System

Abstract

A system is provided.SOLUTION: A system comprising: devices that allow a user to input patient conditions and symptoms; generative model means for analyzing the input patient information in real-time; communication means for transmitting information analyzed by the generative model means to emergency services; and means for receiving responses from the emergency services and notifying the user of an optimal response.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Modern emergency medical systems are under strain due to the spread of new viruses and population growth. Specifically, ambulance arrival times are increasing, making it difficult to quickly transport patients to the appropriate hospital. Furthermore, difficulties in properly assessing patients' conditions and providing optimal emergency care are increasing, resulting in delayed treatment and a lower patient survival rate. Given this current situation, there is a need to develop a new emergency medical system that improves responsiveness and efficiency. [Means for solving the problem]

[0005] To solve this problem, the present invention provides the following system. It includes a device that allows a user to input a patient's condition and symptoms, a generative modeling means that analyzes the input patient information in real time, a communication means that transmits the information analyzed by the generative modeling means to an emergency service, and a means that receives responses from the emergency service and notifies the user of the optimal emergency response. Furthermore, the system includes a means for managing the operation status and traffic information of ambulances in the area, a means for managing the medical capacity and equipment status of each medical institution, and a means for integrating patient information, ambulance operation status, traffic information, and medical institution information to select the optimal transport route and destination. Furthermore, by providing a hospital means for accepting patient information, adjusting medical capacity, and preparing for appropriate acceptance, and a means for updating medical capacity and acceptance status in real time and sharing this throughout the system, rapid and efficient emergency medical response is achieved.

[0006] A "user" is an individual or institution that uses the system to input patient information and receive analysis results and emergency response suggestions.

[0007] A "device" is an information terminal (e.g., smartphone, tablet, computer) through which a user inputs a patient's condition or symptoms.

[0008] The "generative model means" is an AI model that analyzes input patient information in real time and generates suggestions on urgency and emergency response.

[0009] "Communication means" refers to a network communication function for transmitting analyzed patient information to emergency services and receiving responses from emergency services.

[0010] "Ambulance service" refers to an organization that provides ambulances, transports patients, and responds to emergencies at appropriate medical facilities.

[0011] "Emergency response" refers to the immediate first aid and instructions given to patients at the scene of an emergency medical care.

[0012] "Ambulance operation status within the area" refers to the operation status and deployment status of ambulances deployed within the area.

[0013] "Traffic information" includes information on road traffic conditions, congestion, accidents, and the like within the area.

[0014] "Medical institution" means an institution, including a hospital or clinic, that provides emergency medical care.

[0015] "Medical treatment capabilities" refers to the overall capabilities of a medical institution, including facilities, personnel, and technology for medical treatment.

[0016] An "optimal transport route" is a route selected to transport a patient in the fastest and most optimal way.

[0017] "Destination" refers to the medical institution to which the patient is transported for emergency medical treatment.

[0018] "Hospital procedures" are the systems and processes within a hospital that prepare for patient admission and coordinate care capacity.

[0019] "Sharing across the entire system" refers to the sharing of information in real time across multiple devices, servers, medical institutions, etc. [Brief explanation of the drawings]

[0020] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0021] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0022] First, the terms used in the following description will be explained.

[0023] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0024] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0025] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0026] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0027] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0028] [First embodiment]

[0029] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0030] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0031] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0032] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0033] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0034] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0035] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0036] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0038] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0039] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0040] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0041] This invention is a system that uses generative AI to provide real-time support in emergency medical settings, and is implemented with the following configuration.

[0042] User terminal

[0043] The user's device can be a smartphone, tablet, or computer. The user launches the application and is presented with an interface for entering patient information, including symptoms (e.g., chest pain, shortness of breath) and vital signs (e.g., heart rate, blood pressure). This information is sent in real time to the generative AI model on the device for analysis.

[0044] Analysis of generative AI models

[0045] The device uses a generative AI model to analyze the patient information entered. For example, if chest pain and a heart rate of 120 bpm are entered, the AI ​​model will generate the analysis results of "high urgency" and "suspected heart attack." This analysis result is immediately fed back to the user, and the necessary emergency response is suggested.

[0046] Sending information via communication means

[0047] When the user presses the send button, the device sends the patient information and analysis results to the server. This communication is carried out over the Internet using a safe and secure data transmission protocol.

[0048] Server Processing

[0049] The server receives patient information sent from the user's device, which is then stored in a database. The server then connects with related systems and external APIs to obtain information on the operation status and traffic of ambulances in the area. It also collects information on the medical capabilities and available facilities of each medical institution.

[0050] The server integrates this information and runs an algorithm to select the optimal transport route and hospital. For example, the "Central Hospital" is selected as the optimal destination, and the optimal route that avoids traffic jams is determined.

[0051] Notification of results

[0052] The server sends the results of the selection (optimal transport route and hospital) to the user's device and the emergency service. A notification is displayed on the user's device, allowing the user to confirm the next action to be taken.

[0053] Hospital Treatment

[0054] The hospital receives the patient information sent from the server and prepares to accept the patient using its internal system. For example, this includes calling in an emergency medical team and preparing the necessary medical equipment. The hospital also updates its medical capabilities as needed and shares the latest information with the server, ensuring that the entire system is always running with the latest information.

[0055] Specific examples

[0056] For example, if a user experiences a heart attack, they can quickly launch the app on their smartphone and input "chest pain" and "heart rate 120 bpm." The AI ​​analyzes this information, determines it to be a "high urgency," and notifies the user to "call an ambulance immediately." When the user presses the send button, the information is sent to the server.

[0057] The server receives the information and determines the optimal transport route and destination based on the ambulance operation status, traffic information, and the medical capabilities of each hospital in the area. For example, it may determine that transport to the "Central Hospital" is the most appropriate route, as it avoids traffic jams.

[0058] Once the optimal transport route and hospital are notified to the user device and emergency services, an ambulance will be dispatched quickly and preparations for reception at the "Central Hospital" will begin, thus achieving a fast and efficient emergency medical response.

[0059] The processing flow will be explained below.

[0060] Step 1:

[0061] Users launch the application on their smartphone, tablet, or other device and enter their patient's condition and symptoms, such as chest pain and a heart rate of 120 bpm.

[0062] Step 2:

[0063] The device sends the patient information (symptoms and vital signs) entered by the user to the generative AI model, which then analyzes the information in real time and generates a level of urgency and emergency response.

[0064] Step 3:

[0065] The device receives the analysis results from the generative AI model and notifies the user, for example, displaying results such as "high urgency" and "suspected heart attack."

[0066] Step 4:

[0067] The user checks the analysis results and, if necessary, presses the send button to determine the next action to take. Pressing this button will cause the patient information to proceed to the next procedure.

[0068] Step 5:

[0069] The device sends patient information (symptoms, vital signs, and analysis results) to the server. This communication is carried out securely via the Internet.

[0070] Step 6:

[0071] The server receives the patient information sent from the user terminal and stores it in a database, allowing for centralized management of the patient's condition.

[0072] Step 7:

[0073] The server collects real-time information on the operational status and location of ambulances in the area, as well as traffic information, and obtains this information from related systems and external APIs as needed.

[0074] Step 8:

[0075] The server manages the medical capabilities and equipment status of each medical institution and updates this information, making it possible to select the most suitable hospital.

[0076] Step 9:

[0077] The server integrates patient information, ambulance operation status, traffic information, and information from each medical institution, and executes an algorithm to select the optimal transport route and destination hospital. For example, it may determine that the "Central Hospital" is the best option.

[0078] Step 10:

[0079] The server sends the selection result to the user terminal and the emergency service, which then starts arranging an ambulance and transporting the patient.

[0080] Step 11:

[0081] The user can check the optimal transport route and destination displayed on the device and wait for the ambulance to arrive. If necessary, they will be instructed to continue providing first aid.

[0082] Step 12:

[0083] The emergency services receive the notification from the server, follow the designated transport route to the patient, and begin transporting the patient promptly.

[0084] Step 13:

[0085] The hospital begins preparations for admission based on the patient information sent from the server, convening an emergency medical team and preparing the necessary medical equipment.

[0086] Step 14:

[0087] Hospitals update their medical capacity in real time and send the latest information to a server, which makes it easier to adjust the intake of other patients.

[0088] Step 15:

[0089] When the ambulance arrives, the user follows the instructions to transport the patient, and the emergency services and hospitals work together to ensure a prompt response.

[0090] The above processing steps enable a fast and efficient emergency medical response.

[0091] Example 1

[0092] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0093] In emergency medical situations, rapid and appropriate responses are required, but it is not easy to process a large amount of information at once, such as assessing the situation on-site, deciding where to transport patients, and taking into account traffic information. It is also difficult to grasp the medical capabilities and acceptance status of medical institutions. This poses the issue of the possibility of delaying appropriate treatment for patients.

[0094] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0095] In this invention, the server includes terminal means that allows a user to input the patient's condition and symptoms, generative model means that analyzes the input patient information in real time, server means that saves and manages the information analyzed by the generative model means, means that prompts an emergency service to respond via the server means, means that receives a response from the emergency service and notifies the user of the optimal emergency response, means that manages the operation status and traffic information of ambulances in the area, means that manages the medical capacity and equipment status of each medical institution, means that integrates patient information, ambulance operation status, traffic information, and medical institution information to select the optimal transport route and destination, medical institution means that accepts patient information, adjusts medical capacity, and prepares for appropriate acceptance, and means that updates the medical capacity and acceptance status in real time and shares it throughout the system, thereby enabling prompt and appropriate response at the scene of an emergency medical care.

[0096] The "terminal means by which a user can input the condition or symptoms of a patient" is a device such as a smartphone, tablet, or computer, which is an apparatus that allows a user to input patient information using an interface.

[0097] The "generative model means for analyzing input patient information in real time" is a system in which a generative AI model instantly analyzes input patient information and provides information on the urgency of symptoms and medical decisions.

[0098] "Server means for storing and managing information analyzed by the generative model means" refers to a data storage and management device for storing the analysis results by the generative AI model in a database and centrally managing this information.

[0099] The "means for encouraging emergency services to respond via server means" refers to a communication function and processing system that transmits the analysis results to emergency services via a server and encourages emergency response.

[0100] The "means for receiving responses from emergency services and notifying the user of the most appropriate emergency response" is a system for receiving responses from emergency services and notifying the user of the appropriate emergency response method in real time based on that information.

[0101] "Means for managing ambulance operation status and traffic information within the region" refers to a system that collects and manages ambulance usage status and real-time traffic conditions within the region, and consolidates this information in a centralized manner.

[0102] "Means for managing the medical treatment capacity and equipment status of each medical institution" refers to a function for collecting and managing information on the current medical treatment capacity and available equipment of each medical institution.

[0103] "Means of integrating patient information, ambulance operation status, traffic information, and medical institution information to select the optimal transport route and destination" refers to an algorithm and system that integrates this information and automatically selects the optimal transport route and destination.

[0104] "Medical institution means for accepting patient information, adjusting medical treatment capabilities, and making appropriate preparations for acceptance" is a system that allows medical institutions to adjust their medical treatment capabilities and prepare the necessary medical equipment and specialists based on patient information sent from the server.

[0105] "Means for updating medical treatment capacity and admission status in real time and sharing it throughout the system" refers to a means for updating medical institutions' medical treatment capacity and admission status at any time and sharing this information throughout the system in real time.

[0106] This invention is a system that uses generative AI models to provide real-time support in emergency medical settings. The system mainly consists of the following components:

[0107] User terminal

[0108] The user's device can be a smartphone, tablet, or computer. The user launches the application and is presented with an interface for entering patient information, including symptoms (e.g., chest pain, shortness of breath) and vital signs (e.g., heart rate, blood pressure). This information is sent in real time to the generative AI model on the device for analysis.

[0109] Analysis of generative AI models

[0110] The device uses a generative AI model to analyze the patient information entered. For example, if chest pain and a heart rate of 120 bpm are entered, the generative AI model will generate the analysis results of "high urgency" and "suspected heart attack." This analysis result is immediately fed back to the user, and the necessary emergency response is suggested.

[0111] Sending information via communication means

[0112] When the user presses the send button, the terminal sends the patient information and analysis results to the server. This communication is carried out over the Internet using a secure data transmission protocol (e.g., HTTPS).

[0113] Server Processing

[0114] The server receives patient information sent from the user's device. After receiving the data, it stores it in a database. The server also obtains information on the operation status of ambulances in the area, traffic information, and information on the medical capabilities and available facilities of each medical institution, and executes an algorithm that integrates this information to select the optimal transport route and destination. For example, the "Central Hospital" is selected as the optimal destination, and the optimal route that avoids traffic congestion is determined.

[0115] Notification of results

[0116] The server sends the selection results (optimal transport route and hospital) to the user's device and the emergency service. A notification message is displayed on the user's device, allowing the user to confirm the next action to be taken.

[0117] Hospital Treatment

[0118] The hospital receives the patient information sent from the server and prepares to accept the patient using its internal system. For example, this includes calling in an emergency medical team and preparing the necessary medical equipment. The hospital also updates its medical capabilities as needed and shares the latest information with the server, ensuring that the entire system is always running with the latest information.

[0119] Specific examples

[0120] For example, if a user experiences a heart attack, they can launch the app on their smartphone and enter "chest pain" and "heart rate 120 bpm." The generating AI analyzes this information, determines it to be a "high urgency," and notifies the user to "call an ambulance immediately." When the user presses the send button, the information is sent to the server. The server determines the optimal transport route and destination based on the ambulance operation status in the area, traffic information, and the medical capabilities of each medical institution. For example, it may determine that transport to the "Central Hospital" is appropriate, taking the fastest route that avoids traffic jams. The result is notified to the user's device and emergency services, and an ambulance is quickly dispatched, while preparations begin at the "Central Hospital." This enables fast and efficient emergency medical response.

[0121] Prompt Sentence Examples

[0122] "I have a patient with chest pain and a heart rate of 120 bpm. What is the appropriate first aid?"

[0123] These prompts can be fed into a generative AI model to help you get a consultation faster.

[0124] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0125] Step 1:

[0126] The user inputs the patient's condition and symptoms.

[0127] The user launches the application on their smartphone, tablet, or computer. The application displays an interface for entering patient information. The user enters the patient's symptoms (e.g., chest pain, shortness of breath) and vital signs (e.g., heart rate, blood pressure).

[0128] Input: Patient symptoms and vital signs

[0129] Output: Entered patient information

[0130] Specific behavior: The user enters "chest pain" and "heart rate 120 bpm" into the application's input form.

[0131] Step 2:

[0132] Analyzing patient information with generative AI models

[0133] The device sends the patient information entered in step 1 to the generative AI model within the device. The generative AI model analyzes the information in real time and determines the urgency of the patient's symptoms. For example, if chest pain and a heart rate of 120 bpm are entered, the generative AI model will generate the analysis results of "high urgency" and "suspected heart attack."

[0134] Input: Patient information entered

[0135] Output: Analysis result (e.g. high urgency, suspected heart attack)

[0136] Specific operation: The device passes the input patient information to the generative AI model, which then returns an analysis result stating, "This symptom is of high urgency and is suspected to be a heart attack."

[0137] Step 3:

[0138] Analysis results are fed back to the user

[0139] The device immediately provides the user with the analysis results from the generative AI model. Based on the analysis results, the device displays the necessary emergency response instructions. For example, instructions such as "Call an ambulance immediately" are displayed.

[0140] Input: Analysis results of the generative AI model

[0141] Output: Emergency response instructions displayed to the user

[0142] Specific behavior: The device displays a message to the user indicating "High Urgency" and "Call an ambulance immediately."

[0143] Step 4:

[0144] User sends information to server

[0145] When the user presses the send button, the device sends the entered patient information and the analysis results of the generated AI model to the server. This communication is carried out over the internet using a safe and secure data transmission protocol such as HTTPS.

[0146] Input: Entered patient information and analysis results of the generated AI model

[0147] Output: Information sent to the server

[0148] What happens: The user clicks the "Send" button and the device sends the information to the server using HTTPS.

[0149] Step 5:

[0150] The server processes the information and determines the optimal delivery route and destination.

[0151] The server receives patient information sent from the device and stores it in a database. The server then collects information on the operation status of ambulances in the area, traffic information, and information on the medical capabilities and facilities of each medical institution, and executes an algorithm that integrates this information to select the optimal transport route and destination. For example, the "Central Hospital" may be selected as the optimal destination, and the optimal route that avoids traffic congestion may be determined.

[0152] Input: Patient information, traffic information, ambulance operation status, medical institution medical capacity

[0153] Output: Optimal route and destination

[0154] What it does: The server stores the information in a database, retrieves the latest traffic information from an external API, and runs an algorithm to determine that "Central Hospital is the best choice."

[0155] Step 6:

[0156] The server notifies the user device and emergency services

[0157] The server then sends the optimal transport route and hospital to the user's device and the emergency service. A notification message is displayed on the user's device, allowing the user to confirm the next steps.

[0158] Enter: Optimal Route and Destination Information

[0159] Output: Notification message to user terminal and emergency services

[0160] Specific operation: The server sends a message to the user device and emergency services saying, "Central Hospital is the best option. Route A is the best route to avoid traffic jams."

[0161] Step 7:

[0162] Preparations for hospital admission

[0163] The hospital receives the patient information sent from the server and prepares to accept the patient using its internal system, including calling in an emergency medical team and preparing the necessary medical equipment. The hospital also updates its medical capabilities as needed and shares the latest information with the server, ensuring that the entire system is always running with the latest information.

[0164] Input: Patient information sent from the server

[0165] Output: Readiness status

[0166] Specific operations: The hospital receives the patient information, starts preparing the emergency room, and updates the medical capabilities to the server.

[0167] (Application example 1)

[0168] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0169] Modern society demands rapid emergency medical response. However, various factors, such as traffic congestion and overwhelmed hospitals, hinder rapid response. In urban areas in particular, ambulances are often delayed due to traffic congestion. Furthermore, because the quality of emergency response at the scene depends on the experience and knowledge of medical staff, there are cases where an appropriate initial response is not provided. To solve these problems, more efficient and accurate analysis of medical information and a seamless emergency medical support system that includes autonomous driving technology are required.

[0170] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0171] In this invention, the server includes a device that allows a user to input the patient's condition and symptoms, a generative model means that analyzes the input patient information in real time, a communication means that transmits the information analyzed by the generative model means to an emergency service, a means that receives a response from the emergency service and notifies the user of the optimal emergency response, and a means that dispatches an autonomous ambulance along an optimal transport route. This enables rapid analysis of patient information and dispatch of an autonomous ambulance along an optimal route that avoids traffic congestion. This enables faster and more accurate emergency response and more appropriate initial response at the scene.

[0172] "Device that allows a user to input a patient's condition or symptoms" refers to a smartphone, tablet, smart glasses, or other input device that allows a user to directly input a patient's physical condition or specific symptoms in real time.

[0173] "Generative model means for analyzing input patient information in real time" refers to AI models and algorithms for analyzing input patient data, thereby enabling instant medical assessment and determination of urgency.

[0174] "Communication means for transmitting information analyzed by the generative model means to emergency services" refers to network technologies and communication protocols for quickly transmitting the analysis results to the appropriate emergency services, and is typically a means for secure data transmission via the Internet.

[0175] "Means for receiving responses from emergency services and informing the user of the best emergency response" refers to a system or application that receives instructions and information from emergency services and, based on that, notifies the user of the next steps to take and emergency procedures.

[0176] "Means for dispatching autonomous ambulances along the optimal transport route" refers to technologies and systems that calculate the quickest and most efficient route based on input patient information, traffic conditions, medical institution medical capabilities, etc., and dispatch ambulances using autonomous driving technology.

[0177] "Means for managing the operational status and traffic information of ambulances within the region" refers to a system or platform for monitoring and managing the current location, operational status, traffic congestion and accident information, etc. of ambulances in real time.

[0178] "Means for managing the medical treatment capacity and equipment status of each medical institution" refers to a system for managing each medical institution's current capacity, the operating status of medical staff, and information on available medical equipment and facilities, and constantly updating it to the latest version.

[0179] "Means for integrating patient information, ambulance operation status, traffic information, and medical institution information to select the optimal transport route and destination" refers to algorithms and systems for comprehensively processing the various types of information mentioned above and determining the most appropriate transport destination and optimal travel route.

[0180] "Means for providing information on transport routes and destinations to autonomous ambulances" refers to the technology and systems that transmit the calculated optimal transport route and destination information to the autonomous ambulance's system in real time and control autonomous driving based on that information.

[0181] "Hospital means for receiving patient information, adjusting medical treatment capacity, and making appropriate preparations for admission" refers to a system and management means for adjusting the admission system within the hospital and arranging for the necessary medical staff and equipment based on the received patient information.

[0182] "Means for updating medical treatment capacity and admission status in real time and sharing it throughout the system" refers to technologies and platforms for constantly monitoring medical institutions' medical treatment capacity and admission status, and instantly updating and sharing this information within the entire system.

[0183] "Means for predicting the arrival time of an autonomous ambulance and adjusting the hospital's reception readiness" refers to systems and technologies for calculating the estimated arrival time of an ambulance and appropriately adjusting the hospital's reception readiness based on this.

[0184] This invention provides a fast and efficient emergency medical system that utilizes an autonomous ambulance. The specific configuration and operation procedure for implementing this invention are described below.

[0185] First, the user terminal can be a smartphone, tablet, smart glasses, or other device. Users can input the patient's symptoms and vital signs through these devices. For example, if a patient inputs "chest pain" and "heart rate 120 bpm," the generative AI model on the device will instantly analyze this information and generate a diagnosis such as "high urgency" or "suspected heart attack." This analysis uses generative AI models such as TensorFlow and PyTorch.

[0186] The information analyzed by the generative AI model is sent to a server via a communication method using a REST API, which securely transmits patient information and analysis results over the internet. The server stores the received patient information in a database and connects with Google Maps API and the company's own traffic analysis system to obtain real-time information on the operation status of local ambulances and traffic information.

[0187] The server then manages the medical capabilities and equipment status of each medical institution and integrates this information. Based on the stored patient information, ambulance operation status, traffic information, and medical institution information, the optimal transport route and medical institution are selected. This algorithm often applies machine learning models and operations research methods.

[0188] The server provides the selection results (optimal transport route and destination medical institution) to the autonomous ambulance dispatch system. This dispatch system then dispatches the autonomous ambulance along the optimal route based on the selection results. The autonomous driving technology uses control software and sensor systems for autonomous vehicles.

[0189] The information from the server is also sent to the medical institution, and once it has been received, medical staff prepare to accept the patient using their internal system. Medical capacity and acceptance status are updated in real time and shared across the entire server, allowing the hospital to respond quickly.

[0190] As an example, use the following prompt:

[0191] Enter your symptoms: Chest pain

[0192] Enter your heart rate: 120 bpm

[0193] Enter your blood pressure: 140 / 90 mmHg

[0194] The AI ​​then responds with the analysis:

[0195] Analysis result: Suspected heart attack (high urgency)

[0196] Sending data...

[0197] Optimal route: The fastest route avoiding traffic jams

[0198] Self-driving ambulances have been deployed.

[0199] In this way, by using the system of the present invention, it is possible to perform real-time analysis of patients, provide appropriate initial responses, provide optimal transport routes, and quickly dispatch autonomous ambulances, thereby significantly improving the quality and speed of emergency medical care.

[0200] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0201] Step 1:

[0202] Users input the patient's symptoms and vital signs using a device such as a smartphone or tablet. They launch the application and enter specific symptoms (e.g., chest pain) and vital signs (e.g., heart rate 120 bpm, blood pressure 140 / 90 mmHg) through the interface. This data is then analyzed by the generative AI model.

[0203] Input: Symptom and vital sign data

[0204] Output: Input data to a generative AI model

[0205] Step 2:

[0206] A generative AI model installed on the device analyzes input patient information in real time. It uses machine learning libraries such as TensorFlow and PyTorch to determine the urgency and suspected medical condition from the patient information. The results of this analysis are immediately fed back to the user as emergency response suggestions.

[0207] Input: Patient information (symptoms, vital signs)

[0208] Output: Diagnosis result (urgency, condition)

[0209] Step 3:

[0210] The user checks the analysis results of the generative AI model and, if necessary, presses the send button to send the information to the server. The information is securely transmitted to the server via the internet via a REST API.

[0211] Input: Analysis results (urgency, condition), user's sending operation

[0212] Output: Patient information sent to the server

[0213] Step 4:

[0214] The server receives patient information sent from the user's device and stores it in a database. Based on the received data, it connects with Google Maps API and its own traffic analysis system to obtain information on the operation status of ambulances in the area and traffic information. Based on this information, it determines the optimal transport route and medical institution.

[0215] Input: Patient information submitted

[0216] Output: Selection of optimal transport route and medical institution

[0217] Step 5:

[0218] The server provides the results of the selection (optimal transport route and destination medical institution) to the autonomous ambulance dispatch system. The autonomous vehicle's control software then dispatches the ambulance based on the selected route.

[0219] Input: Selection result of optimal transport route and destination

[0220] Output: Dispatch of autonomous ambulance

[0221] Step 6:

[0222] The autonomous ambulance arrives at the patient's location and begins transporting the patient based on the selected route. Meanwhile, the server also sends information to the medical institution, which then prepares to accept the patient.

[0223] Input: Notification of arrival of autonomous ambulance, sending admission information to hospital

[0224] Output: Patient transport by ambulance, preparation for admission at medical institution

[0225] Step 7:

[0226] Medical institutions receive the information sent from the server in real time and proceed with preparations for admission using their internal systems. Medical treatment capabilities and admission status are updated as needed and shared across the entire server.

[0227] Input: Acceptance information from the server

[0228] Output: Ready to accept patients, updated medical capabilities

[0229] Step 8:

[0230] Once the ambulance arrives at the hospital, the patient receives prompt and appropriate medical treatment, and real-time updated information allows medical staff to provide the most appropriate response.

[0231] Input: Ambulance arrival, patient handover

[0232] Output: Patient treatment start, real-time treatment response

[0233] This will enable a series of steps: rapid analysis of patient information, appropriate initial response, provision of optimal transport routes, rapid dispatch of autonomous ambulances, and rapid preparations for admission at medical institutions.

[0234] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0235] This invention is a system that uses generative AI and an emotion engine to provide real-time support in emergency medical settings, and is implemented with the following configuration.

[0236] User terminal

[0237] The user's device can be a smartphone, tablet, or computer. The user launches the application and is presented with an interface for entering patient information, including symptoms (e.g., chest pain, shortness of breath) and vital signs (e.g., heart rate, blood pressure). This information is sent in real time to the generative AI model on the device for analysis.

[0238] Analysis of generative AI models

[0239] The device uses a generative AI model to analyze the patient information entered. For example, if chest pain and a heart rate of 120 bpm are entered, the AI ​​model will generate the analysis results of "high urgency" and "suspected heart attack." This analysis result is immediately fed back to the user, and the necessary emergency response is suggested.

[0240] Incorporating an emotion engine

[0241] The emotion engine recognizes the user's psychological state. When the user inputs information, the device analyzes the user's facial expression, tone of voice, input actions, etc., and recognizes the user's emotions through the emotion engine. For example, if the user is feeling impatient or anxious, the device will suggest appropriate emergency measures.

[0242] Sending information via communication means

[0243] When the user presses the send button, the device sends the patient information and analysis results to the server. This communication is securely carried out over the Internet.

[0244] Server Processing

[0245] The server receives patient information sent from the user's device, which is then stored in a database. The server then connects with related systems and external APIs to obtain information on the operation status and traffic of ambulances in the area. It also collects information on the medical capabilities and available facilities of each medical institution.

[0246] The server integrates this information and runs an algorithm to select the optimal transport route and hospital. For example, the "Central Hospital" is selected as the optimal destination, and the optimal route that avoids traffic jams is determined.

[0247] Notification of results

[0248] The server sends the selection results (optimal transport route and hospital) to the user terminal and the emergency service, which then starts arranging an ambulance and transporting the patient.

[0249] Hospital Treatment

[0250] The hospital prepares to accept the patient based on the patient information sent from the server, convenes an emergency medical team, and prepares the necessary medical equipment. The hospital also updates its medical capabilities as needed and shares the latest information with the server, ensuring that the entire system is always running with the latest information.

[0251] Specific examples

[0252] For example, if a user experiences a heart attack, they can quickly launch the app on their smartphone and input "chest pain" and "heart rate 120 bpm." The generative AI analyzes this information and determines a "high level of urgency." At the same time, the emotion engine analyzes the user's facial expressions and tone of voice and recognizes that the user is experiencing strong anxiety or impatience.

[0253] As a result, the app sends a reassuring notification to the user saying, "Please stay calm, we'll call an ambulance." When the user presses the send button, the information is sent to the server.

[0254] The server receives the information and determines the optimal transport route and destination based on the ambulance operation status, traffic information, and the medical capabilities of each hospital in the area. For example, it may determine that transport to the "Central Hospital" is the most appropriate route, as it avoids traffic jams.

[0255] The selection result is notified to the user's device, and the user confirms the next action to be taken. An ambulance arrives and the patient is safely transported to the "Central Hospital," achieving a fast and efficient emergency medical response. The use of the emotion engine can reduce the user's anxiety and stress.

[0256] The processing flow will be explained below.

[0257] Step 1:

[0258] Users launch the application on their smartphone, tablet, or other device and enter their patient's condition and symptoms, such as chest pain and a heart rate of 120 bpm.

[0259] Step 2:

[0260] The device also sends the patient information (symptoms and vital signs) entered by the user to the emotion engine. At this time, the device analyzes the user's facial expressions, tone of voice, input actions, etc. to recognize the user's emotions.

[0261] Step 3:

[0262] The emotion engine analyzes the user's emotions (e.g., impatience, anxiety, fear) and sends the results to the generative AI model. The generative AI model then combines the patient information and the user's emotion data to generate an analysis result, such as "high urgency" and "suspected heart attack."

[0263] Step 4:

[0264] The device receives the analysis results from the generative AI model and notifies the user, displaying a reassuring message (e.g., "Please stay calm, we will call an ambulance immediately") based on the results of the emotion engine.

[0265] Step 5:

[0266] The user checks the analysis results and, if necessary, presses the send button to determine the next action to take. Pressing this button will cause the patient information to proceed to the next procedure.

[0267] Step 6:

[0268] The device sends patient information (symptoms, vital signs, and analysis results) to the server. This communication is carried out securely via the Internet.

[0269] Step 7:

[0270] The server receives the patient information sent from the user terminal and stores it in a database, allowing for centralized management of the patient's condition.

[0271] Step 8:

[0272] The server collects real-time information on the operational status and location of ambulances in the area, as well as traffic information, and obtains this information from related systems and external APIs as needed.

[0273] Step 9:

[0274] The server manages the medical capabilities and equipment status of each medical institution and updates this information, making it possible to select the most suitable hospital.

[0275] Step 10:

[0276] The server integrates patient information, ambulance operation status, traffic information, and information from each medical institution, and executes an algorithm to select the optimal transport route and destination hospital. For example, it may determine that the "Central Hospital" is the best option.

[0277] Step 11:

[0278] The server sends the selection result to the user terminal and the emergency service, which then starts arranging an ambulance and transporting the patient.

[0279] Step 12:

[0280] The user can check the optimal transport route and destination displayed on the device and wait for the ambulance to arrive. If necessary, they will be instructed to continue providing first aid.

[0281] Step 13:

[0282] The emergency services receive the notification from the server, follow the designated transport route to the patient, and begin transporting the patient promptly.

[0283] Step 14:

[0284] The hospital begins preparations for admission based on the patient information sent from the server, convening an emergency medical team and preparing the necessary medical equipment.

[0285] Step 15:

[0286] Hospitals update their medical capacity in real time and send the latest information to a server, which makes it easier to adjust the intake of other patients.

[0287] Step 16:

[0288] When the ambulance arrives, the user follows the instructions to transport the patient, and the emergency services and hospitals work together to ensure a prompt response.

[0289] Example 2

[0290] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0291] Conventional emergency medical systems have difficulty in quickly and accurately collecting and analyzing patient information, and they also do not propose emergency responses that take into account the user's psychological state. As a result, it is difficult to provide optimal responses in real time according to the patient's condition, which can lead to serious medical accidents and stress for users.

[0292] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes terminal means that allows a user to input the patient's condition and symptoms, generative AI model means that analyzes the input patient information in real time, emotion engine means that analyzes the user's emotions from their facial expressions, tone of voice, and input actions, means for proposing emergency responses based on the information analyzed by the generative AI model means and the user's emotional state, communication means that transmits the information and suggestions to emergency services, and means that receive responses from emergency services and notify the user of the optimal emergency responses. This enables prompt and appropriate medical response that takes into account the patient's condition and the user's psychological state.

[0293] A "user terminal" is a device used by a user to input a patient's condition or symptoms, and includes a smartphone, tablet, computer, etc.

[0294] A "generative AI model" is an artificial intelligence model that analyzes input patient information in real time and generates analysis results based on the urgency and symptoms.

[0295] An "emotion engine" is a means of analyzing a user's psychological state from their facial expressions, tone of voice, and input actions, and recognizing their emotions.

[0296] "Communication means" means for transmitting the information analyzed by the generative AI model and the user's emotional state to emergency services and for receiving a response from emergency services.

[0297] "Emergency services" refers to medical services for transporting emergency patients and providing first aid, including the deployment of ambulances and cooperation with hospitals.

[0298] "Optimal emergency response measures" are measures that suggest necessary emergency responses to users based on the analysis results of the generative AI model and emotion engine.

[0299] "Means for managing the operational status and traffic information of ambulances within the area" refers to means for collecting and managing the current status and traffic information of ambulances deployed within the area.

[0300] "Means for managing the medical treatment capacity and equipment status of each medical institution" refers to means for collecting and managing the status of the medical treatment capacity and available medical equipment of each medical institution.

[0301] The "means for selecting the optimal transport route and destination" refers to a means for selecting the optimal transport route and destination based on patient information, ambulance operation status, traffic information, and information from medical institutions.

[0302] "Hospital means" refers to the means used to receive patient information, adjust medical capabilities, and prepare for appropriate reception.

[0303] "Means for updating medical treatment capacity and admission status in real time and sharing it throughout the system" refers to a means for updating a hospital's medical treatment capacity and patient admission status in real time and sharing that information throughout the system.

[0304] This invention is a system that provides real-time support in emergency medical situations using a generative AI model and an emotion engine. The system operates through collaboration between the user device, the generative AI model, the emotion engine, a server, and the hospital.

[0305] User terminal

[0306] User devices include smartphones, tablets, and computers. The user launches an application on the device and inputs the patient's condition and symptoms. Specifically, the user inputs symptoms such as "chest pain" and "heart rate 120 bpm."

[0307] Generative AI Models

[0308] The information entered by the user is sent to a generative AI model on the device. This generative AI model is implemented in a programming language such as Python, using TensorFlow or PyTorch. The generative AI model analyzes the input information and identifies the urgency and possible symptoms. For example, if the input is "chest pain" and "heart rate 120 bpm," the AI ​​model will generate analysis results such as "high urgency" and "possible heart attack."

[0309] Emotion Engine

[0310] The device also has a built-in emotion engine. This uses a camera and microphone to analyze the user's facial expressions, tone of voice, and input actions to recognize the user's psychological state. For example, if the user is feeling strong anxiety or impatience, the device will send a notification to calm them down. Specifically, the device will display a message saying, "Please stay calm, we'll call an ambulance."

[0311] communication means

[0312] When the user presses the send button, the device sends the analysis results of the generative AI model and the recognition results of the emotion engine to the server. This communication is securely carried out via the HTTPS protocol.

[0313] Server Roles

[0314] The server receives the information sent from the device and stores it in a database. Additionally, the server obtains information such as the ambulance operation status, traffic conditions, and the medical capabilities of each medical institution in the area from related systems and external APIs (e.g., Google Maps API). This information is integrated and an algorithm is run to select the optimal transport route and destination.

[0315] Selection of transport route and destination

[0316] The server uses Dijkstra's algorithm or A algorithm to select the optimal transport route and hospital. For example, if the "Central Hospital" is determined to be the optimal route, the server sends this result to the terminal and emergency services.

[0317] Hospital coordination and preparation

[0318] The hospital prepares for admission based on the patient information sent from the server, including calling in emergency medical teams and preparing the necessary medical equipment. The hospital also updates its medical capacity and admission status in real time and shares it with the entire system.

[0319] Specific examples

[0320] For example, if a user shows symptoms of a heart attack, they would do the following: They would use their smartphone, launch the app, and input "chest pain" and "heart rate 120 bpm." The generative AI model analyzes this information and determines a "high level of urgency." The emotion engine also analyzes the user's psychological state and recognizes that the user is feeling anxious. As a result, it displays the message "Please remain calm, we will call an ambulance." When the user presses the send button, the information is sent to the server.

[0321] The server integrates the operational status of local ambulances, traffic information, and the medical capabilities of each hospital to calculate the optimal transport route and destination. For example, it may determine that transport to the "Central Hospital" is appropriate by avoiding traffic jams. This result is notified to the user's device and emergency services, and the hospital then begins preparations to accept the patient based on this information.

[0322] Prompt Sentence Examples

[0323] Example prompt: "User reports chest pain and a heart rate of 120 bpm. Analyze the emergency level and recommend immediate actions."

[0324] In this way, the present invention can provide highly accurate medical support in real time that takes into account the user's physical and psychological state.

[0325] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0326] Step 1:

[0327] User Action:

[0328] First, the user launches the application on their smartphone, tablet, or computer. Then, they input the patient's condition and symptoms, such as "chest pain" or "heart rate 120 bpm." Once the input data is ready, they are ready to press the send button.

[0329] Input: Patient information such as chest pain, heart rate 120 bpm.

[0330] Output: The entered patient information is stored on the terminal.

[0331] Step 2:

[0332] Terminal behavior:

[0333] The user device sends the input patient information to the generative AI model in real time. The generative AI model is implemented using Python, TensorFlow, and PyTorch. The generative AI model analyzes the input information and determines the urgency and suspected symptoms.

[0334] Input: Patient condition and symptoms (chest pain, heart rate 120 bpm).

[0335] Data processing and computation: A generative AI model analyzes input data and identifies symptoms that may indicate a high level of urgency. For example, a model might derive a high level of urgency and a possible heart attack from input data such as chest pain and a heart rate of 120 bpm.

[0336] Output: Analysis result (high urgency, possible heart attack).

[0337] Step 3:

[0338] Terminal behavior:

[0339] Next, the emotion engine analyzes the user's psychological state. Using the device's camera and microphone, it captures and analyzes the user's facial expressions, tone of voice, and input actions. The emotion engine recognizes the user's psychological state and recommends appropriate emergency responses.

[0340] Input: User facial expressions, tone of voice, and typing behavior.

[0341] Data processing and calculation: The emotion engine analyzes this data and identifies the user's emotional state. For example, it infers "strong anxiety" from the user's facial tension and excited tone of voice.

[0342] Output: Sentiment analysis result (high anxiety).

[0343] Step 4:

[0344] Terminal behavior:

[0345] The analysis results of the generative AI model and the emotion engine are combined to suggest emergency responses to the user. For example, if the analysis result of the generative AI model is "possible heart attack" and the analysis result of the emotion engine is "strong anxiety," the message "Please stay calm, we will call an ambulance" will be displayed.

[0346] Input: Generative AI model analysis (high urgency, possible heart attack), emotion engine analysis (high anxiety).

[0347] Data processing and calculation: Generate suggested messages, such as "Please stay calm, I'll call an ambulance."

[0348] Output: Notification messages that are displayed on the application screen.

[0349] Step 5:

[0350] User Action:

[0351] The user takes action based on the suggested message by pressing the send button to send the information to the server. This action causes the device to send the analysis results of the generative AI model and emotion engine to the server.

[0352] Input: Analysis results of the generative AI model, analysis results of the emotion engine.

[0353] Output: The analysis results are sent to the server.

[0354] Step 6:

[0355] Server Action:

[0356] The server receives the data and stores it in a database. The server then obtains information such as the operating status of ambulances in the area, traffic information, and the medical capabilities of each medical institution. For example, traffic information can be obtained using the Google Maps API.

[0357] Input: Analysis results, ambulance operation status in the area, traffic information, and medical facility capacity.

[0358] Data processing and calculation: The server integrates the data and selects the optimal delivery route and destination.

[0359] Output: Optimal delivery route and destination information.

[0360] Step 7:

[0361] Server Action:

[0362] The server calculates the optimal transport route and destination and notifies the user terminal and emergency services. For example, if the "Central Hospital" is determined to be the optimal destination, that information will be sent along with a route that avoids traffic jams.

[0363] Input: Optimal delivery route and destination.

[0364] Output: User terminal and emergency services are notified.

[0365] Step 8:

[0366] Hospital Actions:

[0367] Based on the patient information sent from the server, the hospital prepares to accept the patient, assembles an emergency medical team, and prepares the necessary medical equipment.In addition, medical capacity and acceptance status are updated in real time and shared throughout the system.

[0368] Input: Patient information, optimal transport route and destination.

[0369] Output: Emergency medical team assembled and ready to receive.

[0370] (Application example 2)

[0371] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0372] Conventional emergency medical support systems were able to analyze the patient's condition and symptoms in real time and respond appropriately, but they did not take into account the user's psychological state, and therefore had the problem of not being able to reduce anxiety and stress at the scene of an emergency response.In addition, while systems exist that provide quick and appropriate emergency response, they lack the functionality to provide psychological support to users, which poses the risk of limiting quick judgment and action.

[0373] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0374] In this invention, the server includes emotion engine means, means for providing appropriate feedback to the user based on the psychological state analyzed by the emotion engine means, and means for managing the medical capabilities and equipment status of each medical institution, thereby enabling prompt and appropriate medical response as well as psychological support for the user.

[0375] A "user terminal" is a device used by a user to input medical or emergency information, and may include a smartphone, tablet, or computer.

[0376] "Generative model means" refers to AI models and machine learning algorithms that perform appropriate analysis based on input patient information and emergency data.

[0377] "Communication means" refers to the internet or network communication means for transmitting the analyzed information from the user terminal to the server or emergency services.

[0378] "Notification means" refers to a method or device for notifying a user in real time of the response or analysis results from emergency services.

[0379] "Emotion engine means" refers to a system or software for recognizing and appropriately evaluating a user's psychological state and emotions.

[0380] The "feedback providing means" refers to a method or device for providing appropriate responses or support based on the user's psychological state analyzed by the emotion engine means.

[0381] "Ambulance availability management means" means a system or method for managing the availability and availability of ambulances in an area in real time and updating them as necessary.

[0382] "Traffic information management means" refers to systems and methods for capturing and managing road congestion and traffic conditions in real time.

[0383] "Medical institution information management means" refers to a system or method for managing information on the medical treatment capabilities and available facilities of each medical institution and updating the necessary information.

[0384] "Hospital means" refers to the medical facilities and systems that receive patient information, coordinate medical capacity, and make appropriate preparations for admission.

[0385] "Sharing tools" refers to methods and systems for updating clinical capacity and availability in real time and sharing that information across the system.

[0386] The present invention is a system that combines a user terminal, a generative model means, a communication means, a notification means, an emotion engine means, a feedback providing means, an ambulance operation status management means, a traffic information management means, a medical institution information management means, a hospital means, a sharing means, and the like.

[0387] System Configuration

[0388] 1. User Device:

[0389] The user device is a smartphone, tablet, or computer that provides an interface for the user to input information about the patient's condition and symptoms. For example, the user might input information such as "chest pain" or "heart rate 120 bpm." This information is then sent to the generative AI model in real time.

[0390] 2. Generative modeling methods:

[0391] The generative AI model installed on the user's device analyzes the input patient information. For example, if "chest pain" and "heart rate 120 bpm" are input, the generative AI model will output the diagnosis "suspected heart attack." This analysis result is immediately fed back to the user, and the necessary emergency response is suggested.

[0392] 3. Means of communication:

[0393] The user device sends the information analyzed by the generative model to the server. The communication is carried out via the Internet, and the information is encrypted and secure.

[0394] 4. Means of notification:

[0395] The server sends a response to the user's device, which notifies the user in real time of the arrival time of the ambulance and the best emergency response.

[0396] 5. Emotion Engine Means:

[0397] It is equipped with an emotion engine that analyzes the user's psychological state. It analyzes the user's facial expressions, tone of voice, input actions, etc. to recognize the anxiety or impatience the user is feeling.

[0398] 6. How to provide feedback:

[0399] The system provides appropriate feedback based on the user's psychological state analyzed by the emotion engine means. For example, if the user is feeling strong anxiety or impatience, the system will provide a reassuring message such as "Please stay calm, we will call an ambulance."

[0400] 7. Ambulance operation status management means and traffic information management means:

[0401] The server manages the operational status and traffic information of ambulances in the area in real time, which allows the system to determine the optimal ambulance dispatch and transport route.

[0402] 8. Medical institution information management measures:

[0403] The server manages the medical capabilities and equipment status of each medical institution, and determines the optimal destination based on this information.

[0404] 9. Hospital Measures:

[0405] The hospital prepares to accept the patient based on the patient information sent from the server, mobilizes an emergency medical team, and prepares the necessary medical equipment. Medical capacity and acceptance status are also updated in real time and shared throughout the system.

[0406] Example of operation

[0407] For example, if a user discovers a fire, they can enter "Fire in Building A - Floor 3" into the app and provide image data. The generative AI model analyzes this data and immediately provides instructions such as "Take evacuation measures." At the same time, the emotion engine detects the user's anxiety and provides specific feedback such as "Remain calm and evacuate."

[0408] Prompt Sentence Examples

[0409] Example input:

[0410] Emergency Type: Fire

[0411] Location: Building A - Floor 3

[0412] Description: Fire in storage room

[0413] Image data: base64_image_string

[0414] User emotional state: scared

[0415] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0416] Step 1:

[0417] The user inputs information about the emergency situation and the patient's condition into the user device. Specifically, the user uses a smartphone or tablet to input the situation, such as "fire outbreak," and patient information, such as "heart rate 120 bpm," into the application interface. The input data is saved on the device in text or image format.

[0418] Input: Text and image data that users enter into the app

[0419] Output: Input information data in the user's terminal

[0420] Step 2:

[0421] The generative AI model analyzes data entered into the user's device, analyzes information about the patient's condition and emergency, and generates a diagnosis such as "high urgency" or "possible heart attack." Machine learning algorithms are used to perform text and image analysis.

[0422] Input: Input information data in the user terminal

[0423] Output: Analyzed diagnostic results

[0424] Step 3:

[0425] The analysis results are sent to a server using a communication method. The data is encrypted and securely transmitted over the Internet. The server stores the received data and performs further analysis.

[0426] Input: Analyzed diagnostic results

[0427] Output: Data sent to the server

[0428] Step 4:

[0429] The emotion engine analyzes the user's psychological state. It recognizes the user's facial expressions and analyzes the tone of their voice to assess whether the user is feeling anxious or impatient. Input data is collected using sensors such as cameras and microphones, and the AI ​​model analyzes it.

[0430] Input: User's facial expression data, voice tone data

[0431] Output: User's psychological state evaluation result

[0432] Step 5:

[0433] The feedback provider provides appropriate feedback to the user based on the analyzed psychological state. For example, it may provide a message such as "Please stay calm" or "Call an ambulance" via text or voice. Text-to-Speech (TTS) technology may also be used.

[0434] Input: User's psychological state evaluation results

[0435] Output: Feedback message

[0436] Step 6:

[0437] The ambulance operation status management means and traffic information management means obtain real-time information on the operation status and traffic of ambulances in the area, and based on this, determine the optimal ambulance arrangements and transport routes.

[0438] Input: Ambulance operation status data and traffic information data stored on the server

[0439] Output: Optimal ambulance dispatch route and transport route

[0440] Step 7:

[0441] The medical institution information management tool selects the optimal destination based on the medical capabilities and equipment status of each medical institution. The selection results are updated in real time and shared throughout the system.

[0442] Input: Medical institution information data in the server

[0443] Output: Optimal destination selection result

[0444] Step 8:

[0445] The result of the selection is notified to the user's terminal using a notification method. The user is given clear instructions on the next action to be taken, and the entire system cooperates to achieve an efficient emergency response.

[0446] Input: Selection result of optimal transport route and destination

[0447] Output: Notification message of selection result

[0448] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0449] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0450] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0451] [Second embodiment]

[0452] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0453] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0454] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0455] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0456] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0457] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0458] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0459] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0460] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0461] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0462] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0463] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0464] This invention is a system that uses generative AI to provide real-time support in emergency medical settings, and is implemented with the following configuration.

[0465] User terminal

[0466] The user's device can be a smartphone, tablet, or computer. The user launches the application and is presented with an interface for entering patient information, including symptoms (e.g., chest pain, shortness of breath) and vital signs (e.g., heart rate, blood pressure). This information is sent in real time to the generative AI model on the device for analysis.

[0467] Analysis of generative AI models

[0468] The device uses a generative AI model to analyze the patient information entered. For example, if chest pain and a heart rate of 120 bpm are entered, the AI ​​model will generate the analysis results of "high urgency" and "suspected heart attack." This analysis result is immediately fed back to the user, and the necessary emergency response is suggested.

[0469] Sending information via communication means

[0470] When the user presses the send button, the device sends the patient information and analysis results to the server. This communication is carried out over the Internet using a safe and secure data transmission protocol.

[0471] Server Processing

[0472] The server receives patient information sent from the user's device, which is then stored in a database. The server then connects with related systems and external APIs to obtain information on the operation status and traffic of ambulances in the area. It also collects information on the medical capabilities and available facilities of each medical institution.

[0473] The server integrates this information and runs an algorithm to select the optimal transport route and hospital. For example, the "Central Hospital" is selected as the optimal destination, and the optimal route that avoids traffic jams is determined.

[0474] Notification of results

[0475] The server sends the results of the selection (optimal transport route and hospital) to the user's device and the emergency service. A notification is displayed on the user's device, allowing the user to confirm the next action to be taken.

[0476] Hospital Treatment

[0477] The hospital receives the patient information sent from the server and prepares to accept the patient using its internal system. For example, this includes calling in an emergency medical team and preparing the necessary medical equipment. The hospital also updates its medical capabilities as needed and shares the latest information with the server, ensuring that the entire system is always running with the latest information.

[0478] Specific examples

[0479] For example, if a user experiences a heart attack, they can quickly launch the app on their smartphone and input "chest pain" and "heart rate 120 bpm." The AI ​​analyzes this information, determines it to be a "high urgency," and notifies the user to "call an ambulance immediately." When the user presses the send button, the information is sent to the server.

[0480] The server receives the information and determines the optimal transport route and destination based on the ambulance operation status, traffic information, and the medical capabilities of each hospital in the area. For example, it may determine that transport to the "Central Hospital" is the most appropriate route, as it avoids traffic jams.

[0481] Once the optimal transport route and hospital are notified to the user device and emergency services, an ambulance will be dispatched quickly and preparations for reception at the "Central Hospital" will begin, thus achieving a fast and efficient emergency medical response.

[0482] The processing flow will be explained below.

[0483] Step 1:

[0484] Users launch the application on their smartphone, tablet, or other device and enter their patient's condition and symptoms, such as chest pain and a heart rate of 120 bpm.

[0485] Step 2:

[0486] The device sends the patient information (symptoms and vital signs) entered by the user to the generative AI model, which then analyzes the information in real time and generates a level of urgency and emergency response.

[0487] Step 3:

[0488] The device receives the analysis results from the generative AI model and notifies the user, for example, displaying results such as "high urgency" and "suspected heart attack."

[0489] Step 4:

[0490] The user checks the analysis results and, if necessary, presses the send button to determine the next action to take. Pressing this button will cause the patient information to proceed to the next procedure.

[0491] Step 5:

[0492] The device sends patient information (symptoms, vital signs, and analysis results) to the server. This communication is carried out securely via the Internet.

[0493] Step 6:

[0494] The server receives the patient information sent from the user terminal and stores it in a database, allowing for centralized management of the patient's condition.

[0495] Step 7:

[0496] The server collects real-time information on the operational status and location of ambulances in the area, as well as traffic information, and obtains this information from related systems and external APIs as needed.

[0497] Step 8:

[0498] The server manages the medical capabilities and equipment status of each medical institution and updates this information, making it possible to select the most suitable hospital.

[0499] Step 9:

[0500] The server integrates patient information, ambulance operation status, traffic information, and information from each medical institution, and executes an algorithm to select the optimal transport route and destination hospital. For example, it may determine that the "Central Hospital" is the best option.

[0501] Step 10:

[0502] The server sends the selection result to the user terminal and the emergency service, which then starts arranging an ambulance and transporting the patient.

[0503] Step 11:

[0504] The user can check the optimal transport route and destination displayed on the device and wait for the ambulance to arrive. If necessary, they will be instructed to continue providing first aid.

[0505] Step 12:

[0506] The emergency services receive the notification from the server, follow the designated transport route to the patient, and begin transporting the patient promptly.

[0507] Step 13:

[0508] The hospital begins preparations for admission based on the patient information sent from the server, convening an emergency medical team and preparing the necessary medical equipment.

[0509] Step 14:

[0510] Hospitals update their medical capacity in real time and send the latest information to a server, which makes it easier to adjust the intake of other patients.

[0511] Step 15:

[0512] When the ambulance arrives, the user follows the instructions to transport the patient, and the emergency services and hospitals work together to ensure a prompt response.

[0513] The above processing steps enable a fast and efficient emergency medical response.

[0514] Example 1

[0515] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0516] In emergency medical situations, rapid and appropriate responses are required, but it is not easy to process a large amount of information at once, such as assessing the situation on-site, deciding where to transport patients, and taking into account traffic information. It is also difficult to grasp the medical capabilities and acceptance status of medical institutions. This poses the issue of the possibility of delaying appropriate treatment for patients.

[0517] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0518] In this invention, the server includes terminal means that allows a user to input the patient's condition and symptoms, generative model means that analyzes the input patient information in real time, server means that saves and manages the information analyzed by the generative model means, means that prompts an emergency service to respond via the server means, means that receives a response from the emergency service and notifies the user of the optimal emergency response, means that manages the operation status and traffic information of ambulances in the area, means that manages the medical capacity and equipment status of each medical institution, means that integrates patient information, ambulance operation status, traffic information, and medical institution information to select the optimal transport route and destination, medical institution means that accepts patient information, adjusts medical capacity, and prepares for appropriate acceptance, and means that updates the medical capacity and acceptance status in real time and shares it throughout the system, thereby enabling prompt and appropriate response at the scene of an emergency medical care.

[0519] The "terminal means by which a user can input the condition or symptoms of a patient" is a device such as a smartphone, tablet, or computer, which is an apparatus that allows a user to input patient information using an interface.

[0520] The "generative model means for analyzing input patient information in real time" is a system in which a generative AI model instantly analyzes input patient information and provides information on the urgency of symptoms and medical decisions.

[0521] "Server means for storing and managing information analyzed by the generative model means" refers to a data storage and management device for storing the analysis results by the generative AI model in a database and centrally managing this information.

[0522] The "means for encouraging emergency services to respond via server means" refers to a communication function and processing system that transmits the analysis results to emergency services via a server and encourages emergency response.

[0523] The "means for receiving responses from emergency services and notifying the user of the most appropriate emergency response" is a system for receiving responses from emergency services and notifying the user of the appropriate emergency response method in real time based on that information.

[0524] "Means for managing ambulance operation status and traffic information within the region" refers to a system that collects and manages ambulance usage status and real-time traffic conditions within the region, and consolidates this information in a centralized manner.

[0525] "Means for managing the medical treatment capacity and equipment status of each medical institution" refers to a function for collecting and managing information on the current medical treatment capacity and available equipment of each medical institution.

[0526] "Means of integrating patient information, ambulance operation status, traffic information, and medical institution information to select the optimal transport route and destination" refers to an algorithm and system that integrates this information and automatically selects the optimal transport route and destination.

[0527] "Medical institution means for accepting patient information, adjusting medical treatment capabilities, and making appropriate preparations for acceptance" is a system that allows medical institutions to adjust their medical treatment capabilities and prepare the necessary medical equipment and specialists based on patient information sent from the server.

[0528] "Means for updating medical treatment capacity and admission status in real time and sharing it throughout the system" refers to a means for updating medical institutions' medical treatment capacity and admission status at any time and sharing this information throughout the system in real time.

[0529] This invention is a system that uses generative AI models to provide real-time support in emergency medical settings. The system mainly consists of the following components:

[0530] User terminal

[0531] The user's device can be a smartphone, tablet, or computer. The user launches the application and is presented with an interface for entering patient information, including symptoms (e.g., chest pain, shortness of breath) and vital signs (e.g., heart rate, blood pressure). This information is sent in real time to the generative AI model on the device for analysis.

[0532] Analysis of generative AI models

[0533] The device uses a generative AI model to analyze the patient information entered. For example, if chest pain and a heart rate of 120 bpm are entered, the generative AI model will generate the analysis results of "high urgency" and "suspected heart attack." This analysis result is immediately fed back to the user, and the necessary emergency response is suggested.

[0534] Sending information via communication means

[0535] When the user presses the send button, the terminal sends the patient information and analysis results to the server. This communication is carried out over the Internet using a secure data transmission protocol (e.g., HTTPS).

[0536] Server Processing

[0537] The server receives patient information sent from the user's device. After receiving the data, it stores it in a database. The server also obtains information on the operation status of ambulances in the area, traffic information, and information on the medical capabilities and available facilities of each medical institution, and executes an algorithm that integrates this information to select the optimal transport route and destination. For example, the "Central Hospital" is selected as the optimal destination, and the optimal route that avoids traffic congestion is determined.

[0538] Notification of results

[0539] The server sends the selection results (optimal transport route and hospital) to the user's device and the emergency service. A notification message is displayed on the user's device, allowing the user to confirm the next action to be taken.

[0540] Hospital Treatment

[0541] The hospital receives the patient information sent from the server and prepares to accept the patient using its internal system. For example, this includes calling in an emergency medical team and preparing the necessary medical equipment. The hospital also updates its medical capabilities as needed and shares the latest information with the server, ensuring that the entire system is always running with the latest information.

[0542] Specific examples

[0543] For example, if a user experiences a heart attack, they can launch the app on their smartphone and enter "chest pain" and "heart rate 120 bpm." The generating AI analyzes this information, determines it to be a "high urgency," and notifies the user to "call an ambulance immediately." When the user presses the send button, the information is sent to the server. The server determines the optimal transport route and destination based on the ambulance operation status in the area, traffic information, and the medical capabilities of each medical institution. For example, it may determine that transport to the "Central Hospital" is appropriate, taking the fastest route that avoids traffic jams. The result is notified to the user's device and emergency services, and an ambulance is quickly dispatched, while preparations begin at the "Central Hospital." This enables fast and efficient emergency medical response.

[0544] Prompt Sentence Examples

[0545] "I have a patient with chest pain and a heart rate of 120 bpm. What is the appropriate first aid?"

[0546] These prompts can be fed into a generative AI model to help you get a consultation faster.

[0547] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0548] Step 1:

[0549] The user inputs the patient's condition and symptoms.

[0550] The user launches the application on their smartphone, tablet, or computer. The application displays an interface for entering patient information. The user enters the patient's symptoms (e.g., chest pain, shortness of breath) and vital signs (e.g., heart rate, blood pressure).

[0551] Input: Patient symptoms and vital signs

[0552] Output: Entered patient information

[0553] Specific behavior: The user enters "chest pain" and "heart rate 120 bpm" into the application's input form.

[0554] Step 2:

[0555] Analyzing patient information with generative AI models

[0556] The device sends the patient information entered in step 1 to the generative AI model within the device. The generative AI model analyzes the information in real time and determines the urgency of the patient's symptoms. For example, if chest pain and a heart rate of 120 bpm are entered, the generative AI model will generate the analysis results of "high urgency" and "suspected heart attack."

[0557] Input: Patient information entered

[0558] Output: Analysis result (e.g. high urgency, suspected heart attack)

[0559] Specific operation: The device passes the input patient information to the generative AI model, which then returns an analysis result stating, "This symptom is of high urgency and is suspected to be a heart attack."

[0560] Step 3:

[0561] Analysis results are fed back to the user

[0562] The device immediately provides the user with the analysis results from the generative AI model. Based on the analysis results, the device displays the necessary emergency response instructions. For example, instructions such as "Call an ambulance immediately" are displayed.

[0563] Input: Analysis results of the generative AI model

[0564] Output: Emergency response instructions displayed to the user

[0565] Specific behavior: The device displays a message to the user indicating "High Urgency" and "Call an ambulance immediately."

[0566] Step 4:

[0567] User sends information to server

[0568] When the user presses the send button, the device sends the entered patient information and the analysis results of the generated AI model to the server. This communication is carried out over the internet using a safe and secure data transmission protocol such as HTTPS.

[0569] Input: Entered patient information and analysis results of the generated AI model

[0570] Output: Information sent to the server

[0571] What happens: The user clicks the "Send" button and the device sends the information to the server using HTTPS.

[0572] Step 5:

[0573] The server processes the information and determines the optimal delivery route and destination.

[0574] The server receives patient information sent from the device and stores it in a database. The server then collects information on the operation status of ambulances in the area, traffic information, and information on the medical capabilities and facilities of each medical institution, and executes an algorithm that integrates this information to select the optimal transport route and destination. For example, the "Central Hospital" may be selected as the optimal destination, and the optimal route that avoids traffic congestion may be determined.

[0575] Input: Patient information, traffic information, ambulance operation status, medical institution medical capacity

[0576] Output: Optimal route and destination

[0577] What it does: The server stores the information in a database, retrieves the latest traffic information from an external API, and runs an algorithm to determine that "Central Hospital is the best choice."

[0578] Step 6:

[0579] The server notifies the user device and emergency services

[0580] The server then sends the optimal transport route and hospital to the user's device and the emergency service. A notification message is displayed on the user's device, allowing the user to confirm the next steps.

[0581] Enter: Optimal Route and Destination Information

[0582] Output: Notification message to user terminal and emergency services

[0583] Specific operation: The server sends a message to the user device and emergency services saying, "Central Hospital is the best option. Route A is the best route to avoid traffic jams."

[0584] Step 7:

[0585] Preparations for hospital admission

[0586] The hospital receives the patient information sent from the server and prepares to accept the patient using its internal system, including calling in an emergency medical team and preparing the necessary medical equipment. The hospital also updates its medical capabilities as needed and shares the latest information with the server, ensuring that the entire system is always running with the latest information.

[0587] Input: Patient information sent from the server

[0588] Output: Readiness status

[0589] Specific operations: The hospital receives the patient information, starts preparing the emergency room, and updates the medical capabilities to the server.

[0590] (Application example 1)

[0591] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0592] Modern society demands rapid emergency medical response. However, various factors, such as traffic congestion and overwhelmed hospitals, hinder rapid response. In urban areas in particular, ambulances are often delayed due to traffic congestion. Furthermore, because the quality of emergency response at the scene depends on the experience and knowledge of medical staff, there are cases where an appropriate initial response is not provided. To solve these problems, more efficient and accurate analysis of medical information and a seamless emergency medical support system that includes autonomous driving technology are required.

[0593] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0594] In this invention, the server includes a device that allows a user to input the patient's condition and symptoms, a generative model means that analyzes the input patient information in real time, a communication means that transmits the information analyzed by the generative model means to an emergency service, a means that receives a response from the emergency service and notifies the user of the optimal emergency response, and a means that dispatches an autonomous ambulance along an optimal transport route. This enables rapid analysis of patient information and dispatch of an autonomous ambulance along an optimal route that avoids traffic congestion. This enables faster and more accurate emergency response and more appropriate initial response at the scene.

[0595] "Device that allows a user to input a patient's condition or symptoms" refers to a smartphone, tablet, smart glasses, or other input device that allows a user to directly input a patient's physical condition or specific symptoms in real time.

[0596] "Generative model means for analyzing input patient information in real time" refers to AI models and algorithms for analyzing input patient data, thereby enabling instant medical assessment and determination of urgency.

[0597] "Communication means for transmitting information analyzed by the generative model means to emergency services" refers to network technologies and communication protocols for quickly transmitting the analysis results to the appropriate emergency services, and is typically a means for secure data transmission via the Internet.

[0598] "Means for receiving responses from emergency services and informing the user of the best emergency response" refers to a system or application that receives instructions and information from emergency services and, based on that, notifies the user of the next steps to take and emergency procedures.

[0599] "Means for dispatching autonomous ambulances along the optimal transport route" refers to technologies and systems that calculate the quickest and most efficient route based on input patient information, traffic conditions, medical institution medical capabilities, etc., and dispatch ambulances using autonomous driving technology.

[0600] "Means for managing the operational status and traffic information of ambulances within the region" refers to a system or platform for monitoring and managing the current location, operational status, traffic congestion and accident information, etc. of ambulances in real time.

[0601] "Means for managing the medical treatment capacity and equipment status of each medical institution" refers to a system for managing each medical institution's current capacity, the operating status of medical staff, and information on available medical equipment and facilities, and constantly updating it to the latest version.

[0602] "Means for integrating patient information, ambulance operation status, traffic information, and medical institution information to select the optimal transport route and destination" refers to algorithms and systems for comprehensively processing the various types of information mentioned above and determining the most appropriate transport destination and optimal travel route.

[0603] "Means for providing information on transport routes and destinations to autonomous ambulances" refers to the technology and systems that transmit the calculated optimal transport route and destination information to the autonomous ambulance's system in real time and control autonomous driving based on that information.

[0604] "Hospital means for receiving patient information, adjusting medical treatment capacity, and making appropriate preparations for admission" refers to a system and management means for adjusting the admission system within the hospital and arranging for the necessary medical staff and equipment based on the received patient information.

[0605] "Means for updating medical treatment capacity and admission status in real time and sharing it throughout the system" refers to technologies and platforms for constantly monitoring medical institutions' medical treatment capacity and admission status, and instantly updating and sharing this information within the entire system.

[0606] "Means for predicting the arrival time of an autonomous ambulance and adjusting the hospital's reception readiness" refers to systems and technologies for calculating the estimated arrival time of an ambulance and appropriately adjusting the hospital's reception readiness based on this.

[0607] This invention provides a fast and efficient emergency medical system that utilizes an autonomous ambulance. The specific configuration and operation procedure for implementing this invention are described below.

[0608] First, the user terminal can be a smartphone, tablet, smart glasses, or other device. Users can input the patient's symptoms and vital signs through these devices. For example, if a patient inputs "chest pain" and "heart rate 120 bpm," the generative AI model on the device will instantly analyze this information and generate a diagnosis such as "high urgency" or "suspected heart attack." This analysis uses generative AI models such as TensorFlow and PyTorch.

[0609] The information analyzed by the generative AI model is sent to a server via a communication method using a REST API, which securely transmits patient information and analysis results over the internet. The server stores the received patient information in a database and connects with Google Maps API and the company's own traffic analysis system to obtain real-time information on the operation status of local ambulances and traffic information.

[0610] The server then manages the medical capabilities and equipment status of each medical institution and integrates this information. Based on the stored patient information, ambulance operation status, traffic information, and medical institution information, the optimal transport route and medical institution are selected. This algorithm often applies machine learning models and operations research methods.

[0611] The server provides the selection results (optimal transport route and destination medical institution) to the autonomous ambulance dispatch system. This dispatch system then dispatches the autonomous ambulance along the optimal route based on the selection results. The autonomous driving technology uses control software and sensor systems for autonomous vehicles.

[0612] The information from the server is also sent to the medical institution, and once it has been received, medical staff prepare to accept the patient using their internal system. Medical capacity and acceptance status are updated in real time and shared across the entire server, allowing the hospital to respond quickly.

[0613] As an example, use the following prompt:

[0614] Enter your symptoms: Chest pain

[0615] Enter your heart rate: 120 bpm

[0616] Enter your blood pressure: 140 / 90 mmHg

[0617] The AI ​​then responds with the analysis:

[0618] Analysis result: Suspected heart attack (high urgency)

[0619] Sending data...

[0620] Optimal route: The fastest route avoiding traffic jams

[0621] Self-driving ambulances have been deployed.

[0622] In this way, by using the system of the present invention, it is possible to perform real-time analysis of patients, provide appropriate initial responses, provide optimal transport routes, and quickly dispatch autonomous ambulances, thereby significantly improving the quality and speed of emergency medical care.

[0623] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0624] Step 1:

[0625] Users input the patient's symptoms and vital signs using a device such as a smartphone or tablet. They launch the application and enter specific symptoms (e.g., chest pain) and vital signs (e.g., heart rate 120 bpm, blood pressure 140 / 90 mmHg) through the interface. This data is then analyzed by the generative AI model.

[0626] Input: Symptom and vital sign data

[0627] Output: Input data to a generative AI model

[0628] Step 2:

[0629] A generative AI model installed on the device analyzes input patient information in real time. It uses machine learning libraries such as TensorFlow and PyTorch to determine the urgency and suspected medical condition from the patient information. The results of this analysis are immediately fed back to the user as emergency response suggestions.

[0630] Input: Patient information (symptoms, vital signs)

[0631] Output: Diagnosis result (urgency, condition)

[0632] Step 3:

[0633] The user checks the analysis results of the generative AI model and, if necessary, presses the send button to send the information to the server. The information is securely transmitted to the server via the internet via a REST API.

[0634] Input: Analysis results (urgency, condition), user's sending operation

[0635] Output: Patient information sent to the server

[0636] Step 4:

[0637] The server receives patient information sent from the user's device and stores it in a database. Based on the received data, it connects with Google Maps API and its own traffic analysis system to obtain information on the operation status of ambulances in the area and traffic information. Based on this information, it determines the optimal transport route and medical institution.

[0638] Input: Patient information submitted

[0639] Output: Selection of optimal transport route and medical institution

[0640] Step 5:

[0641] The server provides the results of the selection (optimal transport route and destination medical institution) to the autonomous ambulance dispatch system. The autonomous vehicle's control software then dispatches the ambulance based on the selected route.

[0642] Input: Selection result of optimal transport route and destination

[0643] Output: Dispatch of autonomous ambulance

[0644] Step 6:

[0645] The autonomous ambulance arrives at the patient's location and begins transporting the patient based on the selected route. Meanwhile, the server also sends information to the medical institution, which then prepares to accept the patient.

[0646] Input: Notification of arrival of autonomous ambulance, sending admission information to hospital

[0647] Output: Patient transport by ambulance, preparation for admission at medical institution

[0648] Step 7:

[0649] Medical institutions receive the information sent from the server in real time and proceed with preparations for admission using their internal systems. Medical treatment capabilities and admission status are updated as needed and shared across the entire server.

[0650] Input: Acceptance information from the server

[0651] Output: Ready to accept patients, updated medical capabilities

[0652] Step 8:

[0653] Once the ambulance arrives at the hospital, the patient receives prompt and appropriate medical treatment, and real-time updated information allows medical staff to provide the most appropriate response.

[0654] Input: Ambulance arrival, patient handover

[0655] Output: Patient treatment start, real-time treatment response

[0656] This will enable a series of steps: rapid analysis of patient information, appropriate initial response, provision of optimal transport routes, rapid dispatch of autonomous ambulances, and rapid preparations for admission at medical institutions.

[0657] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0658] This invention is a system that uses generative AI and an emotion engine to provide real-time support in emergency medical settings, and is implemented with the following configuration.

[0659] User terminal

[0660] The user's device can be a smartphone, tablet, or computer. The user launches the application and is presented with an interface for entering patient information, including symptoms (e.g., chest pain, shortness of breath) and vital signs (e.g., heart rate, blood pressure). This information is sent in real time to the generative AI model on the device for analysis.

[0661] Analysis of generative AI models

[0662] The device uses a generative AI model to analyze the patient information entered. For example, if chest pain and a heart rate of 120 bpm are entered, the AI ​​model will generate the analysis results of "high urgency" and "suspected heart attack." This analysis result is immediately fed back to the user, and the necessary emergency response is suggested.

[0663] Incorporating an emotion engine

[0664] The emotion engine recognizes the user's psychological state. When the user inputs information, the device analyzes the user's facial expression, tone of voice, input actions, etc., and recognizes the user's emotions through the emotion engine. For example, if the user is feeling impatient or anxious, the device will suggest appropriate emergency measures.

[0665] Sending information via communication means

[0666] When the user presses the send button, the device sends the patient information and analysis results to the server. This communication is securely carried out over the Internet.

[0667] Server Processing

[0668] The server receives patient information sent from the user's device, which is then stored in a database. The server then connects with related systems and external APIs to obtain information on the operation status and traffic of ambulances in the area. It also collects information on the medical capabilities and available facilities of each medical institution.

[0669] The server integrates this information and runs an algorithm to select the optimal transport route and hospital. For example, the "Central Hospital" is selected as the optimal destination, and the optimal route that avoids traffic jams is determined.

[0670] Notification of results

[0671] The server sends the selection results (optimal transport route and hospital) to the user terminal and the emergency service, which then starts arranging an ambulance and transporting the patient.

[0672] Hospital Treatment

[0673] The hospital prepares to accept the patient based on the patient information sent from the server, convenes an emergency medical team, and prepares the necessary medical equipment. The hospital also updates its medical capabilities as needed and shares the latest information with the server, ensuring that the entire system is always running with the latest information.

[0674] Specific examples

[0675] For example, if a user experiences a heart attack, they can quickly launch the app on their smartphone and input "chest pain" and "heart rate 120 bpm." The generative AI analyzes this information and determines a "high level of urgency." At the same time, the emotion engine analyzes the user's facial expressions and tone of voice and recognizes that the user is experiencing strong anxiety or impatience.

[0676] As a result, the app sends a reassuring notification to the user saying, "Please stay calm, we'll call an ambulance." When the user presses the send button, the information is sent to the server.

[0677] The server receives the information and determines the optimal transport route and destination based on the ambulance operation status, traffic information, and the medical capabilities of each hospital in the area. For example, it may determine that transport to the "Central Hospital" is the most appropriate route, as it avoids traffic jams.

[0678] The selection result is notified to the user's device, and the user confirms the next action to be taken. An ambulance arrives and the patient is safely transported to the "Central Hospital," achieving a fast and efficient emergency medical response. The use of the emotion engine can reduce the user's anxiety and stress.

[0679] The processing flow will be explained below.

[0680] Step 1:

[0681] Users launch the application on their smartphone, tablet, or other device and enter their patient's condition and symptoms, such as chest pain and a heart rate of 120 bpm.

[0682] Step 2:

[0683] The device also sends the patient information (symptoms and vital signs) entered by the user to the emotion engine. At this time, the device analyzes the user's facial expressions, tone of voice, input actions, etc. to recognize the user's emotions.

[0684] Step 3:

[0685] The emotion engine analyzes the user's emotions (e.g., impatience, anxiety, fear) and sends the results to the generative AI model. The generative AI model then combines the patient information and the user's emotion data to generate an analysis result, such as "high urgency" and "suspected heart attack."

[0686] Step 4:

[0687] The device receives the analysis results from the generative AI model and notifies the user, displaying a reassuring message (e.g., "Please stay calm, we will call an ambulance immediately") based on the results of the emotion engine.

[0688] Step 5:

[0689] The user checks the analysis results and, if necessary, presses the send button to determine the next action to take. Pressing this button will cause the patient information to proceed to the next procedure.

[0690] Step 6:

[0691] The device sends patient information (symptoms, vital signs, and analysis results) to the server. This communication is carried out securely via the Internet.

[0692] Step 7:

[0693] The server receives the patient information sent from the user terminal and stores it in a database, allowing for centralized management of the patient's condition.

[0694] Step 8:

[0695] The server collects real-time information on the operational status and location of ambulances in the area, as well as traffic information, and obtains this information from related systems and external APIs as needed.

[0696] Step 9:

[0697] The server manages the medical capabilities and equipment status of each medical institution and updates this information, making it possible to select the most suitable hospital.

[0698] Step 10:

[0699] The server integrates patient information, ambulance operation status, traffic information, and information from each medical institution, and executes an algorithm to select the optimal transport route and destination hospital. For example, it may determine that the "Central Hospital" is the best option.

[0700] Step 11:

[0701] The server sends the selection result to the user terminal and the emergency service, which then starts arranging an ambulance and transporting the patient.

[0702] Step 12:

[0703] The user can check the optimal transport route and destination displayed on the device and wait for the ambulance to arrive. If necessary, they will be instructed to continue providing first aid.

[0704] Step 13:

[0705] The emergency services receive the notification from the server, follow the designated transport route to the patient, and begin transporting the patient promptly.

[0706] Step 14:

[0707] The hospital begins preparations for admission based on the patient information sent from the server, convening an emergency medical team and preparing the necessary medical equipment.

[0708] Step 15:

[0709] Hospitals update their medical capacity in real time and send the latest information to a server, which makes it easier to adjust the intake of other patients.

[0710] Step 16:

[0711] When the ambulance arrives, the user follows the instructions to transport the patient, and the emergency services and hospitals work together to ensure a prompt response.

[0712] Example 2

[0713] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0714] Conventional emergency medical systems have difficulty in quickly and accurately collecting and analyzing patient information, and they also do not propose emergency responses that take into account the user's psychological state. As a result, it is difficult to provide optimal responses in real time according to the patient's condition, which can lead to serious medical accidents and stress for users.

[0715] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes terminal means that allows a user to input the patient's condition and symptoms, generative AI model means that analyzes the input patient information in real time, emotion engine means that analyzes the user's emotions from their facial expressions, tone of voice, and input actions, means for proposing emergency responses based on the information analyzed by the generative AI model means and the user's emotional state, communication means that transmits the information and suggestions to emergency services, and means that receive responses from emergency services and notify the user of the optimal emergency responses. This enables prompt and appropriate medical response that takes into account the patient's condition and the user's psychological state.

[0716] A "user terminal" is a device used by a user to input a patient's condition or symptoms, and includes a smartphone, tablet, computer, etc.

[0717] A "generative AI model" is an artificial intelligence model that analyzes input patient information in real time and generates analysis results based on the urgency and symptoms.

[0718] An "emotion engine" is a means of analyzing a user's psychological state from their facial expressions, tone of voice, and input actions, and recognizing their emotions.

[0719] "Communication means" means for transmitting the information analyzed by the generative AI model and the user's emotional state to emergency services and for receiving a response from emergency services.

[0720] "Emergency services" refers to medical services for transporting emergency patients and providing first aid, including the deployment of ambulances and cooperation with hospitals.

[0721] "Optimal emergency response measures" are measures that suggest necessary emergency responses to users based on the analysis results of the generative AI model and emotion engine.

[0722] "Means for managing the operational status and traffic information of ambulances within the area" refers to means for collecting and managing the current status and traffic information of ambulances deployed within the area.

[0723] "Means for managing the medical treatment capacity and equipment status of each medical institution" refers to means for collecting and managing the status of the medical treatment capacity and available medical equipment of each medical institution.

[0724] The "means for selecting the optimal transport route and destination" refers to a means for selecting the optimal transport route and destination based on patient information, ambulance operation status, traffic information, and information from medical institutions.

[0725] "Hospital means" refers to the means used to receive patient information, adjust medical capabilities, and prepare for appropriate reception.

[0726] "Means for updating medical treatment capacity and admission status in real time and sharing it throughout the system" refers to a means for updating a hospital's medical treatment capacity and patient admission status in real time and sharing that information throughout the system.

[0727] This invention is a system that provides real-time support in emergency medical situations using a generative AI model and an emotion engine. The system operates through collaboration between the user device, the generative AI model, the emotion engine, a server, and the hospital.

[0728] User terminal

[0729] User devices include smartphones, tablets, and computers. The user launches an application on the device and inputs the patient's condition and symptoms. Specifically, the user inputs symptoms such as "chest pain" and "heart rate 120 bpm."

[0730] Generative AI Models

[0731] The information entered by the user is sent to a generative AI model on the device. This generative AI model is implemented in a programming language such as Python, using TensorFlow or PyTorch. The generative AI model analyzes the input information and identifies the urgency and possible symptoms. For example, if the input is "chest pain" and "heart rate 120 bpm," the AI ​​model will generate analysis results such as "high urgency" and "possible heart attack."

[0732] Emotion Engine

[0733] The device also has a built-in emotion engine. This uses a camera and microphone to analyze the user's facial expressions, tone of voice, and input actions to recognize the user's psychological state. For example, if the user is feeling strong anxiety or impatience, the device will send a notification to calm them down. Specifically, the device will display a message saying, "Please stay calm, we'll call an ambulance."

[0734] communication means

[0735] When the user presses the send button, the device sends the analysis results of the generative AI model and the recognition results of the emotion engine to the server. This communication is securely carried out via the HTTPS protocol.

[0736] Server Roles

[0737] The server receives the information sent from the device and stores it in a database. Additionally, the server obtains information such as the ambulance operation status, traffic conditions, and the medical capabilities of each medical institution in the area from related systems and external APIs (e.g., Google Maps API). This information is integrated and an algorithm is run to select the optimal transport route and destination.

[0738] Selection of transport route and destination

[0739] The server uses Dijkstra's algorithm or A algorithm to select the optimal transport route and hospital. For example, if the "Central Hospital" is determined to be the optimal route, the server sends this result to the terminal and emergency services.

[0740] Hospital coordination and preparation

[0741] The hospital prepares for admission based on the patient information sent from the server, including calling in emergency medical teams and preparing the necessary medical equipment. The hospital also updates its medical capacity and admission status in real time and shares it with the entire system.

[0742] Specific examples

[0743] For example, if a user shows symptoms of a heart attack, they would do the following: They would use their smartphone, launch the app, and input "chest pain" and "heart rate 120 bpm." The generative AI model analyzes this information and determines a "high level of urgency." The emotion engine also analyzes the user's psychological state and recognizes that the user is feeling anxious. As a result, it displays the message "Please remain calm, we will call an ambulance." When the user presses the send button, the information is sent to the server.

[0744] The server integrates the operational status of local ambulances, traffic information, and the medical capabilities of each hospital to calculate the optimal transport route and destination. For example, it may determine that transport to the "Central Hospital" is appropriate by avoiding traffic jams. This result is notified to the user's device and emergency services, and the hospital then begins preparations to accept the patient based on this information.

[0745] Prompt Sentence Examples

[0746] Example prompt: "User reports chest pain and a heart rate of 120 bpm. Analyze the emergency level and recommend immediate actions."

[0747] In this way, the present invention can provide highly accurate medical support in real time that takes into account the user's physical and psychological state.

[0748] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0749] Step 1:

[0750] User Action:

[0751] First, the user launches the application on their smartphone, tablet, or computer. Then, they input the patient's condition and symptoms, such as "chest pain" or "heart rate 120 bpm." Once the input data is ready, they are ready to press the send button.

[0752] Input: Patient information such as chest pain, heart rate 120 bpm.

[0753] Output: The entered patient information is stored on the terminal.

[0754] Step 2:

[0755] Terminal behavior:

[0756] The user device sends the input patient information to the generative AI model in real time. The generative AI model is implemented using Python, TensorFlow, and PyTorch. The generative AI model analyzes the input information and determines the urgency and suspected symptoms.

[0757] Input: Patient condition and symptoms (chest pain, heart rate 120 bpm).

[0758] Data processing and computation: A generative AI model analyzes input data and identifies symptoms that may indicate a high level of urgency. For example, a model might derive a high level of urgency and a possible heart attack from input data such as chest pain and a heart rate of 120 bpm.

[0759] Output: Analysis result (high urgency, possible heart attack).

[0760] Step 3:

[0761] Terminal behavior:

[0762] Next, the emotion engine analyzes the user's psychological state. Using the device's camera and microphone, it captures and analyzes the user's facial expressions, tone of voice, and input actions. The emotion engine recognizes the user's psychological state and recommends appropriate emergency responses.

[0763] Input: User facial expressions, tone of voice, and typing behavior.

[0764] Data processing and calculation: The emotion engine analyzes this data and identifies the user's emotional state. For example, it infers "strong anxiety" from the user's facial tension and excited tone of voice.

[0765] Output: Sentiment analysis result (high anxiety).

[0766] Step 4:

[0767] Terminal behavior:

[0768] The analysis results of the generative AI model and the emotion engine are combined to suggest emergency responses to the user. For example, if the analysis result of the generative AI model is "possible heart attack" and the analysis result of the emotion engine is "strong anxiety," the message "Please stay calm, we will call an ambulance" will be displayed.

[0769] Input: Generative AI model analysis (high urgency, possible heart attack), emotion engine analysis (high anxiety).

[0770] Data processing and calculation: Generate suggested messages, such as "Please stay calm, I'll call an ambulance."

[0771] Output: Notification messages that are displayed on the application screen.

[0772] Step 5:

[0773] User Action:

[0774] The user takes action based on the suggested message by pressing the send button to send the information to the server. This action causes the device to send the analysis results of the generative AI model and emotion engine to the server.

[0775] Input: Analysis results of the generative AI model, analysis results of the emotion engine.

[0776] Output: The analysis results are sent to the server.

[0777] Step 6:

[0778] Server Action:

[0779] The server receives the data and stores it in a database. The server then obtains information such as the operating status of ambulances in the area, traffic information, and the medical capabilities of each medical institution. For example, traffic information can be obtained using the Google Maps API.

[0780] Input: Analysis results, ambulance operation status in the area, traffic information, and medical facility capacity.

[0781] Data processing and calculation: The server integrates the data and selects the optimal delivery route and destination.

[0782] Output: Optimal delivery route and destination information.

[0783] Step 7:

[0784] Server Action:

[0785] The server calculates the optimal transport route and destination and notifies the user terminal and emergency services. For example, if the "Central Hospital" is determined to be the optimal destination, that information will be sent along with a route that avoids traffic jams.

[0786] Input: Optimal delivery route and destination.

[0787] Output: User terminal and emergency services are notified.

[0788] Step 8:

[0789] Hospital Actions:

[0790] Based on the patient information sent from the server, the hospital prepares to accept the patient, assembles an emergency medical team, and prepares the necessary medical equipment.In addition, medical capacity and acceptance status are updated in real time and shared throughout the system.

[0791] Input: Patient information, optimal transport route and destination.

[0792] Output: Emergency medical team assembled and ready to receive.

[0793] (Application example 2)

[0794] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0795] Conventional emergency medical support systems were able to analyze the patient's condition and symptoms in real time and respond appropriately, but they did not take into account the user's psychological state, and therefore had the problem of not being able to reduce anxiety and stress at the scene of an emergency response.In addition, while systems exist that provide quick and appropriate emergency response, they lack the functionality to provide psychological support to users, which poses the risk of limiting quick judgment and action.

[0796] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0797] In this invention, the server includes emotion engine means, means for providing appropriate feedback to the user based on the psychological state analyzed by the emotion engine means, and means for managing the medical capabilities and equipment status of each medical institution, thereby enabling prompt and appropriate medical response as well as psychological support for the user.

[0798] A "user terminal" is a device used by a user to input medical or emergency information, and may include a smartphone, tablet, or computer.

[0799] "Generative model means" refers to AI models and machine learning algorithms that perform appropriate analysis based on input patient information and emergency data.

[0800] "Communication means" refers to the internet or network communication means for transmitting the analyzed information from the user terminal to the server or emergency services.

[0801] "Notification means" refers to a method or device for notifying a user in real time of the response or analysis results from emergency services.

[0802] "Emotion engine means" refers to a system or software for recognizing and appropriately evaluating a user's psychological state and emotions.

[0803] The "feedback providing means" refers to a method or device for providing appropriate responses or support based on the user's psychological state analyzed by the emotion engine means.

[0804] "Ambulance availability management means" means a system or method for managing the availability and availability of ambulances in an area in real time and updating them as necessary.

[0805] "Traffic information management means" refers to systems and methods for capturing and managing road congestion and traffic conditions in real time.

[0806] "Medical institution information management means" refers to a system or method for managing information on the medical treatment capabilities and available facilities of each medical institution and updating the necessary information.

[0807] "Hospital means" refers to the medical facilities and systems that receive patient information, coordinate medical capacity, and make appropriate preparations for admission.

[0808] "Sharing tools" refers to methods and systems for updating clinical capacity and availability in real time and sharing that information across the system.

[0809] The present invention is a system that combines a user terminal, a generative model means, a communication means, a notification means, an emotion engine means, a feedback providing means, an ambulance operation status management means, a traffic information management means, a medical institution information management means, a hospital means, a sharing means, and the like.

[0810] System Configuration

[0811] 1. User Device:

[0812] The user device is a smartphone, tablet, or computer that provides an interface for the user to input information about the patient's condition and symptoms. For example, the user might input information such as "chest pain" or "heart rate 120 bpm." This information is then sent to the generative AI model in real time.

[0813] 2. Generative modeling methods:

[0814] The generative AI model installed on the user's device analyzes the input patient information. For example, if "chest pain" and "heart rate 120 bpm" are input, the generative AI model will output the diagnosis "suspected heart attack." This analysis result is immediately fed back to the user, and the necessary emergency response is suggested.

[0815] 3. Means of communication:

[0816] The user device sends the information analyzed by the generative model to the server. The communication is carried out via the Internet, and the information is encrypted and secure.

[0817] 4. Means of notification:

[0818] The server sends a response to the user's device, which notifies the user in real time of the arrival time of the ambulance and the best emergency response.

[0819] 5. Emotion Engine Means:

[0820] It is equipped with an emotion engine that analyzes the user's psychological state. It analyzes the user's facial expressions, tone of voice, input actions, etc. to recognize the anxiety or impatience the user is feeling.

[0821] 6. How to provide feedback:

[0822] The system provides appropriate feedback based on the user's psychological state analyzed by the emotion engine means. For example, if the user is feeling strong anxiety or impatience, the system will provide a reassuring message such as "Please stay calm, we will call an ambulance."

[0823] 7. Ambulance operation status management means and traffic information management means:

[0824] The server manages the operational status and traffic information of ambulances in the area in real time, which allows the system to determine the optimal ambulance dispatch and transport route.

[0825] 8. Medical institution information management measures:

[0826] The server manages the medical capabilities and equipment status of each medical institution, and determines the optimal destination based on this information.

[0827] 9. Hospital Measures:

[0828] The hospital prepares to accept the patient based on the patient information sent from the server, mobilizes an emergency medical team, and prepares the necessary medical equipment. Medical capacity and acceptance status are also updated in real time and shared throughout the system.

[0829] Example of operation

[0830] For example, if a user discovers a fire, they can enter "Fire in Building A - Floor 3" into the app and provide image data. The generative AI model analyzes this data and immediately provides instructions such as "Take evacuation measures." At the same time, the emotion engine detects the user's anxiety and provides specific feedback such as "Remain calm and evacuate."

[0831] Prompt Sentence Examples

[0832] Example input:

[0833] Emergency Type: Fire

[0834] Location: Building A - Floor 3

[0835] Description: Fire in storage room

[0836] Image data: base64_image_string

[0837] User emotional state: scared

[0838] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0839] Step 1:

[0840] The user inputs information about the emergency situation and the patient's condition into the user device. Specifically, the user uses a smartphone or tablet to input the situation, such as "fire outbreak," and patient information, such as "heart rate 120 bpm," into the application interface. The input data is saved on the device in text or image format.

[0841] Input: Text and image data that users enter into the app

[0842] Output: Input information data in the user's terminal

[0843] Step 2:

[0844] The generative AI model analyzes data entered into the user's device, analyzes information about the patient's condition and emergency, and generates a diagnosis such as "high urgency" or "possible heart attack." Machine learning algorithms are used to perform text and image analysis.

[0845] Input: Input information data in the user terminal

[0846] Output: Analyzed diagnostic results

[0847] Step 3:

[0848] The analysis results are sent to a server using a communication method. The data is encrypted and securely transmitted over the Internet. The server stores the received data and performs further analysis.

[0849] Input: Analyzed diagnostic results

[0850] Output: Data sent to the server

[0851] Step 4:

[0852] The emotion engine analyzes the user's psychological state. It recognizes the user's facial expressions and analyzes the tone of their voice to assess whether the user is feeling anxious or impatient. Input data is collected using sensors such as cameras and microphones, and the AI ​​model analyzes it.

[0853] Input: User's facial expression data, voice tone data

[0854] Output: User's psychological state evaluation result

[0855] Step 5:

[0856] The feedback provider provides appropriate feedback to the user based on the analyzed psychological state. For example, it may provide a message such as "Please stay calm" or "Call an ambulance" via text or voice. Text-to-Speech (TTS) technology may also be used.

[0857] Input: User's psychological state evaluation results

[0858] Output: Feedback message

[0859] Step 6:

[0860] The ambulance operation status management means and traffic information management means obtain real-time information on the operation status and traffic of ambulances in the area, and based on this, determine the optimal ambulance arrangements and transport routes.

[0861] Input: Ambulance operation status data and traffic information data stored on the server

[0862] Output: Optimal ambulance dispatch route and transport route

[0863] Step 7:

[0864] The medical institution information management tool selects the optimal destination based on the medical capabilities and equipment status of each medical institution. The selection results are updated in real time and shared throughout the system.

[0865] Input: Medical institution information data in the server

[0866] Output: Optimal destination selection result

[0867] Step 8:

[0868] The result of the selection is notified to the user's terminal using a notification method. The user is given clear instructions on the next action to be taken, and the entire system cooperates to achieve an efficient emergency response.

[0869] Input: Selection result of optimal transport route and destination

[0870] Output: Notification message of selection result

[0871] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0872] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0873] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0874] [Third embodiment]

[0875] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0876] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0877] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0878] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0879] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0880] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0881] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0882] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0883] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0884] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0885] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0886] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0887] This invention is a system that uses generative AI to provide real-time support in emergency medical settings, and is implemented with the following configuration.

[0888] User terminal

[0889] The user's device can be a smartphone, tablet, or computer. The user launches the application and is presented with an interface for entering patient information, including symptoms (e.g., chest pain, shortness of breath) and vital signs (e.g., heart rate, blood pressure). This information is sent in real time to the generative AI model on the device for analysis.

[0890] Analysis of generative AI models

[0891] The device uses a generative AI model to analyze the patient information entered. For example, if chest pain and a heart rate of 120 bpm are entered, the AI ​​model will generate the analysis results of "high urgency" and "suspected heart attack." This analysis result is immediately fed back to the user, and the necessary emergency response is suggested.

[0892] Sending information via communication means

[0893] When the user presses the send button, the device sends the patient information and analysis results to the server. This communication is carried out over the Internet using a safe and secure data transmission protocol.

[0894] Server Processing

[0895] The server receives patient information sent from the user's device, which is then stored in a database. The server then connects with related systems and external APIs to obtain information on the operation status and traffic of ambulances in the area. It also collects information on the medical capabilities and available facilities of each medical institution.

[0896] The server integrates this information and runs an algorithm to select the optimal transport route and hospital. For example, the "Central Hospital" is selected as the optimal destination, and the optimal route that avoids traffic jams is determined.

[0897] Notification of results

[0898] The server sends the results of the selection (optimal transport route and hospital) to the user's device and the emergency service. A notification is displayed on the user's device, allowing the user to confirm the next action to be taken.

[0899] Hospital Treatment

[0900] The hospital receives the patient information sent from the server and prepares to accept the patient using its internal system. For example, this includes calling in an emergency medical team and preparing the necessary medical equipment. The hospital also updates its medical capabilities as needed and shares the latest information with the server, ensuring that the entire system is always running with the latest information.

[0901] Specific examples

[0902] For example, if a user experiences a heart attack, they can quickly launch the app on their smartphone and input "chest pain" and "heart rate 120 bpm." The AI ​​analyzes this information, determines it to be a "high urgency," and notifies the user to "call an ambulance immediately." When the user presses the send button, the information is sent to the server.

[0903] The server receives the information and determines the optimal transport route and destination based on the ambulance operation status, traffic information, and the medical capabilities of each hospital in the area. For example, it may determine that transport to the "Central Hospital" is the most appropriate route, as it avoids traffic jams.

[0904] Once the optimal transport route and hospital are notified to the user device and emergency services, an ambulance will be dispatched quickly and preparations for reception at the "Central Hospital" will begin, thus achieving a fast and efficient emergency medical response.

[0905] The processing flow will be explained below.

[0906] Step 1:

[0907] Users launch the application on their smartphone, tablet, or other device and enter their patient's condition and symptoms, such as chest pain and a heart rate of 120 bpm.

[0908] Step 2:

[0909] The device sends the patient information (symptoms and vital signs) entered by the user to the generative AI model, which then analyzes the information in real time and generates a level of urgency and emergency response.

[0910] Step 3:

[0911] The device receives the analysis results from the generative AI model and notifies the user, for example, displaying results such as "high urgency" and "suspected heart attack."

[0912] Step 4:

[0913] The user checks the analysis results and, if necessary, presses the send button to determine the next action to take. Pressing this button will cause the patient information to proceed to the next procedure.

[0914] Step 5:

[0915] The device sends patient information (symptoms, vital signs, and analysis results) to the server. This communication is carried out securely via the Internet.

[0916] Step 6:

[0917] The server receives the patient information sent from the user terminal and stores it in a database, allowing for centralized management of the patient's condition.

[0918] Step 7:

[0919] The server collects real-time information on the operational status and location of ambulances in the area, as well as traffic information, and obtains this information from related systems and external APIs as needed.

[0920] Step 8:

[0921] The server manages the medical capabilities and equipment status of each medical institution and updates this information, making it possible to select the most suitable hospital.

[0922] Step 9:

[0923] The server integrates patient information, ambulance operation status, traffic information, and information from each medical institution, and executes an algorithm to select the optimal transport route and destination hospital. For example, it may determine that the "Central Hospital" is the best option.

[0924] Step 10:

[0925] The server sends the selection result to the user terminal and the emergency service, which then starts arranging an ambulance and transporting the patient.

[0926] Step 11:

[0927] The user can check the optimal transport route and destination displayed on the device and wait for the ambulance to arrive. If necessary, they will be instructed to continue providing first aid.

[0928] Step 12:

[0929] The emergency services receive the notification from the server, follow the designated transport route to the patient, and begin transporting the patient promptly.

[0930] Step 13:

[0931] The hospital begins preparations for admission based on the patient information sent from the server, convening an emergency medical team and preparing the necessary medical equipment.

[0932] Step 14:

[0933] Hospitals update their medical capacity in real time and send the latest information to a server, which makes it easier to adjust the intake of other patients.

[0934] Step 15:

[0935] When the ambulance arrives, the user follows the instructions to transport the patient, and the emergency services and hospitals work together to ensure a prompt response.

[0936] The above processing steps enable a fast and efficient emergency medical response.

[0937] Example 1

[0938] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0939] In emergency medical situations, rapid and appropriate responses are required, but it is not easy to process a large amount of information at once, such as assessing the situation on-site, deciding where to transport patients, and taking into account traffic information. It is also difficult to grasp the medical capabilities and acceptance status of medical institutions. This poses the issue of the possibility of delaying appropriate treatment for patients.

[0940] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0941] In this invention, the server includes terminal means that allows a user to input the patient's condition and symptoms, generative model means that analyzes the input patient information in real time, server means that saves and manages the information analyzed by the generative model means, means that prompts an emergency service to respond via the server means, means that receives a response from the emergency service and notifies the user of the optimal emergency response, means that manages the operation status and traffic information of ambulances in the area, means that manages the medical capacity and equipment status of each medical institution, means that integrates patient information, ambulance operation status, traffic information, and medical institution information to select the optimal transport route and destination, medical institution means that accepts patient information, adjusts medical capacity, and prepares for appropriate acceptance, and means that updates the medical capacity and acceptance status in real time and shares it throughout the system, thereby enabling prompt and appropriate response at the scene of an emergency medical care.

[0942] The "terminal means by which a user can input the condition or symptoms of a patient" is a device such as a smartphone, tablet, or computer, which is an apparatus that allows a user to input patient information using an interface.

[0943] The "generative model means for analyzing input patient information in real time" is a system in which a generative AI model instantly analyzes input patient information and provides information on the urgency of symptoms and medical decisions.

[0944] "Server means for storing and managing information analyzed by the generative model means" refers to a data storage and management device for storing the analysis results by the generative AI model in a database and centrally managing this information.

[0945] The "means for encouraging emergency services to respond via server means" refers to a communication function and processing system that transmits the analysis results to emergency services via a server and encourages emergency response.

[0946] The "means for receiving responses from emergency services and notifying the user of the most appropriate emergency response" is a system for receiving responses from emergency services and notifying the user of the appropriate emergency response method in real time based on that information.

[0947] "Means for managing ambulance operation status and traffic information within the region" refers to a system that collects and manages ambulance usage status and real-time traffic conditions within the region, and consolidates this information in a centralized manner.

[0948] "Means for managing the medical treatment capacity and equipment status of each medical institution" refers to a function for collecting and managing information on the current medical treatment capacity and available equipment of each medical institution.

[0949] "Means of integrating patient information, ambulance operation status, traffic information, and medical institution information to select the optimal transport route and destination" refers to an algorithm and system that integrates this information and automatically selects the optimal transport route and destination.

[0950] "Medical institution means for accepting patient information, adjusting medical treatment capabilities, and making appropriate preparations for acceptance" is a system that allows medical institutions to adjust their medical treatment capabilities and prepare the necessary medical equipment and specialists based on patient information sent from the server.

[0951] "Means for updating medical treatment capacity and admission status in real time and sharing it throughout the system" refers to a means for updating medical institutions' medical treatment capacity and admission status at any time and sharing this information throughout the system in real time.

[0952] This invention is a system that uses generative AI models to provide real-time support in emergency medical settings. The system mainly consists of the following components:

[0953] User terminal

[0954] The user's device can be a smartphone, tablet, or computer. The user launches the application and is presented with an interface for entering patient information, including symptoms (e.g., chest pain, shortness of breath) and vital signs (e.g., heart rate, blood pressure). This information is sent in real time to the generative AI model on the device for analysis.

[0955] Analysis of generative AI models

[0956] The device uses a generative AI model to analyze the patient information entered. For example, if chest pain and a heart rate of 120 bpm are entered, the generative AI model will generate the analysis results of "high urgency" and "suspected heart attack." This analysis result is immediately fed back to the user, and the necessary emergency response is suggested.

[0957] Sending information via communication means

[0958] When the user presses the send button, the terminal sends the patient information and analysis results to the server. This communication is carried out over the Internet using a secure data transmission protocol (e.g., HTTPS).

[0959] Server Processing

[0960] The server receives patient information sent from the user's device. After receiving the data, it stores it in a database. The server also obtains information on the operation status of ambulances in the area, traffic information, and information on the medical capabilities and available facilities of each medical institution, and executes an algorithm that integrates this information to select the optimal transport route and destination. For example, the "Central Hospital" is selected as the optimal destination, and the optimal route that avoids traffic congestion is determined.

[0961] Notification of results

[0962] The server sends the selection results (optimal transport route and hospital) to the user's device and the emergency service. A notification message is displayed on the user's device, allowing the user to confirm the next action to be taken.

[0963] Hospital Treatment

[0964] The hospital receives the patient information sent from the server and prepares to accept the patient using its internal system. For example, this includes calling in an emergency medical team and preparing the necessary medical equipment. The hospital also updates its medical capabilities as needed and shares the latest information with the server, ensuring that the entire system is always running with the latest information.

[0965] Specific examples

[0966] For example, if a user experiences a heart attack, they can launch the app on their smartphone and enter "chest pain" and "heart rate 120 bpm." The generating AI analyzes this information, determines it to be a "high urgency," and notifies the user to "call an ambulance immediately." When the user presses the send button, the information is sent to the server. The server determines the optimal transport route and destination based on the ambulance operation status in the area, traffic information, and the medical capabilities of each medical institution. For example, it may determine that transport to the "Central Hospital" is appropriate, taking the fastest route that avoids traffic jams. The result is notified to the user's device and emergency services, and an ambulance is quickly dispatched, while preparations begin at the "Central Hospital." This enables fast and efficient emergency medical response.

[0967] Prompt Sentence Examples

[0968] "I have a patient with chest pain and a heart rate of 120 bpm. What is the appropriate first aid?"

[0969] These prompts can be fed into a generative AI model to help you get a consultation faster.

[0970] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0971] Step 1:

[0972] The user inputs the patient's condition and symptoms.

[0973] The user launches the application on their smartphone, tablet, or computer. The application displays an interface for entering patient information. The user enters the patient's symptoms (e.g., chest pain, shortness of breath) and vital signs (e.g., heart rate, blood pressure).

[0974] Input: Patient symptoms and vital signs

[0975] Output: Entered patient information

[0976] Specific behavior: The user enters "chest pain" and "heart rate 120 bpm" into the application's input form.

[0977] Step 2:

[0978] Analyzing patient information with generative AI models

[0979] The device sends the patient information entered in step 1 to the generative AI model within the device. The generative AI model analyzes the information in real time and determines the urgency of the patient's symptoms. For example, if chest pain and a heart rate of 120 bpm are entered, the generative AI model will generate the analysis results of "high urgency" and "suspected heart attack."

[0980] Input: Patient information entered

[0981] Output: Analysis result (e.g. high urgency, suspected heart attack)

[0982] Specific operation: The device passes the input patient information to the generative AI model, which then returns an analysis result stating, "This symptom is of high urgency and is suspected to be a heart attack."

[0983] Step 3:

[0984] Analysis results are fed back to the user

[0985] The device immediately provides the user with the analysis results from the generative AI model. Based on the analysis results, the device displays the necessary emergency response instructions. For example, instructions such as "Call an ambulance immediately" are displayed.

[0986] Input: Analysis results of the generative AI model

[0987] Output: Emergency response instructions displayed to the user

[0988] Specific behavior: The device displays a message to the user indicating "High Urgency" and "Call an ambulance immediately."

[0989] Step 4:

[0990] User sends information to server

[0991] When the user presses the send button, the device sends the entered patient information and the analysis results of the generated AI model to the server. This communication is carried out over the internet using a safe and secure data transmission protocol such as HTTPS.

[0992] Input: Entered patient information and analysis results of the generated AI model

[0993] Output: Information sent to the server

[0994] What happens: The user clicks the "Send" button and the device sends the information to the server using HTTPS.

[0995] Step 5:

[0996] The server processes the information and determines the optimal delivery route and destination.

[0997] The server receives patient information sent from the device and stores it in a database. The server then collects information on the operation status of ambulances in the area, traffic information, and information on the medical capabilities and facilities of each medical institution, and executes an algorithm that integrates this information to select the optimal transport route and destination. For example, the "Central Hospital" may be selected as the optimal destination, and the optimal route that avoids traffic congestion may be determined.

[0998] Input: Patient information, traffic information, ambulance operation status, medical institution medical capacity

[0999] Output: Optimal route and destination

[1000] What it does: The server stores the information in a database, retrieves the latest traffic information from an external API, and runs an algorithm to determine that "Central Hospital is the best choice."

[1001] Step 6:

[1002] The server notifies the user device and emergency services

[1003] The server then sends the optimal transport route and hospital to the user's device and the emergency service. A notification message is displayed on the user's device, allowing the user to confirm the next steps.

[1004] Enter: Optimal Route and Destination Information

[1005] Output: Notification message to user terminal and emergency services

[1006] Specific operation: The server sends a message to the user device and emergency services saying, "Central Hospital is the best option. Route A is the best route to avoid traffic jams."

[1007] Step 7:

[1008] Preparations for hospital admission

[1009] The hospital receives the patient information sent from the server and prepares to accept the patient using its internal system, including calling in an emergency medical team and preparing the necessary medical equipment. The hospital also updates its medical capabilities as needed and shares the latest information with the server, ensuring that the entire system is always running with the latest information.

[1010] Input: Patient information sent from the server

[1011] Output: Readiness status

[1012] Specific operations: The hospital receives the patient information, starts preparing the emergency room, and updates the medical capabilities to the server.

[1013] (Application example 1)

[1014] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1015] Modern society demands rapid emergency medical response. However, various factors, such as traffic congestion and overwhelmed hospitals, hinder rapid response. In urban areas in particular, ambulances are often delayed due to traffic congestion. Furthermore, because the quality of emergency response at the scene depends on the experience and knowledge of medical staff, there are cases where an appropriate initial response is not provided. To solve these problems, more efficient and accurate analysis of medical information and a seamless emergency medical support system that includes autonomous driving technology are required.

[1016] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1017] In this invention, the server includes a device that allows a user to input the patient's condition and symptoms, a generative model means that analyzes the input patient information in real time, a communication means that transmits the information analyzed by the generative model means to an emergency service, a means that receives a response from the emergency service and notifies the user of the optimal emergency response, and a means that dispatches an autonomous ambulance along an optimal transport route. This enables rapid analysis of patient information and dispatch of an autonomous ambulance along an optimal route that avoids traffic congestion. This enables faster and more accurate emergency response and more appropriate initial response at the scene.

[1018] "Device that allows a user to input a patient's condition or symptoms" refers to a smartphone, tablet, smart glasses, or other input device that allows a user to directly input a patient's physical condition or specific symptoms in real time.

[1019] "Generative model means for analyzing input patient information in real time" refers to AI models and algorithms for analyzing input patient data, thereby enabling instant medical assessment and determination of urgency.

[1020] "Communication means for transmitting information analyzed by the generative model means to emergency services" refers to network technologies and communication protocols for quickly transmitting the analysis results to the appropriate emergency services, and is typically a means for secure data transmission via the Internet.

[1021] "Means for receiving responses from emergency services and informing the user of the best emergency response" refers to a system or application that receives instructions and information from emergency services and, based on that, notifies the user of the next steps to take and emergency procedures.

[1022] "Means for dispatching autonomous ambulances along the optimal transport route" refers to technologies and systems that calculate the quickest and most efficient route based on input patient information, traffic conditions, medical institution medical capabilities, etc., and dispatch ambulances using autonomous driving technology.

[1023] "Means for managing the operational status and traffic information of ambulances within the region" refers to a system or platform for monitoring and managing the current location, operational status, traffic congestion and accident information, etc. of ambulances in real time.

[1024] "Means for managing the medical treatment capacity and equipment status of each medical institution" refers to a system for managing each medical institution's current capacity, the operating status of medical staff, and information on available medical equipment and facilities, and constantly updating it to the latest version.

[1025] "Means for integrating patient information, ambulance operation status, traffic information, and medical institution information to select the optimal transport route and destination" refers to algorithms and systems for comprehensively processing the various types of information mentioned above and determining the most appropriate transport destination and optimal travel route.

[1026] "Means for providing information on transport routes and destinations to autonomous ambulances" refers to the technology and systems that transmit the calculated optimal transport route and destination information to the autonomous ambulance's system in real time and control autonomous driving based on that information.

[1027] "Hospital means for receiving patient information, adjusting medical treatment capacity, and making appropriate preparations for admission" refers to a system and management means for adjusting the admission system within the hospital and arranging for the necessary medical staff and equipment based on the received patient information.

[1028] "Means for updating medical treatment capacity and admission status in real time and sharing it throughout the system" refers to technologies and platforms for constantly monitoring medical institutions' medical treatment capacity and admission status, and instantly updating and sharing this information within the entire system.

[1029] "Means for predicting the arrival time of an autonomous ambulance and adjusting the hospital's reception readiness" refers to systems and technologies for calculating the estimated arrival time of an ambulance and appropriately adjusting the hospital's reception readiness based on this.

[1030] This invention provides a fast and efficient emergency medical system that utilizes an autonomous ambulance. The specific configuration and operation procedure for implementing this invention are described below.

[1031] First, the user terminal can be a smartphone, tablet, smart glasses, or other device. Users can input the patient's symptoms and vital signs through these devices. For example, if a patient inputs "chest pain" and "heart rate 120 bpm," the generative AI model on the device will instantly analyze this information and generate a diagnosis such as "high urgency" or "suspected heart attack." This analysis uses generative AI models such as TensorFlow and PyTorch.

[1032] The information analyzed by the generative AI model is sent to a server via a communication method using a REST API, which securely transmits patient information and analysis results over the internet. The server stores the received patient information in a database and connects with Google Maps API and the company's own traffic analysis system to obtain real-time information on the operation status of local ambulances and traffic information.

[1033] The server then manages the medical capabilities and equipment status of each medical institution and integrates this information. Based on the stored patient information, ambulance operation status, traffic information, and medical institution information, the optimal transport route and medical institution are selected. This algorithm often applies machine learning models and operations research methods.

[1034] The server provides the selection results (optimal transport route and destination medical institution) to the autonomous ambulance dispatch system. This dispatch system then dispatches the autonomous ambulance along the optimal route based on the selection results. The autonomous driving technology uses control software and sensor systems for autonomous vehicles.

[1035] The information from the server is also sent to the medical institution, and once it has been received, medical staff prepare to accept the patient using their internal system. Medical capacity and acceptance status are updated in real time and shared across the entire server, allowing the hospital to respond quickly.

[1036] As an example, use the following prompt:

[1037] Enter your symptoms: Chest pain

[1038] Enter your heart rate: 120 bpm

[1039] Enter your blood pressure: 140 / 90 mmHg

[1040] The AI ​​then responds with the analysis:

[1041] Analysis result: Suspected heart attack (high urgency)

[1042] Sending data...

[1043] Optimal route: The fastest route avoiding traffic jams

[1044] Self-driving ambulances have been deployed.

[1045] In this way, by using the system of the present invention, it is possible to perform real-time analysis of patients, provide appropriate initial responses, provide optimal transport routes, and quickly dispatch autonomous ambulances, thereby significantly improving the quality and speed of emergency medical care.

[1046] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1047] Step 1:

[1048] Users input the patient's symptoms and vital signs using a device such as a smartphone or tablet. They launch the application and enter specific symptoms (e.g., chest pain) and vital signs (e.g., heart rate 120 bpm, blood pressure 140 / 90 mmHg) through the interface. This data is then analyzed by the generative AI model.

[1049] Input: Symptom and vital sign data

[1050] Output: Input data to a generative AI model

[1051] Step 2:

[1052] A generative AI model installed on the device analyzes input patient information in real time. It uses machine learning libraries such as TensorFlow and PyTorch to determine the urgency and suspected medical condition from the patient information. The results of this analysis are immediately fed back to the user as emergency response suggestions.

[1053] Input: Patient information (symptoms, vital signs)

[1054] Output: Diagnosis result (urgency, condition)

[1055] Step 3:

[1056] The user checks the analysis results of the generative AI model and, if necessary, presses the send button to send the information to the server. The information is securely transmitted to the server via the internet via a REST API.

[1057] Input: Analysis results (urgency, condition), user's sending operation

[1058] Output: Patient information sent to the server

[1059] Step 4:

[1060] The server receives patient information sent from the user's device and stores it in a database. Based on the received data, it connects with Google Maps API and its own traffic analysis system to obtain information on the operation status of ambulances in the area and traffic information. Based on this information, it determines the optimal transport route and medical institution.

[1061] Input: Patient information submitted

[1062] Output: Selection of optimal transport route and medical institution

[1063] Step 5:

[1064] The server provides the results of the selection (optimal transport route and destination medical institution) to the autonomous ambulance dispatch system. The autonomous vehicle's control software then dispatches the ambulance based on the selected route.

[1065] Input: Selection result of optimal transport route and destination

[1066] Output: Dispatch of autonomous ambulance

[1067] Step 6:

[1068] The autonomous ambulance arrives at the patient's location and begins transporting the patient based on the selected route. Meanwhile, the server also sends information to the medical institution, which then prepares to accept the patient.

[1069] Input: Notification of arrival of autonomous ambulance, sending admission information to hospital

[1070] Output: Patient transport by ambulance, preparation for admission at medical institution

[1071] Step 7:

[1072] Medical institutions receive the information sent from the server in real time and proceed with preparations for admission using their internal systems. Medical treatment capabilities and admission status are updated as needed and shared across the entire server.

[1073] Input: Acceptance information from the server

[1074] Output: Ready to accept patients, updated medical capabilities

[1075] Step 8:

[1076] Once the ambulance arrives at the hospital, the patient receives prompt and appropriate medical treatment, and real-time updated information allows medical staff to provide the most appropriate response.

[1077] Input: Ambulance arrival, patient handover

[1078] Output: Patient treatment start, real-time treatment response

[1079] This will enable a series of steps: rapid analysis of patient information, appropriate initial response, provision of optimal transport routes, rapid dispatch of autonomous ambulances, and rapid preparations for admission at medical institutions.

[1080] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1081] This invention is a system that uses generative AI and an emotion engine to provide real-time support in emergency medical settings, and is implemented with the following configuration.

[1082] User terminal

[1083] The user's device can be a smartphone, tablet, or computer. The user launches the application and is presented with an interface for entering patient information, including symptoms (e.g., chest pain, shortness of breath) and vital signs (e.g., heart rate, blood pressure). This information is sent in real time to the generative AI model on the device for analysis.

[1084] Analysis of generative AI models

[1085] The device uses a generative AI model to analyze the patient information entered. For example, if chest pain and a heart rate of 120 bpm are entered, the AI ​​model will generate the analysis results of "high urgency" and "suspected heart attack." This analysis result is immediately fed back to the user, and the necessary emergency response is suggested.

[1086] Incorporating an emotion engine

[1087] The emotion engine recognizes the user's psychological state. When the user inputs information, the device analyzes the user's facial expression, tone of voice, input actions, etc., and recognizes the user's emotions through the emotion engine. For example, if the user is feeling impatient or anxious, the device will suggest appropriate emergency measures.

[1088] Sending information via communication means

[1089] When the user presses the send button, the device sends the patient information and analysis results to the server. This communication is securely carried out over the Internet.

[1090] Server Processing

[1091] The server receives patient information sent from the user's device, which is then stored in a database. The server then connects with related systems and external APIs to obtain information on the operation status and traffic of ambulances in the area. It also collects information on the medical capabilities and available facilities of each medical institution.

[1092] The server integrates this information and runs an algorithm to select the optimal transport route and hospital. For example, the "Central Hospital" is selected as the optimal destination, and the optimal route that avoids traffic jams is determined.

[1093] Notification of results

[1094] The server sends the selection results (optimal transport route and hospital) to the user terminal and the emergency service, which then starts arranging an ambulance and transporting the patient.

[1095] Hospital Treatment

[1096] The hospital prepares to accept the patient based on the patient information sent from the server, convenes an emergency medical team, and prepares the necessary medical equipment. The hospital also updates its medical capabilities as needed and shares the latest information with the server, ensuring that the entire system is always running with the latest information.

[1097] Specific examples

[1098] For example, if a user experiences a heart attack, they can quickly launch the app on their smartphone and input "chest pain" and "heart rate 120 bpm." The generative AI analyzes this information and determines a "high level of urgency." At the same time, the emotion engine analyzes the user's facial expressions and tone of voice and recognizes that the user is experiencing strong anxiety or impatience.

[1099] As a result, the app sends a reassuring notification to the user saying, "Please stay calm, we'll call an ambulance." When the user presses the send button, the information is sent to the server.

[1100] The server receives the information and determines the optimal transport route and destination based on the ambulance operation status, traffic information, and the medical capabilities of each hospital in the area. For example, it may determine that transport to the "Central Hospital" is the most appropriate route, as it avoids traffic jams.

[1101] The selection result is notified to the user's device, and the user confirms the next action to be taken. An ambulance arrives and the patient is safely transported to the "Central Hospital," achieving a fast and efficient emergency medical response. The use of the emotion engine can reduce the user's anxiety and stress.

[1102] The processing flow will be explained below.

[1103] Step 1:

[1104] Users launch the application on their smartphone, tablet, or other device and enter their patient's condition and symptoms, such as chest pain and a heart rate of 120 bpm.

[1105] Step 2:

[1106] The device also sends the patient information (symptoms and vital signs) entered by the user to the emotion engine. At this time, the device analyzes the user's facial expressions, tone of voice, input actions, etc. to recognize the user's emotions.

[1107] Step 3:

[1108] The emotion engine analyzes the user's emotions (e.g., impatience, anxiety, fear) and sends the results to the generative AI model. The generative AI model then combines the patient information and the user's emotion data to generate an analysis result, such as "high urgency" and "suspected heart attack."

[1109] Step 4:

[1110] The device receives the analysis results from the generative AI model and notifies the user, displaying a reassuring message (e.g., "Please stay calm, we will call an ambulance immediately") based on the results of the emotion engine.

[1111] Step 5:

[1112] The user checks the analysis results and, if necessary, presses the send button to determine the next action to take. Pressing this button will cause the patient information to proceed to the next procedure.

[1113] Step 6:

[1114] The device sends patient information (symptoms, vital signs, and analysis results) to the server. This communication is carried out securely via the Internet.

[1115] Step 7:

[1116] The server receives the patient information sent from the user terminal and stores it in a database, allowing for centralized management of the patient's condition.

[1117] Step 8:

[1118] The server collects real-time information on the operational status and location of ambulances in the area, as well as traffic information, and obtains this information from related systems and external APIs as needed.

[1119] Step 9:

[1120] The server manages the medical capabilities and equipment status of each medical institution and updates this information, making it possible to select the most suitable hospital.

[1121] Step 10:

[1122] The server integrates patient information, ambulance operation status, traffic information, and information from each medical institution, and executes an algorithm to select the optimal transport route and destination hospital. For example, it may determine that the "Central Hospital" is the best option.

[1123] Step 11:

[1124] The server sends the selection result to the user terminal and the emergency service, which then starts arranging an ambulance and transporting the patient.

[1125] Step 12:

[1126] The user can check the optimal transport route and destination displayed on the device and wait for the ambulance to arrive. If necessary, they will be instructed to continue providing first aid.

[1127] Step 13:

[1128] The emergency services receive the notification from the server, follow the designated transport route to the patient, and begin transporting the patient promptly.

[1129] Step 14:

[1130] The hospital begins preparations for admission based on the patient information sent from the server, convening an emergency medical team and preparing the necessary medical equipment.

[1131] Step 15:

[1132] Hospitals update their medical capacity in real time and send the latest information to a server, which makes it easier to adjust the intake of other patients.

[1133] Step 16:

[1134] When the ambulance arrives, the user follows the instructions to transport the patient, and the emergency services and hospitals work together to ensure a prompt response.

[1135] Example 2

[1136] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1137] Conventional emergency medical systems have difficulty in quickly and accurately collecting and analyzing patient information, and they also do not propose emergency responses that take into account the user's psychological state. As a result, it is difficult to provide optimal responses in real time according to the patient's condition, which can lead to serious medical accidents and stress for users.

[1138] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes terminal means that allows a user to input the patient's condition and symptoms, generative AI model means that analyzes the input patient information in real time, emotion engine means that analyzes the user's emotions from their facial expressions, tone of voice, and input actions, means for proposing emergency responses based on the information analyzed by the generative AI model means and the user's emotional state, communication means that transmits the information and suggestions to emergency services, and means that receive responses from emergency services and notify the user of the optimal emergency responses. This enables prompt and appropriate medical response that takes into account the patient's condition and the user's psychological state.

[1139] A "user terminal" is a device used by a user to input a patient's condition or symptoms, and includes a smartphone, tablet, computer, etc.

[1140] A "generative AI model" is an artificial intelligence model that analyzes input patient information in real time and generates analysis results based on the urgency and symptoms.

[1141] An "emotion engine" is a means of analyzing a user's psychological state from their facial expressions, tone of voice, and input actions, and recognizing their emotions.

[1142] "Communication means" means for transmitting the information analyzed by the generative AI model and the user's emotional state to emergency services and for receiving a response from emergency services.

[1143] "Emergency services" refers to medical services for transporting emergency patients and providing first aid, including the deployment of ambulances and cooperation with hospitals.

[1144] "Optimal emergency response measures" are measures that suggest necessary emergency responses to users based on the analysis results of the generative AI model and emotion engine.

[1145] "Means for managing the operational status and traffic information of ambulances within the area" refers to means for collecting and managing the current status and traffic information of ambulances deployed within the area.

[1146] "Means for managing the medical treatment capacity and equipment status of each medical institution" refers to means for collecting and managing the status of the medical treatment capacity and available medical equipment of each medical institution.

[1147] The "means for selecting the optimal transport route and destination" refers to a means for selecting the optimal transport route and destination based on patient information, ambulance operation status, traffic information, and information from medical institutions.

[1148] "Hospital means" refers to the means used to receive patient information, adjust medical capabilities, and prepare for appropriate reception.

[1149] "Means for updating medical treatment capacity and admission status in real time and sharing it throughout the system" refers to a means for updating a hospital's medical treatment capacity and patient admission status in real time and sharing that information throughout the system.

[1150] This invention is a system that provides real-time support in emergency medical situations using a generative AI model and an emotion engine. The system operates through collaboration between the user device, the generative AI model, the emotion engine, a server, and the hospital.

[1151] User terminal

[1152] User devices include smartphones, tablets, and computers. The user launches an application on the device and inputs the patient's condition and symptoms. Specifically, the user inputs symptoms such as "chest pain" and "heart rate 120 bpm."

[1153] Generative AI Models

[1154] The information entered by the user is sent to a generative AI model on the device. This generative AI model is implemented in a programming language such as Python, using TensorFlow or PyTorch. The generative AI model analyzes the input information and identifies the urgency and possible symptoms. For example, if the input is "chest pain" and "heart rate 120 bpm," the AI ​​model will generate analysis results such as "high urgency" and "possible heart attack."

[1155] Emotion Engine

[1156] The device also has a built-in emotion engine. This uses a camera and microphone to analyze the user's facial expressions, tone of voice, and input actions to recognize the user's psychological state. For example, if the user is feeling strong anxiety or impatience, the device will send a notification to calm them down. Specifically, the device will display a message saying, "Please stay calm, we'll call an ambulance."

[1157] communication means

[1158] When the user presses the send button, the device sends the analysis results of the generative AI model and the recognition results of the emotion engine to the server. This communication is securely carried out via the HTTPS protocol.

[1159] Server Roles

[1160] The server receives the information sent from the device and stores it in a database. Additionally, the server obtains information such as the ambulance operation status, traffic conditions, and the medical capabilities of each medical institution in the area from related systems and external APIs (e.g., Google Maps API). This information is integrated and an algorithm is run to select the optimal transport route and destination.

[1161] Selection of transport route and destination

[1162] The server uses Dijkstra's algorithm or A algorithm to select the optimal transport route and hospital. For example, if the "Central Hospital" is determined to be the optimal route, the server sends this result to the terminal and emergency services.

[1163] Hospital coordination and preparation

[1164] The hospital prepares for admission based on the patient information sent from the server, including calling in emergency medical teams and preparing the necessary medical equipment. The hospital also updates its medical capacity and admission status in real time and shares it with the entire system.

[1165] Specific examples

[1166] For example, if a user shows symptoms of a heart attack, they would do the following: They would use their smartphone, launch the app, and input "chest pain" and "heart rate 120 bpm." The generative AI model analyzes this information and determines a "high level of urgency." The emotion engine also analyzes the user's psychological state and recognizes that the user is feeling anxious. As a result, it displays the message "Please remain calm, we will call an ambulance." When the user presses the send button, the information is sent to the server.

[1167] The server integrates the operational status of local ambulances, traffic information, and the medical capabilities of each hospital to calculate the optimal transport route and destination. For example, it may determine that transport to the "Central Hospital" is appropriate by avoiding traffic jams. This result is notified to the user's device and emergency services, and the hospital then begins preparations to accept the patient based on this information.

[1168] Prompt Sentence Examples

[1169] Example prompt: "User reports chest pain and a heart rate of 120 bpm. Analyze the emergency level and recommend immediate actions."

[1170] In this way, the present invention can provide highly accurate medical support in real time that takes into account the user's physical and psychological state.

[1171] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1172] Step 1:

[1173] User Action:

[1174] First, the user launches the application on their smartphone, tablet, or computer. Then, they input the patient's condition and symptoms, such as "chest pain" or "heart rate 120 bpm." Once the input data is ready, they are ready to press the send button.

[1175] Input: Patient information such as chest pain, heart rate 120 bpm.

[1176] Output: The entered patient information is stored on the terminal.

[1177] Step 2:

[1178] Terminal behavior:

[1179] The user device sends the input patient information to the generative AI model in real time. The generative AI model is implemented using Python, TensorFlow, and PyTorch. The generative AI model analyzes the input information and determines the urgency and suspected symptoms.

[1180] Input: Patient condition and symptoms (chest pain, heart rate 120 bpm).

[1181] Data processing and computation: A generative AI model analyzes input data and identifies symptoms that may indicate a high level of urgency. For example, a model might derive a high level of urgency and a possible heart attack from input data such as chest pain and a heart rate of 120 bpm.

[1182] Output: Analysis result (high urgency, possible heart attack).

[1183] Step 3:

[1184] Terminal behavior:

[1185] Next, the emotion engine analyzes the user's psychological state. Using the device's camera and microphone, it captures and analyzes the user's facial expressions, tone of voice, and input actions. The emotion engine recognizes the user's psychological state and recommends appropriate emergency responses.

[1186] Input: User facial expressions, tone of voice, and typing behavior.

[1187] Data processing and calculation: The emotion engine analyzes this data and identifies the user's emotional state. For example, it infers "strong anxiety" from the user's facial tension and excited tone of voice.

[1188] Output: Sentiment analysis result (high anxiety).

[1189] Step 4:

[1190] Terminal behavior:

[1191] The analysis results of the generative AI model and the emotion engine are combined to suggest emergency responses to the user. For example, if the analysis result of the generative AI model is "possible heart attack" and the analysis result of the emotion engine is "strong anxiety," the message "Please stay calm, we will call an ambulance" will be displayed.

[1192] Input: Generative AI model analysis (high urgency, possible heart attack), emotion engine analysis (high anxiety).

[1193] Data processing and calculation: Generate suggested messages, such as "Please stay calm, I'll call an ambulance."

[1194] Output: Notification messages that are displayed on the application screen.

[1195] Step 5:

[1196] User Action:

[1197] The user takes action based on the suggested message by pressing the send button to send the information to the server. This action causes the device to send the analysis results of the generative AI model and emotion engine to the server.

[1198] Input: Analysis results of the generative AI model, analysis results of the emotion engine.

[1199] Output: The analysis results are sent to the server.

[1200] Step 6:

[1201] Server Action:

[1202] The server receives the data and stores it in a database. The server then obtains information such as the operating status of ambulances in the area, traffic information, and the medical capabilities of each medical institution. For example, traffic information can be obtained using the Google Maps API.

[1203] Input: Analysis results, ambulance operation status in the area, traffic information, and medical facility capacity.

[1204] Data processing and calculation: The server integrates the data and selects the optimal delivery route and destination.

[1205] Output: Optimal delivery route and destination information.

[1206] Step 7:

[1207] Server Action:

[1208] The server calculates the optimal transport route and destination and notifies the user terminal and emergency services. For example, if the "Central Hospital" is determined to be the optimal destination, that information will be sent along with a route that avoids traffic jams.

[1209] Input: Optimal delivery route and destination.

[1210] Output: User terminal and emergency services are notified.

[1211] Step 8:

[1212] Hospital Actions:

[1213] Based on the patient information sent from the server, the hospital prepares to accept the patient, assembles an emergency medical team, and prepares the necessary medical equipment.In addition, medical capacity and acceptance status are updated in real time and shared throughout the system.

[1214] Input: Patient information, optimal transport route and destination.

[1215] Output: Emergency medical team assembled and ready to receive.

[1216] (Application example 2)

[1217] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1218] Conventional emergency medical support systems were able to analyze the patient's condition and symptoms in real time and respond appropriately, but they did not take into account the user's psychological state, and therefore had the problem of not being able to reduce anxiety and stress at the scene of an emergency response.In addition, while systems exist that provide quick and appropriate emergency response, they lack the functionality to provide psychological support to users, which poses the risk of limiting quick judgment and action.

[1219] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1220] In this invention, the server includes emotion engine means, means for providing appropriate feedback to the user based on the psychological state analyzed by the emotion engine means, and means for managing the medical capabilities and equipment status of each medical institution, thereby enabling prompt and appropriate medical response as well as psychological support for the user.

[1221] A "user terminal" is a device used by a user to input medical or emergency information, and may include a smartphone, tablet, or computer.

[1222] "Generative model means" refers to AI models and machine learning algorithms that perform appropriate analysis based on input patient information and emergency data.

[1223] "Communication means" refers to the internet or network communication means for transmitting the analyzed information from the user terminal to the server or emergency services.

[1224] "Notification means" refers to a method or device for notifying a user in real time of the response or analysis results from emergency services.

[1225] "Emotion engine means" refers to a system or software for recognizing and appropriately evaluating a user's psychological state and emotions.

[1226] The "feedback providing means" refers to a method or device for providing appropriate responses or support based on the user's psychological state analyzed by the emotion engine means.

[1227] "Ambulance availability management means" means a system or method for managing the availability and availability of ambulances in an area in real time and updating them as necessary.

[1228] "Traffic information management means" refers to systems and methods for capturing and managing road congestion and traffic conditions in real time.

[1229] "Medical institution information management means" refers to a system or method for managing information on the medical treatment capabilities and available facilities of each medical institution and updating the necessary information.

[1230] "Hospital means" refers to the medical facilities and systems that receive patient information, coordinate medical capacity, and make appropriate preparations for admission.

[1231] "Sharing tools" refers to methods and systems for updating clinical capacity and availability in real time and sharing that information across the system.

[1232] The present invention is a system that combines a user terminal, a generative model means, a communication means, a notification means, an emotion engine means, a feedback providing means, an ambulance operation status management means, a traffic information management means, a medical institution information management means, a hospital means, a sharing means, and the like.

[1233] System Configuration

[1234] 1. User Device:

[1235] The user device is a smartphone, tablet, or computer that provides an interface for the user to input information about the patient's condition and symptoms. For example, the user might input information such as "chest pain" or "heart rate 120 bpm." This information is then sent to the generative AI model in real time.

[1236] 2. Generative modeling methods:

[1237] The generative AI model installed on the user's device analyzes the input patient information. For example, if "chest pain" and "heart rate 120 bpm" are input, the generative AI model will output the diagnosis "suspected heart attack." This analysis result is immediately fed back to the user, and the necessary emergency response is suggested.

[1238] 3. Means of communication:

[1239] The user device sends the information analyzed by the generative model to the server. The communication is carried out via the Internet, and the information is encrypted and secure.

[1240] 4. Means of notification:

[1241] The server sends a response to the user's device, which notifies the user in real time of the arrival time of the ambulance and the best emergency response.

[1242] 5. Emotion Engine Means:

[1243] It is equipped with an emotion engine that analyzes the user's psychological state. It analyzes the user's facial expressions, tone of voice, input actions, etc. to recognize the anxiety or impatience the user is feeling.

[1244] 6. How to provide feedback:

[1245] The system provides appropriate feedback based on the user's psychological state analyzed by the emotion engine means. For example, if the user is feeling strong anxiety or impatience, the system will provide a reassuring message such as "Please stay calm, we will call an ambulance."

[1246] 7. Ambulance operation status management means and traffic information management means:

[1247] The server manages the operational status and traffic information of ambulances in the area in real time, which allows the system to determine the optimal ambulance dispatch and transport route.

[1248] 8. Medical institution information management measures:

[1249] The server manages the medical capabilities and equipment status of each medical institution, and determines the optimal destination based on this information.

[1250] 9. Hospital Measures:

[1251] The hospital prepares to accept the patient based on the patient information sent from the server, mobilizes an emergency medical team, and prepares the necessary medical equipment. Medical capacity and acceptance status are also updated in real time and shared throughout the system.

[1252] Example of operation

[1253] For example, if a user discovers a fire, they can enter "Fire in Building A - Floor 3" into the app and provide image data. The generative AI model analyzes this data and immediately provides instructions such as "Take evacuation measures." At the same time, the emotion engine detects the user's anxiety and provides specific feedback such as "Remain calm and evacuate."

[1254] Prompt Sentence Examples

[1255] Example input:

[1256] Emergency Type: Fire

[1257] Location: Building A - Floor 3

[1258] Description: Fire in storage room

[1259] Image data: base64_image_string

[1260] User emotional state: scared

[1261] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1262] Step 1:

[1263] The user inputs information about the emergency situation and the patient's condition into the user device. Specifically, the user uses a smartphone or tablet to input the situation, such as "fire outbreak," and patient information, such as "heart rate 120 bpm," into the application interface. The input data is saved on the device in text or image format.

[1264] Input: Text and image data that users enter into the app

[1265] Output: Input information data in the user's terminal

[1266] Step 2:

[1267] The generative AI model analyzes data entered into the user's device, analyzes information about the patient's condition and emergency, and generates a diagnosis such as "high urgency" or "possible heart attack." Machine learning algorithms are used to perform text and image analysis.

[1268] Input: Input information data in the user terminal

[1269] Output: Analyzed diagnostic results

[1270] Step 3:

[1271] The analysis results are sent to a server using a communication method. The data is encrypted and securely transmitted over the Internet. The server stores the received data and performs further analysis.

[1272] Input: Analyzed diagnostic results

[1273] Output: Data sent to the server

[1274] Step 4:

[1275] The emotion engine analyzes the user's psychological state. It recognizes the user's facial expressions and analyzes the tone of their voice to assess whether the user is feeling anxious or impatient. Input data is collected using sensors such as cameras and microphones, and the AI ​​model analyzes it.

[1276] Input: User's facial expression data, voice tone data

[1277] Output: User's psychological state evaluation result

[1278] Step 5:

[1279] The feedback provider provides appropriate feedback to the user based on the analyzed psychological state. For example, it may provide a message such as "Please stay calm" or "Call an ambulance" via text or voice. Text-to-Speech (TTS) technology may also be used.

[1280] Input: User's psychological state evaluation results

[1281] Output: Feedback message

[1282] Step 6:

[1283] The ambulance operation status management means and traffic information management means obtain real-time information on the operation status and traffic of ambulances in the area, and based on this, determine the optimal ambulance arrangements and transport routes.

[1284] Input: Ambulance operation status data and traffic information data stored on the server

[1285] Output: Optimal ambulance dispatch route and transport route

[1286] Step 7:

[1287] The medical institution information management tool selects the optimal destination based on the medical capabilities and equipment status of each medical institution. The selection results are updated in real time and shared throughout the system.

[1288] Input: Medical institution information data in the server

[1289] Output: Optimal destination selection result

[1290] Step 8:

[1291] The result of the selection is notified to the user's terminal using a notification method. The user is given clear instructions on the next action to be taken, and the entire system cooperates to achieve an efficient emergency response.

[1292] Input: Selection result of optimal transport route and destination

[1293] Output: Notification message of selection result

[1294] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1295] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1296] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1297] [Fourth embodiment]

[1298] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1299] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1300] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1301] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1302] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1303] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1304] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1305] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1306] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1307] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1308] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1309] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1310] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1311] This invention is a system that uses generative AI to provide real-time support in emergency medical settings, and is implemented with the following configuration.

[1312] User terminal

[1313] The user's device can be a smartphone, tablet, or computer. The user launches the application and is presented with an interface for entering patient information, including symptoms (e.g., chest pain, shortness of breath) and vital signs (e.g., heart rate, blood pressure). This information is sent in real time to the generative AI model on the device for analysis.

[1314] Analysis of generative AI models

[1315] The device uses a generative AI model to analyze the patient information entered. For example, if chest pain and a heart rate of 120 bpm are entered, the AI ​​model will generate the analysis results of "high urgency" and "suspected heart attack." This analysis result is immediately fed back to the user, and the necessary emergency response is suggested.

[1316] Sending information via communication means

[1317] When the user presses the send button, the device sends the patient information and analysis results to the server. This communication is carried out over the Internet using a safe and secure data transmission protocol.

[1318] Server Processing

[1319] The server receives patient information sent from the user's device, which is then stored in a database. The server then connects with related systems and external APIs to obtain information on the operation status and traffic of ambulances in the area. It also collects information on the medical capabilities and available facilities of each medical institution.

[1320] The server integrates this information and runs an algorithm to select the optimal transport route and hospital. For example, the "Central Hospital" is selected as the optimal destination, and the optimal route that avoids traffic jams is determined.

[1321] Notification of results

[1322] The server sends the results of the selection (optimal transport route and hospital) to the user's device and the emergency service. A notification is displayed on the user's device, allowing the user to confirm the next action to be taken.

[1323] Hospital Treatment

[1324] The hospital receives the patient information sent from the server and prepares to accept the patient using its internal system. For example, this includes calling in an emergency medical team and preparing the necessary medical equipment. The hospital also updates its medical capabilities as needed and shares the latest information with the server, ensuring that the entire system is always running with the latest information.

[1325] Specific examples

[1326] For example, if a user experiences a heart attack, they can quickly launch the app on their smartphone and input "chest pain" and "heart rate 120 bpm." The AI ​​analyzes this information, determines it to be a "high urgency," and notifies the user to "call an ambulance immediately." When the user presses the send button, the information is sent to the server.

[1327] The server receives the information and determines the optimal transport route and destination based on the ambulance operation status, traffic information, and the medical capabilities of each hospital in the area. For example, it may determine that transport to the "Central Hospital" is the most appropriate route, as it avoids traffic jams.

[1328] Once the optimal transport route and hospital are notified to the user device and emergency services, an ambulance will be dispatched quickly and preparations for reception at the "Central Hospital" will begin, thus achieving a fast and efficient emergency medical response.

[1329] The processing flow will be explained below.

[1330] Step 1:

[1331] Users launch the application on their smartphone, tablet, or other device and enter their patient's condition and symptoms, such as chest pain and a heart rate of 120 bpm.

[1332] Step 2:

[1333] The device sends the patient information (symptoms and vital signs) entered by the user to the generative AI model, which then analyzes the information in real time and generates a level of urgency and emergency response.

[1334] Step 3:

[1335] The device receives the analysis results from the generative AI model and notifies the user, for example, displaying results such as "high urgency" and "suspected heart attack."

[1336] Step 4:

[1337] The user checks the analysis results and, if necessary, presses the send button to determine the next action to take. Pressing this button will cause the patient information to proceed to the next procedure.

[1338] Step 5:

[1339] The device sends patient information (symptoms, vital signs, and analysis results) to the server. This communication is carried out securely via the Internet.

[1340] Step 6:

[1341] The server receives the patient information sent from the user terminal and stores it in a database, allowing for centralized management of the patient's condition.

[1342] Step 7:

[1343] The server collects real-time information on the operational status and location of ambulances in the area, as well as traffic information, and obtains this information from related systems and external APIs as needed.

[1344] Step 8:

[1345] The server manages the medical capabilities and equipment status of each medical institution and updates this information, making it possible to select the most suitable hospital.

[1346] Step 9:

[1347] The server integrates patient information, ambulance operation status, traffic information, and information from each medical institution, and executes an algorithm to select the optimal transport route and destination hospital. For example, it may determine that the "Central Hospital" is the best option.

[1348] Step 10:

[1349] The server sends the selection result to the user terminal and the emergency service, which then starts arranging an ambulance and transporting the patient.

[1350] Step 11:

[1351] The user can check the optimal transport route and destination displayed on the device and wait for the ambulance to arrive. If necessary, they will be instructed to continue providing first aid.

[1352] Step 12:

[1353] The emergency services receive the notification from the server, follow the designated transport route to the patient, and begin transporting the patient promptly.

[1354] Step 13:

[1355] The hospital begins preparations for admission based on the patient information sent from the server, convening an emergency medical team and preparing the necessary medical equipment.

[1356] Step 14:

[1357] Hospitals update their medical capacity in real time and send the latest information to a server, which makes it easier to adjust the intake of other patients.

[1358] Step 15:

[1359] When the ambulance arrives, the user follows the instructions to transport the patient, and the emergency services and hospitals work together to ensure a prompt response.

[1360] The above processing steps enable a fast and efficient emergency medical response.

[1361] Example 1

[1362] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1363] In emergency medical situations, rapid and appropriate responses are required, but it is not easy to process a large amount of information at once, such as assessing the situation on-site, deciding where to transport patients, and taking into account traffic information. It is also difficult to grasp the medical capabilities and acceptance status of medical institutions. This poses the issue of the possibility of delaying appropriate treatment for patients.

[1364] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1365] In this invention, the server includes terminal means that allows a user to input the patient's condition and symptoms, generative model means that analyzes the input patient information in real time, server means that saves and manages the information analyzed by the generative model means, means that prompts an emergency service to respond via the server means, means that receives a response from the emergency service and notifies the user of the optimal emergency response, means that manages the operation status and traffic information of ambulances in the area, means that manages the medical capacity and equipment status of each medical institution, means that integrates patient information, ambulance operation status, traffic information, and medical institution information to select the optimal transport route and destination, medical institution means that accepts patient information, adjusts medical capacity, and prepares for appropriate acceptance, and means that updates the medical capacity and acceptance status in real time and shares it throughout the system, thereby enabling prompt and appropriate response at the scene of an emergency medical care.

[1366] The "terminal means by which a user can input the condition or symptoms of a patient" is a device such as a smartphone, tablet, or computer, which is an apparatus that allows a user to input patient information using an interface.

[1367] The "generative model means for analyzing input patient information in real time" is a system in which a generative AI model instantly analyzes input patient information and provides information on the urgency of symptoms and medical decisions.

[1368] "Server means for storing and managing information analyzed by the generative model means" refers to a data storage and management device for storing the analysis results by the generative AI model in a database and centrally managing this information.

[1369] The "means for encouraging emergency services to respond via server means" refers to a communication function and processing system that transmits the analysis results to emergency services via a server and encourages emergency response.

[1370] The "means for receiving responses from emergency services and notifying the user of the most appropriate emergency response" is a system for receiving responses from emergency services and notifying the user of the appropriate emergency response method in real time based on that information.

[1371] "Means for managing ambulance operation status and traffic information within the region" refers to a system that collects and manages ambulance usage status and real-time traffic conditions within the region, and consolidates this information in a centralized manner.

[1372] "Means for managing the medical treatment capacity and equipment status of each medical institution" refers to a function for collecting and managing information on the current medical treatment capacity and available equipment of each medical institution.

[1373] "Means of integrating patient information, ambulance operation status, traffic information, and medical institution information to select the optimal transport route and destination" refers to an algorithm and system that integrates this information and automatically selects the optimal transport route and destination.

[1374] "Medical institution means for accepting patient information, adjusting medical treatment capabilities, and making appropriate preparations for acceptance" is a system that allows medical institutions to adjust their medical treatment capabilities and prepare the necessary medical equipment and specialists based on patient information sent from the server.

[1375] "Means for updating medical treatment capacity and admission status in real time and sharing it throughout the system" refers to a means for updating medical institutions' medical treatment capacity and admission status at any time and sharing this information throughout the system in real time.

[1376] This invention is a system that uses generative AI models to provide real-time support in emergency medical settings. The system mainly consists of the following components:

[1377] User terminal

[1378] The user's device can be a smartphone, tablet, or computer. The user launches the application and is presented with an interface for entering patient information, including symptoms (e.g., chest pain, shortness of breath) and vital signs (e.g., heart rate, blood pressure). This information is sent in real time to the generative AI model on the device for analysis.

[1379] Analysis of generative AI models

[1380] The device uses a generative AI model to analyze the patient information entered. For example, if chest pain and a heart rate of 120 bpm are entered, the generative AI model will generate the analysis results of "high urgency" and "suspected heart attack." This analysis result is immediately fed back to the user, and the necessary emergency response is suggested.

[1381] Sending information via communication means

[1382] When the user presses the send button, the terminal sends the patient information and analysis results to the server. This communication is carried out over the Internet using a secure data transmission protocol (e.g., HTTPS).

[1383] Server Processing

[1384] The server receives patient information sent from the user's device. After receiving the data, it stores it in a database. The server also obtains information on the operation status of ambulances in the area, traffic information, and information on the medical capabilities and available facilities of each medical institution, and executes an algorithm that integrates this information to select the optimal transport route and destination. For example, the "Central Hospital" is selected as the optimal destination, and the optimal route that avoids traffic congestion is determined.

[1385] Notification of results

[1386] The server sends the selection results (optimal transport route and hospital) to the user's device and the emergency service. A notification message is displayed on the user's device, allowing the user to confirm the next action to be taken.

[1387] Hospital Treatment

[1388] The hospital receives the patient information sent from the server and prepares to accept the patient using its internal system. For example, this includes calling in an emergency medical team and preparing the necessary medical equipment. The hospital also updates its medical capabilities as needed and shares the latest information with the server, ensuring that the entire system is always running with the latest information.

[1389] Specific examples

[1390] For example, if a user experiences a heart attack, they can launch the app on their smartphone and enter "chest pain" and "heart rate 120 bpm." The generating AI analyzes this information, determines it to be a "high urgency," and notifies the user to "call an ambulance immediately." When the user presses the send button, the information is sent to the server. The server determines the optimal transport route and destination based on the ambulance operation status in the area, traffic information, and the medical capabilities of each medical institution. For example, it may determine that transport to the "Central Hospital" is appropriate, taking the fastest route that avoids traffic jams. The result is notified to the user's device and emergency services, and an ambulance is quickly dispatched, while preparations begin at the "Central Hospital." This enables fast and efficient emergency medical response.

[1391] Prompt Sentence Examples

[1392] "I have a patient with chest pain and a heart rate of 120 bpm. What is the appropriate first aid?"

[1393] These prompts can be fed into a generative AI model to help you get a consultation faster.

[1394] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1395] Step 1:

[1396] The user inputs the patient's condition and symptoms.

[1397] The user launches the application on their smartphone, tablet, or computer. The application displays an interface for entering patient information. The user enters the patient's symptoms (e.g., chest pain, shortness of breath) and vital signs (e.g., heart rate, blood pressure).

[1398] Input: Patient symptoms and vital signs

[1399] Output: Entered patient information

[1400] Specific behavior: The user enters "chest pain" and "heart rate 120 bpm" into the application's input form.

[1401] Step 2:

[1402] Analyzing patient information with generative AI models

[1403] The device sends the patient information entered in step 1 to the generative AI model within the device. The generative AI model analyzes the information in real time and determines the urgency of the patient's symptoms. For example, if chest pain and a heart rate of 120 bpm are entered, the generative AI model will generate the analysis results of "high urgency" and "suspected heart attack."

[1404] Input: Patient information entered

[1405] Output: Analysis result (e.g. high urgency, suspected heart attack)

[1406] Specific operation: The device passes the input patient information to the generative AI model, which then returns an analysis result stating, "This symptom is of high urgency and is suspected to be a heart attack."

[1407] Step 3:

[1408] Analysis results are fed back to the user

[1409] The device immediately provides the user with the analysis results from the generative AI model. Based on the analysis results, the device displays the necessary emergency response instructions. For example, instructions such as "Call an ambulance immediately" are displayed.

[1410] Input: Analysis results of the generative AI model

[1411] Output: Emergency response instructions displayed to the user

[1412] Specific behavior: The device displays a message to the user indicating "High Urgency" and "Call an ambulance immediately."

[1413] Step 4:

[1414] User sends information to server

[1415] When the user presses the send button, the device sends the entered patient information and the analysis results of the generated AI model to the server. This communication is carried out over the internet using a safe and secure data transmission protocol such as HTTPS.

[1416] Input: Entered patient information and analysis results of the generated AI model

[1417] Output: Information sent to the server

[1418] What happens: The user clicks the "Send" button and the device sends the information to the server using HTTPS.

[1419] Step 5:

[1420] The server processes the information and determines the optimal delivery route and destination.

[1421] The server receives patient information sent from the device and stores it in a database. The server then collects information on the operation status of ambulances in the area, traffic information, and information on the medical capabilities and facilities of each medical institution, and executes an algorithm that integrates this information to select the optimal transport route and destination. For example, the "Central Hospital" may be selected as the optimal destination, and the optimal route that avoids traffic congestion may be determined.

[1422] Input: Patient information, traffic information, ambulance operation status, medical institution medical capacity

[1423] Output: Optimal route and destination

[1424] What it does: The server stores the information in a database, retrieves the latest traffic information from an external API, and runs an algorithm to determine that "Central Hospital is the best choice."

[1425] Step 6:

[1426] The server notifies the user device and emergency services

[1427] The server then sends the optimal transport route and hospital to the user's device and the emergency service. A notification message is displayed on the user's device, allowing the user to confirm the next steps.

[1428] Enter: Optimal Route and Destination Information

[1429] Output: Notification message to user terminal and emergency services

[1430] Specific operation: The server sends a message to the user device and emergency services saying, "Central Hospital is the best option. Route A is the best route to avoid traffic jams."

[1431] Step 7:

[1432] Preparations for hospital admission

[1433] The hospital receives the patient information sent from the server and prepares to accept the patient using its internal system, including calling in an emergency medical team and preparing the necessary medical equipment. The hospital also updates its medical capabilities as needed and shares the latest information with the server, ensuring that the entire system is always running with the latest information.

[1434] Input: Patient information sent from the server

[1435] Output: Readiness status

[1436] Specific operations: The hospital receives the patient information, starts preparing the emergency room, and updates the medical capabilities to the server.

[1437] (Application example 1)

[1438] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1439] Modern society demands rapid emergency medical response. However, various factors, such as traffic congestion and overwhelmed hospitals, hinder rapid response. In urban areas in particular, ambulances are often delayed due to traffic congestion. Furthermore, because the quality of emergency response at the scene depends on the experience and knowledge of medical staff, there are cases where an appropriate initial response is not provided. To solve these problems, more efficient and accurate analysis of medical information and a seamless emergency medical support system that includes autonomous driving technology are required.

[1440] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1441] In this invention, the server includes a device that allows a user to input the patient's condition and symptoms, a generative model means that analyzes the input patient information in real time, a communication means that transmits the information analyzed by the generative model means to an emergency service, a means that receives a response from the emergency service and notifies the user of the optimal emergency response, and a means that dispatches an autonomous ambulance along an optimal transport route. This enables rapid analysis of patient information and dispatch of an autonomous ambulance along an optimal route that avoids traffic congestion. This enables faster and more accurate emergency response and more appropriate initial response at the scene.

[1442] "Device that allows a user to input a patient's condition or symptoms" refers to a smartphone, tablet, smart glasses, or other input device that allows a user to directly input a patient's physical condition or specific symptoms in real time.

[1443] "Generative model means for analyzing input patient information in real time" refers to AI models and algorithms for analyzing input patient data, thereby enabling instant medical assessment and determination of urgency.

[1444] "Communication means for transmitting information analyzed by the generative model means to emergency services" refers to network technologies and communication protocols for quickly transmitting the analysis results to the appropriate emergency services, and is typically a means for secure data transmission via the Internet.

[1445] "Means for receiving responses from emergency services and informing the user of the best emergency response" refers to a system or application that receives instructions and information from emergency services and, based on that, notifies the user of the next steps to take and emergency procedures.

[1446] "Means for dispatching autonomous ambulances along the optimal transport route" refers to technologies and systems that calculate the quickest and most efficient route based on input patient information, traffic conditions, medical institution medical capabilities, etc., and dispatch ambulances using autonomous driving technology.

[1447] "Means for managing the operational status and traffic information of ambulances within the region" refers to a system or platform for monitoring and managing the current location, operational status, traffic congestion and accident information, etc. of ambulances in real time.

[1448] "Means for managing the medical treatment capacity and equipment status of each medical institution" refers to a system for managing each medical institution's current capacity, the operating status of medical staff, and information on available medical equipment and facilities, and constantly updating it to the latest version.

[1449] "Means for integrating patient information, ambulance operation status, traffic information, and medical institution information to select the optimal transport route and destination" refers to algorithms and systems for comprehensively processing the various types of information mentioned above and determining the most appropriate transport destination and optimal travel route.

[1450] "Means for providing information on transport routes and destinations to autonomous ambulances" refers to the technology and systems that transmit the calculated optimal transport route and destination information to the autonomous ambulance's system in real time and control autonomous driving based on that information.

[1451] "Hospital means for receiving patient information, adjusting medical treatment capacity, and making appropriate preparations for admission" refers to a system and management means for adjusting the admission system within the hospital and arranging for the necessary medical staff and equipment based on the received patient information.

[1452] "Means for updating medical treatment capacity and admission status in real time and sharing it throughout the system" refers to technologies and platforms for constantly monitoring medical institutions' medical treatment capacity and admission status, and instantly updating and sharing this information within the entire system.

[1453] "Means for predicting the arrival time of an autonomous ambulance and adjusting the hospital's reception readiness" refers to systems and technologies for calculating the estimated arrival time of an ambulance and appropriately adjusting the hospital's reception readiness based on this.

[1454] This invention provides a fast and efficient emergency medical system that utilizes an autonomous ambulance. The specific configuration and operation procedure for implementing this invention are described below.

[1455] First, the user terminal can be a smartphone, tablet, smart glasses, or other device. Users can input the patient's symptoms and vital signs through these devices. For example, if a patient inputs "chest pain" and "heart rate 120 bpm," the generative AI model on the device will instantly analyze this information and generate a diagnosis such as "high urgency" or "suspected heart attack." This analysis uses generative AI models such as TensorFlow and PyTorch.

[1456] The information analyzed by the generative AI model is sent to a server via a communication method using a REST API, which securely transmits patient information and analysis results over the internet. The server stores the received patient information in a database and connects with Google Maps API and the company's own traffic analysis system to obtain real-time information on the operation status of local ambulances and traffic information.

[1457] The server then manages the medical capabilities and equipment status of each medical institution and integrates this information. Based on the stored patient information, ambulance operation status, traffic information, and medical institution information, the optimal transport route and medical institution are selected. This algorithm often applies machine learning models and operations research methods.

[1458] The server provides the selection results (optimal transport route and destination medical institution) to the autonomous ambulance dispatch system. This dispatch system then dispatches the autonomous ambulance along the optimal route based on the selection results. The autonomous driving technology uses control software and sensor systems for autonomous vehicles.

[1459] The information from the server is also sent to the medical institution, and once it has been received, medical staff prepare to accept the patient using their internal system. Medical capacity and acceptance status are updated in real time and shared across the entire server, allowing the hospital to respond quickly.

[1460] As an example, use the following prompt:

[1461] Enter your symptoms: Chest pain

[1462] Enter your heart rate: 120 bpm

[1463] Enter your blood pressure: 140 / 90 mmHg

[1464] The AI ​​then responds with the analysis:

[1465] Analysis result: Suspected heart attack (high urgency)

[1466] Sending data...

[1467] Optimal route: The fastest route avoiding traffic jams

[1468] Self-driving ambulances have been deployed.

[1469] In this way, by using the system of the present invention, it is possible to perform real-time analysis of patients, provide appropriate initial responses, provide optimal transport routes, and quickly dispatch autonomous ambulances, thereby significantly improving the quality and speed of emergency medical care.

[1470] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1471] Step 1:

[1472] Users input the patient's symptoms and vital signs using a device such as a smartphone or tablet. They launch the application and enter specific symptoms (e.g., chest pain) and vital signs (e.g., heart rate 120 bpm, blood pressure 140 / 90 mmHg) through the interface. This data is then analyzed by the generative AI model.

[1473] Input: Symptom and vital sign data

[1474] Output: Input data to a generative AI model

[1475] Step 2:

[1476] A generative AI model installed on the device analyzes input patient information in real time. It uses machine learning libraries such as TensorFlow and PyTorch to determine the urgency and suspected medical condition from the patient information. The results of this analysis are immediately fed back to the user as emergency response suggestions.

[1477] Input: Patient information (symptoms, vital signs)

[1478] Output: Diagnosis result (urgency, condition)

[1479] Step 3:

[1480] The user checks the analysis results of the generative AI model and, if necessary, presses the send button to send the information to the server. The information is securely transmitted to the server via the internet via a REST API.

[1481] Input: Analysis results (urgency, condition), user's sending operation

[1482] Output: Patient information sent to the server

[1483] Step 4:

[1484] The server receives patient information sent from the user's device and stores it in a database. Based on the received data, it connects with Google Maps API and its own traffic analysis system to obtain information on the operation status of ambulances in the area and traffic information. Based on this information, it determines the optimal transport route and medical institution.

[1485] Input: Patient information submitted

[1486] Output: Selection of optimal transport route and medical institution

[1487] Step 5:

[1488] The server provides the results of the selection (optimal transport route and destination medical institution) to the autonomous ambulance dispatch system. The autonomous vehicle's control software then dispatches the ambulance based on the selected route.

[1489] Input: Selection result of optimal transport route and destination

[1490] Output: Dispatch of autonomous ambulance

[1491] Step 6:

[1492] The autonomous ambulance arrives at the patient's location and begins transporting the patient based on the selected route. Meanwhile, the server also sends information to the medical institution, which then prepares to accept the patient.

[1493] Input: Notification of arrival of autonomous ambulance, sending admission information to hospital

[1494] Output: Patient transport by ambulance, preparation for admission at medical institution

[1495] Step 7:

[1496] Medical institutions receive the information sent from the server in real time and proceed with preparations for admission using their internal systems. Medical treatment capabilities and admission status are updated as needed and shared across the entire server.

[1497] Input: Acceptance information from the server

[1498] Output: Ready to accept patients, updated medical capabilities

[1499] Step 8:

[1500] Once the ambulance arrives at the hospital, the patient receives prompt and appropriate medical treatment, and real-time updated information allows medical staff to provide the most appropriate response.

[1501] Input: Ambulance arrival, patient handover

[1502] Output: Patient treatment start, real-time treatment response

[1503] This will enable a series of steps: rapid analysis of patient information, appropriate initial response, provision of optimal transport routes, rapid dispatch of autonomous ambulances, and rapid preparations for admission at medical institutions.

[1504] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1505] This invention is a system that uses generative AI and an emotion engine to provide real-time support in emergency medical settings, and is implemented with the following configuration.

[1506] User terminal

[1507] The user's device can be a smartphone, tablet, or computer. The user launches the application and is presented with an interface for entering patient information, including symptoms (e.g., chest pain, shortness of breath) and vital signs (e.g., heart rate, blood pressure). This information is sent in real time to the generative AI model on the device for analysis.

[1508] Analysis of generative AI models

[1509] The device uses a generative AI model to analyze the patient information entered. For example, if chest pain and a heart rate of 120 bpm are entered, the AI ​​model will generate the analysis results of "high urgency" and "suspected heart attack." This analysis result is immediately fed back to the user, and the necessary emergency response is suggested.

[1510] Incorporating an emotion engine

[1511] The emotion engine recognizes the user's psychological state. When the user inputs information, the device analyzes the user's facial expression, tone of voice, input actions, etc., and recognizes the user's emotions through the emotion engine. For example, if the user is feeling impatient or anxious, the device will suggest appropriate emergency measures.

[1512] Sending information via communication means

[1513] When the user presses the send button, the device sends the patient information and analysis results to the server. This communication is securely carried out over the Internet.

[1514] Server Processing

[1515] The server receives patient information sent from the user's device, which is then stored in a database. The server then connects with related systems and external APIs to obtain information on the operation status and traffic of ambulances in the area. It also collects information on the medical capabilities and available facilities of each medical institution.

[1516] The server integrates this information and runs an algorithm to select the optimal transport route and hospital. For example, the "Central Hospital" is selected as the optimal destination, and the optimal route that avoids traffic jams is determined.

[1517] Notification of results

[1518] The server sends the selection results (optimal transport route and hospital) to the user terminal and the emergency service, which then starts arranging an ambulance and transporting the patient.

[1519] Hospital Treatment

[1520] The hospital prepares to accept the patient based on the patient information sent from the server, convenes an emergency medical team, and prepares the necessary medical equipment. The hospital also updates its medical capabilities as needed and shares the latest information with the server, ensuring that the entire system is always running with the latest information.

[1521] Specific examples

[1522] For example, if a user experiences a heart attack, they can quickly launch the app on their smartphone and input "chest pain" and "heart rate 120 bpm." The generative AI analyzes this information and determines a "high level of urgency." At the same time, the emotion engine analyzes the user's facial expressions and tone of voice and recognizes that the user is experiencing strong anxiety or impatience.

[1523] As a result, the app sends a reassuring notification to the user saying, "Please stay calm, we'll call an ambulance." When the user presses the send button, the information is sent to the server.

[1524] The server receives the information and determines the optimal transport route and destination based on the ambulance operation status, traffic information, and the medical capabilities of each hospital in the area. For example, it may determine that transport to the "Central Hospital" is the most appropriate route, as it avoids traffic jams.

[1525] The selection result is notified to the user's device, and the user confirms the next action to be taken. An ambulance arrives and the patient is safely transported to the "Central Hospital," achieving a fast and efficient emergency medical response. The use of the emotion engine can reduce the user's anxiety and stress.

[1526] The processing flow will be explained below.

[1527] Step 1:

[1528] Users launch the application on their smartphone, tablet, or other device and enter their patient's condition and symptoms, such as chest pain and a heart rate of 120 bpm.

[1529] Step 2:

[1530] The device also sends the patient information (symptoms and vital signs) entered by the user to the emotion engine. At this time, the device analyzes the user's facial expressions, tone of voice, input actions, etc. to recognize the user's emotions.

[1531] Step 3:

[1532] The emotion engine analyzes the user's emotions (e.g., impatience, anxiety, fear) and sends the results to the generative AI model. The generative AI model then combines the patient information and the user's emotion data to generate an analysis result, such as "high urgency" and "suspected heart attack."

[1533] Step 4:

[1534] The device receives the analysis results from the generative AI model and notifies the user, displaying a reassuring message (e.g., "Please stay calm, we will call an ambulance immediately") based on the results of the emotion engine.

[1535] Step 5:

[1536] The user checks the analysis results and, if necessary, presses the send button to determine the next action to take. Pressing this button will cause the patient information to proceed to the next procedure.

[1537] Step 6:

[1538] The device sends patient information (symptoms, vital signs, and analysis results) to the server. This communication is carried out securely via the Internet.

[1539] Step 7:

[1540] The server receives the patient information sent from the user terminal and stores it in a database, allowing for centralized management of the patient's condition.

[1541] Step 8:

[1542] The server collects real-time information on the operational status and location of ambulances in the area, as well as traffic information, and obtains this information from related systems and external APIs as needed.

[1543] Step 9:

[1544] The server manages the medical capabilities and equipment status of each medical institution and updates this information, making it possible to select the most suitable hospital.

[1545] Step 10:

[1546] The server integrates patient information, ambulance operation status, traffic information, and information from each medical institution, and executes an algorithm to select the optimal transport route and destination hospital. For example, it may determine that the "Central Hospital" is the best option.

[1547] Step 11:

[1548] The server sends the selection result to the user terminal and the emergency service, which then starts arranging an ambulance and transporting the patient.

[1549] Step 12:

[1550] The user can check the optimal transport route and destination displayed on the device and wait for the ambulance to arrive. If necessary, they will be instructed to continue providing first aid.

[1551] Step 13:

[1552] The emergency services receive the notification from the server, follow the designated transport route to the patient, and begin transporting the patient promptly.

[1553] Step 14:

[1554] The hospital begins preparations for admission based on the patient information sent from the server, convening an emergency medical team and preparing the necessary medical equipment.

[1555] Step 15:

[1556] Hospitals update their medical capacity in real time and send the latest information to a server, which makes it easier to adjust the intake of other patients.

[1557] Step 16:

[1558] When the ambulance arrives, the user follows the instructions to transport the patient, and the emergency services and hospitals work together to ensure a prompt response.

[1559] Example 2

[1560] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1561] Conventional emergency medical systems have difficulty in quickly and accurately collecting and analyzing patient information, and they also do not propose emergency responses that take into account the user's psychological state. As a result, it is difficult to provide optimal responses in real time according to the patient's condition, which can lead to serious medical accidents and stress for users.

[1562] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes terminal means that allows a user to input the patient's condition and symptoms, generative AI model means that analyzes the input patient information in real time, emotion engine means that analyzes the user's emotions from their facial expressions, tone of voice, and input actions, means for proposing emergency responses based on the information analyzed by the generative AI model means and the user's emotional state, communication means that transmits the information and suggestions to emergency services, and means that receive responses from emergency services and notify the user of the optimal emergency responses. This enables prompt and appropriate medical response that takes into account the patient's condition and the user's psychological state.

[1563] A "user terminal" is a device used by a user to input a patient's condition or symptoms, and includes a smartphone, tablet, computer, etc.

[1564] A "generative AI model" is an artificial intelligence model that analyzes input patient information in real time and generates analysis results based on the urgency and symptoms.

[1565] An "emotion engine" is a means of analyzing a user's psychological state from their facial expressions, tone of voice, and input actions, and recognizing their emotions.

[1566] "Communication means" means for transmitting the information analyzed by the generative AI model and the user's emotional state to emergency services and for receiving a response from emergency services.

[1567] "Emergency services" refers to medical services for transporting emergency patients and providing first aid, including the deployment of ambulances and cooperation with hospitals.

[1568] "Optimal emergency response measures" are measures that suggest necessary emergency responses to users based on the analysis results of the generative AI model and emotion engine.

[1569] "Means for managing the operational status and traffic information of ambulances within the area" refers to means for collecting and managing the current status and traffic information of ambulances deployed within the area.

[1570] "Means for managing the medical treatment capacity and equipment status of each medical institution" refers to means for collecting and managing the status of the medical treatment capacity and available medical equipment of each medical institution.

[1571] The "means for selecting the optimal transport route and destination" refers to a means for selecting the optimal transport route and destination based on patient information, ambulance operation status, traffic information, and information from medical institutions.

[1572] "Hospital means" refers to the means used to receive patient information, adjust medical capabilities, and prepare for appropriate reception.

[1573] "Means for updating medical treatment capacity and admission status in real time and sharing it throughout the system" refers to a means for updating a hospital's medical treatment capacity and patient admission status in real time and sharing that information throughout the system.

[1574] This invention is a system that provides real-time support in emergency medical situations using a generative AI model and an emotion engine. The system operates through collaboration between the user device, the generative AI model, the emotion engine, a server, and the hospital.

[1575] User terminal

[1576] User devices include smartphones, tablets, and computers. The user launches an application on the device and inputs the patient's condition and symptoms. Specifically, the user inputs symptoms such as "chest pain" and "heart rate 120 bpm."

[1577] Generative AI Models

[1578] The information entered by the user is sent to a generative AI model on the device. This generative AI model is implemented in a programming language such as Python, using TensorFlow or PyTorch. The generative AI model analyzes the input information and identifies the urgency and possible symptoms. For example, if the input is "chest pain" and "heart rate 120 bpm," the AI ​​model will generate analysis results such as "high urgency" and "possible heart attack."

[1579] Emotion Engine

[1580] The device also has a built-in emotion engine. This uses a camera and microphone to analyze the user's facial expressions, tone of voice, and input actions to recognize the user's psychological state. For example, if the user is feeling strong anxiety or impatience, the device will send a notification to calm them down. Specifically, the device will display a message saying, "Please stay calm, we'll call an ambulance."

[1581] communication means

[1582] When the user presses the send button, the device sends the analysis results of the generative AI model and the recognition results of the emotion engine to the server. This communication is securely carried out via the HTTPS protocol.

[1583] Server Roles

[1584] The server receives the information sent from the device and stores it in a database. Additionally, the server obtains information such as the ambulance operation status, traffic conditions, and the medical capabilities of each medical institution in the area from related systems and external APIs (e.g., Google Maps API). This information is integrated and an algorithm is run to select the optimal transport route and destination.

[1585] Selection of transport route and destination

[1586] The server uses Dijkstra's algorithm or A algorithm to select the optimal transport route and hospital. For example, if the "Central Hospital" is determined to be the optimal route, the server sends this result to the terminal and emergency services.

[1587] Hospital coordination and preparation

[1588] The hospital prepares for admission based on the patient information sent from the server, including calling in emergency medical teams and preparing the necessary medical equipment. The hospital also updates its medical capacity and admission status in real time and shares it with the entire system.

[1589] Specific examples

[1590] For example, if a user shows symptoms of a heart attack, they would do the following: They would use their smartphone, launch the app, and input "chest pain" and "heart rate 120 bpm." The generative AI model analyzes this information and determines a "high level of urgency." The emotion engine also analyzes the user's psychological state and recognizes that the user is feeling anxious. As a result, it displays the message "Please remain calm, we will call an ambulance." When the user presses the send button, the information is sent to the server.

[1591] The server integrates the operational status of local ambulances, traffic information, and the medical capabilities of each hospital to calculate the optimal transport route and destination. For example, it may determine that transport to the "Central Hospital" is appropriate by avoiding traffic jams. This result is notified to the user's device and emergency services, and the hospital then begins preparations to accept the patient based on this information.

[1592] Prompt Sentence Examples

[1593] Example prompt: "User reports chest pain and a heart rate of 120 bpm. Analyze the emergency level and recommend immediate actions."

[1594] In this way, the present invention can provide highly accurate medical support in real time that takes into account the user's physical and psychological state.

[1595] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1596] Step 1:

[1597] User Action:

[1598] First, the user launches the application on their smartphone, tablet, or computer. Then, they input the patient's condition and symptoms, such as "chest pain" or "heart rate 120 bpm." Once the input data is ready, they are ready to press the send button.

[1599] Input: Patient information such as chest pain, heart rate 120 bpm.

[1600] Output: The entered patient information is stored on the terminal.

[1601] Step 2:

[1602] Terminal behavior:

[1603] The user device sends the input patient information to the generative AI model in real time. The generative AI model is implemented using Python, TensorFlow, and PyTorch. The generative AI model analyzes the input information and determines the urgency and suspected symptoms.

[1604] Input: Patient condition and symptoms (chest pain, heart rate 120 bpm).

[1605] Data processing and computation: A generative AI model analyzes input data and identifies symptoms that may indicate a high level of urgency. For example, a model might derive a high level of urgency and a possible heart attack from input data such as chest pain and a heart rate of 120 bpm.

[1606] Output: Analysis result (high urgency, possible heart attack).

[1607] Step 3:

[1608] Terminal behavior:

[1609] Next, the emotion engine analyzes the user's psychological state. Using the device's camera and microphone, it captures and analyzes the user's facial expressions, tone of voice, and input actions. The emotion engine recognizes the user's psychological state and recommends appropriate emergency responses.

[1610] Input: User facial expressions, tone of voice, and typing behavior.

[1611] Data processing and calculation: The emotion engine analyzes this data and identifies the user's emotional state. For example, it infers "strong anxiety" from the user's facial tension and excited tone of voice.

[1612] Output: Sentiment analysis result (high anxiety).

[1613] Step 4:

[1614] Terminal behavior:

[1615] The analysis results of the generative AI model and the emotion engine are combined to suggest emergency responses to the user. For example, if the analysis result of the generative AI model is "possible heart attack" and the analysis result of the emotion engine is "strong anxiety," the message "Please stay calm, we will call an ambulance" will be displayed.

[1616] Input: Generative AI model analysis (high urgency, possible heart attack), emotion engine analysis (high anxiety).

[1617] Data processing and calculation: Generate suggested messages, such as "Please stay calm, I'll call an ambulance."

[1618] Output: Notification messages that are displayed on the application screen.

[1619] Step 5:

[1620] User Action:

[1621] The user takes action based on the suggested message by pressing the send button to send the information to the server. This action causes the device to send the analysis results of the generative AI model and emotion engine to the server.

[1622] Input: Analysis results of the generative AI model, analysis results of the emotion engine.

[1623] Output: The analysis results are sent to the server.

[1624] Step 6:

[1625] Server Action:

[1626] The server receives the data and stores it in a database. The server then obtains information such as the operating status of ambulances in the area, traffic information, and the medical capabilities of each medical institution. For example, traffic information can be obtained using the Google Maps API.

[1627] Input: Analysis results, ambulance operation status in the area, traffic information, and medical facility capacity.

[1628] Data processing and calculation: The server integrates the data and selects the optimal delivery route and destination.

[1629] Output: Optimal delivery route and destination information.

[1630] Step 7:

[1631] Server Action:

[1632] The server calculates the optimal transport route and destination and notifies the user terminal and emergency services. For example, if the "Central Hospital" is determined to be the optimal destination, that information will be sent along with a route that avoids traffic jams.

[1633] Input: Optimal delivery route and destination.

[1634] Output: User terminal and emergency services are notified.

[1635] Step 8:

[1636] Hospital Actions:

[1637] Based on the patient information sent from the server, the hospital prepares to accept the patient, assembles an emergency medical team, and prepares the necessary medical equipment.In addition, medical capacity and acceptance status are updated in real time and shared throughout the system.

[1638] Input: Patient information, optimal transport route and destination.

[1639] Output: Emergency medical team assembled and ready to receive.

[1640] (Application example 2)

[1641] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1642] Conventional emergency medical support systems were able to analyze the patient's condition and symptoms in real time and respond appropriately, but they did not take into account the user's psychological state, and therefore had the problem of not being able to reduce anxiety and stress at the scene of an emergency response.In addition, while systems exist that provide quick and appropriate emergency response, they lack the functionality to provide psychological support to users, which poses the risk of limiting quick judgment and action.

[1643] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1644] In this invention, the server includes emotion engine means, means for providing appropriate feedback to the user based on the psychological state analyzed by the emotion engine means, and means for managing the medical capabilities and equipment status of each medical institution, thereby enabling prompt and appropriate medical response as well as psychological support for the user.

[1645] A "user terminal" is a device used by a user to input medical or emergency information, and may include a smartphone, tablet, or computer.

[1646] "Generative model means" refers to AI models and machine learning algorithms that perform appropriate analysis based on input patient information and emergency data.

[1647] "Communication means" refers to the internet or network communication means for transmitting the analyzed information from the user terminal to the server or emergency services.

[1648] "Notification means" refers to a method or device for notifying a user in real time of the response or analysis results from emergency services.

[1649] "Emotion engine means" refers to a system or software for recognizing and appropriately evaluating a user's psychological state and emotions.

[1650] The "feedback providing means" refers to a method or device for providing appropriate responses or support based on the user's psychological state analyzed by the emotion engine means.

[1651] "Ambulance availability management means" means a system or method for managing the availability and availability of ambulances in an area in real time and updating them as necessary.

[1652] "Traffic information management means" refers to systems and methods for capturing and managing road congestion and traffic conditions in real time.

[1653] "Medical institution information management means" refers to a system or method for managing information on the medical treatment capabilities and available facilities of each medical institution and updating the necessary information.

[1654] "Hospital means" refers to the medical facilities and systems that receive patient information, coordinate medical capacity, and make appropriate preparations for admission.

[1655] "Sharing tools" refers to methods and systems for updating clinical capacity and availability in real time and sharing that information across the system.

[1656] The present invention is a system that combines a user terminal, a generative model means, a communication means, a notification means, an emotion engine means, a feedback providing means, an ambulance operation status management means, a traffic information management means, a medical institution information management means, a hospital means, a sharing means, and the like.

[1657] System Configuration

[1658] 1. User Device:

[1659] The user device is a smartphone, tablet, or computer that provides an interface for the user to input information about the patient's condition and symptoms. For example, the user might input information such as "chest pain" or "heart rate 120 bpm." This information is then sent to the generative AI model in real time.

[1660] 2. Generative modeling methods:

[1661] The generative AI model installed on the user's device analyzes the input patient information. For example, if "chest pain" and "heart rate 120 bpm" are input, the generative AI model will output the diagnosis "suspected heart attack." This analysis result is immediately fed back to the user, and the necessary emergency response is suggested.

[1662] 3. Means of communication:

[1663] The user device sends the information analyzed by the generative model to the server. The communication is carried out via the Internet, and the information is encrypted and secure.

[1664] 4. Means of notification:

[1665] The server sends a response to the user's device, which notifies the user in real time of the arrival time of the ambulance and the best emergency response.

[1666] 5. Emotion Engine Means:

[1667] It is equipped with an emotion engine that analyzes the user's psychological state. It analyzes the user's facial expressions, tone of voice, input actions, etc. to recognize the anxiety or impatience the user is feeling.

[1668] 6. How to provide feedback:

[1669] The system provides appropriate feedback based on the user's psychological state analyzed by the emotion engine means. For example, if the user is feeling strong anxiety or impatience, the system will provide a reassuring message such as "Please stay calm, we will call an ambulance."

[1670] 7. Ambulance operation status management means and traffic information management means:

[1671] The server manages the operational status and traffic information of ambulances in the area in real time, which allows the system to determine the optimal ambulance dispatch and transport route.

[1672] 8. Medical institution information management measures:

[1673] The server manages the medical capabilities and equipment status of each medical institution, and determines the optimal destination based on this information.

[1674] 9. Hospital Measures:

[1675] The hospital prepares to accept the patient based on the patient information sent from the server, mobilizes an emergency medical team, and prepares the necessary medical equipment. Medical capacity and acceptance status are also updated in real time and shared throughout the system.

[1676] Example of operation

[1677] For example, if a user discovers a fire, they can enter "Fire in Building A - Floor 3" into the app and provide image data. The generative AI model analyzes this data and immediately provides instructions such as "Take evacuation measures." At the same time, the emotion engine detects the user's anxiety and provides specific feedback such as "Remain calm and evacuate."

[1678] Prompt Sentence Examples

[1679] Example input:

[1680] Emergency Type: Fire

[1681] Location: Building A - Floor 3

[1682] Description: Fire in storage room

[1683] Image data: base64_image_string

[1684] User emotional state: scared

[1685] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1686] Step 1:

[1687] The user inputs information about the emergency situation and the patient's condition into the user device. Specifically, the user uses a smartphone or tablet to input the situation, such as "fire outbreak," and patient information, such as "heart rate 120 bpm," into the application interface. The input data is saved on the device in text or image format.

[1688] Input: Text and image data that users enter into the app

[1689] Output: Input information data in the user's terminal

[1690] Step 2:

[1691] The generative AI model analyzes data entered ...

Claims

1. a device that allows a user to input a patient's condition and symptoms; a generative modeling means for analyzing input patient information in real time; communication means for transmitting the information analysed by the generative model means to emergency services; means for receiving a response from emergency services and informing the user of the best emergency response; A system including:

2. 10. The system of claim 1, A means of managing ambulance operation status and traffic information within the area; A means of managing the medical capabilities and equipment status of each medical institution; A means for integrating patient information, ambulance operation status, traffic information, and information on medical institutions to select the optimal transport route and destination; The system further comprises:

3. 10. The system of claim 1, Hospital means for receiving patient information and adjusting medical capacity to prepare for appropriate admission; A means to update and share clinical capacity and availability in real time across the system; The system further comprises:

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