system

Abstract

We provide the system. [Solution] A means of analyzing health consultations from patients using a generative AI model and responding with health information, A means of analyzing data collected from medical devices and notifying in case of abnormalities, A means of enabling communication with medical staff via a terminal, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] There are problems such as unbalanced medical access, shortage of medical staff, and insufficient rapid access of patients to appropriate medical information. In particular, for elderly people and patients living in remote areas, when it is difficult to visit a medical institution, it is required to provide medical services efficiently and quickly. It is difficult for conventional systems to sufficiently solve these problems, and new technical means are needed.

Means for Solving the Problems

[0005] This invention provides a means to analyze health consultations from patients and immediately respond with appropriate health information by utilizing a generative AI model. Furthermore, it includes a means to detect the presence or absence of abnormalities by analyzing data collected from medical devices and notify medical staff as necessary. In addition, it has developed a means to manage online medical appointments, enabling communication with medical staff from home and efficiently managing appointment scheduling. This will improve access to medical care and increase the efficiency of medical institutions.

[0006] A "generative AI model" is a computer program that uses machine learning techniques to process and analyze data and make responses and decisions in natural language.

[0007] "Patient health consultations" refer to the act of an individual seeking professional information and advice to resolve questions or concerns they have about their health condition.

[0008] "Health information" refers to information about an individual's health status, as well as data or knowledge that provides guidelines for maintaining or improving health.

[0009] A "medical device" is a machine or device used to monitor, diagnose, or treat a patient's health condition.

[0010] "Data analysis" refers to a series of processes or calculations that take collected data as a basis for understanding and evaluating the information and patterns that the data reveals.

[0011] A "notification" is a communication or alert sent to inform a target person of specific information.

[0012] A "terminal" is an electronic or computer device used by a user to input information or to display received information.

[0013] "Communication with medical staff" refers to the means and processes for exchanging medical information and interacting as needed.

[0014] A "medication recipe" is a document or piece of information that specifies the type, dosage, and method of administration of medication to be given to a patient.

[0015] "Online medical appointment management" refers to the process of making appointments for medical consultations remotely via the internet, and properly saving and managing that appointment information. [Brief explanation of the drawing]

[0016] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Embodiments for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), etc.

[0020] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] This invention is a system that supports the last mile of healthcare using a generative AI model. This system communicates data between the user's terminal and the server to provide medical consultations, medical device monitoring, home healthcare support, recipe management, and online medical consultations.

[0038] Users enter health-related questions through an interface on their device for medical consultation. This input is sent to a server, where a generative AI model analyzes the questions. The server then generates health information based on the analysis results and sends it back to the device, allowing users to quickly obtain the necessary information.

[0039] In medical device monitoring, a server periodically collects data from medical devices installed in patients' homes or medical facilities. This data is analyzed on the server, and if an abnormality is detected, the server immediately notifies medical staff, enabling a rapid response.

[0040] To support home healthcare, users input information about their health condition and symptoms via their device. This information is sent to a server, and medical staff are notified as needed. This allows medical staff to understand the user's health status and take appropriate action, even when they are in a remote location.

[0041] Furthermore, in recipe management, when a user requests a drug recipe via their terminal, the server searches the treatment history database for relevant information and provides the necessary recipe information. This allows users to obtain medications efficiently.

[0042] In online medical consultations, when a user makes an appointment from their device, the server checks the doctor's schedule and assigns an appropriate appointment time. Once the appointment time is confirmed, the user can receive an online consultation with the doctor on their device. This process makes it possible for users to receive appropriate medical support even from remote locations.

[0043] As a concrete example, if an elderly patient experiences a headache, they can input "I have a headache" into their terminal, and the server immediately analyzes the information and suggests "drink fluids and rest." If the symptoms persist the next day, they can receive advice from a doctor through online consultation. Furthermore, the server will arrange for prescribed medication to be picked up from a nearby pharmacy. This allows patients to receive a series of medical services without having to travel.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The user enters health consultation questions through the terminal's interface.

[0047] Step 2:

[0048] The terminal converts the entered question into the appropriate data format and sends it to the server.

[0049] Step 3:

[0050] The server receives the question, inputs it into the generative AI model, and performs the analysis.

[0051] Step 4:

[0052] Based on the analysis results from the server-generated AI model, it generates appropriate health information and advice.

[0053] Step 5:

[0054] The server converts the generated information into data packets and sends them back to the terminal.

[0055] Step 6:

[0056] The device displays the information it receives on the screen in a format that is easy for the user to understand.

[0057] Step 7:

[0058] If necessary, the user may enter further questions or choose an action based on the information provided.

[0059] This series of processes allows users to quickly and easily obtain health-related information.

[0060] (Example 1)

[0061] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0062] In the modern medical field, there is a need for patients to receive medical information quickly and accurately, for the timing of anomaly detection to be improved, and for efficient communication with healthcare professionals. However, conventional systems cannot adequately meet these requirements. For example, there are problems such as the time it takes for patients to receive specific health advice, delays in detecting abnormalities, and cumbersome appointment scheduling. These challenges need to be addressed.

[0063] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0064] In this invention, the server includes means for analyzing health inquiries from users using a generative AI model and returning health information, means for analyzing information obtained from medical devices and issuing warnings in the event of an abnormality, and means for enabling dialogue with medical professionals via a communication terminal. This enables users to quickly and efficiently obtain medical information and communicate appropriately with medical professionals.

[0065] A "generative AI model" is a program that utilizes artificial intelligence to analyze input data through natural language processing and generate corresponding information.

[0066] A "user" is an individual who uses the system to receive health consultations or medical services.

[0067] A "medical device" is hardware that monitors a patient's health status and collects necessary data.

[0068] A "communication terminal" is a device used by users to access a system and input or receive information.

[0069] A "healthcare professional" is a professional who provides medical services to patients.

[0070] This invention is a system that provides medical services using a generative AI model. Users input health-related questions and medical information using a communication terminal. This communication terminal includes devices such as smartphones and computers, and transmits the input data to a server.

[0071] The server analyzes the received user data using a generative AI model. This generative AI model includes a program that utilizes natural language processing technology to highly analyze user questions and symptoms. Specifically, when a user enters a specific query regarding help desk or health in text format, that text data is sent to the server.

[0072] The AI ​​model on the server understands the user's intent during the analysis process and generates appropriate advice and health information. This information is then sent back to the communication terminal and presented to the user. For example, if a user enters a prompt such as, "I've been coughing a lot at night lately; I'd like some advice on what to do," the server can use this information to return advice such as, "We recommend using a humidifier to maintain humidity in your bedroom and consulting a specialist if necessary."

[0073] Furthermore, the server can collect and analyze monitoring data from medical devices. If an anomaly is detected, the server will issue a warning to healthcare professionals to prompt a quick response. The system also has a scheduling function, and when users make online medical appointments, it takes into account the healthcare professionals' schedules to allocate appropriate appointment times.

[0074] This system allows users to efficiently and quickly access necessary medical information and services, and can also contribute to reducing the burden on healthcare professionals.

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] Users enter health-related inquiries using a communication terminal. Specifically, they enter their questions into a text box provided on a dedicated application on the terminal and press the submit button. The data entered is a prompt message, such as "I have a persistent cough at night, what should I do?" This user input constitutes the initial data entry into the system.

[0078] Step 2:

[0079] The terminal sends input data obtained from the user to the server. The data is transmitted securely through encrypted communication such as SSL. Once the server confirms receipt of the data, the analysis process starts automatically. At this stage, the data transfer to the server is completed while ensuring the security of the data.

[0080] Step 3:

[0081] The server inputs the received data into a generating AI model. The AI ​​model uses natural language processing techniques to analyze the user's intent and relevant health information from the prompt text. Specifically, it performs processes such as word analysis, contextual understanding, and inference of health-related information. This analysis yields hypotheses about the user's symptoms and recommended actions as analysis results.

[0082] Step 4:

[0083] The server generates specific advice and health information based on the analysis results from the generation AI model. For example, it might output something like, "We recommend using a humidifier to maintain humidity in your bedroom and consulting a specialist if necessary." In this generation phase, natural language is generated based on the analysis results, and advice is constructed in a format that is easy for the user to understand.

[0084] Step 5:

[0085] The server sends the generated information back to the communication terminal. The data is encrypted during transmission, ensuring secure delivery to the terminal. Upon receiving the data, the terminal sends a notification and displays the analysis results and advice on the screen. Throughout this process, the user is supported in confirming the system's response.

[0086] Step 6:

[0087] Users review the advice displayed on their communication terminal and use it to manage their own health. Specifically, they might decide on subsequent actions while viewing the terminal screen, and, if necessary, consult with or schedule appointments with healthcare professionals. In this final stage, users will take the next steps based on the information obtained through the system.

[0088] (Application Example 1)

[0089] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0090] In modern society, home-based health management is becoming increasingly important as the population ages. However, conventional systems have struggled to monitor users' physiological states in real time and provide appropriate health advice immediately. Furthermore, there is a need for rapid and effective responses when accepting medical services from remote locations.

[0091] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0092] In this invention, the server includes means for analyzing health consultations from users using a generative AI model and responding with health information, means for analyzing biometric data collected from medical devices and issuing alarms in case of abnormalities, and means for enabling information exchange with medical professionals via a communication terminal. This enables real-time monitoring of the user's physiological functions and rapid health management.

[0093] A "generative AI model" is an artificial intelligence technology that analyzes user input information and suggests appropriate health information and actions.

[0094] A "user" is an individual who receives health consultations or medical services through this system.

[0095] "Health consultation" refers to the act of a user asking questions about their own health condition and symptoms and receiving information in response.

[0096] "Health information" refers to health advice and recommendations proposed by generative AI models.

[0097] A "medical device" is a hardware device used to collect a user's biometric data.

[0098] "Biometric data" refers to measurement results that indicate the user's physiological state, such as body temperature, heart rate, and blood pressure.

[0099] An "alarm" is a notification issued when an anomaly is detected in the user's biometric data.

[0100] A "communication terminal" is an electronic device used by users to exchange information with a system.

[0101] "Healthcare professionals" are specialists such as doctors and nurses who provide medical services to users.

[0102] "Information exchange" refers to the act of users and healthcare professionals sending and receiving messages and data.

[0103] "Physiological function" refers to the physical processes related to the activity of the user's body and the function of various organs.

[0104] The system for realizing this invention mainly consists of a server, a user communication terminal, and a medical device. The server uses a generative AI model to analyze health consultation input from the user, generates health information based on that analysis, and responds to the user. It also periodically collects biometric data from the medical device and issues an alarm if an abnormality is detected. The communication terminal is used by the user to send health consultations and monitors physiological functions in real time. Furthermore, it enables information exchange with medical professionals via the communication terminal, allowing medical professionals to provide necessary advice and treatment remotely.

[0105] The hardware used includes heart rate monitors, thermometers, and blood pressure monitors, which connect to communication terminals via Bluetooth or Wi-Fi. The software consists of generative AI models and data analysis platforms that run on the cloud. This enables real-time data processing and analysis.

[0106] For example, if an elderly person feels "heavy in the head" during their daily life, a generative AI model can analyze this information and immediately provide health advice such as "drink some water and rest a little." Furthermore, if there are abnormalities in heart rate or body temperature, the communication device will immediately notify healthcare professionals, and remote consultation options will be provided as needed.

[0107] An example of a prompt message is, "If the user feels unwell, collect health data in real time and use a generative AI model to suggest appropriate measures."

[0108] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0109] Step 1:

[0110] Users input data about their health concerns and symptoms via a communication terminal. The terminal receives this input and converts it into a format for transmission to the server. The input data includes consultation details and symptoms in text format, as well as collected biometric data.

[0111] Step 2:

[0112] The server inputs the received data into a generative AI model for analysis. Text-based consultation content and symptom data are preprocessed using natural language processing techniques to generate dynamic prompt sentences. This enables the AI ​​model to generate appropriate health information.

[0113] Step 3:

[0114] The generative AI model analyzes input data based on prompt messages and generates health information. The generated information is presented as advice and recommendations in a user-friendly format.

[0115] Step 4:

[0116] The server retrieves output from the generated AI model and sends it back to the user's communication terminal in an appropriate format. This includes appropriate health advice and next steps to take. It clearly outlines specific action steps that the user can immediately implement.

[0117] Step 5:

[0118] Simultaneously, biometric data from medical devices is transmitted to a server. The server analyzes this data and, if it detects any abnormal patterns or values, generates an alarm and notifies medical personnel. This data includes heart rate, body temperature, blood pressure, and other parameters.

[0119] Step 6:

[0120] Healthcare professionals receive notifications and, if necessary, contact the user to confirm the situation in detail and provide medical care. Through this process, users can continue to receive prompt medical support even when they are in a remote location.

[0121] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0122] This invention is a system that utilizes an emotion engine to accurately recognize the user's emotions and provide more individualized and optimized medical support. This system communicates data between the user's terminal and the server, and uses a generated AI model to provide health consultations, medical device monitoring, home healthcare support, recipe management, and online medical consultations. Furthermore, by incorporating an emotion engine, it provides advice and support tailored to the user's psychological state.

[0123] When users enter health information via their device, they can simultaneously input their emotions. This input is captured by the device as natural speech or text and sent to the server. On the server side, an emotion engine analyzes the user's emotions from the input and incorporates it into a generative AI model to generate health information and advice that corresponds to the user's emotions. As a result, the advice is more personalized, and users receive responses that are best suited to their emotions.

[0124] As a concrete example of its use, if a user is feeling anxious, they can type "I haven't been able to sleep at night lately" into their device, and the server will analyze the text using an emotion engine. Based on this analysis, the server will detect emotions such as "anxiety" and "stress," and then provide specific relaxation techniques and support information. Furthermore, if necessary, medical staff will be notified, enabling follow-up if the specific emotions persist.

[0125] In addition, the emotion engine, in conjunction with doctors' scheduling, enables efficient appointment scheduling based on the user's psychological state and urgency. This allows users to receive medical support optimized for their emotional state, enabling them to live with greater peace of mind. Through this system, support in the last mile of healthcare will be provided more effectively than ever before.

[0126] The following describes the processing flow.

[0127] Step 1:

[0128] The user inputs health consultation questions and their own emotional state through the device's interface.

[0129] Step 2:

[0130] The terminal converts the entered question and sentiment data into the appropriate data format and sends it to the server.

[0131] Step 3:

[0132] The server receives questions and sentiment data and inputs them into the generative AI model and sentiment engine.

[0133] Step 4:

[0134] The server uses an emotion engine to analyze the user's emotions and feeds the results back into the generating AI model.

[0135] Step 5:

[0136] Based on the analysis results from the server-generated AI model, the system generates health information and advice tailored to the user's emotions.

[0137] Step 6:

[0138] The server converts the generated information into data packets and sends them back to the terminal.

[0139] Step 7:

[0140] The device displays the information it receives on the screen in a format that is easy for the user to understand.

[0141] Step 8:

[0142] Users review the information provided and, if necessary, communicate further with medical staff or choose the recommended course of action.

[0143] By incorporating an emotion engine, personalized health management that takes into account the user's psychological state becomes possible.

[0144] (Example 2)

[0145] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0146] In modern medicine, providing individualized medical support that takes into account the patient's emotional state is a challenge. While conventional systems can respond to one-sided health consultations from patients, they struggle to adequately analyze those emotions and provide personalized support. This makes it difficult to provide optimal medical support that considers the patient's psychological state.

[0147] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0148] This invention includes a server that analyzes the user's health consultation and emotional data using a generative AI model and provides personalized health information and advice tailored to their psychological state; a server that adapts medical support based on the emotional data analyzed via a communication device; and a server that enables communication with medical technicians and continuous monitoring. This makes it possible to provide more personalized and effective medical support rooted in the user's emotional state.

[0149] A "generative AI model" is a type of artificial intelligence that has the ability to generate new information or solutions based on given data.

[0150] "Users" refer to individuals who use the system, primarily those who provide information about their health status and emotions.

[0151] "Health consultation" refers to the act of a user seeking inquiries or advice about their own health condition or symptoms.

[0152] "Emotional data" refers to information that indicates the user's psychological state, and is collected as text or audio data.

[0153] "Personalized health information" refers to information optimized for a specific individual based on their health status and emotional state.

[0154] "Medical support" refers to the support and advice provided regarding the user's health and medical needs.

[0155] A "communication device" is a device used for sending and receiving data, enabling communication between users and systems.

[0156] A "medical technologist" is a professional who works in a medical setting and is responsible for providing advice and follow-up to users through systems.

[0157] A "timetable" is a schedule that shows the times when medical professionals are available to provide consultations and services.

[0158] This invention is a system that provides more personalized medical support by allowing users to conduct health consultations and reflect their emotions in the process. This system utilizes components such as the user's terminal, server, emotion engine, and generative AI model.

[0159] Users can input health-related questions and emotional information via the device. The device features voice recognition and text input capabilities and functions as the user's interface. The device then sends this data to a server.

[0160] The server analyzes the received data using an emotion engine. The emotion engine uses natural language processing to extract emotions from text and audio and analyzes their content. Based on this, the server uses a generative AI model to generate personalized health information and advice for the user.

[0161] The generative AI model combines collected emotional data with health consultation information to create optimal advice. This allows users to receive more personalized medical support. For example, if a user inputs "I haven't been able to sleep at night recently," the server's emotional engine analyzes emotions such as "anxiety" and "stress," and based on that, provides advice such as relaxation techniques.

[0162] Communication equipment is necessary for smooth data transmission and reception, enabling communication and follow-up between users and medical technicians. Furthermore, by linking with doctors' schedules, it allows for optimal appointment scheduling tailored to the user's psychological and health condition.

[0163] Examples of prompts include, "How can I relax when I feel anxious?" and "Please tell me some ways to reduce stress right now." In this way, this invention makes it easier to provide personalized, emotion-based medical support.

[0164] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0165] Step 1:

[0166] The user inputs information about health concerns and emotions into the device via voice or text. This includes using specific prompts, such as "I've been having trouble sleeping at night lately." The input data is generated as either an audio file or text data. The device then prepares this data for transmission to the server as digital data.

[0167] Step 2:

[0168] The terminal sends input data obtained from the user to the server. The input includes text and voice data entered by the user, and data packets are sent as output to the server. Here, reliable and rapid data transmission takes place via a communication protocol.

[0169] Step 3:

[0170] The server inputs the received text or audio data into the emotion engine. The emotion engine applies natural language processing to the input data to analyze the user's emotions. Here, the text data is converted into emotion metadata, and emotion labels such as "anxiety" or "stress" are output.

[0171] Step 4:

[0172] The server inputs the emotion labels obtained from the emotion engine into the generating AI model. The generating AI model takes in the emotion labels and health consultation information together and starts the generation process. The engine performs data analysis to generate more personalized health information and advice, which is then converted into text-based advice.

[0173] Step 5:

[0174] The server sends the generated advice data to the terminal. The output data is formatted in a user-friendly format. The terminal notifies the user of this data visually or audibly, allowing them to review the advice.

[0175] Step 6:

[0176] In some cases, the server sends alerts to medical technicians based on the analysis results. This plays a crucial role in cases requiring follow-up, such as when emotions exceed a certain threshold. The data, including prompts, indicates the need for medical attention.

[0177] (Application Example 2)

[0178] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0179] Providing personalized health support based on the user's emotions is challenging in medical and nursing care settings. In particular, accurately understanding the user's emotional state and providing appropriate advice and follow-up is not adequately achieved with conventional systems. Efficient communication and coordination with medical staff and caregivers also remain challenges.

[0180] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0181] In this invention, the server includes means for analyzing health consultations from users using a generative AI model and responding with health information, means for analyzing information collected from medical devices and notifying in the event of an abnormality, means for enabling communication with medical personnel via a terminal, means for converting voice input into text using speech recognition technology, means for analyzing the user's emotions using natural language processing technology, means for generating personalized advice based on the user's emotional state, and means for sending notifications to caregivers and coordinating to provide necessary support. This enables optimal health support tailored to the user's emotions, realizing a rapid and appropriate response in medical and nursing care settings.

[0182] A "generative AI model" is an artificial intelligence framework that learns from large amounts of data and produces appropriate outputs and predictions even for new data.

[0183] "Speech recognition technology" is a technology that allows machines to understand human speech and convert it into text data.

[0184] "Natural language processing technology" refers to technologies that enable machines to understand, analyze, and generate human language.

[0185] "User" refers to the individual person who uses this system.

[0186] "Health support" refers to activities that provide users with advice and information regarding their health, and assist them in maintaining their health and resolving health problems.

[0187] "Medical devices" refer to equipment and instruments designed for use in medical settings.

[0188] "Emotional analysis" is a technology that determines emotions from the user's input and infers their psychological state.

[0189] "Medical staff" refers to specialists and staff who are responsible for their duties in a medical setting.

[0190] A "caregiver" refers to someone who assists elderly people or people with physical disabilities with their daily lives and medical care.

[0191] The system for implementing this invention mainly consists of a server and a user terminal. The user terminal has the function of converting the user's voice input into text using speech recognition technology. Specifically, it is conceivable to use speech recognition software such as Google® Cloud Speech-to-Text.

[0192] The data converted to text on the terminal is sent to the server. On the server, an emotion engine is used to analyze the emotions in the text data using natural language processing techniques (e.g., NLTK, spaCy). Based on the results of this emotion analysis, a generative AI model (e.g., a model built with TENSORFLOW® or PyTorch) generates personalized health support advice.

[0193] The server also monitors information from medical devices and notifies medical personnel if an abnormality is detected. Furthermore, it sends necessary notifications to caregivers and adjusts support based on the user's emotional state. Using APIs such as Google Calendar is suitable for these notifications and adjustments.

[0194] For example, if a user voice-inputs "I've been feeling stressed lately," the voice is converted to text on the device, and the server analyzes the emotion of "stress." The generative AI model then suggests specific relaxation methods to reduce stress and notifies the caregiver as needed. An example of a prompt message would be, "Please suggest appropriate relaxation methods when the user feels stressed."

[0195] In this way, this system provides optimal health support based on the user's emotions, enabling prompt and appropriate care services and medical support.

[0196] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0197] Step 1:

[0198] The user inputs their health consultation request via voice into the device. The device then uses speech recognition technology to convert this voice input into text data. Here, the input is voice data, and the output is text data. Specifically, the Google Cloud Speech-to-Text API is used to convert the voice to text.

[0199] Step 2:

[0200] The server receives text data sent from the terminal. The server analyzes this text data using natural language processing techniques to identify the user's emotions. The input is text data, and the output is emotion data. Specifically, it uses the NLTK library to perform emotion analysis and identify emotions such as "stress" and "anxiety."

[0201] Step 3:

[0202] The server uses a generative AI model to generate health support advice based on emotional data. The input is emotional data, and the output is personalized advice. Specifically, a generative AI model is built using TensorFlow, etc., and optimal advice is derived in response to prompts such as "suggest relaxation methods."

[0203] Step 4:

[0204] If the sentiment analysis determines the situation is urgent, the server will send a notification to caregivers or medical personnel. The input is sentiment data and urgency level, and the output is the sending of a notification. As a concrete example, the Google Calendar API is used to adjust schedules and issue a notification requesting immediate attention from caregivers.

[0205] Step 5:

[0206] The generated advice is fed back to the user through the device. The input is the generated advice, and the output is what is displayed to the user. Specifically, the advice is clearly displayed on the device screen, and the advice is also played back audibly using the audio playback function.

[0207] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0208] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0209] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0210] [Second Embodiment]

[0211] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0212] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0213] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0214] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0215] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0216] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0217] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0218] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0219] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0220] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0221] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0222] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0223] This invention is a system that supports the last mile of healthcare using a generative AI model. This system communicates data between the user's terminal and the server to provide medical consultations, medical device monitoring, home healthcare support, recipe management, and online medical consultations.

[0224] Users enter health-related questions through an interface on their device for medical consultation. This input is sent to a server, where a generative AI model analyzes the questions. The server then generates health information based on the analysis results and sends it back to the device, allowing users to quickly obtain the necessary information.

[0225] In medical device monitoring, a server periodically collects data from medical devices installed in patients' homes or medical facilities. This data is analyzed on the server, and if an abnormality is detected, the server immediately notifies medical staff, enabling a rapid response.

[0226] To support home healthcare, users input information about their health condition and symptoms via their device. This information is sent to a server, and medical staff are notified as needed. This allows medical staff to understand the user's health status and take appropriate action, even when they are in a remote location.

[0227] Furthermore, in recipe management, when a user requests a drug recipe via their terminal, the server searches the treatment history database for relevant information and provides the necessary recipe information. This allows users to obtain medications efficiently.

[0228] In online medical consultations, when a user makes an appointment from their device, the server checks the doctor's schedule and assigns an appropriate appointment time. Once the appointment time is confirmed, the user can receive an online consultation with the doctor on their device. This process makes it possible for users to receive appropriate medical support even from remote locations.

[0229] As a concrete example, if an elderly patient experiences a headache, they can input "I have a headache" into their terminal, and the server immediately analyzes the information and suggests "drink fluids and rest." If the symptoms persist the next day, they can receive advice from a doctor through online consultation. Furthermore, the server will arrange for prescribed medication to be picked up from a nearby pharmacy. This allows patients to receive a series of medical services without having to travel.

[0230] The following describes the processing flow.

[0231] Step 1:

[0232] The user enters health consultation questions through the terminal's interface.

[0233] Step 2:

[0234] The terminal converts the entered question into the appropriate data format and sends it to the server.

[0235] Step 3:

[0236] The server receives the question, inputs it into the generative AI model, and performs the analysis.

[0237] Step 4:

[0238] Based on the analysis results from the server-generated AI model, it generates appropriate health information and advice.

[0239] Step 5:

[0240] The server converts the generated information into data packets and sends them back to the terminal.

[0241] Step 6:

[0242] The device displays the information it receives on the screen in a format that is easy for the user to understand.

[0243] Step 7:

[0244] If necessary, the user may enter further questions or choose an action based on the information provided.

[0245] This series of processes allows users to quickly and easily obtain health-related information.

[0246] (Example 1)

[0247] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0248] In the modern medical field, there is a need for patients to receive medical information quickly and accurately, for the timing of anomaly detection to be improved, and for efficient communication with healthcare professionals. However, conventional systems cannot adequately meet these requirements. For example, there are problems such as the time it takes for patients to receive specific health advice, delays in detecting abnormalities, and cumbersome appointment scheduling. These challenges need to be addressed.

[0249] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0250] In this invention, the server includes means for analyzing health inquiries from users using a generative AI model and returning health information, means for analyzing information obtained from medical devices and issuing warnings in the event of an abnormality, and means for enabling dialogue with medical professionals via a communication terminal. This enables users to quickly and efficiently obtain medical information and communicate appropriately with medical professionals.

[0251] A "generative AI model" is a program that utilizes artificial intelligence to analyze input data through natural language processing and generate corresponding information.

[0252] A "user" is an individual who uses the system to receive health consultations or medical services.

[0253] A "medical device" is hardware that monitors a patient's health status and collects necessary data.

[0254] A "communication terminal" is a device used by users to access a system and input or receive information.

[0255] A "healthcare professional" is a professional who provides medical services to patients.

[0256] This invention is a system that provides medical services using a generative AI model. Users input health-related questions and medical information using a communication terminal. This communication terminal includes devices such as smartphones and computers, and transmits the input data to a server.

[0257] The server analyzes the received user data using a generative AI model. This generative AI model includes a program that utilizes natural language processing technology to highly analyze user questions and symptoms. Specifically, when a user enters a specific query regarding help desk or health in text format, that text data is sent to the server.

[0258] The AI ​​model on the server understands the user's intent during the analysis process and generates appropriate advice and health information. This information is then sent back to the communication terminal and presented to the user. For example, if a user enters a prompt such as, "I've been coughing a lot at night lately; I'd like some advice on what to do," the server can use this information to return advice such as, "We recommend using a humidifier to maintain humidity in your bedroom and consulting a specialist if necessary."

[0259] Furthermore, the server can collect and analyze monitoring data from medical devices. If an anomaly is detected, the server will issue a warning to healthcare professionals to prompt a quick response. The system also has a scheduling function, and when users make online medical appointments, it takes into account the healthcare professionals' schedules to allocate appropriate appointment times.

[0260] This system allows users to efficiently and quickly access necessary medical information and services, and can also contribute to reducing the burden on healthcare professionals.

[0261] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0262] Step 1:

[0263] Users enter health-related inquiries using a communication terminal. Specifically, they enter their questions into a text box provided on a dedicated application on the terminal and press the submit button. The data entered is a prompt message, such as "I have a persistent cough at night, what should I do?" This user input constitutes the initial data entry into the system.

[0264] Step 2:

[0265] The terminal sends input data obtained from the user to the server. The data is transmitted securely through encrypted communication such as SSL. Once the server confirms receipt of the data, the analysis process starts automatically. At this stage, the data transfer to the server is completed while ensuring the security of the data.

[0266] Step 3:

[0267] The server inputs the received data into a generating AI model. The AI ​​model uses natural language processing techniques to analyze the user's intent and relevant health information from the prompt text. Specifically, it performs processes such as word analysis, contextual understanding, and inference of health-related information. This analysis yields hypotheses about the user's symptoms and recommended actions as analysis results.

[0268] Step 4:

[0269] The server generates specific advice and health information based on the analysis results from the generation AI model. For example, it might output something like, "We recommend using a humidifier to maintain humidity in your bedroom and consulting a specialist if necessary." In this generation phase, natural language is generated based on the analysis results, and advice is constructed in a format that is easy for the user to understand.

[0270] Step 5:

[0271] The server sends the generated information back to the communication terminal. The data is encrypted during transmission, ensuring secure delivery to the terminal. Upon receiving the data, the terminal sends a notification and displays the analysis results and advice on the screen. Throughout this process, the user is supported in confirming the system's response.

[0272] Step 6:

[0273] Users review the advice displayed on their communication terminal and use it to manage their own health. Specifically, they might decide on subsequent actions while viewing the terminal screen, and, if necessary, consult with or schedule appointments with healthcare professionals. In this final stage, users will take the next steps based on the information obtained through the system.

[0274] (Application Example 1)

[0275] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0276] In modern society, home-based health management is becoming increasingly important as the population ages. However, conventional systems have struggled to monitor users' physiological states in real time and provide appropriate health advice immediately. Furthermore, there is a need for rapid and effective responses when accepting medical services from remote locations.

[0277] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0278] In this invention, the server includes means for analyzing health consultations from users using a generative AI model and responding with health information, means for analyzing biometric data collected from medical devices and issuing alarms in case of abnormalities, and means for enabling information exchange with medical professionals via a communication terminal. This enables real-time monitoring of the user's physiological functions and rapid health management.

[0279] A "generative AI model" is an artificial intelligence technology that analyzes the input information of users and proposes appropriate health information and actions.

[0280] A "user" is an individual who receives health consultations and medical services through this system.

[0281] A "health consultation" is an act in which a user asks questions about their health condition and symptoms and receives information in response.

[0282] "Health information" refers to health advice and recommendations proposed by the generative AI model.

[0283] A "medical device" is a hardware device used to collect biometric data of users.

[0284] "Biometric data" refers to measurement results indicating the physiological state of a user, such as body temperature, heart rate, blood pressure, etc.

[0285] An "alarm" is a notification issued when an abnormality is detected from the biometric data of a user.

[0286] A "communication terminal" is an electronic device through which a user exchanges information with the system.

[0287] "Medical staff" refers to professionals such as doctors and nurses who provide medical services to users.

[0288] "Information exchange" refers to the act of sending and receiving messages and data between users and medical staff.

[0289] "Physiological function" refers to the physical processes related to the internal activities of a user and the functions of each organ.

[0290] The system for realizing this invention mainly consists of a server, a user communication terminal, and a medical device. The server uses a generative AI model to analyze health consultation input from the user, generates health information based on that analysis, and responds to the user. It also periodically collects biometric data from the medical device and issues an alarm if an abnormality is detected. The communication terminal is used by the user to send health consultations and monitors physiological functions in real time. Furthermore, it enables information exchange with medical professionals via the communication terminal, allowing medical professionals to provide necessary advice and treatment remotely.

[0291] The hardware used includes heart rate monitors, thermometers, and blood pressure monitors, which connect to communication terminals via Bluetooth or Wi-Fi. The software consists of generative AI models and data analysis platforms that run on the cloud. This enables real-time data processing and analysis.

[0292] For example, if an elderly person feels "heavy in the head" during their daily life, a generative AI model can analyze this information and immediately provide health advice such as "drink some water and rest a little." Furthermore, if there are abnormalities in heart rate or body temperature, the communication device will immediately notify healthcare professionals, and remote consultation options will be provided as needed.

[0293] An example of a prompt message is, "If the user feels unwell, collect health data in real time and use a generative AI model to suggest appropriate measures."

[0294] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0295] Step 1:

[0296] Users input data about their health concerns and symptoms via a communication terminal. The terminal receives this input and converts it into a format for transmission to the server. The input data includes consultation details and symptoms in text format, as well as collected biometric data.

[0297] Step 2:

[0298] The server inputs the received data into a generative AI model for analysis. Text-based consultation content and symptom data are preprocessed using natural language processing techniques to generate dynamic prompt sentences. This enables the AI ​​model to generate appropriate health information.

[0299] Step 3:

[0300] The generative AI model analyzes input data based on prompt messages and generates health information. The generated information is presented as advice and recommendations in a user-friendly format.

[0301] Step 4:

[0302] The server retrieves output from the generated AI model and sends it back to the user's communication terminal in an appropriate format. This includes appropriate health advice and next steps to take. It clearly outlines specific action steps that the user can immediately implement.

[0303] Step 5:

[0304] Simultaneously, biometric data from medical devices is transmitted to a server. The server analyzes this data and, if it detects any abnormal patterns or values, generates an alarm and notifies medical personnel. This data includes heart rate, body temperature, blood pressure, and other parameters.

[0305] Step 6:

[0306] Medical staff receive the notification, contact the user as needed, and conduct detailed situation checks and medical consultations. Through this process, users can continue to receive prompt medical support even when they are in a remote location.

[0307] Furthermore, an emotion engine for estimating the user's emotions may be combined. That is, the specific processing unit 290 may estimate the user's emotions using the emotion identification model 59 and perform specific processing using the user's emotions.

[0308] The present invention is a system that accurately recognizes the user's emotions and makes medical support more personalized and optimized by utilizing an emotion engine. This system not only conducts data communication between the user's terminal and the server and realizes health consultations, monitoring of medical devices, home medical support, recipe management, and online medical consultations using a generated AI model, but also provides advice and support according to the user's psychological state by incorporating an emotion engine.

[0309] When the user makes a health consultation input from the terminal, the user can simultaneously input while reflecting their own emotions. This input is captured by the terminal as natural voice or text and transmitted to the server. On the server side, the emotion engine analyzes the emotions from the user's input and incorporates them into the generated AI model, thereby generating health information and advice corresponding to the user's emotions. As a result, the advice is more personalized, and the user can obtain the optimal response to their emotions.

[0310] As a specific usage example, when a certain user feels anxious and inputs "I can't sleep well recently" from the terminal, the server analyzes the text with the emotion engine. Based on the analysis result, the server detects emotions such as "anxiety" and "stress" and provides specific relaxation techniques and support information based on them. Furthermore, if necessary, a notification is sent to medical staff, enabling follow-up when specific emotions persist.

[0311] In addition, the emotion engine, in conjunction with doctors' scheduling, enables efficient appointment scheduling based on the user's psychological state and urgency. This allows users to receive medical support optimized for their emotional state, enabling them to live with greater peace of mind. Through this system, support in the last mile of healthcare will be provided more effectively than ever before.

[0312] The following describes the processing flow.

[0313] Step 1:

[0314] The user inputs health consultation questions and their own emotional state through the device's interface.

[0315] Step 2:

[0316] The terminal converts the entered question and sentiment data into the appropriate data format and sends it to the server.

[0317] Step 3:

[0318] The server receives questions and sentiment data and inputs them into the generative AI model and sentiment engine.

[0319] Step 4:

[0320] The server uses an emotion engine to analyze the user's emotions and feeds the results back into the generating AI model.

[0321] Step 5:

[0322] Based on the analysis results from the server-generated AI model, the system generates health information and advice tailored to the user's emotions.

[0323] Step 6:

[0324] The server converts the generated information into data packets and sends them back to the terminal.

[0325] Step 7:

[0326] The device displays the information it receives on the screen in a format that is easy for the user to understand.

[0327] Step 8:

[0328] Users review the information provided and, if necessary, communicate further with medical staff or choose the recommended course of action.

[0329] By incorporating an emotion engine, personalized health management that takes into account the user's psychological state becomes possible.

[0330] (Example 2)

[0331] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0332] In modern medicine, providing individualized medical support that takes into account the patient's emotional state is a challenge. While conventional systems can respond to one-sided health consultations from patients, they struggle to adequately analyze those emotions and provide personalized support. This makes it difficult to provide optimal medical support that considers the patient's psychological state.

[0333] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0334] This invention includes a server that analyzes the user's health consultation and emotional data using a generative AI model and provides personalized health information and advice tailored to their psychological state; a server that adapts medical support based on the emotional data analyzed via a communication device; and a server that enables communication with medical technicians and continuous monitoring. This makes it possible to provide more personalized and effective medical support rooted in the user's emotional state.

[0335] A "generative AI model" is a type of artificial intelligence that has the ability to generate new information or solutions based on given data.

[0336] "Users" refer to individuals who use the system, primarily those who provide information about their health status and emotions.

[0337] "Health consultation" refers to the act of a user seeking inquiries or advice about their own health condition or symptoms.

[0338] "Emotional data" refers to information that indicates the user's psychological state, and is collected as text or audio data.

[0339] "Personalized health information" refers to information optimized for a specific individual based on their health status and emotional state.

[0340] "Medical support" refers to the support and advice provided regarding the user's health and medical needs.

[0341] A "communication device" is a device used for sending and receiving data, enabling communication between users and systems.

[0342] A "medical technologist" is a professional who works in a medical setting and is responsible for providing advice and follow-up to users through systems.

[0343] A "timetable" is a schedule that shows the times when medical professionals are available to provide consultations and services.

[0344] This invention is a system that provides more personalized medical support by allowing users to conduct health consultations and reflect their emotions in the process. This system utilizes components such as the user's terminal, server, emotion engine, and generative AI model.

[0345] Users can input health-related questions and emotional information via the device. The device features voice recognition and text input capabilities and functions as the user's interface. The device then sends this data to a server.

[0346] The server analyzes the received data using an emotion engine. The emotion engine uses natural language processing to extract emotions from text and audio and analyzes their content. Based on this, the server uses a generative AI model to generate personalized health information and advice for the user.

[0347] The generative AI model combines collected emotional data with health consultation information to create optimal advice. This allows users to receive more personalized medical support. For example, if a user inputs "I haven't been able to sleep at night recently," the server's emotional engine analyzes emotions such as "anxiety" and "stress," and based on that, provides advice such as relaxation techniques.

[0348] Communication equipment is necessary for smooth data transmission and reception, enabling communication and follow-up between users and medical technicians. Furthermore, by linking with doctors' schedules, it allows for optimal appointment scheduling tailored to the user's psychological and health condition.

[0349] Examples of prompts include, "How can I relax when I feel anxious?" and "Please tell me some ways to reduce stress right now." In this way, this invention makes it easier to provide personalized, emotion-based medical support.

[0350] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0351] Step 1:

[0352] The user inputs information about health concerns and emotions into the device via voice or text. This includes using specific prompts, such as "I've been having trouble sleeping at night lately." The input data is generated as either an audio file or text data. The device then prepares this data for transmission to the server as digital data.

[0353] Step 2:

[0354] The terminal sends input data obtained from the user to the server. The input includes text and voice data entered by the user, and data packets are sent as output to the server. Here, reliable and rapid data transmission takes place via a communication protocol.

[0355] Step 3:

[0356] The server inputs the received text or audio data into the emotion engine. The emotion engine applies natural language processing to the input data to analyze the user's emotions. Here, the text data is converted into emotion metadata, and emotion labels such as "anxiety" or "stress" are output.

[0357] Step 4:

[0358] The server inputs the emotion labels obtained from the emotion engine into the generating AI model. The generating AI model takes in the emotion labels and health consultation information together and starts the generation process. The engine performs data analysis to generate more personalized health information and advice, which is then converted into text-based advice.

[0359] Step 5:

[0360] The server sends the generated advice data to the terminal. The output data is formatted in a user-friendly format. The terminal notifies the user of this data visually or audibly, allowing them to review the advice.

[0361] Step 6:

[0362] In some cases, the server sends alerts to medical technicians based on the analysis results. This plays a crucial role in cases requiring follow-up, such as when emotions exceed a certain threshold. The data, including prompts, indicates the need for medical attention.

[0363] (Application Example 2)

[0364] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0365] Providing personalized health support based on the user's emotions is challenging in medical and nursing care settings. In particular, accurately understanding the user's emotional state and providing appropriate advice and follow-up is not adequately achieved with conventional systems. Efficient communication and coordination with medical staff and caregivers also remain challenges.

[0366] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0367] In this invention, the server includes means for analyzing health consultations from users using a generative AI model and responding with health information, means for analyzing information collected from medical devices and notifying in the event of an abnormality, means for enabling communication with medical personnel via a terminal, means for converting voice input into text using speech recognition technology, means for analyzing the user's emotions using natural language processing technology, means for generating personalized advice based on the user's emotional state, and means for sending notifications to caregivers and coordinating to provide necessary support. This enables optimal health support tailored to the user's emotions, realizing a rapid and appropriate response in medical and nursing care settings.

[0368] A "generative AI model" is an artificial intelligence framework that learns from large amounts of data and produces appropriate outputs and predictions even for new data.

[0369] "Speech recognition technology" is a technology that allows machines to understand human speech and convert it into text data.

[0370] "Natural language processing technology" refers to technologies that enable machines to understand, analyze, and generate human language.

[0371] "User" refers to the individual person who uses this system.

[0372] "Health support" refers to activities that provide users with advice and information regarding their health, and assist them in maintaining their health and resolving health problems.

[0373] "Medical devices" refer to equipment and instruments designed for use in medical settings.

[0374] "Emotional analysis" is a technology that determines emotions from the user's input and infers their psychological state.

[0375] "Medical staff" refers to specialists and staff who are responsible for their duties in a medical setting.

[0376] A "caregiver" refers to someone who assists elderly people or people with physical disabilities with their daily lives and medical care.

[0377] The system for implementing this invention mainly consists of a server and a user terminal. The user terminal has the function of converting the user's voice input into text using speech recognition technology. Specifically, it is conceivable to use speech recognition software such as Google Cloud Speech-to-Text.

[0378] The data, converted to text on the device, is sent to the server. On the server, an emotion engine is used to analyze the emotions in the text data using natural language processing techniques (e.g., NLTK, spaCy). Based on the results of this emotion analysis, a generative AI model (e.g., a model built with TensorFlow or PyTorch) generates personalized health support advice.

[0379] The server also monitors information from medical devices and notifies medical personnel if an abnormality is detected. Furthermore, it sends necessary notifications to caregivers and adjusts support based on the user's emotional state. Using APIs such as Google Calendar is suitable for these notifications and adjustments.

[0380] For example, if a user voice-inputs "I've been feeling stressed lately," the voice is converted to text on the device, and the server analyzes the emotion of "stress." The generative AI model then suggests specific relaxation methods to reduce stress and notifies the caregiver as needed. An example of a prompt message would be, "Please suggest appropriate relaxation methods when the user feels stressed."

[0381] In this way, this system provides optimal health support based on the user's emotions, enabling prompt and appropriate care services and medical support.

[0382] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0383] Step 1:

[0384] The user inputs their health consultation request via voice into the device. The device then uses speech recognition technology to convert this voice input into text data. Here, the input is voice data, and the output is text data. Specifically, the Google Cloud Speech-to-Text API is used to convert the voice to text.

[0385] Step 2:

[0386] The server receives text data sent from the terminal. The server analyzes this text data using natural language processing techniques to identify the user's emotions. The input is text data, and the output is emotion data. Specifically, it uses the NLTK library to perform emotion analysis and identify emotions such as "stress" and "anxiety."

[0387] Step 3:

[0388] The server uses a generative AI model to generate health support advice based on emotional data. The input is emotional data, and the output is personalized advice. Specifically, a generative AI model is built using TensorFlow, etc., and optimal advice is derived in response to prompts such as "suggest relaxation methods."

[0389] Step 4:

[0390] If the sentiment analysis determines the situation is urgent, the server will send a notification to caregivers or medical personnel. The input is sentiment data and urgency level, and the output is the sending of a notification. As a concrete example, the Google Calendar API is used to adjust schedules and issue a notification requesting immediate attention from caregivers.

[0391] Step 5:

[0392] The generated advice is fed back to the user through the device. The input is the generated advice, and the output is what is displayed to the user. Specifically, the advice is clearly displayed on the device screen, and the advice is also played back audibly using the audio playback function.

[0393] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0394] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0395] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0396] [Third Embodiment]

[0397] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0398] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0399] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0400] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0401] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0402] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0403] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0404] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0405] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0406] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0407] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0408] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0409] This invention is a system that supports the last mile of healthcare using a generative AI model. This system communicates data between the user's terminal and the server to provide medical consultations, medical device monitoring, home healthcare support, recipe management, and online medical consultations.

[0410] Users enter health-related questions through an interface on their device for medical consultation. This input is sent to a server, where a generative AI model analyzes the questions. The server then generates health information based on the analysis results and sends it back to the device, allowing users to quickly obtain the necessary information.

[0411] In medical device monitoring, a server periodically collects data from medical devices installed in patients' homes or medical facilities. This data is analyzed on the server, and if an abnormality is detected, the server immediately notifies medical staff, enabling a rapid response.

[0412] To support home healthcare, users input information about their health condition and symptoms via their device. This information is sent to a server, and medical staff are notified as needed. This allows medical staff to understand the user's health status and take appropriate action, even when they are in a remote location.

[0413] Furthermore, in recipe management, when a user requests a drug recipe via their terminal, the server searches the treatment history database for relevant information and provides the necessary recipe information. This allows users to obtain medications efficiently.

[0414] In online medical consultations, when a user makes an appointment from their device, the server checks the doctor's schedule and assigns an appropriate appointment time. Once the appointment time is confirmed, the user can receive an online consultation with the doctor on their device. This process makes it possible for users to receive appropriate medical support even from remote locations.

[0415] As a concrete example, if an elderly patient experiences a headache, they can input "I have a headache" into their terminal, and the server immediately analyzes the information and suggests "drink fluids and rest." If the symptoms persist the next day, they can receive advice from a doctor through online consultation. Furthermore, the server will arrange for prescribed medication to be picked up from a nearby pharmacy. This allows patients to receive a series of medical services without having to travel.

[0416] The following describes the processing flow.

[0417] Step 1:

[0418] The user enters health consultation questions through the terminal's interface.

[0419] Step 2:

[0420] The terminal converts the entered question into the appropriate data format and sends it to the server.

[0421] Step 3:

[0422] The server receives the question, inputs it into the generative AI model, and performs the analysis.

[0423] Step 4:

[0424] Based on the analysis results from the server-generated AI model, it generates appropriate health information and advice.

[0425] Step 5:

[0426] The server converts the generated information into data packets and sends them back to the terminal.

[0427] Step 6:

[0428] The device displays the information it receives on the screen in a format that is easy for the user to understand.

[0429] Step 7:

[0430] If necessary, the user may enter further questions or choose an action based on the information provided.

[0431] This series of processes allows users to quickly and easily obtain health-related information.

[0432] (Example 1)

[0433] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0434] In the modern medical field, there is a need for patients to receive medical information quickly and accurately, for the timing of anomaly detection to be improved, and for efficient communication with healthcare professionals. However, conventional systems cannot adequately meet these requirements. For example, there are problems such as the time it takes for patients to receive specific health advice, delays in detecting abnormalities, and cumbersome appointment scheduling. These challenges need to be addressed.

[0435] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0436] In this invention, the server includes means for analyzing health inquiries from users using a generative AI model and returning health information, means for analyzing information obtained from medical devices and issuing warnings in the event of an abnormality, and means for enabling dialogue with medical professionals via a communication terminal. This enables users to quickly and efficiently obtain medical information and communicate appropriately with medical professionals.

[0437] A "generative AI model" is a program that utilizes artificial intelligence to analyze input data through natural language processing and generate corresponding information.

[0438] A "user" is an individual who uses the system to receive health consultations or medical services.

[0439] A "medical device" is hardware that monitors a patient's health status and collects necessary data.

[0440] A "communication terminal" is a device used by users to access a system and input or receive information.

[0441] A "healthcare professional" is a professional who provides medical services to patients.

[0442] This invention is a system that provides medical services using a generative AI model. Users input health-related questions and medical information using a communication terminal. This communication terminal includes devices such as smartphones and computers, and transmits the input data to a server.

[0443] The server analyzes the received user data using a generative AI model. This generative AI model includes a program that utilizes natural language processing technology to highly analyze user questions and symptoms. Specifically, when a user enters a specific query regarding help desk or health in text format, that text data is sent to the server.

[0444] The AI ​​model on the server understands the user's intent during the analysis process and generates appropriate advice and health information. This information is then sent back to the communication terminal and presented to the user. For example, if a user enters a prompt such as, "I've been coughing a lot at night lately; I'd like some advice on what to do," the server can use this information to return advice such as, "We recommend using a humidifier to maintain humidity in your bedroom and consulting a specialist if necessary."

[0445] Furthermore, the server can collect and analyze monitoring data from medical devices. If an anomaly is detected, the server will issue a warning to healthcare professionals to prompt a quick response. The system also has a scheduling function, and when users make online medical appointments, it takes into account the healthcare professionals' schedules to allocate appropriate appointment times.

[0446] This system allows users to efficiently and quickly access necessary medical information and services, and can also contribute to reducing the burden on healthcare professionals.

[0447] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0448] Step 1:

[0449] Users enter health-related inquiries using a communication terminal. Specifically, they enter their questions into a text box provided on a dedicated application on the terminal and press the submit button. The data entered is a prompt message, such as "I have a persistent cough at night, what should I do?" This user input constitutes the initial data entry into the system.

[0450] Step 2:

[0451] The terminal sends input data obtained from the user to the server. The data is transmitted securely through encrypted communication such as SSL. Once the server confirms receipt of the data, the analysis process starts automatically. At this stage, the data transfer to the server is completed while ensuring the security of the data.

[0452] Step 3:

[0453] The server inputs the received data into a generating AI model. The AI ​​model uses natural language processing techniques to analyze the user's intent and relevant health information from the prompt text. Specifically, it performs processes such as word analysis, contextual understanding, and inference of health-related information. This analysis yields hypotheses about the user's symptoms and recommended actions as analysis results.

[0454] Step 4:

[0455] The server generates specific advice and health information based on the analysis results from the generation AI model. For example, it might output something like, "We recommend using a humidifier to maintain humidity in your bedroom and consulting a specialist if necessary." In this generation phase, natural language is generated based on the analysis results, and advice is constructed in a format that is easy for the user to understand.

[0456] Step 5:

[0457] The server sends the generated information back to the communication terminal. The data is encrypted during transmission, ensuring secure delivery to the terminal. Upon receiving the data, the terminal sends a notification and displays the analysis results and advice on the screen. Throughout this process, the user is supported in confirming the system's response.

[0458] Step 6:

[0459] Users review the advice displayed on their communication terminal and use it to manage their own health. Specifically, they might decide on subsequent actions while viewing the terminal screen, and, if necessary, consult with or schedule appointments with healthcare professionals. In this final stage, users will take the next steps based on the information obtained through the system.

[0460] (Application Example 1)

[0461] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0462] In modern society, home-based health management is becoming increasingly important as the population ages. However, conventional systems have struggled to monitor users' physiological states in real time and provide appropriate health advice immediately. Furthermore, there is a need for rapid and effective responses when accepting medical services from remote locations.

[0463] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0464] In this invention, the server includes means for analyzing health consultations from users using a generative AI model and responding with health information, means for analyzing biometric data collected from medical devices and issuing alarms in case of abnormalities, and means for enabling information exchange with medical professionals via a communication terminal. This enables real-time monitoring of the user's physiological functions and rapid health management.

[0465] A "generative AI model" is an artificial intelligence technology that analyzes user input information and suggests appropriate health information and actions.

[0466] A "user" is an individual who receives health consultations or medical services through this system.

[0467] "Health consultation" refers to the act of a user asking questions about their own health condition and symptoms and receiving information in response.

[0468] "Health information" refers to health advice and recommendations proposed by generative AI models.

[0469] A "medical device" is a hardware device used to collect a user's biometric data.

[0470] "Biometric data" refers to measurement results that indicate the user's physiological state, such as body temperature, heart rate, and blood pressure.

[0471] An "alarm" is a notification issued when an anomaly is detected in the user's biometric data.

[0472] A "communication terminal" is an electronic device used by users to exchange information with a system.

[0473] "Healthcare professionals" are specialists such as doctors and nurses who provide medical services to users.

[0474] "Information exchange" refers to the act of users and healthcare professionals sending and receiving messages and data.

[0475] "Physiological function" refers to the physical processes related to the activity of the user's body and the function of various organs.

[0476] The system for realizing this invention mainly consists of a server, a user communication terminal, and a medical device. The server uses a generative AI model to analyze health consultation input from the user, generates health information based on that analysis, and responds to the user. It also periodically collects biometric data from the medical device and issues an alarm if an abnormality is detected. The communication terminal is used by the user to send health consultations and monitors physiological functions in real time. Furthermore, it enables information exchange with medical professionals via the communication terminal, allowing medical professionals to provide necessary advice and treatment remotely.

[0477] The hardware used includes heart rate monitors, thermometers, and blood pressure monitors, which connect to communication terminals via Bluetooth or Wi-Fi. The software consists of generative AI models and data analysis platforms that run on the cloud. This enables real-time data processing and analysis.

[0478] For example, if an elderly person feels "heavy in the head" during their daily life, a generative AI model can analyze this information and immediately provide health advice such as "drink some water and rest a little." Furthermore, if there are abnormalities in heart rate or body temperature, the communication device will immediately notify healthcare professionals, and remote consultation options will be provided as needed.

[0479] An example of a prompt message is, "If the user feels unwell, collect health data in real time and use a generative AI model to suggest appropriate measures."

[0480] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0481] Step 1:

[0482] Users input data about their health concerns and symptoms via a communication terminal. The terminal receives this input and converts it into a format for transmission to the server. The input data includes consultation details and symptoms in text format, as well as collected biometric data.

[0483] Step 2:

[0484] The server inputs the received data into a generative AI model for analysis. Text-based consultation content and symptom data are preprocessed using natural language processing techniques to generate dynamic prompt sentences. This enables the AI ​​model to generate appropriate health information.

[0485] Step 3:

[0486] The generative AI model analyzes input data based on prompt messages and generates health information. The generated information is presented as advice and recommendations in a user-friendly format.

[0487] Step 4:

[0488] The server retrieves output from the generated AI model and sends it back to the user's communication terminal in an appropriate format. This includes appropriate health advice and next steps to take. It clearly outlines specific action steps that the user can immediately implement.

[0489] Step 5:

[0490] Simultaneously, biometric data from medical devices is transmitted to a server. The server analyzes this data and, if it detects any abnormal patterns or values, generates an alarm and notifies medical personnel. This data includes heart rate, body temperature, blood pressure, and other parameters.

[0491] Step 6:

[0492] Healthcare professionals receive notifications and, if necessary, contact the user to confirm the situation in detail and provide medical care. Through this process, users can continue to receive prompt medical support even when they are in a remote location.

[0493] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0494] This invention is a system that utilizes an emotion engine to accurately recognize the user's emotions and provide more individualized and optimized medical support. This system communicates data between the user's terminal and the server, and uses a generated AI model to provide health consultations, medical device monitoring, home healthcare support, recipe management, and online medical consultations. Furthermore, by incorporating an emotion engine, it provides advice and support tailored to the user's psychological state.

[0495] When users enter health information via their device, they can simultaneously input their emotions. This input is captured by the device as natural speech or text and sent to the server. On the server side, an emotion engine analyzes the user's emotions from the input and incorporates it into a generative AI model to generate health information and advice that corresponds to the user's emotions. As a result, the advice is more personalized, and users receive responses that are best suited to their emotions.

[0496] As a concrete example of its use, if a user is feeling anxious, they can type "I haven't been able to sleep at night lately" into their device, and the server will analyze the text using an emotion engine. Based on this analysis, the server will detect emotions such as "anxiety" and "stress," and then provide specific relaxation techniques and support information. Furthermore, if necessary, medical staff will be notified, enabling follow-up if the specific emotions persist.

[0497] In addition, the emotion engine, in conjunction with doctors' scheduling, enables efficient appointment scheduling based on the user's psychological state and urgency. This allows users to receive medical support optimized for their emotional state, enabling them to live with greater peace of mind. Through this system, support in the last mile of healthcare will be provided more effectively than ever before.

[0498] The following describes the processing flow.

[0499] Step 1:

[0500] The user inputs health consultation questions and their own emotional state through the device's interface.

[0501] Step 2:

[0502] The terminal converts the entered question and sentiment data into the appropriate data format and sends it to the server.

[0503] Step 3:

[0504] The server receives questions and sentiment data and inputs them into the generative AI model and sentiment engine.

[0505] Step 4:

[0506] The server uses an emotion engine to analyze the user's emotions and feeds the results back into the generating AI model.

[0507] Step 5:

[0508] Based on the analysis results from the server-generated AI model, the system generates health information and advice tailored to the user's emotions.

[0509] Step 6:

[0510] The server converts the generated information into data packets and sends them back to the terminal.

[0511] Step 7:

[0512] The device displays the information it receives on the screen in a format that is easy for the user to understand.

[0513] Step 8:

[0514] Users review the information provided and, if necessary, communicate further with medical staff or choose the recommended course of action.

[0515] By incorporating an emotion engine, personalized health management that takes into account the user's psychological state becomes possible.

[0516] (Example 2)

[0517] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0518] In modern medicine, providing individualized medical support that takes into account the patient's emotional state is a challenge. While conventional systems can respond to one-sided health consultations from patients, they struggle to adequately analyze those emotions and provide personalized support. This makes it difficult to provide optimal medical support that considers the patient's psychological state.

[0519] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0520] This invention includes a server that analyzes the user's health consultation and emotional data using a generative AI model and provides personalized health information and advice tailored to their psychological state; a server that adapts medical support based on the emotional data analyzed via a communication device; and a server that enables communication with medical technicians and continuous monitoring. This makes it possible to provide more personalized and effective medical support rooted in the user's emotional state.

[0521] A "generative AI model" is a type of artificial intelligence that has the ability to generate new information or solutions based on given data.

[0522] "Users" refer to individuals who use the system, primarily those who provide information about their health status and emotions.

[0523] "Health consultation" refers to the act of a user seeking inquiries or advice about their own health condition or symptoms.

[0524] "Emotional data" refers to information that indicates the user's psychological state, and is collected as text or audio data.

[0525] "Personalized health information" refers to information optimized for a specific individual based on their health status and emotional state.

[0526] "Medical support" refers to the support and advice provided regarding the user's health and medical needs.

[0527] A "communication device" is a device used for sending and receiving data, enabling communication between users and systems.

[0528] A "medical technologist" is a professional who works in a medical setting and is responsible for providing advice and follow-up to users through systems.

[0529] A "timetable" is a schedule that shows the times when medical professionals are available to provide consultations and services.

[0530] This invention is a system that provides more personalized medical support by allowing users to conduct health consultations and reflect their emotions in the process. This system utilizes components such as the user's terminal, server, emotion engine, and generative AI model.

[0531] Users can input health-related questions and emotional information via the device. The device features voice recognition and text input capabilities and functions as the user's interface. The device then sends this data to a server.

[0532] The server analyzes the received data using an emotion engine. The emotion engine uses natural language processing to extract emotions from text and audio and analyzes their content. Based on this, the server uses a generative AI model to generate personalized health information and advice for the user.

[0533] The generative AI model combines collected emotional data with health consultation information to create optimal advice. This allows users to receive more personalized medical support. For example, if a user inputs "I haven't been able to sleep at night recently," the server's emotional engine analyzes emotions such as "anxiety" and "stress," and based on that, provides advice such as relaxation techniques.

[0534] Communication equipment is necessary for smooth data transmission and reception, enabling communication and follow-up between users and medical technicians. Furthermore, by linking with doctors' schedules, it allows for optimal appointment scheduling tailored to the user's psychological and health condition.

[0535] Examples of prompts include, "How can I relax when I feel anxious?" and "Please tell me some ways to reduce stress right now." In this way, this invention makes it easier to provide personalized, emotion-based medical support.

[0536] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0537] Step 1:

[0538] The user inputs information about health concerns and emotions into the device via voice or text. This includes using specific prompts, such as "I've been having trouble sleeping at night lately." The input data is generated as either an audio file or text data. The device then prepares this data for transmission to the server as digital data.

[0539] Step 2:

[0540] The terminal sends input data obtained from the user to the server. The input includes text and voice data entered by the user, and data packets are sent as output to the server. Here, reliable and rapid data transmission takes place via a communication protocol.

[0541] Step 3:

[0542] The server inputs the received text or audio data into the emotion engine. The emotion engine applies natural language processing to the input data to analyze the user's emotions. Here, the text data is converted into emotion metadata, and emotion labels such as "anxiety" or "stress" are output.

[0543] Step 4:

[0544] The server inputs the emotion labels obtained from the emotion engine into the generating AI model. The generating AI model takes in the emotion labels and health consultation information together and starts the generation process. The engine performs data analysis to generate more personalized health information and advice, which is then converted into text-based advice.

[0545] Step 5:

[0546] The server sends the generated advice data to the terminal. The output data is formatted in a user-friendly format. The terminal notifies the user of this data visually or audibly, allowing them to review the advice.

[0547] Step 6:

[0548] In some cases, the server sends alerts to medical technicians based on the analysis results. This plays a crucial role in cases requiring follow-up, such as when emotions exceed a certain threshold. The data, including prompts, indicates the need for medical attention.

[0549] (Application Example 2)

[0550] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0551] Providing personalized health support based on the user's emotions is challenging in medical and nursing care settings. In particular, accurately understanding the user's emotional state and providing appropriate advice and follow-up is not adequately achieved with conventional systems. Efficient communication and coordination with medical staff and caregivers also remain challenges.

[0552] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0553] In this invention, the server includes means for analyzing health consultations from users using a generative AI model and responding with health information, means for analyzing information collected from medical devices and notifying in the event of an abnormality, means for enabling communication with medical personnel via a terminal, means for converting voice input into text using speech recognition technology, means for analyzing the user's emotions using natural language processing technology, means for generating personalized advice based on the user's emotional state, and means for sending notifications to caregivers and coordinating to provide necessary support. This enables optimal health support tailored to the user's emotions, realizing a rapid and appropriate response in medical and nursing care settings.

[0554] A "generative AI model" is an artificial intelligence framework that learns from large amounts of data and produces appropriate outputs and predictions even for new data.

[0555] "Speech recognition technology" is a technology that allows machines to understand human speech and convert it into text data.

[0556] "Natural language processing technology" refers to technologies that enable machines to understand, analyze, and generate human language.

[0557] "User" refers to the individual person who uses this system.

[0558] "Health support" refers to activities that provide users with advice and information regarding their health, and assist them in maintaining their health and resolving health problems.

[0559] "Medical devices" refer to equipment and instruments designed for use in medical settings.

[0560] "Emotional analysis" is a technology that determines emotions from the user's input and infers their psychological state.

[0561] "Medical staff" refers to specialists and staff who are responsible for their duties in a medical setting.

[0562] A "caregiver" refers to someone who assists elderly people or people with physical disabilities with their daily lives and medical care.

[0563] The system for implementing this invention mainly consists of a server and a user terminal. The user terminal has the function of converting the user's voice input into text using speech recognition technology. Specifically, it is conceivable to use speech recognition software such as Google Cloud Speech-to-Text.

[0564] The data, converted to text on the device, is sent to the server. On the server, an emotion engine is used to analyze the emotions in the text data using natural language processing techniques (e.g., NLTK, spaCy). Based on the results of this emotion analysis, a generative AI model (e.g., a model built with TensorFlow or PyTorch) generates personalized health support advice.

[0565] The server also monitors information from medical devices and notifies medical personnel if an abnormality is detected. Furthermore, it sends necessary notifications to caregivers and adjusts support based on the user's emotional state. Using APIs such as Google Calendar is suitable for these notifications and adjustments.

[0566] For example, if a user voice-inputs "I've been feeling stressed lately," the voice is converted to text on the device, and the server analyzes the emotion of "stress." The generative AI model then suggests specific relaxation methods to reduce stress and notifies the caregiver as needed. An example of a prompt message would be, "Please suggest appropriate relaxation methods when the user feels stressed."

[0567] In this way, this system provides optimal health support based on the user's emotions, enabling prompt and appropriate care services and medical support.

[0568] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0569] Step 1:

[0570] The user inputs their health consultation request via voice into the device. The device then uses speech recognition technology to convert this voice input into text data. Here, the input is voice data, and the output is text data. Specifically, the Google Cloud Speech-to-Text API is used to convert the voice to text.

[0571] Step 2:

[0572] The server receives text data sent from the terminal. The server analyzes this text data using natural language processing techniques to identify the user's emotions. The input is text data, and the output is emotion data. Specifically, it uses the NLTK library to perform emotion analysis and identify emotions such as "stress" and "anxiety."

[0573] Step 3:

[0574] The server uses a generative AI model to generate health support advice based on emotional data. The input is emotional data, and the output is personalized advice. Specifically, a generative AI model is built using TensorFlow, etc., and optimal advice is derived in response to prompts such as "suggest relaxation methods."

[0575] Step 4:

[0576] If the sentiment analysis determines the situation is urgent, the server will send a notification to caregivers or medical personnel. The input is sentiment data and urgency level, and the output is the sending of a notification. As a concrete example, the Google Calendar API is used to adjust schedules and issue a notification requesting immediate attention from caregivers.

[0577] Step 5:

[0578] The generated advice is fed back to the user through the device. The input is the generated advice, and the output is what is displayed to the user. Specifically, the advice is clearly displayed on the device screen, and the advice is also played back audibly using the audio playback function.

[0579] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0580] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0581] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0582] [Fourth Embodiment]

[0583] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0584] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0585] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0586] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0587] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0588] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0589] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0590] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0591] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0592] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0593] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0594] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0595] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0596] This invention is a system that supports the last mile of healthcare using a generative AI model. This system communicates data between the user's terminal and the server to provide medical consultations, medical device monitoring, home healthcare support, recipe management, and online medical consultations.

[0597] Users enter health-related questions through an interface on their device for medical consultation. This input is sent to a server, where a generative AI model analyzes the questions. The server then generates health information based on the analysis results and sends it back to the device, allowing users to quickly obtain the necessary information.

[0598] In medical device monitoring, a server periodically collects data from medical devices installed in patients' homes or medical facilities. This data is analyzed on the server, and if an abnormality is detected, the server immediately notifies medical staff, enabling a rapid response.

[0599] To support home healthcare, users input information about their health condition and symptoms via their device. This information is sent to a server, and medical staff are notified as needed. This allows medical staff to understand the user's health status and take appropriate action, even when they are in a remote location.

[0600] Furthermore, in recipe management, when a user requests a drug recipe via their terminal, the server searches the treatment history database for relevant information and provides the necessary recipe information. This allows users to obtain medications efficiently.

[0601] In online medical consultations, when a user makes an appointment from their device, the server checks the doctor's schedule and assigns an appropriate appointment time. Once the appointment time is confirmed, the user can receive an online consultation with the doctor on their device. This process makes it possible for users to receive appropriate medical support even from remote locations.

[0602] As a concrete example, if an elderly patient experiences a headache, they can input "I have a headache" into their terminal, and the server immediately analyzes the information and suggests "drink fluids and rest." If the symptoms persist the next day, they can receive advice from a doctor through online consultation. Furthermore, the server will arrange for prescribed medication to be picked up from a nearby pharmacy. This allows patients to receive a series of medical services without having to travel.

[0603] The following describes the processing flow.

[0604] Step 1:

[0605] The user enters health consultation questions through the terminal's interface.

[0606] Step 2:

[0607] The terminal converts the entered question into the appropriate data format and sends it to the server.

[0608] Step 3:

[0609] The server receives the question, inputs it into the generative AI model, and performs the analysis.

[0610] Step 4:

[0611] Based on the analysis results from the server-generated AI model, it generates appropriate health information and advice.

[0612] Step 5:

[0613] The server converts the generated information into data packets and sends them back to the terminal.

[0614] Step 6:

[0615] The device displays the information it receives on the screen in a format that is easy for the user to understand.

[0616] Step 7:

[0617] If necessary, the user may enter further questions or choose an action based on the information provided.

[0618] This series of processes allows users to quickly and easily obtain health-related information.

[0619] (Example 1)

[0620] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0621] In the modern medical field, there is a need for patients to receive medical information quickly and accurately, for the timing of anomaly detection to be improved, and for efficient communication with healthcare professionals. However, conventional systems cannot adequately meet these requirements. For example, there are problems such as the time it takes for patients to receive specific health advice, delays in detecting abnormalities, and cumbersome appointment scheduling. These challenges need to be addressed.

[0622] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0623] In this invention, the server includes means for analyzing health inquiries from users using a generative AI model and returning health information, means for analyzing information obtained from medical devices and issuing warnings in the event of an abnormality, and means for enabling dialogue with medical professionals via a communication terminal. This enables users to quickly and efficiently obtain medical information and communicate appropriately with medical professionals.

[0624] A "generative AI model" is a program that utilizes artificial intelligence to analyze input data through natural language processing and generate corresponding information.

[0625] A "user" is an individual who uses the system to receive health consultations or medical services.

[0626] A "medical device" is hardware that monitors a patient's health status and collects necessary data.

[0627] A "communication terminal" is a device used by users to access a system and input or receive information.

[0628] A "healthcare professional" is a professional who provides medical services to patients.

[0629] This invention is a system that provides medical services using a generative AI model. Users input health-related questions and medical information using a communication terminal. This communication terminal includes devices such as smartphones and computers, and transmits the input data to a server.

[0630] The server analyzes the received user data using a generative AI model. This generative AI model includes a program that utilizes natural language processing technology to highly analyze user questions and symptoms. Specifically, when a user enters a specific query regarding help desk or health in text format, that text data is sent to the server.

[0631] The AI ​​model on the server understands the user's intent during the analysis process and generates appropriate advice and health information. This information is then sent back to the communication terminal and presented to the user. For example, if a user enters a prompt such as, "I've been coughing a lot at night lately; I'd like some advice on what to do," the server can use this information to return advice such as, "We recommend using a humidifier to maintain humidity in your bedroom and consulting a specialist if necessary."

[0632] Furthermore, the server can collect and analyze monitoring data from medical devices. If an anomaly is detected, the server will issue a warning to healthcare professionals to prompt a quick response. The system also has a scheduling function, and when users make online medical appointments, it takes into account the healthcare professionals' schedules to allocate appropriate appointment times.

[0633] This system allows users to efficiently and quickly access necessary medical information and services, and can also contribute to reducing the burden on healthcare professionals.

[0634] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0635] Step 1:

[0636] Users enter health-related inquiries using a communication terminal. Specifically, they enter their questions into a text box provided on a dedicated application on the terminal and press the submit button. The data entered is a prompt message, such as "I have a persistent cough at night, what should I do?" This user input constitutes the initial data entry into the system.

[0637] Step 2:

[0638] The terminal sends input data obtained from the user to the server. The data is transmitted securely through encrypted communication such as SSL. Once the server confirms receipt of the data, the analysis process starts automatically. At this stage, the data transfer to the server is completed while ensuring the security of the data.

[0639] Step 3:

[0640] The server inputs the received data into a generating AI model. The AI ​​model uses natural language processing techniques to analyze the user's intent and relevant health information from the prompt text. Specifically, it performs processes such as word analysis, contextual understanding, and inference of health-related information. This analysis yields hypotheses about the user's symptoms and recommended actions as analysis results.

[0641] Step 4:

[0642] The server generates specific advice and health information based on the analysis results from the generation AI model. For example, it might output something like, "We recommend using a humidifier to maintain humidity in your bedroom and consulting a specialist if necessary." In this generation phase, natural language is generated based on the analysis results, and advice is constructed in a format that is easy for the user to understand.

[0643] Step 5:

[0644] The server sends the generated information back to the communication terminal. The data is encrypted during transmission, ensuring secure delivery to the terminal. Upon receiving the data, the terminal sends a notification and displays the analysis results and advice on the screen. Throughout this process, the user is supported in confirming the system's response.

[0645] Step 6:

[0646] Users review the advice displayed on their communication terminal and use it to manage their own health. Specifically, they might decide on subsequent actions while viewing the terminal screen, and, if necessary, consult with or schedule appointments with healthcare professionals. In this final stage, users will take the next steps based on the information obtained through the system.

[0647] (Application Example 1)

[0648] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0649] In modern society, home-based health management is becoming increasingly important as the population ages. However, conventional systems have struggled to monitor users' physiological states in real time and provide appropriate health advice immediately. Furthermore, there is a need for rapid and effective responses when accepting medical services from remote locations.

[0650] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0651] In this invention, the server includes means for analyzing health consultations from users using a generative AI model and responding with health information, means for analyzing biometric data collected from medical devices and issuing alarms in case of abnormalities, and means for enabling information exchange with medical professionals via a communication terminal. This enables real-time monitoring of the user's physiological functions and rapid health management.

[0652] A "generative AI model" is an artificial intelligence technology that analyzes user input information and suggests appropriate health information and actions.

[0653] A "user" is an individual who receives health consultations or medical services through this system.

[0654] "Health consultation" refers to the act of a user asking questions about their own health condition and symptoms and receiving information in response.

[0655] "Health information" refers to health advice and recommendations proposed by generative AI models.

[0656] A "medical device" is a hardware device used to collect a user's biometric data.

[0657] "Biometric data" refers to measurement results that indicate the user's physiological state, such as body temperature, heart rate, and blood pressure.

[0658] An "alarm" is a notification issued when an anomaly is detected in the user's biometric data.

[0659] A "communication terminal" is an electronic device used by users to exchange information with a system.

[0660] "Healthcare professionals" are specialists such as doctors and nurses who provide medical services to users.

[0661] "Information exchange" refers to the act of users and healthcare professionals sending and receiving messages and data.

[0662] "Physiological function" refers to the physical processes related to the activity of the user's body and the function of various organs.

[0663] The system for realizing this invention mainly consists of a server, a user communication terminal, and a medical device. The server uses a generative AI model to analyze health consultation input from the user, generates health information based on that analysis, and responds to the user. It also periodically collects biometric data from the medical device and issues an alarm if an abnormality is detected. The communication terminal is used by the user to send health consultations and monitors physiological functions in real time. Furthermore, it enables information exchange with medical professionals via the communication terminal, allowing medical professionals to provide necessary advice and treatment remotely.

[0664] The hardware used includes heart rate monitors, thermometers, and blood pressure monitors, which connect to communication terminals via Bluetooth or Wi-Fi. The software consists of generative AI models and data analysis platforms that run on the cloud. This enables real-time data processing and analysis.

[0665] For example, if an elderly person feels "heavy in the head" during their daily life, a generative AI model can analyze this information and immediately provide health advice such as "drink some water and rest a little." Furthermore, if there are abnormalities in heart rate or body temperature, the communication device will immediately notify healthcare professionals, and remote consultation options will be provided as needed.

[0666] An example of a prompt message is, "If the user feels unwell, collect health data in real time and use a generative AI model to suggest appropriate measures."

[0667] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0668] Step 1:

[0669] Users input data about their health concerns and symptoms via a communication terminal. The terminal receives this input and converts it into a format for transmission to the server. The input data includes consultation details and symptoms in text format, as well as collected biometric data.

[0670] Step 2:

[0671] The server inputs the received data into a generative AI model for analysis. Text-based consultation content and symptom data are preprocessed using natural language processing techniques to generate dynamic prompt sentences. This enables the AI ​​model to generate appropriate health information.

[0672] Step 3:

[0673] The generative AI model analyzes input data based on prompt messages and generates health information. The generated information is presented as advice and recommendations in a user-friendly format.

[0674] Step 4:

[0675] The server retrieves output from the generated AI model and sends it back to the user's communication terminal in an appropriate format. This includes appropriate health advice and next steps to take. It clearly outlines specific action steps that the user can immediately implement.

[0676] Step 5:

[0677] Simultaneously, biometric data from medical devices is transmitted to a server. The server analyzes this data and, if it detects any abnormal patterns or values, generates an alarm and notifies medical personnel. This data includes heart rate, body temperature, blood pressure, and other parameters.

[0678] Step 6:

[0679] Healthcare professionals receive notifications and, if necessary, contact the user to confirm the situation in detail and provide medical care. Through this process, users can continue to receive prompt medical support even when they are in a remote location.

[0680] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0681] This invention is a system that utilizes an emotion engine to accurately recognize the user's emotions and provide more individualized and optimized medical support. This system communicates data between the user's terminal and the server, and uses a generated AI model to provide health consultations, medical device monitoring, home healthcare support, recipe management, and online medical consultations. Furthermore, by incorporating an emotion engine, it provides advice and support tailored to the user's psychological state.

[0682] When users enter health information via their device, they can simultaneously input their emotions. This input is captured by the device as natural speech or text and sent to the server. On the server side, an emotion engine analyzes the user's emotions from the input and incorporates it into a generative AI model to generate health information and advice that corresponds to the user's emotions. As a result, the advice is more personalized, and users receive responses that are best suited to their emotions.

[0683] As a concrete example of its use, if a user is feeling anxious, they can type "I haven't been able to sleep at night lately" into their device, and the server will analyze the text using an emotion engine. Based on this analysis, the server will detect emotions such as "anxiety" and "stress," and then provide specific relaxation techniques and support information. Furthermore, if necessary, medical staff will be notified, enabling follow-up if the specific emotions persist.

[0684] In addition, the emotion engine, in conjunction with doctors' scheduling, enables efficient appointment scheduling based on the user's psychological state and urgency. This allows users to receive medical support optimized for their emotional state, enabling them to live with greater peace of mind. Through this system, support in the last mile of healthcare will be provided more effectively than ever before.

[0685] The following describes the processing flow.

[0686] Step 1:

[0687] The user inputs health consultation questions and their own emotional state through the device's interface.

[0688] Step 2:

[0689] The terminal converts the entered question and sentiment data into the appropriate data format and sends it to the server.

[0690] Step 3:

[0691] The server receives questions and sentiment data and inputs them into the generative AI model and sentiment engine.

[0692] Step 4:

[0693] The server uses an emotion engine to analyze the user's emotions and feeds the results back into the generating AI model.

[0694] Step 5:

[0695] Based on the analysis results from the server-generated AI model, the system generates health information and advice tailored to the user's emotions.

[0696] Step 6:

[0697] The server converts the generated information into data packets and sends them back to the terminal.

[0698] Step 7:

[0699] The device displays the information it receives on the screen in a format that is easy for the user to understand.

[0700] Step 8:

[0701] Users review the information provided and, if necessary, communicate further with medical staff or choose the recommended course of action.

[0702] By incorporating an emotion engine, personalized health management that takes into account the user's psychological state becomes possible.

[0703] (Example 2)

[0704] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0705] In modern medicine, providing individualized medical support that takes into account the patient's emotional state is a challenge. While conventional systems can respond to one-sided health consultations from patients, they struggle to adequately analyze those emotions and provide personalized support. This makes it difficult to provide optimal medical support that considers the patient's psychological state.

[0706] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0707] This invention includes a server that analyzes the user's health consultation and emotional data using a generative AI model and provides personalized health information and advice tailored to their psychological state; a server that adapts medical support based on the emotional data analyzed via a communication device; and a server that enables communication with medical technicians and continuous monitoring. This makes it possible to provide more personalized and effective medical support rooted in the user's emotional state.

[0708] A "generative AI model" is a type of artificial intelligence that has the ability to generate new information or solutions based on given data.

[0709] "Users" refer to individuals who use the system, primarily those who provide information about their health status and emotions.

[0710] "Health consultation" refers to the act of a user seeking inquiries or advice about their own health condition or symptoms.

[0711] "Emotional data" refers to information that indicates the user's psychological state, and is collected as text or audio data.

[0712] "Personalized health information" refers to information optimized for a specific individual based on their health status and emotional state.

[0713] "Medical support" refers to the support and advice provided regarding the user's health and medical needs.

[0714] A "communication device" is a device used for sending and receiving data, enabling communication between users and systems.

[0715] A "medical technologist" is a professional who works in a medical setting and is responsible for providing advice and follow-up to users through systems.

[0716] A "timetable" is a schedule that shows the times when medical professionals are available to provide consultations and services.

[0717] This invention is a system that provides more personalized medical support by allowing users to conduct health consultations and reflect their emotions in the process. This system utilizes components such as the user's terminal, server, emotion engine, and generative AI model.

[0718] Users can input health-related questions and emotional information via the device. The device features voice recognition and text input capabilities and functions as the user's interface. The device then sends this data to a server.

[0719] The server analyzes the received data using an emotion engine. The emotion engine uses natural language processing to extract emotions from text and audio and analyzes their content. Based on this, the server uses a generative AI model to generate personalized health information and advice for the user.

[0720] The generative AI model combines collected emotional data with health consultation information to create optimal advice. This allows users to receive more personalized medical support. For example, if a user inputs "I haven't been able to sleep at night recently," the server's emotional engine analyzes emotions such as "anxiety" and "stress," and based on that, provides advice such as relaxation techniques.

[0721] Communication equipment is necessary for smooth data transmission and reception, enabling communication and follow-up between users and medical technicians. Furthermore, by linking with doctors' schedules, it allows for optimal appointment scheduling tailored to the user's psychological and health condition.

[0722] Examples of prompts include, "How can I relax when I feel anxious?" and "Please tell me some ways to reduce stress right now." In this way, this invention makes it easier to provide personalized, emotion-based medical support.

[0723] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0724] Step 1:

[0725] The user inputs information about health concerns and emotions into the device via voice or text. This includes using specific prompts, such as "I've been having trouble sleeping at night lately." The input data is generated as either an audio file or text data. The device then prepares this data for transmission to the server as digital data.

[0726] Step 2:

[0727] The terminal sends input data obtained from the user to the server. The input includes text and voice data entered by the user, and data packets are sent as output to the server. Here, reliable and rapid data transmission takes place via a communication protocol.

[0728] Step 3:

[0729] The server inputs the received text or audio data into the emotion engine. The emotion engine applies natural language processing to the input data to analyze the user's emotions. Here, the text data is converted into emotion metadata, and emotion labels such as "anxiety" or "stress" are output.

[0730] Step 4:

[0731] The server inputs the emotion labels obtained from the emotion engine into the generating AI model. The generating AI model takes in the emotion labels and health consultation information together and starts the generation process. The engine performs data analysis to generate more personalized health information and advice, which is then converted into text-based advice.

[0732] Step 5:

[0733] The server sends the generated advice data to the terminal. The output data is formatted in a user-friendly format. The terminal notifies the user of this data visually or audibly, allowing them to review the advice.

[0734] Step 6:

[0735] In some cases, the server sends alerts to medical technicians based on the analysis results. This plays a crucial role in cases requiring follow-up, such as when emotions exceed a certain threshold. The data, including prompts, indicates the need for medical attention.

[0736] (Application Example 2)

[0737] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0738] Providing personalized health support based on the user's emotions is challenging in medical and nursing care settings. In particular, accurately understanding the user's emotional state and providing appropriate advice and follow-up is not adequately achieved with conventional systems. Efficient communication and coordination with medical staff and caregivers also remain challenges.

[0739] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0740] In this invention, the server includes means for analyzing health consultations from users using a generative AI model and responding with health information, means for analyzing information collected from medical devices and notifying in the event of an abnormality, means for enabling communication with medical personnel via a terminal, means for converting voice input into text using speech recognition technology, means for analyzing the user's emotions using natural language processing technology, means for generating personalized advice based on the user's emotional state, and means for sending notifications to caregivers and coordinating to provide necessary support. This enables optimal health support tailored to the user's emotions, realizing a rapid and appropriate response in medical and nursing care settings.

[0741] A "generative AI model" is an artificial intelligence framework that learns from large amounts of data and produces appropriate outputs and predictions even for new data.

[0742] "Speech recognition technology" is a technology that allows machines to understand human speech and convert it into text data.

[0743] "Natural language processing technology" refers to technologies that enable machines to understand, analyze, and generate human language.

[0744] "User" refers to the individual person who uses this system.

[0745] "Health support" refers to activities that provide users with advice and information regarding their health, and assist them in maintaining their health and resolving health problems.

[0746] "Medical devices" refer to equipment and instruments designed for use in medical settings.

[0747] "Emotional analysis" is a technology that determines emotions from the user's input and infers their psychological state.

[0748] "Medical staff" refers to specialists and staff who are responsible for their duties in a medical setting.

[0749] A "caregiver" refers to someone who assists elderly people or people with physical disabilities with their daily lives and medical care.

[0750] The system for implementing this invention mainly consists of a server and a user terminal. The user terminal has the function of converting the user's voice input into text using speech recognition technology. Specifically, it is conceivable to use speech recognition software such as Google Cloud Speech-to-Text.

[0751] The data, converted to text on the device, is sent to the server. On the server, an emotion engine is used to analyze the emotions in the text data using natural language processing techniques (e.g., NLTK, spaCy). Based on the results of this emotion analysis, a generative AI model (e.g., a model built with TensorFlow or PyTorch) generates personalized health support advice.

[0752] The server also monitors information from medical devices and notifies medical personnel if an abnormality is detected. Furthermore, it sends necessary notifications to caregivers and adjusts support based on the user's emotional state. Using APIs such as Google Calendar is suitable for these notifications and adjustments.

[0753] For example, if a user voice-inputs "I've been feeling stressed lately," the voice is converted to text on the device, and the server analyzes the emotion of "stress." The generative AI model then suggests specific relaxation methods to reduce stress and notifies the caregiver as needed. An example of a prompt message would be, "Please suggest appropriate relaxation methods when the user feels stressed."

[0754] In this way, this system provides optimal health support based on the user's emotions, enabling prompt and appropriate care services and medical support.

[0755] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0756] Step 1:

[0757] The user inputs their health consultation request via voice into the device. The device then uses speech recognition technology to convert this voice input into text data. Here, the input is voice data, and the output is text data. Specifically, the Google Cloud Speech-to-Text API is used to convert the voice to text.

[0758] Step 2:

[0759] The server receives text data sent from the terminal. The server analyzes this text data using natural language processing techniques to identify the user's emotions. The input is text data, and the output is emotion data. Specifically, it uses the NLTK library to perform emotion analysis and identify emotions such as "stress" and "anxiety."

[0760] Step 3:

[0761] The server uses a generative AI model to generate health support advice based on emotional data. The input is emotional data, and the output is personalized advice. Specifically, a generative AI model is built using TensorFlow, etc., and optimal advice is derived in response to prompts such as "suggest relaxation methods."

[0762] Step 4:

[0763] If the sentiment analysis determines the situation is urgent, the server will send a notification to caregivers or medical personnel. The input is sentiment data and urgency level, and the output is the sending of a notification. As a concrete example, the Google Calendar API is used to adjust schedules and issue a notification requesting immediate attention from caregivers.

[0764] Step 5:

[0765] The generated advice is fed back to the user through the device. The input is the generated advice, and the output is what is displayed to the user. Specifically, the advice is clearly displayed on the device screen, and the advice is also played back audibly using the audio playback function.

[0766] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0767] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0768] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0769] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0770] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0771] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0772] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0773] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0774] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0775] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0776] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0777] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0778] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0779] 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.

[0780] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0781] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0782] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0783] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0784] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0785] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0786] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0787] The following is further disclosed regarding the embodiments described above.

[0788] (Claim 1)

[0789] A means of analyzing health consultations from patients using a generative AI model and responding with health information,

[0790] A means of analyzing data collected from medical devices and notifying in case of abnormalities,

[0791] A means of enabling communication with medical staff via a terminal,

[0792] A system that includes this.

[0793] (Claim 2)

[0794] The system according to claim 1, further comprising means for providing a drug recipe based on a patient's past treatment data.

[0795] (Claim 3)

[0796] The system according to claim 1, further comprising means for managing online medical appointments based on a doctor's schedule.

[0797] "Example 1"

[0798] (Claim 1)

[0799] A method for analyzing health-related inquiries from users using a generative AI model and returning health information,

[0800] A means of analyzing information obtained from medical devices and issuing warnings in case of abnormalities,

[0801] A means of enabling communication with medical professionals via a communication terminal,

[0802] A means of sending user input information to a server and providing the user with advice generated by the server based on that information,

[0803] A system that includes this.

[0804] (Claim 2)

[0805] The system according to claim 1, further comprising means for providing treatment instruction information based on the user's past medical history.

[0806] (Claim 3)

[0807] The system according to claim 1, further comprising means for scheduling telemedicine appointments based on the schedules of healthcare professionals.

[0808] "Application Example 1"

[0809] (Claim 1)

[0810] A means of analyzing health consultations from users using a generative AI model and responding with health information,

[0811] A means of analyzing biological data collected from medical devices and issuing an alarm in case of abnormalities,

[0812] A means of enabling information exchange with medical professionals via communication terminals,

[0813] A means of monitoring the user's physiological functions in real time using a health monitoring device and proposing recommended actions using a generative AI model,

[0814] A system that includes this.

[0815] (Claim 2)

[0816] The system according to claim 1, further comprising means for providing drug information based on the user's past medical history.

[0817] (Claim 3)

[0818] The system according to claim 1, further comprising means for managing appointments for telemedicine based on the work schedules of healthcare professionals.

[0819] "Example 2 of combining an emotion engine"

[0820] (Claim 1)

[0821] A means of analyzing users' health consultation and emotional data using a generative AI model, and responding with personalized health information and advice tailored to their psychological state,

[0822] A means of adapting medical support based on emotional data analyzed via communication devices,

[0823] Means that enable communication with medical technicians and continuous monitoring,

[0824] A system that includes this.

[0825] (Claim 2)

[0826] The system according to claim 1, further comprising means for providing medical policies based on the results of user emotion analysis.

[0827] (Claim 3)

[0828] The system according to claim 1, further comprising means for optimizing online medical appointments based on the schedules of medical professionals.

[0829] "Application example 2 when combining with an emotional engine"

[0830] (Claim 1)

[0831] A means of analyzing health consultations from users using a generative AI model and responding with health information,

[0832] A means of analyzing information collected from medical devices and notifying in case of abnormalities,

[0833] A means of enabling communication with medical personnel via a terminal,

[0834] A means of converting voice input into text using speech recognition technology,

[0835] A means of analyzing the user's emotions using natural language processing technology,

[0836] A means of generating personalized advice based on the user's emotional state,

[0837] A means of sending notifications to caregivers and coordinating to provide necessary support,

[0838] A system that includes this.

[0839] (Claim 2)

[0840] The system according to claim 1, further comprising means for providing appropriate drug information based on past medical data.

[0841] (Claim 3)

[0842] The system according to claim 1, further comprising means for managing online medical appointment bookings based on the operational schedule of medical professionals. [Explanation of Symbols]

[0843] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of analyzing health consultations from patients using a generative AI model and responding with health information, A means of analyzing data collected from medical devices and notifying in case of abnormalities, A means of enabling communication with medical staff via a terminal, A system that includes this.

2. The system according to claim 1, further comprising means for providing a drug recipe based on the patient's past treatment data.

3. The system according to claim 1, further comprising means for managing online medical appointments based on a doctor's schedule.

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