who can see the feedback, "Your current blood pressure is 130 / 85 mmHg, which is within the normal range."

[1351] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1352] This invention is a remote medical consultation system that utilizes a chatbot with a generative AI model and an emotion engine to provide medical support to patients with mobility issues. The system receives patient input, generates appropriate responses, analyzes biometric data collected from a health management device, and provides the results to the patient. The emotion engine also recognizes the patient's emotional state and adjusts responses and feedback based on this emotional information, enabling more personalized support.

[1353] System configuration

[1354] This system mainly consists of the following parts:

[1355] 1. User Device:

[1356] This refers to a device that patients use to input medical consultations, such as a smartphone, tablet, or PC. The user terminal provides an interface for inputting medical consultation text, collecting data from health management devices, and recognizing emotions.

[1357] 2. Server:

[1358] This refers to a processing device that uses a generative AI model and emotion engine to analyze input information and biometric data received from a user device and provides responses and feedback based on that. The server includes a generative AI model, emotion engine, data analysis engine, and database.

[1359] 3. Health management equipment:

[1360] A health management device is a device that collects a patient's biological data, such as a fitness tracker, blood pressure monitor, or heart rate monitor. The health management device transmits the collected data to a server via a user terminal.

[1361] System Operation

[1362] The specific flow of operation of this system is shown below.

[1363] Medical consultation handling

[1364] 1. User Input:

[1365] The patient inputs a medical inquiry through the chat interface of the user terminal. For example, the patient may input, "I've been having frequent headaches recently."

[1366] 2. Data transmission:

[1367] The terminal transmits the user's input information to the server.

[1368] 3. Emotion recognition:

[1369] The server inputs the received input information into an emotion engine to recognize the patient's emotional state (e.g., anxiety, tension, relief, etc.).

[1370] 4. Response Generation:

[1371] The server inputs the input information, along with the emotional information recognized by the emotion engine, into the generative AI model. The generative AI model then uses natural language processing technology to generate the optimal response. For example, it might generate a response such as, "Possible causes of your headache include stress, lack of sleep, or other health conditions. If you are concerned, consult your doctor."

[1372] 5. Send Response:

[1373] The server sends the generated response to the user terminal.

[1374] 6. User Verification:

[1375] The terminal displays the received response to the user, who can then review the generated response and re-enter the question if necessary.

[1376] Health data management

[1377] 1. Data Collection:

[1378] The health management device collects biological data of the patient (e.g., activity level, heart rate, blood pressure, etc.).

[1379] 2. Data transmission:

[1380] The health management device transmits the collected data to the user terminal, which then periodically transmits this data to the server.

[1381] 3. Data reception:

[1382] The server receives the biometric data transmitted from the user terminal.

[1383] 4. Data Analysis:

[1384] The server analyzes the received biometric data using a data analysis engine.

[1385] 5. Emotional reflection:

[1386] The server reflects the patient's emotional information recognized by the emotion engine in the analysis results and generates feedback, such as "Your current blood pressure is 130 / 85 mmHg, which is within the normal range. Please continue to take appropriate health care measures."

[1387] 6. Send results:

[1388] The server transmits the generated feedback to the user terminal.

[1389] 7. User Feedback:

[1390] The device displays the analysis results to the patient, who then checks the feedback and initiates medical consultation if any abnormalities are detected.

[1391] Specific examples

[1392] As a concrete example, consider the following scenario.

[1393] Scenario 1: Headache consultation

[1394] A user types into a chat interface, "I've been having a lot of headaches lately."

[1395] The server uses an emotion engine to recognize "anxiety" from the user's input.

[1396] The server uses a generative AI model to generate a response such as, "Possible causes of headaches include stress, lack of sleep, and other health conditions. If you are concerned, consult your doctor."

[1397] The user terminal displays this response to the user.

[1398] Scenario 2: Blood Pressure Management

[1399] The health management device measures the user's blood pressure to be 130 / 85 mmHg.

[1400] The data is transmitted to the server via the user terminal.

[1401] The server analyzes the data using a data analysis engine, and then recognizes the feeling of "security" using an emotion engine.

[1402] Generate feedback such as "Your current blood pressure is 130 / 85 mmHg, which is within the normal range. Please continue to take appropriate care of your health."

[1403] The user terminal displays this feedback to the user.

[1404] Through this series of processes, the system provides an environment where even patients who have difficulty moving around can receive medical consultations and health management with peace of mind.

[1405] The processing flow will be explained below.

[1406] Medical consultation handling

[1407] Step 1: User Input

[1408] The user inputs a medical consultation request via a chat interface on the user terminal. For example, the user may input "I've been having frequent headaches recently."

[1409] Step 2: Send data

[1410] The terminal transmits the user's input information to the server.

[1411] Step 3: Emotion Recognition

[1412] The server inputs the received input information into an emotion engine to recognize the patient's emotional state (e.g., anxiety, tension, relief, etc.).

[1413] Step 4: Response Generation

[1414] The server inputs the input information, along with the emotions recognized by the emotion engine, into the generative AI model. The generative AI model then uses natural language processing techniques to generate the optimal response. For example, it might generate a response such as, "Possible causes of your headache include stress, lack of sleep, or other health conditions. If you are concerned, consult your doctor."

[1415] Step 5: Send response

[1416] The server sends the generated response to the user terminal.

[1417] Step 6: User Verification

[1418] The terminal displays the received response to the user, who can then review the generated response and re-enter the question if necessary.

[1419] Health data management

[1420] Step 1: Data collection

[1421] The health management device collects biological data of the patient (e.g., activity level, heart rate, blood pressure, etc.).

[1422] Step 2: Send data

[1423] The health management device transmits the collected data to the user terminal, which then periodically transmits this data to the server.

[1424] Step 3: Receiving data

[1425] The server receives the biometric data transmitted from the user terminal.

[1426] Step 4: Data analysis

[1427] The server analyzes the received biometric data using a data analysis engine.

[1428] Step 5: Emotional reflection

[1429] The server reflects the patient's emotional information recognized by the emotion engine in the analysis results and generates feedback, such as "Your current blood pressure is 130 / 85 mmHg, which is within the normal range. Please continue to take appropriate health care measures."

[1430] Step 6: Send results

[1431] The server transmits the generated feedback to the user terminal.

[1432] Step 7: User Feedback

[1433] The device displays the analysis results to the patient, who then checks the feedback and initiates medical consultation if any abnormalities are detected.

[1434] These are the specific processing steps. This system allows patients to receive medical consultations and manage their health from the comfort of their own homes or nursing homes.

[1435] Example 2

[1436] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1437] The present invention relates to a telemedicine system that allows patients with mobility issues to receive medical consultations and health management from the comfort of their own homes. Conventional telemedicine systems often provide mechanical responses without considering the patient's current emotional state, which can lead to unsatisfactory results for the patient. To solve this problem, a more personalized response that also takes the patient's emotional state into account is needed.

[1438] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1439] In this invention, the server includes means for receiving input information from a patient, means for generating a response based on the received input information using a generative AI model, means for receiving biometric data of the patient from a health management device, means for recognizing the emotional state of the patient using an emotion engine and reflecting the recognized emotional state in generating the response, and means for reflecting the emotional state of the patient in feedback generated based on the analyzed biometric data, thereby making it possible to provide personalized responses and feedback to patients that take emotions into consideration.

[1440] The "means for receiving patient input information" is a mechanism for transmitting text data and questions input by the patient via a user terminal to a server, and for the server to receive the information.

[1441] "Means for generating a response based on received input information using a generative AI model" refers to a mechanism for inputting received input information into a generative AI model and automatically generating an optimal response using natural language processing technology.

[1442] The "means for receiving patient biometric data from a health management device" is a mechanism by which the server receives biometric data sent from a health management device such as a fitness tracker or blood pressure monitor.

[1443] "Means for recognizing the emotional state of the patient using an emotion engine and reflecting the recognized emotional state in the generation of the response" refers to a mechanism for inputting received input information into an emotion engine, extracting the user's emotional state (e.g., anxiety, relief, etc.), and reflecting that emotional information in the response generation process via a generative AI model.

[1444] The "means for reflecting the patient's emotional state in the feedback generated based on the analyzed biometric data" is a mechanism for analyzing the received biometric data using a data analysis engine, integrating the patient's emotional state recognized by the emotion engine, and adjusting the feedback message based on the analysis results to provide to the patient.

[1445] This invention is a remote medical consultation system that utilizes a chatbot with a generative AI model and an emotion engine to provide medical support to patients with mobility issues. The system receives patient input and generates appropriate responses. It also has the ability to analyze biometric data collected from health management devices and provide the results to the patient. Furthermore, the emotion engine recognizes the patient's emotional state and adjusts responses and feedback based on this emotional information, thereby achieving more personalized support.

[1446] System configuration

[1447] This system mainly consists of the following parts:

[1448] 1. User Device:

[1449] This is a device for patients to input medical consultations, and can be a smartphone, tablet, PC, etc. The user terminal provides an interface for inputting medical consultation text, collecting data from health management devices, and recognizing emotions.

[1450] 2. Server:

[1451] This refers to a processing device that uses a generative AI model and emotion engine to analyze input information and biometric data received from a user device and generate responses and feedback based on that. The server includes a generative AI model, emotion engine, data analysis engine, and database.

[1452] 3. Health management equipment:

[1453] These are devices that collect patient biometric data, such as fitness trackers, blood pressure monitors, and heart rate monitors. Health management devices send the collected data to a server via a user terminal.

[1454] Medical consultation handling

[1455] Specific examples

[1456] 1. Receiving user input

[1457] A user inputs a medical consultation request via a chat interface on a smartphone or PC. For example, the user might input, "I've been having frequent headaches recently."

[1458] 2. Data Transmission

[1459] The terminal sends the user's input information to the server using the HTTPS protocol.

[1460] 3. Emotion Recognition

[1461] The server inputs the received text data into an emotion engine to recognize the user's emotional state, for example, extracting emotions such as anxiety or tension.

[1462] 4. Response Generation

[1463] The server inputs text data, including emotional information, into the generative AI model. The generative AI model uses natural language processing techniques to generate an appropriate response to the user's input. For example, it might generate a response like, "Possible causes of headaches include stress and lack of sleep. If you're concerned, consult a doctor."

[1464] 5. Sending the Response

[1465] The server sends the generated response to the user terminal.

[1466] 6. User Display

[1467] The terminal displays the received response to the user, who can then check the displayed response and re-enter the question if necessary.

[1468] Health data management

[1469] Specific examples

[1470] 1. Data Collection

[1471] Health monitoring devices (such as fitness trackers and blood pressure monitors) collect patient biometric data. For example, a blood pressure monitor measures a blood pressure of 130 / 85 mmHg.

[1472] 2. Data Transmission

[1473] Health management devices send collected biometric data to a user's device, which then periodically transmits the data to a server, often via Bluetooth or Wi-Fi.

[1474] 3. Data Reception

[1475] The server receives the biometric data sent from the user terminal and stores it in a database.

[1476] 4. Data Analysis

[1477] The server then analyzes the received biometric data using a data analysis engine, which uses statistical analysis and machine learning algorithms.

[1478] 5. Emotional reflection

[1479] The server reflects the user's emotional information recognized by the emotion engine in the analysis results and generates feedback, such as a message like "Your current blood pressure is 130 / 85 mmHg, which is within the normal range. Please continue to take care of your health."

[1480] 6. Send results

[1481] The server transmits the generated feedback to the user terminal.

[1482] 7. User Presentation

[1483] The device displays the feedback sent from the server to the user, who then checks the analysis results and begins medical consultation if any abnormalities are detected.

[1484] Prompt Sentence Examples

[1485] Prompt example 1: Medical consultation

[1486] When a patient types, "I've been having frequent headaches lately," use an emotion engine to recognize the emotion and a generative AI model to generate the optimal response.

[1487] Prompt example 2: Health data management

[1488] When the patient's blood pressure is measured at 130 / 85 mmHg and the emotion engine recognizes relief, generate appropriate feedback.

[1489] In this way, this system uses an emotion engine and generative AI model to provide advanced remote medical support to patients, creating an environment where even patients with difficulty traveling can receive medical consultations and manage their health with peace of mind.

[1490] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1491] Medical consultation handling

[1492] Step 1: User inputs medical consultation

[1493] A user inputs a medical consultation through a chat interface, for example, inputting the text "I've been having frequent headaches lately."

[1494] Input: User text input

[1495] Output: The text entered

[1496] Step 2: Send text data

[1497] The device sends the entered text data to the server using the HTTPS protocol.

[1498] Input: Text entered by the user

[1499] Output: Text data sent to the server

[1500] Step 3: Performing emotion recognition

[1501] The server inputs the received text data into an emotion engine to recognize the patient's emotional state, for example, by extracting emotions such as "anxiety" or "tension."

[1502] Input: Received text data

[1503] Output: Recognized emotion information (e.g., "anxiety")

[1504] Step 4: Generate a response

[1505] The server inputs text data based on the emotional information into the generative AI model, which then uses natural language processing technology to generate the optimal response. For example, the model might generate a response such as, "Possible causes of headaches include stress and lack of sleep. If you're concerned, consult a doctor."

[1506] Input: Text data and emotion information

[1507] Output: The generated response

[1508] Step 5: Sending a Response

[1509] The server sends the generated response to the user's terminal using the HTTPS protocol.

[1510] Input: The generated response

[1511] Output: Response sent to the user's device

[1512] Step 6: View the response

[1513] The terminal displays the received response to the user, who can then review the displayed response and re-enter the question if necessary.

[1514] Input: Response received from the server

[1515] Output: The response displayed to the user

[1516] Health data management

[1517] Step 1: Collect biometric data

[1518] Health management devices collect patient biometric data, for example, a blood pressure monitor measures "130 / 85 mmHg."

[1519] Input: Patient biometric data (e.g. blood pressure)

[1520] Output: Measured biometric data

[1521] Step 2: Sending data

[1522] The health management device transmits the collected biometric data to the user's terminal, which then transmits the data to a server via Bluetooth or Wi-Fi.

[1523] Input: Measured biometric data

[1524] Output: Biometric data sent to the server

[1525] Step 3: Receiving the data

[1526] The server receives the biometric data sent from the user terminal and stores it in a database.

[1527] Input: Data sent from the user's device

[1528] Output: Stored biometric data

[1529] Step 4: Analyze the data

[1530] The server then analyzes the received biometric data using a data analysis engine, which uses statistical analysis and machine learning algorithms.

[1531] Input: Stored biometric data

[1532] Output: Analysis results

[1533] Step 5: Reflecting emotional information

[1534] The server then incorporates the emotional information recognized by the emotion engine into the analysis results and generates final feedback, such as a message saying, "Your current blood pressure is 130 / 85 mmHg, which is within the normal range. Please continue to take care of your health."

[1535] Input: Analysis results and emotion information

[1536] Output: Final feedback message

[1537] Step 6: Sending the results

[1538] The server sends the generated feedback to the user terminal using the HTTPS protocol.

[1539] Input: Final feedback message

[1540] Output: Feedback message sent to the user's device

[1541] Step 7: View your feedback

[1542] The terminal displays the feedback sent from the server to the user, who can check the feedback and start another medical consultation if there is any abnormality.

[1543] Input: Feedback message received from the server

[1544] Output: Feedback displayed to the user

[1545] (Application example 2)

[1546] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1547] In today's world, patients with mobility issues have limited access to medical support, making it particularly difficult for those living in remote areas to receive appropriate medical services. Furthermore, traditional telemedicine systems typically provide responses and feedback without considering the patient's emotional state, making it difficult to provide personalized, optimal support. This can lead to a lack of reassurance and trust for patients, resulting in a decline in the quality of medical consultations.

[1548] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving patient input information, means for generating a response based on the received input information using a generative AI model, means for receiving the patient's biometric data from the health management device, means for recognizing the patient's emotional state using an emotion engine, and means for adjusting the response based on the recognized emotional state. This not only enables patients with limited mobility to receive medical consultations remotely, but also provides personalized responses through emotion recognition, thereby providing patients with an appropriate sense of security and trust.

[1549] "Patient input information" is text data entered by patients as questions or messages regarding medical consultations or health care.

[1550] A "generative AI model" is an artificial intelligence algorithm used to generate natural language responses based on large datasets.

[1551] A "health management device" is a device that measures and collects a patient's biometric data, such as a fitness tracker, blood pressure monitor, or heart rate monitor.

[1552] "Biometric data" refers to data that indicates physical indicators such as a patient's heart rate, blood pressure, and activity level.

[1553] The "emotion engine" is an artificial intelligence algorithm that analyzes the patient's input information and recognizes their emotional state (e.g., anxiety, relief, tension, etc.).

[1554] A "means for tailoring responses" is a system that customizes the responses generated based on the emotional state of an individual patient.

[1555] "Natural language processing technology" is an information processing technology for analyzing, understanding, and generating human language.

[1556] MODE FOR CARRYING OUT THE INVENTION

[1557] System configuration

[1558] This invention is a remote medical consultation system for providing medical support to patients with mobility issues. The system receives patient input, generates responses using a generative AI model, and uses an emotion engine to recognize the patient's emotional state and adjust responses based on that emotional information. The system primarily consists of the following hardware and software:

[1559] 1. User Device

[1560] Smartphones, tablets, computers, etc. are used.

[1561] It is an interface through which patients input their medical consultations, and is responsible for collecting biometric data from health management devices and sending it to the server.

[1562] 2. Generative AI Models

[1563] It is an algorithm that uses natural language processing technology to generate optimal responses based on the patient's input information received.

[1564] Model name example: "text-davinci-003"

[1565] 3. Emotion Engine

[1566] It includes algorithms for analyzing patient input and recognizing their emotional state.

[1567] Example of technology used: Emotion recognition model using deep learning

[1568] 4. Health management device

[1569] These are devices that measure a patient's biometric data, such as fitness trackers, blood pressure monitors, and heart rate monitors.

[1570] System Operation

[1571] Medical consultation handling

[1572] 1. A patient inputs a medical consultation through a user terminal. For example, the patient inputs, "I've been having frequent headaches recently."

[1573] 2. The device sends this input information to the server.

[1574] 3. The server inputs this input information into an emotion engine to recognize the patient's emotional state (e.g., anxiety).

[1575] 4. The server passes the recognized emotion information to a generative AI model to generate an optimal response.

[1576] Example generated response: "Possible causes of headaches include stress, lack of sleep, or other health conditions. If you are concerned, consult your doctor."

[1577] 5. The server sends the generated response to the user terminal, which displays it to the patient.

[1578] Health data management

[1579] 1. Collect patient biometric data (e.g., heart rate, blood pressure) using health monitoring devices.

[1580] 2. The user terminal periodically sends the collected data to the server.

[1581] 3. The server receives the biometric data and analyzes it using a data analysis engine.

[1582] 4. The generative AI model provides feedback based on the emotional information recognized by the emotion engine.

[1583] Example of generated feedback: "Your current blood pressure is 130 / 85 mmHg, which is within the normal range. Please continue to take good care of your health."

[1584] 5. The server sends this feedback to the user terminal, which displays the analysis results to the patient.

[1585] Prompt Sentence Examples

[1586] For medical consultations

[1587] User Input: I've been having frequent headaches lately.

[1588] Emotion: Anxiety

[1589] Appropriate response: Headaches can be caused by stress, lack of sleep, or other health conditions. If you're concerned, consult your doctor.

[1590] In the case of biometric data

[1591] Example of vital data: {"heart_rate": 75, "blood_pressure": "130 / 85"}

[1592] Health feedback: Health is normal.

[1593] In this way, an environment is provided where even patients who have difficulty moving around can receive medical consultations and health management remotely with peace of mind.

[1594] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1595] Program processing steps

[1596] Step 1:

[1597] A user inputs a medical consultation request using a smartphone. For example, the user might input, "I've been having frequent headaches recently." This input information is saved as text data on the device.

[1598] Input: User's medical consultation text

[1599] Output: Medical consultation in text data format

[1600] Step 2:

[1601] The terminal transmits the stored medical consultation text of the user to the server, where the terminal makes a request using internet communication.

[1602] Input: Medical consultation in text format

[1603] Output: Medical consultation data sent to the server

[1604] Step 3:

[1605] The server inputs the received medical consultation data into the emotion engine, which uses a deep learning model to analyze the patient's emotional state from the input text. For example, it can recognize an emotional state such as "anxiety."

[1606] Input: Medical consultation data

[1607] Output: Perceived emotional state (e.g., anxiety)

[1608] Step 4:

[1609] The server passes the emotional state to a generative AI model, which generates a response. The generative AI model uses natural language processing technology to generate the optimal response based on the medical consultation and emotional information. For example, it might generate text like, "Possible causes of your headache include stress, lack of sleep, or other health conditions. If you're concerned, consult a doctor."

[1610] Input: medical consultation data, recognized emotional state

[1611] Output: Generated response

[1612] Step 5:

[1613] The server sends the generated response to the user's terminal, which receives the response and displays it to the user.

[1614] Input: Generated response sentence

[1615] Output: Response text displayed on the terminal

[1616] Step 6:

[1617] The health management device is used to collect biometric data of the user, such as measuring heart rate and blood pressure, and this data is transmitted to the terminal.

[1618] Input: Biometric data from health management device

[1619] Output: Biometric data stored on the device

[1620] Step 7:

[1621] The device transmits the stored biometric data to a server, and the device is programmed to periodically transmit this data to the server.

[1622] Input: Stored biometric data

[1623] Output: Biometric data sent to the server

[1624] Step 8:

[1625] The server analyzes the received biometric data using a data analysis engine. For example, if the heart rate is over 100, it is determined to be a "high heart rate."

[1626] Input: Biometric data

[1627] Output: Analysis results (e.g. high heart rate)

[1628] Step 9:

[1629] The server reflects the patient's emotional information recognized by the emotion engine in the analysis results, and the generative AI model provides feedback, such as, "Your current blood pressure is 130 / 85 mmHg, which is within the normal range. Please continue to take appropriate health care measures."

[1630] Input: Analysis results, recognized emotional state

[1631] Output: Generated feedback statement

[1632] Step 10:

[1633] The server sends the generated feedback sentence to the user terminal, which displays the feedback to the user.

[1634] Input: Generated feedback sentence

[1635] Output: Feedback text displayed on the terminal

[1636] In this way, patients can receive medical consultations and health management remotely through each step of the system. The combination of generative AI models and emotion engines makes it possible to provide personalized responses and feedback.

[1637] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1638] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1639] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1640] [Fourth embodiment]

[1641] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1642] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1643] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1644] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1645] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1646] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1647] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1648] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1649] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1650] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1651] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1652] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1653] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1654] This invention is a remote medical consultation system that utilizes a chatbot with a generative AI model to provide medical support to patients with mobility issues. The system receives patient input, generates appropriate responses, analyzes biometric data collected from health management devices, and provides the results to the patient.

[1655] System configuration

[1656] This system mainly consists of the following parts:

[1657] 1. User Device:

[1658] A device used by patients to input medical consultations, such as a smartphone, tablet, or PC. The user terminal provides an interface for inputting medical consultation text and collecting data from health management devices.

[1659] 2. Server:

[1660] This refers to a processing device that uses a generative AI model to generate responses based on input information received from a user device, and analyzes biometric data from a health management device to provide results. The server includes a generative AI model, a data analysis engine, and a database.

[1661] 3. Health management equipment:

[1662] A health management device is a device that collects a patient's biological data, such as a fitness tracker, blood pressure monitor, or heart rate monitor. The health management device transmits the collected data to a server via a user terminal.

[1663] System Operation

[1664] The specific flow of operation of this system is shown below.

[1665] Medical consultation handling

[1666] 1. User Input:

[1667] The patient inputs a medical inquiry through the chat interface of the user terminal. For example, the patient may input, "I've been having frequent headaches recently."

[1668] 2. Data transmission:

[1669] The user terminal transmits this input information to the server.

[1670] 3. Response generation:

[1671] The server inputs the received input information into the generative AI model, which then uses natural language processing techniques to generate the optimal response. For example, it might generate a response like, "Possible causes of your headache include stress, lack of sleep, or other health conditions. Consult your doctor for a detailed diagnosis."

[1672] 4. Send response:

[1673] The server generates a response and sends it to the user terminal.

[1674] 5. User Verification:

[1675] The user terminal displays this response to the patient, who can review the generated response and re-enter any further questions.

[1676] Health data management

[1677] 1. Data Collection:

[1678] The health management device collects the patient's biometric data (e.g., activity level, heart rate, blood pressure, etc.).

[1679] 2. Data transmission:

[1680] The health management device transmits the collected data to the user terminal, which then transmits the data to the server.

[1681] 3. Data Analysis:

[1682] The server analyzes the received biometric data using a data analysis engine, and the analysis results are converted into natural language using a generative AI model to generate information in a format that is easy for the patient to understand.

[1683] 4. Send results:

[1684] The server transmits the analysis results to the user terminal.

[1685] 5. User Feedback:

[1686] The user device displays the analysis results to the patient. For example, the patient may receive feedback such as, "Your current blood pressure is 130 / 85 mmHg, which is within the normal range. Please continue to take appropriate health care measures."

[1687] Specific examples

[1688] As a concrete example, consider the following scenario.

[1689] Scenario 1: Headache consultation

[1690] A user types into a chat interface, "I've been having a lot of headaches lately."

[1691] The server uses a generative AI model to generate a response such as, "Possible causes of your headache include stress, lack of sleep, or other health conditions."

[1692] The user terminal displays this response to the user.

[1693] Scenario 2: Blood Pressure Management

[1694] The health management device measures the user's blood pressure to be 130 / 85 mmHg.

[1695] The data is transmitted to the server via the user terminal.

[1696] The server analyzes the data and generates feedback such as, "Your current blood pressure is within the normal range."

[1697] The user terminal displays this feedback to the user.

[1698] Through this series of processes, the system provides an environment where even patients who have difficulty moving around can receive medical consultations and health management with peace of mind.

[1699] The processing flow will be explained below.

[1700] Medical consultation handling

[1701] Step 1: User Input

[1702] The user inputs a medical consultation request via a chat interface on the user terminal. For example, the user may input "I've been having frequent headaches recently."

[1703] Step 2: Send data

[1704] The terminal transmits the text of the medical consultation sent by the user to the server.

[1705] Step 3: Response Generation

[1706] The server inputs the received medical consultation text into a generative AI model.

[1707] Step 4: Natural Language Processing

[1708] The generative AI model uses natural language processing techniques to analyze the input text and generate an optimal response, such as, "Your headache may be due to stress, lack of sleep, or other health conditions. Please consult your doctor for a detailed diagnosis."

[1709] Step 5: Send response

[1710] The server sends the generated response to the user terminal.

[1711] Step 6: User Verification

[1712] The terminal displays the received response to the user, who can then review the generated response and re-enter the question if necessary.

[1713] Health data management

[1714] Step 1: Data collection

[1715] The health management device collects biological data of the patient (e.g., activity level, heart rate, blood pressure, etc.).

[1716] Step 2: Send data

[1717] The health management device transmits the collected data to the user terminal, which then periodically transmits this data to the server.

[1718] Step 3: Receiving data

[1719] The server receives the biometric data transmitted from the user terminal.

[1720] Step 4: Data analysis

[1721] The server analyzes the received biometric data using a data analysis engine.

[1722] Step 5: Interpretation by generative AI models

[1723] The server uses a generative AI model to interpret the results obtained by the data analysis engine in natural language. For example, it generates feedback such as, "Your current blood pressure is 130 / 85 mmHg, which is within the normal range. Please continue to take appropriate measures to manage your health."

[1724] Step 6: Send results

[1725] The server transmits the generated feedback to the user terminal.

[1726] Step 7: User Feedback

[1727] The device displays the analysis results to the user, who then checks the feedback and initiates medical consultation if any abnormalities are detected.

[1728] These are the specific processing steps. This system allows patients to receive medical consultations and manage their health from the comfort of their own homes or nursing homes.

[1729] Example 1

[1730] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1731] In remote medical consultation systems, real-time assessment of health status and accurate feedback based on that assessment are essential for patients with mobility issues to receive appropriate medical support. However, existing systems lack the means to effectively integrate and analyze patient input information and biometric data from health management devices, and provide rapid and accurate feedback to users. Furthermore, the lack of smooth intercommunication makes it difficult to provide a user-friendly service. Therefore, there is a need for an effective system that can appropriately integrate input information and biometric data, generate responses in natural language, and provide accurate feedback.

[1732] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1733] In this invention, the server

[1734] means for generating a response based on the received input information using a generative AI model;

[1735] means for receiving patient biometric data from a health management device;

[1736] means for transmitting the biometric data to a server using a user terminal;

[1737] means for inputting the input information and biometric data as prompts to a generative AI model;

[1738] means for generating the response and analysis results in natural language and providing feedback to the patient;

[1739] means for analyzing biometric data from the health management device and detecting abnormal values;

[1740] a means for determining whether the level is within normal limits;

[1741] means for displaying said responses and feedback on a user terminal;

[1742] This allows for efficient and accurate analysis of patient input and biometric data, and provides appropriate responses and feedback in real time.

[1743] "Patient" refers to a person who receives medical consultation or health management services using the remote medical consultation system.

[1744] "Input information" refers to text data such as symptoms and questions that a patient provides to the system via a user terminal.

[1745] A "generative AI model" refers to artificial intelligence that generates responses in natural language based on input data.

[1746] "Response" refers to the answer or advice to a medical consultation that the generative AI model generates based on the patient's input information.

[1747] "Health management devices" are devices used to measure and collect patients' biometric data, and generally refer to devices such as fitness trackers, heart rate monitors, and blood pressure monitors.

[1748] "Biometric data" refers to data that indicates the patient's health condition, such as blood pressure, heart rate, and activity level, collected by a health management device.

[1749] The "analysis result" refers to information generated as a result of the server analyzing the biometric data received from the health management device.

[1750] "User terminal" refers to a device used by a patient to access the system, send input information, and receive feedback, such as a smartphone, tablet, or PC.

[1751] A "server" is the central processing unit of the system, which uses a generative AI model to generate responses based on input information, analyzes biometric data, and provides the results.

[1752] "Feedback" refers to information provided to the patient regarding the analysis results and generated responses.

[1753] This invention is a remote medical consultation system that utilizes a generative AI model to provide medical support to patients who have difficulty traveling. The main components of the system are a user terminal for patients to input medical consultation information, a server that processes the input information and biometric data, and a health management device that collects the patient's biometric data.

[1754] System configuration

[1755] User terminal

[1756] The user terminal is a device such as a smartphone, tablet, or PC that provides an interface for patients to input medical consultation information and transmit biometric data. The terminal communicates with the server via the Internet.

[1757] server

[1758] The server uses a generative AI model to generate responses based on input information received from the user device, analyzes biometric data received from the health management device, and provides the results to the patient. The server includes the following main components:

[1759] Generative AI models (e.g., natural language processing models such as GPT-3)

[1760] Data Analysis Engine

[1761] Database

[1762] health management device

[1763] Health management devices are devices for collecting patient biometric data, and examples include fitness trackers, blood pressure monitors, heart rate monitors, etc. The devices transmit the collected data to a server via a user terminal.

[1764] System Operation

[1765] Medical consultation handling

[1766] A patient inputs a medical inquiry through a chat interface on the user's device. For example, they might input, "I've been having frequent headaches lately." The user's device then sends this input information to the server. The server then inputs the received input information into a generative AI model and generates an optimal response using natural language processing technology. The generated response is then sent from the server to the user's device and displayed to the user. The user can review the generated response and re-enter any further questions they may have.

[1767] Health data management

[1768] The health management device collects the patient's biometric data (e.g., activity level, heart rate, blood pressure, etc.). The collected data is sent to the user's terminal, which then sends it to the server. The server analyzes the received biometric data using a data analysis engine and converts it into natural language using a generative AI model. The analysis results are sent from the server to the user's terminal and displayed to the user. For example, feedback such as "Your current blood pressure is 130 / 85 mmHg, which is within the normal range" is generated.

[1769] Specific examples

[1770] Scenario 1: Headache consultation

[1771] The patient types into the chat interface, "I've been having frequent headaches lately."

[1772] The user terminal transmits this input content to the server.

[1773] The server uses a generative AI model to generate a response such as, "Possible causes of your headache include stress, lack of sleep, or other health conditions."

[1774] The user terminal displays this response to the user.

[1775] Scenario 2: Blood Pressure Management

[1776] The health management device measures the user's blood pressure to be 130 / 85 mmHg.

[1777] The data is transmitted to the server via the user terminal.

[1778] The server analyzes the data and generates feedback such as, "Your current blood pressure is within the normal range."

[1779] The user terminal displays this feedback to the user.

[1780] This system allows patients with mobility issues to receive medical consultations and health management in real time. By utilizing generative AI models and data analysis engines, appropriate and prompt responses to patients can be provided, improving the quality of medical care.

[1781] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1782] Medical consultation handling

[1783] Step 1:

[1784] The user inputs a medical consultation request via a chat interface on the user terminal. The input is text data including "I've been having frequent headaches recently." This input data becomes the input for the next step.

[1785] Step 2:

[1786] The user device sends the entered consultation details to the server via the Internet. The text data is transferred to the server in JSON format. The input is the text data entered in step 1, and the output is the data sent to the server.

[1787] Step 3:

[1788] The server analyzes the received consultation content and inputs it as a prompt to the generative AI model. Here, the generative AI model (e.g., GPT-3) generates the optimal response in natural language based on the input data. The input is the received text data, and the output is a response in natural language.

[1789] Step 4:

[1790] The server then sends the generated response back to the user's device. This response is in text format and is displayed on the user's device. The input is the response data generated by the generative AI model, and the output is the data sent to the user's device.

[1791] Step 5:

[1792] The user terminal displays the received response on the chat interface. The user can then review it and decide whether they understand the content or need to ask again. The input is the response data sent from the server, and the output is the user's confirmation action.

[1793] Health data management

[1794] Step 1:

[1795] The health management device collects the user's biometric data (e.g., blood pressure and heart rate). The data is collected in real time and set to be sent periodically. The input is the user's biometric data, and the output is the accumulation of collected data.

[1796] Step 2:

[1797] The health management device transmits the collected biometric data to the user terminal, which then transmits the data to the server. The input is the collected biometric data, and the output is the data transmitted to the server.

[1798] Step 3:

[1799] The server analyzes the received biometric data using a data analysis engine. Specifically, it checks whether the data is within the normal range and detects abnormal values. The analysis results are then input back into the generative AI model, which generates feedback in natural language. The input is the received biometric data, and the output is the generated analysis results.

[1800] Step 4:

[1801] The server sends the analysis results to the user's device. Specific feedback includes, for example, "Your current blood pressure is 130 / 85 mmHg, which is within the normal range." The input is the analysis results, and the output is the data sent to the user's device.

[1802] Step 5:

[1803] The user terminal displays the received feedback. The user can check their health status and contact a medical institution if necessary. The input is the feedback data sent from the server, and the output is the user's confirmation action.

[1804] Through this series of steps, the system enables real-time health management and appropriate medical consultation for patients. The combination of generative AI models and a data analysis engine enables fast and accurate responses and feedback.

[1805] (Application example 1)

[1806] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1807] Conventional telemedicine systems allow patients to receive medical consultations even when travel is difficult, but they are limited in their ability to provide both medical consultations and health management in real time. Furthermore, text-based interfaces alone are insufficient to provide a clear understanding of the patient. This creates the challenge of making it difficult for patients to feel at ease. Furthermore, there is a lack of a way to provide the results of biometric data analysis in an intuitive, easy-to-understand format.

[1808] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1809] In this invention, the server includes a means for receiving patient input information, a means for generating a response based on the received input information using a generative AI model, a means for receiving the patient's biometric data from a health management device, a means for analyzing the received biometric data and providing the results to the patient, and a means for performing remote medical consultations and health management in real time using a virtual reality device. This allows patients to receive real-time medical consultations and health management in a virtual space. Providing real-time medical support can increase patients' sense of security and promote the efficiency and understanding of health management.

[1810] "Patient input information" refers to text data of medical consultation details and questions entered by the patient via a chat interface or the like.

[1811] A "generative AI model" is an algorithm or program that uses machine learning and natural language processing techniques to generate responses to input patient questions and data.

[1812] "Health management devices" are devices used to collect patient biometric data, including fitness trackers, blood pressure monitors, and heart rate monitors.

[1813] "Biometric data" refers to data that indicates the patient's physical condition, and includes information such as activity level, heart rate, and blood pressure.

[1814] A "virtual reality device" is a device that allows patients to experience images and information in a virtual space, and includes head-mounted displays.

[1815] "Telemedical consultation" is a service that allows patients to receive medical consultation using communication technology without having to go to a medical institution in person.

[1816] "Data analysis" is the process of analyzing collected biometric data and interpreting it as meaningful information.

[1817] "Real-time" refers to instantaneous data processing and response, with little or no time delay.

[1818] "Natural language processing technology" is a technology that enables computers to understand the natural language used by humans on a daily basis and generate appropriate responses.

[1819] MODE FOR CARRYING OUT THE INVENTION

[1820] This invention is a system that utilizes a chatbot with a generative AI model and a virtual reality device to provide real-time remote medical consultations and health management for patients who have difficulty moving around. The system features a means for receiving patient input information and generating responses based on that information, and a means for receiving and analyzing biometric data from a health management device and providing the results to the patient.

[1821] System configuration

[1822] 1. User Device:

[1823] A device for patients to input medical consultations, such as a smartphone, tablet, or personal computer, provides a chat interface and an interface for collecting data from health management devices.

[1824] 2. Server:

[1825] The system uses a generative AI model to generate responses based on input information received from the user's device, analyzes biometric data from the health management device, and provides results. The server includes the generative AI model, a data analysis engine, and a database. The server can use cloud services such as AWS EC2 instances.

[1826] 3. Health management equipment:

[1827] A device that collects a patient's biometric data, such as a fitness tracker, blood pressure monitor, or heart rate monitor. The health management device transmits the collected data to a server via the user's terminal.

[1828] 4. Virtual reality devices:

[1829] A head-mounted display (HMD) is a device that allows patients to experience medical consultations and health management in a virtual space. Examples include Oculus Rift, HTC Vive, and Sony PlayStation VR.

[1830] System Operation

[1831] Medical consultation handling

[1832] 1. Patient Entry:

[1833] The user inputs a medical inquiry through the HMD's chat interface, for example, "I've been having frequent headaches lately."

[1834] 2. Data transmission:

[1835] The user terminal and the HMD device transmit this input information to the server.

[1836] 3. Response generation:

[1837] The server then inputs the received input information into a generative AI model (e.g., OpenAI's GPT-4), which uses natural language processing techniques to generate the optimal response. For example, it generates a response such as, "Possible causes of your headache include stress, lack of sleep, or other health conditions."

[1838] 4. Response sending and display:

[1839] The server sends the generated response to the user terminal and the HMD device, which displays the response to the user.

[1840] Health data management

[1841] 1. Data Collection:

[1842] The health management device collects the patient's biometric data (e.g., activity level, heart rate, blood pressure, etc.).

[1843] 2. Data transmission:

[1844] The health management device transmits the collected data to the user terminal, which then transmits the data to the server.

[1845] 3. Data Analysis:

[1846] The server analyzes the received biometric data using a data analysis engine and converts it into natural language using a generative AI model. For example, it generates feedback such as, "Your current blood pressure is 130 / 85 mmHg, which is within the normal range."

[1847] 4. Send and display results:

[1848] The server transmits the analysis results to the user terminal and the HMD device, which displays this feedback to the user.

[1849] Specific examples

[1850] Scenario: Health consultation

[1851] 1. Patient entry:

[1852] A user types into a chat interface, "I've been having a lot of headaches lately."

[1853] 2. Generative AI model prompt:

[1854] I've been having frequent headaches lately. What could be the cause?

[1855] 3. Example of generated response:

[1856] "Headaches can be caused by stress, lack of sleep, or other health conditions."

[1857] Scenario: Blood Pressure Management

[1858] 1. Data Collection:

[1859] The health management device measures the patient's blood pressure to be 130 / 85 mmHg.

[1860] 2. Data Analysis:

[1861] The server analyzes the data and generates feedback such as, "Your current blood pressure is within the normal range."

[1862] This system allows patients to efficiently receive real-time medical consultations and health management in a virtual space.

[1863] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1864] Step 1:

[1865] The patient wears a virtual reality device (HMD) and inputs a medical consultation request into the chat interface. The patient inputs, "I've been having frequent headaches recently."

[1866] Step 2:

[1867] The user terminal and the HMD device receive this input information and send it to the server, and the user terminal transfers the input text data to the server.

[1868] Step 3:

[1869] The server inputs the received input information into a generative AI model (e.g., OpenAI's GPT-4). The server passes the prompt sentence "I've been having frequent headaches lately. What could be the cause?" to the generative AI model and obtains its response.

[1870] Step 4:

[1871] The generative AI model uses natural language processing techniques to generate appropriate responses based on the input information, such as "Possible causes of headaches include stress, lack of sleep, or other health conditions" based on the prompt.

[1872] Step 5:

[1873] The server sends the generated response to the user terminal and the HMD device. The server then transfers the text data obtained from the generated AI model to the user terminal and the HMD device.

[1874] Step 6:

[1875] The HMD device displays this response to the patient, who can then review the generated response and ask further questions.

[1876] Step 7:

[1877] The health management device collects the patient's biological data (e.g., activity level, heart rate, blood pressure, etc.). For example, the health management device measures "130 / 85 mmHg" using a blood pressure monitor.

[1878] Step 8:

[1879] The health management device transmits the collected data to the user terminal. The health management device transfers the measured data to the user terminal.

[1880] Step 9:

[1881] The user terminal transmits the biometric data to the server, and the user terminal transfers the data received from the health management device to the server.

[1882] Step 10:

[1883] The server analyzes the received biometric data using a data analysis engine. For example, the server analyzes blood pressure data and generates feedback such as, "Your current blood pressure is within the normal range."

[1884] Step 11:

[1885] The server converts the analysis results into natural language using a generative AI model, and inputs the analysis results into the generative AI model to obtain easy-to-understand feedback text.

[1886] Step 12:

[1887] The server transmits the generated feedback to the user terminal and the HMD device. The server transfers the feedback text to the user terminal and the HMD device.

[1888] Step 13:

[1889] The HMD device displays the analysis results and feedback to the patient, who can see the feedback, "Your current blood pressure is 130 / 85 mmHg, which is within the normal range."

[1890] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1891] This invention is a remote medical consultation system that utilizes a chatbot with a generative AI model and an emotion engine to provide medical support to patients with mobility issues. The system receives patient input, generates appropriate responses, analyzes biometric data collected from a health management device, and provides the results to the patient. The emotion engine also recognizes the patient's emotional state and adjusts responses and feedback based on this emotional information, enabling more personalized support.

[1892] System configuration

[1893] This system mainly consists of the following parts:

[1894] 1. User Device:

[1895] This refers to a device that patients use to input medical consultations, such as a smartphone, tablet, or PC. The user terminal provides an interface for inputting medical consultation text, collecting data from health management devices, and recognizing emotions.

[1896] 2. Server:

[1897] This refers to a processing device that uses a generative AI model and emotion engine to analyze input information and biometric data received from a user device and provides responses and feedback based on that. The server includes a generative AI model, emotion engine, data analysis engine, and database.

[1898] 3. Health management equipment:

[1899] A health management device is a device that collects a patient's biological data, such as a fitness tracker, blood pressure monitor, or heart rate monitor. The health management device transmits the collected data to a server via a user terminal.

[1900] System Operation

[1901] The specific flow of operation of this system is shown below.

[1902] Medical consultation handling

[1903] 1. User Input:

[1904] The patient inputs a medical inquiry through the chat interface of the user terminal. For example, the patient may input, "I've been having frequent headaches recently."

[1905] 2. Data transmission:

[1906] The terminal transmits the user's input information to the server.

[1907] 3. Emotion recognition:

[1908] The server inputs the received input information into an emotion engine to recognize the patient's emotional state (e.g., anxiety, tension, relief, etc.).

[1909] 4. Response Generation:

[1910] The server inputs the input information, along with the emotional information recognized by the emotion engine, into the generative AI model. The generative AI model then uses natural language processing technology to generate the optimal response. For example, it might generate a response such as, "Possible causes of your headache include stress, lack of sleep, or other health conditions. If you are concerned, consult your doctor."

[1911] 5. Send Response:

[1912] The server sends the generated response to the user terminal.

[1913] 6. User Verification:

[1914] The terminal displays the received response to the user, who can then review the generated response and re-enter the question if necessary.

[1915] Health data management

[1916] 1. Data Collection:

[1917] The health management device collects biological data of the patient (e.g., activity level, heart rate, blood pressure, etc.).

[1918] 2. Data transmission:

[1919] The health management device transmits the collected data to the user terminal, which then periodically transmits this data to the server.

[1920] 3. Data reception:

[1921] The server receives the biometric data transmitted from the user terminal.

[1922] 4. Data Analysis:

[1923] The server analyzes the received biometric data using a data analysis engine.

[1924] 5. Emotional reflection:

[1925] The server reflects the patient's emotional information recognized by the emotion engine in the analysis results and generates feedback, such as "Your current blood pressure is 130 / 85 mmHg, which is within the normal range. Please continue to take appropriate health care measures."

[1926] 6. Send results:

[1927] The server transmits the generated feedback to the user terminal.

[1928] 7. User Feedback:

[1929] The device displays the analysis results to the patient, who then checks the feedback and initiates medical consultation if any abnormalities are detected.

[1930] Specific examples

[1931] As a concrete example, consider the following scenario.

[1932] Scenario 1: Headache consultation

[1933] A user types into a chat interface, "I've been having a lot of headaches lately."

[1934] The server uses an emotion engine to recognize "anxiety" from the user's input.

[1935] The server uses a generative AI model to generate a response such as, "Possible causes of headaches include stress, lack of sleep, and other health conditions. If you are concerned, consult your doctor."

[1936] The user terminal displays this response to the user.

[1937] Scenario 2: Blood Pressure Management

[1938] The health management device measures the user's blood pressure to be 130 / 85 mmHg.

[1939] The data is transmitted to the server via the user terminal.

[1940] The server analyzes the data using a data analysis engine, and then recognizes the feeling of "security" using an emotion engine.

[1941] Generate feedback such as "Your current blood pressure is 130 / 85 mmHg, which is within the normal range. Please continue to take appropriate care of your health."

[1942] The user terminal displays this feedback to the user.

[1943] Through this series of processes, the system provides an environment where even patients who have difficulty moving around can receive medical consultations and health management with peace of mind.

[1944] The processing flow will be explained below.

[1945] Medical consultation handling

[1946] Step 1: User Input

[1947] The user inputs a medical consultation request via a chat interface on the user terminal. For example, the user may input "I've been having frequent headaches recently."

[1948] Step 2: Send data

[1949] The terminal transmits the user's input information to the server.

[1950] Step 3: Emotion Recognition

[1951] The server inputs the received input information into an emotion engine to recognize the patient's emotional state (e.g., anxiety, tension, relief, etc.).

[1952] Step 4: Response Generation

[1953] The server inputs the input information, along with the emotions recognized by the emotion engine, into the generative AI model. The generative AI model then uses natural language processing techniques to generate the optimal response. For example, it might generate a response such as, "Possible causes of your headache include stress, lack of sleep, or other health conditions. If you are concerned, consult your doctor."

[1954] Step 5: Send response

[1955] The server sends the generated response to the user terminal.

[1956] Step 6: User Verification

[1957] The terminal displays the received response to the user, who can then review the generated response and re-enter the question if necessary.

[1958] Health data management

[1959] Step 1: Data collection

[1960] The health management device collects biological data of the patient (e.g., activity level, heart rate, blood pressure, etc.).

[1961] Step 2: Send data

[1962] The health management device transmits the collected data to the user terminal, which then periodically transmits this data to the server.

[1963] Step 3: Receiving data

[1964] The server receives the biometric data transmitted from the user terminal.

[1965] Step 4: Data analysis

[1966] The server analyzes the received biometric data using a data analysis engine.

[1967] Step 5: Emotional reflection

[1968] The server reflects the patient's emotional information recognized by the emotion engine in the analysis results and generates feedback, such as "Your current blood pressure is 130 / 85 mmHg, which is within the normal range. Please continue to take appropriate health care measures."

[1969] Step 6: Send results

[1970] The server transmits the generated feedback to the user terminal.

[1971] Step 7: User Feedback

[1972] The device displays the analysis results to the patient, who then checks the feedback and initiates medical consultation if any abnormalities are detected.

[1973] These are the specific processing steps. This system allows patients to receive medical consultations and manage their health from the comfort of their own homes or nursing homes.

[1974] Example 2

[1975] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1976] The present invention relates to a telemedicine system that allows patients with mobility issues to receive medical consultations and health management from the comfort of their own homes. Conventional telemedicine systems often provide mechanical responses without considering the patient's current emotional state, which can lead to unsatisfactory results for the patient. To solve this problem, a more personalized response that also takes the patient's emotional state into account is needed.

[1977] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1978] In this invention, the server includes means for receiving input information from a patient, means for generating a response based on the received input information using a generative AI model, means for receiving biometric data of the patient from a health management device, means for recognizing the emotional state of the patient using an emotion engine and reflecting the recognized emotional state in generating the response, and means for reflecting the emotional state of the patient in feedback generated based on the analyzed biometric data, thereby making it possible to provide personalized responses and feedback to patients that take emotions into consideration.

[1979] The "means for receiving patient input information" is a mechanism for transmitting text data and questions input by the patient via a user terminal to a server, and for the server to receive the information.

[1980] "Means for generating a response based on received input information using a generative AI model" refers to a mechanism for inputting received input information into a generative AI model and automatically generating an optimal response using natural language processing technology.

[1981] The "means for receiving patient biometric data from a health management device" is a mechanism by which the server receives biometric data sent from a health management device such as a fitness tracker or blood pressure monitor.

[1982] "Means for recognizing the emotional state of the patient using an emotion engine and reflecting the recognized emotional state in the generation of the response" refers to a mechanism for inputting received input information into an emotion engine, extracting the user's emotional state (e.g., anxiety, relief, etc.), and reflecting that emotional information in the response generation process via a generative AI model.

[1983] The "means for reflecting the patient's emotional state in the feedback generated based on the analyzed biometric data" is a mechanism for analyzing the received biometric data using a data analysis engine, integrating the patient's emotional state recognized by the emotion engine, and adjusting the feedback message based on the analysis results to provide to the patient.

[1984] This invention is a remote medical consultation system that utilizes a chatbot with a generative AI model and an emotion engine to provide medical support to patients with mobility issues. The system receives patient input and generates appropriate responses. It also has the ability to analyze biometric data collected from health management devices and provide the results to the patient. Furthermore, the emotion engine recognizes the patient's emotional state and adjusts responses and feedback based on this emotional information, thereby achieving more personalized support.

[1985] System configuration

[1986] This system mainly consists of the following parts:

[1987] 1. User Device:

[1988] This is a device for patients to input medical consultations, and can be a smartphone, tablet, PC, etc. The user terminal provides an interface for inputting medical consultation text, collecting data from health management devices, and recognizing emotions.

[1989] 2. Server:

[1990] This refers to a processing device that uses a generative AI model and emotion engine to analyze input information and biometric data received from a user device and generate responses and feedback based on that. The server includes a generative AI model, emotion engine, data analysis engine, and database.

[1991] 3. Health management equipment:

[1992] These are devices that collect patient biometric data, such as fitness trackers, blood pressure monitors, and heart rate monitors. Health management devices send the collected data to a server via a user terminal.

[1993] Medical consultation handling

[1994] Specific examples

[1995] 1. Receiving user input

[1996] A user inputs a medical consultation request via a chat interface on a smartphone or PC. For example, the user might input, "I've been having frequent headaches recently."

[1997] 2. Data Transmission

[1998] The terminal sends the user's input information to the server using the HTTPS protocol.

[1999] 3. Emotion Recognition

[2000] The server inputs the received text data into an emotion engine to recognize the user's emotional state, for example, extracting emotions such as anxiety or tension.

[2001] 4. Response Generation

[2002] The server inputs text data, including emotional information, into the generative AI model. The generative AI model uses natural language processing techniques to generate an appropriate response to the user's input. For example, it might generate a response like, "Possible causes of headaches include stress and lack of sleep. If you're concerned, consult a doctor."

[2003] 5. Sending the Response

[2004] The server sends the generated response to the user terminal.

[2005] 6. User Display

[2006] The terminal displays the received response to the user, who can then check the displayed response and re-enter the question if necessary.

[2007] Health data management

[2008] Specific examples

[2009] 1. Data Collection

[2010] Health monitoring devices (such as fitness trackers and blood pressure monitors) collect patient biometric data. For example, a blood pressure monitor measures a blood pressure of 130 / 85 mmHg.

[2011] 2. Data Transmission

[2012] Health management devices send collected biometric data to a user's device, which then periodically transmits the data to a server, often via Bluetooth or Wi-Fi.

[2013] 3. Data Reception

[2014] The server receives the biometric data sent from the user terminal and stores it in a database.

[2015] 4. Data Analysis

[2016] The server then analyzes the received biometric data using a data analysis engine, which uses statistical analysis and machine learning algorithms.

[2017] 5. Emotional reflection

[2018] The server reflects the user's emotional information recognized by the emotion engine in the analysis results and generates feedback, such as a message like "Your current blood pressure is 130 / 85 mmHg, which is within the normal range. Please continue to take care of your health."

[2019] 6. Send results

[2020] The server transmits the generated feedback to the user terminal.

[2021] 7. User Presentation

[2022] The device displays the feedback sent from the server to the user, who then checks the analysis results and begins medical consultation if any abnormalities are detected.

[2023] Prompt Sentence Examples

[2024] Prompt example 1: Medical consultation

[2025] When a patient types, "I've been having frequent headaches lately," use an emotion engine to recognize the emotion and a generative AI model to generate the optimal response.

[2026] Prompt example 2: Health data management

[2027] When the patient's blood pressure is measured at 130 / 85 mmHg and the emotion engine recognizes relief, generate appropriate feedback.

[2028] In this way, this system uses an emotion engine and generative AI model to provide advanced remote medical support to patients, creating an environment where even patients with difficulty traveling can receive medical consultations and manage their health with peace of mind.

[2029] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2030] Medical consultation handling

[2031] Step 1: User inputs medical consultation

[2032] A user inputs a medical consultation through a chat interface, for example, inputting the text "I've been having frequent headaches lately."

[2033] Input: User text input

[2034] Output: The text entered

[2035] Step 2: Send text data

[2036] The device sends the entered text data to the server using the HTTPS protocol.

[2037] Input: Text entered by the user

[2038] Output: Text data sent to the server

[2039] Step 3: Performing emotion recognition

[2040] The server inputs the received text data into an emotion engine to recognize the patient's emotional state, for example, by extracting emotions such as "anxiety" or "tension."

[2041] Input: Received text data

[2042] Output: Recognized emotion information (e.g., "anxiety")

[2043] Step 4: Generate a response

[2044] The server inputs text data based on the emotional information into the generative AI model, which then uses natural language processing technology to generate the optimal response. For example, the model might generate a response such as, "Possible causes of headaches include stress and lack of sleep. If you're concerned, consult a doctor."

[2045] Input: Text data and emotion information

[2046] Output: The generated response

[2047] Step 5: Sending a Response

[2048] The server sends the generated response to the user's terminal using the HTTPS protocol.

[2049] Input: The generated response

[2050] Output: Response sent to the user's device

[2051] Step 6: View the response

[2052] The terminal displays the received response to the user, who can then review the displayed response and re-enter the question if necessary.

[2053] Input: Response received from the server

[2054] Output: The response displayed to the user

[2055] Health data management

[2056] Step 1: Collect biometric data

[2057] Health management devices collect patient biometric data, for example, a blood pressure monitor measures "130 / 85 mmHg."

[2058] Input: Patient biometric data (e.g. blood pressure)

[2059] Output: Measured biometric data

[2060] Step 2: Sending data

[2061] The health management device transmits the collected biometric data to the user's terminal, which then transmits the data to a server via Bluetooth or Wi-Fi.

[2062] Input: Measured biometric data

[2063] Output: Biometric data sent to the server

[2064] Step 3: Receiving the data

[2065] The server receives the biometric data sent from the user terminal and stores it in a database.

[2066] Input: Data sent from the user's device

[2067] Output: Stored biometric data

[2068] Step 4: Analyze the data

[2069] The server then analyzes the received biometric data using a data analysis engine, which uses statistical analysis and machine learning algorithms.

[2070] Input: Stored biometric data

[2071] Output: Analysis results

[2072] Step 5: Reflecting emotional information

[2073] The server then incorporates the emotional information recognized by the emotion engine into the analysis results and generates final feedback, such as a message saying, "Your current blood pressure is 130 / 85 mmHg, which is within the normal range. Please continue to take care of your health."

[2074] Input: Analysis results and emotion information

[2075] Output: Final feedback message

[2076] Step 6: Sending the results

[2077] The server sends the generated feedback to the user terminal using the HTTPS protocol.

[2078] Input: Final feedback message

[2079] Output: Feedback message sent to the user's device

[2080] Step 7: View your feedback

[2081] The terminal displays the feedback sent from the server to the user, who can check the feedback and start another medical consultation if there is any abnormality.

[2082] Input: Feedback message received from the server

[2083] Output: Feedback displayed to the user

[2084] (Application example 2)

[2085] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2086] In today's world, patients with mobility issues have limited access to medical support, making it particularly difficult for those living in remote areas to receive appropriate medical services. Furthermore, traditional telemedicine systems typically provide responses and feedback without considering the patient's emotional state, making it difficult to provide personalized, optimal support. This can lead to a lack of reassurance and trust for patients, resulting in a decline in the quality of medical consultations.

[2087] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving patient input information, means for generating a response based on the received input information using a generative AI model, means for receiving the patient's biometric data from the health management device, means for recognizing the patient's emotional state using an emotion engine, and means for adjusting the response based on the recognized emotional state. This not only enables patients with limited mobility to receive medical consultations remotely, but also provides personalized responses through emotion recognition, thereby providing patients with an appropriate sense of security and trust.

[2088] "Patient input information" is text data entered by patients as questions or messages regarding medical consultations or health care.

[2089] A "generative AI model" is an artificial intelligence algorithm used to generate natural language responses based on large datasets.

[2090] A "health management device" is a device that measures and collects a patient's biometric data, such as a fitness tracker, blood pressure monitor, or heart rate monitor.

[2091] "Biometric data" refers to data that indicates physical indicators such as a patient's heart rate, blood pressure, and activity level.

[2092] The "emotion engine" is an artificial intelligence algorithm that analyzes the patient's input information and recognizes their emotional state (e.g., anxiety, relief, tension, etc.).

[2093] A "means for tailoring responses" is a system that customizes the responses generated based on the emotional state of an individual patient.

[2094] "Natural language processing technology" is an information processing technology for analyzing, understanding, and generating human language.

[2095] MODE FOR CARRYING OUT THE INVENTION

[2096] System configuration

[2097] This invention is a remote medical consultation system for providing medical support to patients with mobility issues. The system receives patient input, generates responses using a generative AI model, and uses an emotion engine to recognize the patient's emotional state and adjust responses based on that emotional information. The system primarily consists of the following hardware and software:

[2098] 1. User Device

[2099] Smartphones, tablets, computers, etc. are used.

[2100] It is an interface through which patients input their medical consultations, and is responsible for collecting biometric data from health management devices and sending it to the server.

[2101] 2. Generative AI Models

[2102] It is an algorithm that uses natural language processing technology to generate optimal responses based on the patient's input information received.

[2103] Model name example: "text-davinci-003"

[2104] 3. Emotion Engine

[2105] It includes algorithms for analyzing patient input and recognizing their emotional state.

[2106] Example of technology used: Emotion recognition model using deep learning

[2107] 4. Health management device

[2108] These are devices that measure a patient's biometric data, such as fitness trackers, blood pressure monitors, and heart rate monitors.

[2109] System Operation

[2110] Medical consultation handling

[2111] 1. A patient inputs a medical consultation through a user terminal. For example, the patient inputs, "I've been having frequent headaches recently."

[2112] 2. The device sends this input information to the server.

[2113] 3. The server inputs this input information into an emotion engine to recognize the patient's emotional state (e.g., anxiety).

[2114] 4. The server passes the recognized emotion information to a generative AI model to generate an optimal response.

[2115] Example generated response: "Possible causes of headaches include stress, lack of sleep, or other health conditions. If you are concerned, consult your doctor."

[2116] 5. The server sends the generated response to the user terminal, which displays it to the patient.

[2117] Health data management

[2118] 1. Collect patient biometric data (e.g., heart rate, blood pressure) using health monitoring devices.

[2119] 2. The user terminal periodically sends the collected data to the server.

[2120] 3. The server receives the biometric data and analyzes it using a data analysis engine.

[2121] 4. The generative AI model provides feedback based on the emotional information recognized by the emotion engine.

[2122] Example of generated feedback: "Your current blood pressure is 130 / 85 mmHg, which is within the normal range. Please continue to take good care of your health."

[2123] 5. The server sends this feedback to the user terminal, which displays the analysis results to the patient.

[2124] Prompt Sentence Examples

[2125] For medical consultations

[2126] User Input: I've been having frequent headaches lately.

[2127] Emotion: Anxiety

[2128] Appropriate response: Headaches can be caused by stress, lack of sleep, or other health conditions. If you're concerned, consult your doctor.

[2129] In the case of biometric data

[2130] Example of vital data: {"heart_rate": 75, "blood_pressure": "130 / 85"}

[2131] Health feedback: Health is normal.

[2132] In this way, an environment is provided where even patients who have difficulty moving around can receive medical consultations and health management remotely with peace of mind.

[2133] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2134] Program processing steps

[2135] Step 1:

[2136] A user inputs a medical consultation request using a smartphone. For example, the user might input, "I've been having frequent headaches recently." This input information is saved as text data on the device.

[2137] Input: User's medical consultation text

[2138] Output: Medical consultation in text data format

[2139] Step 2:

[2140] The terminal transmits the stored medical consultation text of the user to the server, where the terminal makes a request using internet communication.

[2141] Input: Medical consultation in text format

[2142] Output: Medical consultation data sent to the server

[2143] Step 3:

[2144] The server inputs the received medical consultation data into the emotion engine, which uses a deep learning model to analyze the patient's emotional state from the input text. For example, it can recognize an emotional state such as "anxiety."

[2145] Input: Medical consultation data

[2146] Output: Perceived emotional state (e.g., anxiety)

[2147] Step 4:

[2148] The server passes the emotional state to a generative AI model, which generates a response. The generative AI model uses natural language processing technology to generate the optimal response based on the medical consultation and emotional information. For example, it might generate text like, "Possible causes of your headache include stress, lack of sleep, or other health conditions. If you're concerned, consult a doctor."

[2149] Input: medical consultation data, recognized emotional state

[2150] Output: Generated response

[2151] Step 5:

[2152] The server sends the generated response to the user's terminal, which receives the response and displays it to the user.

[2153] Input: Generated response sentence

[2154] Output: Response text displayed on the terminal

[2155] Step 6:

[2156] The health management device is used to collect biometric data of the user, such as measuring heart rate and blood pressure, and this data is transmitted to the terminal.

[2157] Input: Biometric data from health management device

[2158] Output: Biometric data stored on the device

[2159] Step 7:

[2160] The device transmits the stored biometric data to a server, and the device is programmed to periodically transmit this data to the server.

[2161] Input: Stored biometric data

[2162] Output: Biometric data sent to the server

[2163] Step 8:

[2164] The server analyzes the received biometric data using a data analysis engine. For example, if the heart rate is over 100, it is determined to be a "high heart rate."

[2165] Input: Biometric data

[2166] Output: Analysis results (e.g. high heart rate)

[2167] Step 9:

[2168] The server reflects the patient's emotional information recognized by the emotion engine in the analysis results, and the generative AI model provides feedback, such as, "Your current blood pressure is 130 / 85 mmHg, which is within the normal range. Please continue to take appropriate health care measures."

[2169] Input: Analysis results, recognized emotional state

[2170] Output: Generated feedback statement

[2171] Step 10:

[2172] The server sends the generated feedback sentence to the user terminal, which displays the feedback to the user.

[2173] Input: Generated feedback sentence

[2174] Output: Feedback text displayed on the terminal

[2175] In this way, patients can receive medical consultations and health management remotely through each step of the system. The combination of generative AI models and emotion engines makes it possible to provide personalized responses and feedback.

[2176] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2177] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2178] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2179] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2180] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2181] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2182] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2183] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[2184] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[2185] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[2186] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[2187] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[2188] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[2189] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[2190] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[2191] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[2192] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[2193] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[2194] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[2195] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[2196] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[2197] The following is further disclosed regarding the above embodiment.

[2198] (Claim 1)

[2199] means for receiving patient input information;

[2200] means for generating a response based on the received input information using a generative AI model;

[2201] means for receiving patient biometric data from a health management device;

[2202] The system includes means for analyzing the received biometric data and providing the results to the patient.

[2203] (Claim 2)

[2204] 2. The system according to claim 1, wherein the means for generating a response uses natural language processing technology to generate a response to a medical inquiry input by a patient.

[2205] (Claim 3)

[2206] The system according to claim 1, wherein the analysis results of the biometric data are interpreted by a generative AI model using natural language processing technology and feedback is provided to the patient.

[2207] "Example 1"

[2208] (Claim 1)

[2209] means for receiving patient input information;

[2210] means for generating a response based on the received input information using a generative AI model;

[2211] means for receiving patient biometric data from a health management device;

[2212] means for analyzing the received biometric data and providing the results to the patient;

[2213] means for transmitting the biometric data to a server using a user terminal;

[2214] means for inputting the input information and biometric data as prompts to a generative AI model;

[2215] means for generating the response and analysis results in natural language and providing feedback to the patient;

[2216] means for analyzing biometric data from the health management device and detecting abnormal values;

[2217] a means for determining whether the level is within normal limits;

[2218] means for displaying said responses and feedback on a user terminal;

[2219] A system including:

[2220] (Claim 2)

[2221] 2. The system according to claim 1, wherein the means for generating a response uses natural language processing technology to generate a response to a medical inquiry input by a patient.

[2222] (Claim 3)

[2223] The system according to claim 1, wherein the analysis results of the biometric data are interpreted by a generative AI model using natural language processing technology and feedback is provided to the patient.

[2224] "Application Example 1"

[2225] (Claim 1)

[2226] means for receiving patient input information;

[2227] means for generating a response based on the received input information using a generative AI model;

[2228] means for receiving patient biometric data from a health management device;

[2229] means for analyzing the received biometric data and providing the results to the patient;

[2230] A system including a means for remote medical consultation and health management in real time using a virtual reality device.

[2231] (Claim 2)

[2232] The system of claim 1, wherein the generative AI model is used to generate answers to medical inquiries entered by a patient and provide the answers to the patient through a virtual reality device.

[2233] (Claim 3)

[2234] The system of claim 1, wherein the analysis results of the biometric data are interpreted by a generative AI model and feedback is provided to the patient through a virtual reality device.

[2235] "Example 2: Combining Emotion Engines"

[2236] (Claim 1)

[2237] means for receiving patient input information;

[2238] means for generating a response based on the received input information using a generative AI model;

[2239] means for receiving patient biometric data from a health management device;

[2240] means for analyzing the received biometric data and providing the results to the patient;

[2241] a means for recognizing an emotional state of a patient by an emotion engine and reflecting the recognized emotional state in generating the response;

[2242] The system includes means for reflecting the emotional state of the patient in the feedback generated based on the analyzed biometric data.

[2243] (Claim 2)

[2244] The system according to claim 1, wherein the means for generating a response uses natural language processing technology to generate a response to a medical inquiry entered by a patient, and reflects emotional information recognized by an emotion engine in the generative AI model.

[2245] (Claim 3)

[2246] The system according to claim 1, wherein the analysis results of the biometric data are interpreted by a generative AI model using natural language processing technology, and feedback is provided to the patient, and emotional information recognized by an emotion engine is reflected in the feedback.

[2247] "Application example 2 when combining emotion engines"

[2248] (Claim 1)

[2249] means for receiving patient input information;

[2250] means for generating a response based on the received input information using a generative AI model;

[2251] means for receiving patient biometric data from a health management device;

[2252] means for analyzing the received biometric data and providing the results to the patient;

[2253] means for recognizing an emotional state of a patient using an emotion engine;

[2254] The system includes a means for adjusting a response based on the perceived emotional state.

[2255] (Claim 2)

[2256] 2. The system according to claim 1, wherein the means for generating a response uses natural language processing technology to generate a response to a medical inquiry input by a patient.

[2257] (Claim 3)

[2258] The system according to claim 1, wherein the analysis results of the biometric data are interpreted by a generative AI model using natural language processing technology and feedback is provided to the patient. [Explanation of symbols]

[2259] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving patient input information; means for generating a response based on the received input information using a generative AI model; means for receiving patient biometric data from a health management device; The system includes means for analyzing the received biometric data and providing the results to the patient.

2. 2. The system according to claim 1, wherein the means for generating a response uses natural language processing techniques to generate a response to a medical inquiry input by a patient.

3. The system according to claim 1, wherein the analysis results of the biometric data are interpreted by a generative AI model using natural language processing technology and feedback is provided to the patient.

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