System

Abstract

A system is provided.SOLUTION: The system according to claim 1, further comprising: means for selecting an optimum transport destination by inputting the collected information into a generative artificial intelligence and analyzing the collected information; and means for automatically transmitting the information on the condition of the and the estimated time of arrival to the selected transport destination. circumstance for acceptance.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional emergency transport systems often rely on the judgment of emergency medical teams, who typically transport patients to the nearest medical institution. However, there is a risk that an appropriate medical institution will not be selected depending on the patient's level of urgency or the need for specialized treatment. Furthermore, because it is not possible to accurately grasp the admission status of medical institutions, the allocation of specialists, the number of hospital beds, etc. in real time, patients may miss opportunities to receive the best medical care. The objective of the present invention is to solve these problems and provide a system that enables patients to receive prompt and appropriate medical care. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a system including: means for collecting data on the patient's location, urgency, past medical history, medical institution's acceptance status, and available medical departments and facilities; means for inputting the collected data into a generative artificial intelligence for analysis, selecting the optimal transport destination, and means for automatically transmitting the patient's condition information and estimated arrival time to the selected transport destination. Furthermore, by inputting the patient's location and condition information into a terminal in the ambulance and updating the acceptance status and available medical treatment area information in real time on a terminal at the medical institution, it becomes possible to quickly and appropriately select the transport destination.

[0006] "Patient location" refers to the location information of the patient when an ambulance responds to an emergency.

[0007] "Urgency" refers to a measure that assesses the severity of a patient's illness or injury and indicates how quickly medical attention is required.

[0008] "Past medical history" refers to records of medical examinations and treatment history that a patient has previously received at a medical institution.

[0009] "Acceptance status of medical institutions" refers to the current level of congestion at each medical institution, the availability of hospital beds, and the number of patients they can accept.

[0010] "Available medical departments and facilities" refers to the specialized medical departments owned by the medical institution and the medical equipment and facilities available.

[0011] "Means of collection" refers to the devices and methods used to obtain, store, or transmit the required data.

[0012] "Generative AI" refers to an AI technology that has the ability to analyze large amounts of data, find patterns, and derive optimal results.

[0013] "Analyze" refers to the process of making a diagnosis or prediction based on collected data.

[0014] The "optimal destination" refers to the medical institution that is determined, based on analysis of the collected data, to provide the patient with the most appropriate treatment.

[0015] "Means for selecting" refers to a method or device for determining the best option based on evaluation criteria.

[0016] "Condition information" refers to detailed information about a patient's current health condition and symptoms.

[0017] "Estimated time of arrival" refers to the estimated time that the ambulance will arrive at the designated medical facility.

[0018] "Automatic transmission means" refers to a device or method for transmitting collected data without manual intervention.

[0019] A "system" refers to a comprehensive mechanism that is made up of multiple elements.

[0020] "Ambulance terminal means" refers to an information input and transmission device used in an ambulance.

[0021] "Terminal means of medical institution" refers to information receiving and transmitting devices used within the medical institution. [Brief explanation of the drawings]

[0022] [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

[0023] 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.

[0024] First, the terms used in the following description will be explained.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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."

[0030] [First embodiment]

[0031] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0032] 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.

[0033] 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).

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0039] 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.

[0040] 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.

[0041] 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.

[0042] 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."

[0043] This invention is a system for optimizing the process of selecting a destination for ambulances. This system collects data such as the patient's location, urgency, past medical history, medical institution acceptance status, available medical departments and facilities, and analyzes it in real time using generative artificial intelligence (AI), selecting the most suitable medical institution and contacting the patient.

[0044] Program processing

[0045] 1. Data Collection

[0046] When the ambulance arrives at the scene, it inputs information about the patient's condition (vital data, symptoms, injury details, etc.) and also inputs insurance card information to identify the patient.

[0047] The server retrieves past medical history, allergy information, and prescription drug information from a medical database based on insurance card information.

[0048] The medical institution's terminal sends information on the current congestion situation, number of beds, available medical departments and medical equipment to the server in real time.

[0049] 2. Real-time analysis

[0050] The server inputs the collected data into generative artificial intelligence, which then analyzes the data based on the patient's condition, medical history, and the medical institution's acceptance status.

[0051] The generative AI considers the following factors to determine the best destination for a patient:

[0052] Patient's condition and urgency

[0053] Past medical history and allergy information

[0054] The congestion level of nearby medical institutions and the capacity of specialized medical departments

[0055] Medical equipment available at each medical institution

[0056] 3. Selection and Notification

[0057] The server then determines the optimal destination based on the results of the generative AI analysis, taking into account factors such as distance, the current capacity of medical institutions, the availability of specialists, and the availability of necessary medical equipment.

[0058] The server automatically sends the patient's condition information and estimated time of arrival to the selected medical institution.

[0059] Specific examples

[0060] For example, consider a scenario in which an ambulance transports a patient to the scene of a traffic accident.

[0061] 1. Data Collection

[0062] The ambulance terminal arrives at the scene of an accident and inputs the patient's blood pressure, pulse, respiratory rate, and injury status, and also scans the patient's insurance card to enter the patient's information into the system.

[0063] The server obtains the patient's past medical history and allergy information from the health insurance card information.

[0064] Medical institutions' terminals send information about congestion and available medical equipment to the server in real time.

[0065] 2. Real-time analysis

[0066] The server inputs all collected data into the generation AI.

[0067] The generative AI analyzes the patient's urgency, the severity of the injury, past medical history, and whether or not they have any allergies, and then lists the most suitable medical institutions to recommend.

[0068] 3. Selection and Notification

[0069] Based on the analysis results of the generating AI, the server determines, for example, that trauma center A is the optimal destination for transport.

[0070] The server automatically notifies trauma center A of the patient's condition and estimated time of arrival.

[0071] The ambulance terminal initiates transport of the patient to the selected trauma center A.

[0072] In this way, the system of the present invention quickly selects the most appropriate medical institution when transporting a patient by ambulance, enabling the patient to receive appropriate medical care promptly.

[0073] The processing flow will be explained below.

[0074] Step 1:

[0075] The terminal (ambulance terminal) arrives at the scene and inputs the patient's vital data (blood pressure, pulse rate, respiratory rate, etc.) and condition information (presence or absence of trauma, description of symptoms, etc.).

[0076] The user (paramedic) scans the patient's insurance card and enters the patient's identifying information into the system.

[0077] Step 2:

[0078] Based on the entered insurance card information, the server queries an internal medical database to obtain the patient's past medical history, allergy information, and prescription drug information.

[0079] Step 3:

[0080] The terminal (the medical institution's terminal) sends information to the server in real time regarding the current admission situation, number of hospital beds, the allocation of specialists, available medical departments, and medical equipment.

[0081] Step 4:

[0082] The server inputs all data sent from the ambulance terminal and the medical institution's terminal into the generative AI.

[0083] Step 5:

[0084] Generative AI analyzes the patient's condition, past medical history, medical institution acceptance status, and available medical capabilities, and creates a list of the most appropriate medical institutions to which the patient should be transported.

[0085] Based on the analysis results, the generative AI ranks the most suitable medical institutions and selects the optimal destination for transport.

[0086] Step 6:

[0087] The server automatically sends the patient's condition information and estimated arrival time to the most appropriate medical institution selected by the generative AI.

[0088] Step 7:

[0089] The terminal (ambulance terminal) transports the patient to the designated medical institution based on the transport instructions sent from the server.

[0090] Step 8:

[0091] The terminal (terminal at the medical institution) checks the information received from the server and begins preparations for admission (preparing the emergency operating room, arranging specialists, etc.).

[0092] In this way, the ambulance destination is selected quickly and accurately through each step, allowing the patient to receive treatment promptly at the appropriate medical institution.

[0093] Example 1

[0094] 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."

[0095] In emergencies such as sudden illness or accidents, it is extremely important to quickly transport patients to the appropriate medical institution. However, it is difficult for on-site medical staff to select the optimal destination with limited information and time. Furthermore, if the medical institution's acceptance status and facility capacity cannot be grasped in real time, the selection of the destination may be delayed, putting the patient's life at risk. A system that can solve these issues is needed.

[0096] 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.

[0097] In this invention, the server includes means for collecting data on the patient's location, urgency, past medical history, medical institution acceptance status, and available medical departments and facilities, means for inputting the collected data into a generative artificial intelligence model and selecting the optimal transport destination taking into consideration the patient's condition and urgency, past medical history, and the response capabilities of nearby medical institutions, means for automatically transmitting the patient's condition information and estimated arrival time to the selected transport destination, means for acquiring data including the patient information from a medical database and using it for analysis, and means for updating the acceptance status of each medical institution in real time and reflecting it in the processing results. This makes it possible to quickly and accurately select the optimal transport destination and ensure that patients receive appropriate medical care promptly.

[0098] "Location" refers to information that indicates the patient's current location or position.

[0099] "Urgency" refers to information that serves as a criterion for assessing the severity of a patient's symptoms and condition and determining the priority of transportation.

[0100] "Medical history" refers to records of medical examinations and treatments that a patient has received at medical institutions in the past.

[0101] "Medical institution acceptance status" refers to information that indicates how many patients a medical institution currently has the capacity to accept.

[0102] "Available medical departments" refers to information indicating the specialized medical services and medical departments that a medical institution can provide.

[0103] "Facilities" refers to information indicating medical equipment and treatment devices owned by a medical institution.

[0104] A "generative artificial intelligence model" is an artificial intelligence that analyzes collected data and selects the optimal destination.

[0105] "Condition information" refers to detailed medical information that indicates a patient's current health condition and symptoms.

[0106] "Estimated arrival time" is information indicating the estimated time when the ambulance is to arrive at the selected medical institution.

[0107] A "medical database" is a database that stores medical data such as a patient's past medical history, allergy information, and prescription drug information.

[0108] This invention is a system that optimizes the process of selecting an ambulance destination. This system collects data on the patient's location, urgency, past medical history, medical institution acceptance status, available medical departments, and facilities, and analyzes it in real time using a generative artificial intelligence model. It then selects the most suitable medical institution and automatically transmits the patient's condition information and estimated arrival time to the selected medical institution.

[0109] Hardware and Software Configuration

[0110] The system consists of the following main components:

[0111] 1. Ambulance terminal

[0112] A tablet or smartphone for paramedics to enter information about the patient's condition.

[0113] A scanner for scanning insurance card information.

[0114] 2. Server

[0115] A high-performance server for running generative artificial intelligence (AI) models.

[0116] An interface for real-time communication with medical databases.

[0117] 3. Medical institution terminals

[0118] A computer terminal for inputting and sending information on current admission status, number of hospital beds, available medical departments, and medical equipment.

[0119] Specific operation of the system

[0120] 1. Data Collection

[0121] When the ambulance arrives at the scene, the ambulance staff enters the patient's condition information (e.g., blood pressure, pulse rate, respiratory rate, and the state of injuries) into the terminal. In addition, the ambulance scans the patient's insurance card to obtain basic information about the patient.

[0122] Based on the insurance card information received, the server retrieves the patient's past medical history, allergy information, prescription drug information, etc. from a medical database.

[0123] The medical institution's terminal sends information on each institution's current admission status, number of beds, available medical departments, and medical equipment to the server in real time.

[0124] 2. Real-time analysis and selection

[0125] The server inputs all collected data into a generative AI model, which analyzes the patient's condition, past medical history, allergy information, and medical institution acceptance status to select the medical institution that best suits the patient.

[0126] 3. Selection and Notification

[0127] The server automatically sends the patient's condition information and estimated time of arrival to the most appropriate medical institution selected by the generative AI model.

[0128] The ambulance's terminal provides emergency personnel with information about the destination medical institution and the optimal route.

[0129] Specific examples

[0130] For example, consider the scenario of an ambulance transporting a patient to the scene of a traffic accident:

[0131] The ambulance terminal arrives at the scene of an accident and inputs the patient's blood pressure, pulse, respiratory rate, and injury status. It also scans the patient's insurance card and inputs the patient's information into the system.

[0132] The server obtains the patient's past medical history and allergy information from the health insurance card information.

[0133] Medical institution terminals provide real-time information to the server about current congestion and available medical equipment.

[0134] Next, prompt the generative AI model with the following sentence:

[0135] "The ambulance has arrived at the scene. Please enter the patient's information. We will provide the following information: blood pressure, pulse rate, respiratory rate, injury status, past medical history, allergy information, and prescription drug information. We will also provide information on the availability and medical facilities of nearby medical institutions. Please select the most appropriate destination."

[0136] Based on this prompt, the generative AI model performs an analysis and selects the optimal transport destination. The system then automatically notifies the destination of the patient's condition and estimated arrival time. By linking systems in this way, the patient can be transported quickly and accurately to the optimal medical institution.

[0137] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0138] Step 1: Data collection

[0139] Ambulance terminal: After arriving at the patient's location, the ambulance crew uses the terminal to input the patient's condition information, including blood pressure, pulse rate, respiratory rate, and the state of injuries. The terminal also scans the insurance card, digitizing the patient's basic information and sending it to the server.

[0140] Input: Patient vital data and insurance card information.

[0141] Output: Digitized patient information.

[0142] Server: After receiving the insurance card information, it accesses the medical database and retrieves the patient's past medical history, allergy information, and prescription drug information.

[0143] Input: Digitized insurance card information.

[0144] Output: Past medical history, allergy information, prescription drug information.

[0145] Medical institution terminals: Each medical institution inputs information on its current admission status, number of beds, available medical departments, and medical equipment, and sends this information to the server in real time.

[0146] Input: Details of admission status, number of beds, available medical specialties, and medical facilities.

[0147] Output: Real-time updated medical institution admission information.

[0148] Step 2: Real-time analysis

[0149] Server: Inputs all collected data into the generative AI model, including patient condition information, past medical history, allergy information, and medical institution acceptance status.

[0150] Input: Patient condition information, past medical history, allergy information, and medical institution acceptance status.

[0151] Output: A consolidated dataset that is fed into the generative AI.

[0152] Generative AI: Analyzes all provided data and selects the most appropriate medical institution for the patient, taking into account the patient's urgency, symptoms, past medical history, and the capacity of nearby medical institutions.

[0153] Data processing and calculation: Urgency assessment, algorithmic optimization calculation, ranking of medical institutions that can accept patients.

[0154] Input: Unified dataset.

[0155] Output: Recommended destination list.

[0156] Step 3: Selection and Notification

[0157] Server: Selects the most suitable medical institution based on the list of recommended destinations received from the generative AI model, taking into account the distance, response capacity, and availability of facilities of each medical institution.

[0158] Input: Recommended Destination List.

[0159] Output: Best destination information.

[0160] Server: Automatically sends the patient's condition information and estimated time of arrival to the selected medical institution.

[0161] Input: Best destination information, patient condition information, estimated time of arrival.

[0162] Output:Notification to healthcare provider.

[0163] Ambulance terminal: Information on selected medical institutions and the optimal route are set and displayed to paramedics.

[0164] Input: Best destination information.

[0165] Output: Navigation information and route instructions.

[0166] In this way, the system of the present invention can optimize the patient transport process and transport patients to the appropriate medical institution quickly and accurately.

[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] In today's medical and logistics fields, efficient resource allocation and rapid response are required, but conventional systems have difficulty providing optimal options in real time. In particular, the process of selecting the destination for emergency medical care and optimizing product placement and picking routes in logistics warehouses rely on human judgment, which is time-consuming and labor-intensive, and prone to judgment errors and delays. New and efficient systems are needed to solve these problems.

[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 means for collecting data on the patient's location, urgency, past medical history, medical institution acceptance status, and available medical departments and facilities, means for inputting the collected data into a generative artificial intelligence (AI) for analysis and selecting an optimal destination, means for automatically transmitting the patient's condition information and estimated arrival time to the selected destination, means for collecting the type, quantity, location information, and in / out data of products in the warehouse, means for inputting the collected product data into a generative AI for analysis and proposing optimal product placement locations and picking routes, and means for notifying warehouse workers of the proposed product placement locations and picking routes. This enables quick and accurate selection of a destination in emergency medical care and optimal product placement and efficient picking in logistics warehouses.

[0172] "Location" refers to the location information of a patient or product, and includes the patient's location in emergency medical care and the location where the product is stored in logistics.

[0173] "Urgency" is an index showing the urgency of a patient's condition, and indicates the degree to which prompt medical response is required.

[0174] "Medical history" is a general term for the medical records of a patient's past medical visits, including medical history and past treatment details.

[0175] "Acceptance status of medical institution" is information indicating whether the medical institution is in a state where it can accept new patients based on the current number of patients and the status of its facilities.

[0176] "Medical departments available" refers to the specialized medical care that a medical institution can provide, including medical departments that deal with specific diseases or conditions.

[0177] "Facilities" refers to physical facilities and equipment, including medical instruments and logistics equipment, owned by medical institutions and logistics warehouses.

[0178] "Means for collection" refers to devices or systems for collecting patient or product-related data, including sensors and input devices.

[0179] "Generative artificial intelligence" refers to an AI model that analyzes collected data and derives optimal decisions.

[0180] "Means for analyzing and selecting the optimal destination" refers to the process by which generative artificial intelligence analyzes data and, based on the results, determines the optimal medical institution or optimal location within a warehouse.

[0181] "Means for automatic transmission" refers to a system or method for automatically transmitting analysis results and necessary information to a designated recipient.

[0182] "Product data" refers to data including product type, quantity, location, and related inventory information.

[0183] "Location" refers to the location where the goods are stored within the warehouse, taking into consideration the most appropriate storage method.

[0184] A "picking route" refers to the optimal route for collecting products within a warehouse, including an efficient route for retrieving products.

[0185] "Means of notification" refers to the methods and devices used to communicate analysis results and work instructions to workers or personnel in charge.

[0186] As an embodiment of the present invention, we provide a system that includes both a process for selecting a destination for emergency medical care and a process for optimizing the placement of goods in a logistics warehouse. The specific configuration and operation procedure are described below.

[0187] 1. Emergency medical system configuration and operation procedures

[0188] Hardware and software:

[0189] Ambulance terminal: A mobile device for entering patient location, condition, and insurance card information.

[0190] Server: A central computer that aggregates collected data and analyzes it using generative artificial intelligence (e.g., OpenAI's GPT-4).

[0191] Medical institution terminal: A device that transmits real-time information on current congestion status, number of hospital beds, available medical departments, and medical equipment to a server.

[0192] Examples:

[0193] When an ambulance arrives at the scene of a traffic accident, the patient's blood pressure, pulse rate, respiratory rate, and the state of any injuries are entered into a terminal in the ambulance. The patient's insurance card is also scanned to obtain information about the patient. The server inputs the collected data into generative artificial intelligence. The AI ​​performs analysis, selects the optimal transport destination, and automatically notifies the selected medical institution of this information.

[0194] 2. Logistics Warehouse System Configuration and Operation Procedures

[0195] Hardware and software:

[0196] Warehouse worker's smartphone: A device used to scan product barcodes and enter information such as product type, quantity, and time of receipt.

[0197] Logistics system server: A central computer that aggregates collected product data and analyzes it using generative artificial intelligence.

[0198] GPS device: A device used to track the location of goods within a warehouse.

[0199] Examples:

[0200] Warehouse workers scan product barcodes and input the product type, quantity, and location. The server then inputs the collected data into generative artificial intelligence. The AI ​​then suggests product placement locations and optimal picking routes, and sends this information to the workers' smartphones in real time.

[0201] Example prompt sentence:

[0202] "Please suggest the optimal product placement and picking route based on the following warehouse data: {"items": [{"id": "A001", "quantity": 10, "location": "Shelf 1"}, {"id": "B002", "quantity": 15, "location": "Shelf 2"}], "orders": [{"id": "O123", "items": ["A001", "B002"], "priority": "high"}]}"

[0203] In this way, the system of the present invention enables rapid and accurate selection of destinations for emergency medical care, and enables efficient product placement and picking in logistics warehouses.

[0204] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0205] Step 1:

[0206] Data collection

[0207] Input: The ambulance terminal inputs the patient's location, condition information (e.g., blood pressure, pulse, respiratory rate, and injury status), and insurance card information. The warehouse worker's smartphone scans the product barcode and inputs the product type, quantity, and time of arrival.

[0208] How it works: The ambulance crew manually enters the patient's condition data into a dedicated terminal and scans the insurance card, while the warehouse worker scans the product's barcode with a smartphone app and enters the product information.

[0209] Output: The collected data is sent to a server.

[0210] Step 2:

[0211] Data Aggregation

[0212] Input: Patient and product data collected in step 1.

[0213] Specific operation: The server automatically receives data sent from the ambulance terminal and the warehouse worker's smartphone and stores it in a database.

[0214] Output: The stored data is prepared to be input into a generative AI model.

[0215] Step 3:

[0216] Real-time analytics

[0217] Input: Curated patient and product data.

[0218] Specific operation: The server inputs the stored data into the generation AI, which analyzes the data and calculates the optimal delivery destination, product placement location, and picking route.

[0219] Output: The analysis results provide the optimal delivery destination, product placement location, and picking route.

[0220] Step 4:

[0221] Selection and Notification

[0222] Input: The optimal delivery destination obtained as a result of the analysis, as well as the product placement location and picking route.

[0223] Specific operation: Based on the analysis results of the generation AI, the server automatically selects the optimal destination medical institution, product placement location, and picking route. The selection results are then sent to the medical institution's terminal and the warehouse worker's smartphone.

[0224] Output: The medical institution's terminal is notified of the patient's condition and estimated arrival time, and the warehouse worker's smartphone is notified of the product location and picking route.

[0225] Step 5:

[0226] execution

[0227] Input: Information notified by the server.

[0228] Specific operations: The medical institution prepares to accept the patient at the selected destination, and the ambulance heads to the designated medical institution. The warehouse worker moves the product to the notified location and works according to the designated picking route.

[0229] Output: Ambulances safely transport patients to the most appropriate medical facility, and warehouse workers efficiently place and pick products.

[0230] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0231] This invention is a system for optimizing the process of selecting ambulance destinations, and also combines it with an emotion engine that recognizes user emotions. This system collects data such as the patient's location, urgency, past medical history, medical institution acceptance status, available medical departments, and facilities, and analyzes it in real time using generative artificial intelligence (AI) to select and contact the most appropriate medical institution. In addition, collecting and analyzing user emotion data can improve the quality of medical care.

[0232] Program processing

[0233] 1. Data Collection

[0234] When the ambulance arrives at the scene, it inputs the patient's vital signs (blood pressure, pulse rate, respiratory rate, etc.) and condition information (presence or absence of external injuries, description of symptoms, etc.) into the terminal in the ambulance. It also inputs insurance card information to identify the patient.

[0235] The user (paramedic) scans the patient's insurance card and enters the patient's identifying information into the system.

[0236] The emotion engine collects and analyzes paramedic emotional data (voice tone, facial recognition data, etc.).

[0237] 2. Data acquisition and analysis by the server

[0238] Based on the entered insurance card information, the server queries an internal medical database to obtain the patient's past medical history, allergy information, and prescription drug information.

[0239] The medical institution's terminal sends information to the server in real time about the current admission situation, number of beds, specialist allocation, available medical departments, and medical equipment.

[0240] The emotion engine collects and analyzes emotional data (stress levels, fatigue levels, etc.) of medical institution staff.

[0241] 3. Real-time analysis

[0242] The server inputs all data sent from the ambulance terminal, the medical institution's terminal, and the emotion engine into the generative AI.

[0243] Generative AI comprehensively analyzes the patient's condition, past medical history, medical institution acceptance status, available medical capabilities, and the user's emotional data to create a list of the most suitable medical institutions to which the patient should be transported.

[0244] Based on the analysis results, the generative AI ranks the most suitable medical institutions and selects the optimal destination for transport.

[0245] 4. Selection and Notification

[0246] The server automatically sends the patient's condition information and estimated arrival time to the most appropriate medical institution selected by the generative AI.

[0247] The ambulance terminal transports the patient to the designated medical institution based on the transport instructions sent from the server.

[0248] Specific examples

[0249] For example, consider a scenario in which an ambulance transports a patient to the scene of a traffic accident.

[0250] 1. Data Collection

[0251] When the ambulance arrives at the scene of an accident, it inputs the patient's blood pressure, pulse, respiratory rate, and injury status, and also scans the patient's insurance card to enter the patient's information into the system.

[0252] The user (paramedic) scans their insurance card and inputs the patient's specific information into the system. At the same time, the emotion engine collects and analyzes the paramedic's emotional data (voice tone and facial expression data).

[0253] 2. Data acquisition and analysis by the server

[0254] The server obtains the patient's past medical history and allergy information based on the insurance card information.

[0255] The medical institution's terminal sends information about the current congestion situation and available medical equipment to the server in real time.

[0256] The emotion engine collects and analyzes emotional data (e.g., stress levels and fatigue levels) of medical staff.

[0257] 3. Real-time analysis

[0258] The server inputs all collected data into a generative AI.

[0259] The generated AI comprehensively analyzes the patient's urgency, the severity of the injury, past medical history, whether or not they have allergies, the acceptance status of surrounding medical institutions, and staff emotional data to select the most suitable medical institution.

[0260] 4. Selection and Notification

[0261] Based on the analysis results of the generating AI, the server determines, for example, that trauma center A is the optimal destination for transport.

[0262] The server automatically notifies trauma center A of the patient's condition and estimated time of arrival.

[0263] The ambulance terminal transports the patient to the selected trauma center A.

[0264] In this way, not only is the ambulance's destination selected quickly and accurately at each step, but the emotion engine can also analyze the emotions of emergency medical technicians and medical facility staff, further improving the selection of the most appropriate medical facility and the quality of patient care.

[0265] The processing flow will be explained below.

[0266] Step 1:

[0267] The terminal (ambulance terminal) arrives at the scene and inputs the patient's vital data (blood pressure, pulse rate, respiratory rate, etc.) and condition information (presence or absence of external injuries, description of symptoms, etc.). It also inputs insurance card information to identify the patient.

[0268] The user (paramedic) scans the patient's insurance card and enters the patient's identifying information into the system.

[0269] The emotion engine collects emotional data such as the paramedic's voice tone and facial expression data and analyzes it in real time.

[0270] Step 2:

[0271] Based on the entered insurance card information, the server queries an internal medical database to obtain the patient's past medical history, allergy information, and prescription drug information.

[0272] Step 3:

[0273] The terminal (the medical institution's terminal) sends information to the server in real time regarding the current admission situation, number of hospital beds, the allocation of specialists, available medical departments, and medical equipment.

[0274] The emotion engine collects and analyzes emotional data such as stress levels and fatigue levels of medical institution staff.

[0275] Step 4:

[0276] The server inputs data from the ambulance terminal, the medical institution's terminal, and emotion data from the emotion engine into the generative AI.

[0277] Step 5:

[0278] Generative AI comprehensively analyzes the patient's condition, past medical history, the medical institution's acceptance status, available medical capabilities, and the user's emotional data.

[0279] Based on the analysis results, the generative AI ranks the most suitable medical institutions and determines the optimal destination for transport.

[0280] Step 6:

[0281] The server automatically sends the patient's condition information and estimated arrival time to the most appropriate medical institution determined by the generative AI.

[0282] Step 7:

[0283] The terminal (ambulance terminal) transports the patient to the designated medical institution based on the transport instructions sent from the server.

[0284] Step 8:

[0285] The terminal (terminal at the medical institution) checks the information received from the server and begins preparations for admission (preparing the emergency operating room, arranging specialists, etc.).

[0286] By executing each step in this way, the ambulance's destination can be selected quickly and accurately, and the emotion engine can also analyze the emotions of emergency medical technicians and medical institution staff, further improving the quality of overall medical response.

[0287] Example 2

[0288] 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."

[0289] Conventional emergency transport systems select the optimal destination based on information such as the patient's location, urgency, past medical history, and the medical institution's acceptance status. However, this does not take into account the emotional state of the emergency medical technicians and medical institution staff, which means that the quality of emergency response is not sufficiently improved.

[0290] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting data on the patient's location, urgency, past medical history, medical institution acceptance status, and available medical departments and facilities; means including an emotion engine for collecting and analyzing emotion data; means for inputting the collected data and emotion data into a generative artificial intelligence for analysis and selecting the optimal destination; and means for automatically transmitting information on the patient's condition and estimated time of arrival to the destination selected from the analysis results. This enables quick and accurate selection of the destination taking into account the emotional states of paramedics and medical institution staff.

[0291] "Patient location" is information indicating the location where the ambulance or paramedic found the patient.

[0292] "Urgency" is an index that evaluates the urgency of the health condition that the patient is currently facing.

[0293] "Past medical history" refers to information that includes records of medical services and treatments that a patient has received.

[0294] "Acceptance status of medical institutions" is information indicating the capacity of a particular medical institution to accept patients and its current congestion status.

[0295] "Available medical departments and facilities" refers to information about the types of medical treatments that a medical institution can provide and the medical equipment and facilities that can be used.

[0296] "Emotional data" refers to data that indicates the current emotional state of emergency medical technicians and medical institution staff, including stress levels and fatigue levels.

[0297] The "emotion engine" is a technology that analyzes collected voice and facial expression data and generates emotional data.

[0298] "Generative artificial intelligence" refers to machine learning models and algorithms that analyze collected data and generate appropriate results.

[0299] "Destination" refers to the most appropriate medical facility to which the patient should be transported for treatment.

[0300] "Condition information" is data related to the patient's current health condition and vital signs.

[0301] The "estimated arrival time" is information indicating the time when the ambulance is scheduled to arrive at the medical institution to which the patient is being transported.

[0302] An "ambulance terminal" is an electronic device installed in an ambulance for inputting and transmitting patient data on-site.

[0303] "Medical institution terminal" refers to an electronic device used within a medical institution to update admission status and medical treatment information in real time.

[0304] The system of the present invention optimizes the ambulance destination selection process and improves the quality of emergency response. The hardware used includes mobile devices installed in ambulances, cloud-based servers, and computers at medical institutions. The software includes an emotion engine and a generative artificial intelligence (AI) model. A specific embodiment of this system is described below.

[0305] Data collection

[0306] After arriving at the scene of an accident, the ambulance terminal inputs the patient's vital data (blood pressure, pulse rate, respiratory rate) and condition information (presence or absence of external injuries, description of symptoms), and also scans the patient's insurance card to transmit the patient's identifying information to the system.

[0307] The user (paramedic) scans the patient's insurance card and inputs specific information. At the same time, the emotion engine analyzes the paramedic's voice tone and facial expression data to collect emotional data.

[0308] Data acquisition and analysis by the server

[0309] Based on the entered health insurance card information, the server retrieves the patient's past medical history, allergy information, and prescription drug information from an internal medical database. It also receives real-time information from the medical institution's terminal about the current admission situation, number of hospital beds, allocation of specialists, available medical departments, and medical equipment.

[0310] The emotion engine collects and analyzes emotional data (e.g., stress levels, fatigue levels) of medical institution staff.

[0311] Real-time analytics

[0312] The server aggregates all data sent from the ambulance terminals, medical institution terminals, and emotion engines, and inputs it into a generative AI (e.g., GPT-4).

[0313] Generative AI comprehensively analyzes the patient's condition, past medical history, medical institution acceptance status, available medical capabilities, and the user's emotional data, and then lists and ranks the most suitable medical institutions to which the patient should be transported.

[0314] Selection and Notification

[0315] The server automatically sends the patient's condition information and estimated arrival time to the most appropriate medical institution selected by the generative AI.

[0316] The ambulance terminal transports the patient to the designated medical institution based on the transport instructions sent from the server.

[0317] Specific examples

[0318] For example, consider a scenario in which an ambulance transports a patient to the scene of a traffic accident.

[0319] When the ambulance arrives at the scene of an accident, it inputs the patient's blood pressure, pulse, respiratory rate, and injury status, and also scans the patient's insurance card to enter the patient's information into the system.

[0320] The user (paramedic) scans their insurance card and inputs the patient's specific information. At the same time, the emotion engine collects and analyzes the paramedic's emotional data (voice tone and facial expression data).

[0321] The server obtains the patient's past medical history and allergy information based on the health insurance card information.

[0322] The medical institution's terminal sends information about the current congestion situation and available medical equipment to the server in real time.

[0323] The emotion engine collects and analyzes emotional data (e.g., stress levels and fatigue levels) of medical staff.

[0324] The server inputs the various collected data into the generative AI.

[0325] The generative AI comprehensively analyzes the patient's urgency, the severity of the injury, past medical history, whether or not they have allergies, the acceptance status of surrounding medical institutions, and staff emotional data to select the most suitable medical institution.

[0326] The server determines that "the best destination is Trauma Center A, and the reason is that it has surgeons and all the necessary medical equipment," and automatically notifies Trauma Center A of the details.

[0327] The ambulance terminal follows the notified instructions and transports the patient to trauma center A.

[0328] Prompt Sentence Examples

[0329] "Comprehensively analyze the patient's past medical history, current vital signs, the acceptance status of the nearest medical institution, and the emotional data of the paramedics and medical institution staff to determine the optimal destination."

[0330] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0331] Step 1: Data collection

[0332] After arriving at the scene, the ambulance terminal inputs the patient's vital signs (blood pressure 130 / 80, pulse rate 90, respiratory rate 20) and condition information (presence or absence of trauma and description of symptoms). The input data is sent to the server. In addition, the insurance card is scanned and the patient's specific information is entered into the system.

[0333] Input: Patient's vital data, condition information, insurance card information

[0334] Output: Sending the entered data to the server

[0335] The user (paramedic) scans their insurance card and inputs the patient's specific information. At the same time, the emotion engine analyzes the paramedic's voice tone and facial expression data to collect emotional data.

[0336] Input: Insurance card information, voice tone, facial expression data

[0337] Output: Sending collected specific information and emotion data to the server

[0338] Step 2: Data acquisition and analysis by the server

[0339] The server accesses an internal medical database based on the entered health insurance card information to obtain the patient's past medical history, allergy information, and prescription drug information. It also receives real-time information from the medical institution's terminal on the current admission status, number of hospital beds, allocation of specialists, available medical departments, and medical equipment.

[0340] Input: Insurance card information

[0341] Output: Obtaining past medical history, allergy information, and prescription drug information

[0342] The emotion engine collects voice and facial expression data from medical institution staff and analyzes emotional data (stress levels, fatigue levels).

[0343] Input: Voice data, facial expression data

[0344] Output: Emotion data generation and transmission to the server

[0345] Step 3: Real-time analysis

[0346] The server aggregates all data sent from the ambulance terminal, medical institution terminals, and emotion engine, and inputs it into a generative AI (e.g., GPT-4). The generative AI comprehensively analyzes the patient's condition, past medical history, medical institution acceptance status, available medical capabilities, and the user's emotional data, and lists and ranks the most suitable medical institutions to which the patient should be transported.

[0347] Input: Patient condition information, past medical history, medical institution acceptance status, available medical capabilities, emotional data

[0348] Output: List of optimal destinations and ranking

[0349] Step 4: Selection and Notification

[0350] The server automatically sends the patient's condition information and estimated arrival time to the most appropriate medical institution selected by the generative AI, including detailed information on the patient's health, necessary medical equipment, and the doctor in charge.

[0351] Input: List of optimal destinations and patient status information

[0352] Output: Notification to selected medical institutions

[0353] The ambulance terminal receives the transport instructions sent from the server and transports the patient to the designated medical institution.

[0354] Input: Transport instructions from the server

[0355] Output: Patient transport

[0356] As described above, by analyzing the data collected at each step in real time and selecting the optimal destination, it is possible to significantly improve the efficiency and accuracy of emergency transport.

[0357] (Application example 2)

[0358] 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."

[0359] In the conventional process for selecting ambulance destinations, data collection was often done manually, making it difficult to select a destination quickly and accurately. Furthermore, because information on medical institution acceptance status, available medical departments, and medical equipment could not be obtained in real time, it was not possible to quickly select an appropriate destination. Furthermore, in emergency response for security services, there was no established method for analyzing various data, including staff emotional data, in real time to select the optimal response, which increased the risk of inappropriate responses.

[0360] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting data on the patient's location, urgency, past medical history, medical institution acceptance status, and available medical departments and facilities; means for inputting the collected data into a generative artificial intelligence for analysis and selecting the optimal transport destination; means for automatically transmitting information on the patient's condition and estimated arrival time to the selected transport destination; means for collecting information on the on-site situation, past security history, security staff deployment status, and the status of available equipment; and means for collecting and analyzing emotional data of the security staff. This enables quick and accurate selection of the transport destination and optimal emergency response.

[0361] "Location" refers to the place or position where a particular person or thing exists.

[0362] "Urgency" is a measure of the level of danger of an event and the need for immediate action.

[0363] "Past medical history" refers to records of medical services and treatments a patient has received.

[0364] A "medical institution" is a facility or hospital that provides medical services.

[0365] "Acceptance status" refers to a medical institution's ability and readiness to accept patients.

[0366] "Available medical specialties" refers to the specialties and subjects of treatment that a medical institution can provide.

[0367] "Equipment" refers to medical equipment and facilities owned by a medical institution.

[0368] "Data collection means" refers to the methods and devices used to obtain the required data.

[0369] "Generative AI" is AI that analyzes input data and generates optimal results for a specific purpose.

[0370] "Analysis" is the process of examining input data in detail to understand its meaning and structure.

[0371] "Destination" refers to the destination or medical facility to which the patient is being transported by ambulance or other means of transportation.

[0372] "Condition information" refers to information about a patient's health condition and symptoms.

[0373] "Estimated time of arrival" refers to the time the patient is expected to arrive at the designated location.

[0374] "Situation on the ground" refers to the current state or environment at a particular location or event.

[0375] "Security history" refers to a record of past security-related events and responses.

[0376] "Security staff" refers to professional employees in charge of security operations.

[0377] "Staffing" refers to how staff are deployed in a particular location or shift.

[0378] "Equipment" refers to tools and devices used for a particular task or operation.

[0379] "Emotional data" is information that indicates an individual's emotional state, and includes data such as voice tone, facial expression, and heart rate.

[0380] "Real-time" refers to processing and responding to events in the real world almost as they occur.

[0381] The following is a detailed description of an embodiment of the present invention. First, an overview of the entire system will be given, followed by a detailed description of the specific roles and operations of each element.

[0382] System Overview

[0383] The present invention is a system for optimizing the ambulance routing process and the security services emergency response process. The system consists of three main components:

[0384] 1. Data collection terminal: A terminal in the ambulance that inputs information on the patient's location and condition, as well as a means of collecting information on the situation at the scene, past security history, security staff deployment status, and the status of available equipment.

[0385] 2. Server: A means of inputting collected data into generative artificial intelligence for analysis and selecting the optimal destination and emergency response.

[0386] 3. Notification Device: A means of automatically transmitting patient status information and estimated time of arrival to selected destinations.

[0387] Hardware and software used

[0388] Hardware

[0389] Ambulance devices: tablets, smartphones, etc.

[0390] Sensor devices: environmental sensors, security cameras, heart rate monitors, etc.

[0391] Server: A cloud server for data collection, analysis, and storage.

[0392] Notification devices: ambulance and security staff's smart devices (e.g. smartphones, tablets, or smart glasses).

[0393] software

[0394] Data collection software: Used to collect data from sensors and cameras.

[0395] Emotion analysis software: Voice analysis and facial expression analysis engine.

[0396] Generative artificial intelligence (AI) models: Used to analyze input data and optimize.

[0397] Notification system: A system for providing immediate notifications to staff.

[0398] Data processing and calculation

[0399] 1. Processing of data collection terminals

[0400] The ambulance terminal inputs the patient's condition information and insurance card information, and enters the patient's specific information into the system.

[0401] The sensor device collects and analyzes the situation on-site and emotional data of security staff (voice tone and facial expression data).

[0402] 2. Server analysis process

[0403] The server queries an internal database based on the entered data to obtain the patient's medical history and allergy information.

[0404] The server inputs all collected data into generative artificial intelligence, which analyzes the patient's condition, past medical history, medical institution acceptance status, available medical capabilities, security history, and emotional data to create a list of the most appropriate medical institutions and countermeasures.

[0405] 3. Notification System Processing

[0406] The server sends the most appropriate medical institution and emergency response measures selected by the generative AI to the notification device.

[0407] The ambulance terminal and the security staff's smart devices will receive notifications and initiate immediate response.

[0408] Specific examples

[0409] For example, in a scenario where a suspicious individual is detected in security operations at a large event venue, the process would proceed as follows:

[0410] 1. Data Collection

[0411] Security cameras collect footage of suspicious people in real time and send the data to a server for analysis.

[0412] The situation on the scene is entered into a smart device, and the emotion engine analyzes the voice tone and facial expression data of the security staff.

[0413] 2. Analysis processing

[0414] -The server inputs the collected video data, on-site conditions, and staff emotional data into generative AI to calculate the optimal response.

[0415] 3. Notification and Response

[0416] Based on the analysis results, the server notifies security staff of the most appropriate response (for example, sealing off a specific area or notifying the police).

[0417] Security staff will be notified and will begin responding immediately.

[0418] Prompt Sentence Examples

[0419] "A suspicious individual has entered the venue. Please propose the best course of action based on the video data from Camera 5, the on-site input data, and the emotional data of Staff A."

[0420] In this way, the system of the present invention automates the data collection, analysis and notification process in emergency situations, enabling a rapid and accurate response.

[0421] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0422] Step 1:

[0423] The server collects data on the patient's location, urgency, past medical history, medical institution acceptance status, and available medical departments and facilities. The ambulance terminal inputs the patient's location and condition information, and security cameras and sensor devices collect data on the situation at the scene and the emotions of security staff. The input data is sent to the server.

[0424] Step 2:

[0425] The server queries its internal database based on the input data to retrieve the patient's medical history, allergy information, and prescription drug information. Specifically, it performs a database search and retrieves the relevant patient information. The patient's medical history and medical information are returned to the server as output.

[0426] Step 3:

[0427] The server collects real-time information from medical institutions on their admission status, number of beds, the allocation of specialists, available medical departments, and medical equipment. It also collects information on the situation on-site and past security history from security agencies. This data is sent from medical institutions' terminals and security systems and stored on the server.

[0428] Step 4:

[0429] The server inputs all collected data into a generative AI model. Specifically, the AI ​​model is passed information on the patient's condition, past medical history, medical institution acceptance status, security history, emotional data, etc. The input data is analyzed by the AI ​​model, and the optimal transport destination and emergency response measures are output.

[0430] Step 5:

[0431] Generative AI analyzes input data and comprehensively evaluates the patient's urgency, the severity of the injury, past medical history, whether they have any allergies, the acceptance status of nearby medical institutions, and the emotional state of the staff. Based on this evaluation, it selects the most suitable medical institution and response measures and lists them in a ranked format. This list is sent to a server.

[0432] Step 6:

[0433] The server considers the optimal transport destination and emergency response measures selected by the generative AI model and selects the most appropriate one from among them. The output is the optimal medical institution to transport the patient to and the response measures.

[0434] Step 7:

[0435] The server sends the selected medical institution and emergency response measures to the notification device. Specifically, the selection results are sent to the ambulance terminal and the security staff's smart device for notification. The input includes the selection result, and the output includes notification completion.

[0436] Step 8:

[0437] Users (e.g., paramedics or security personnel) receive notifications and initiate actions based on the designated medical facility or response plan, such as dispatching an ambulance or implementing an emergency response, so that the patient is transported to the appropriate medical facility or the emergency situation is appropriately addressed.

[0438] 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.

[0439] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0440] 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.

[0441] [Second embodiment]

[0442] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0443] 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.

[0444] 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).

[0445] 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.

[0446] 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.

[0447] 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).

[0448] 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.

[0449] 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.

[0450] 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.

[0451] 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.

[0452] 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.

[0453] 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."

[0454] This invention is a system for optimizing the process of selecting a destination for ambulances. This system collects data such as the patient's location, urgency, past medical history, medical institution acceptance status, available medical departments and facilities, and analyzes it in real time using generative artificial intelligence (AI), selecting the most suitable medical institution and contacting the patient.

[0455] Program processing

[0456] 1. Data Collection

[0457] When the ambulance arrives at the scene, it inputs information about the patient's condition (vital data, symptoms, injury details, etc.) and also inputs insurance card information to identify the patient.

[0458] The server retrieves past medical history, allergy information, and prescription drug information from a medical database based on insurance card information.

[0459] The medical institution's terminal sends information on the current congestion situation, number of beds, available medical departments and medical equipment to the server in real time.

[0460] 2. Real-time analysis

[0461] The server inputs the collected data into generative artificial intelligence, which then analyzes the data based on the patient's condition, medical history, and the medical institution's acceptance status.

[0462] The generative AI considers the following factors to determine the best destination for a patient:

[0463] Patient's condition and urgency

[0464] Past medical history and allergy information

[0465] The congestion level of nearby medical institutions and the capacity of specialized medical departments

[0466] Medical equipment available at each medical institution

[0467] 3. Selection and Notification

[0468] The server then determines the optimal destination based on the results of the generative AI analysis, taking into account factors such as distance, the current capacity of medical institutions, the availability of specialists, and the availability of necessary medical equipment.

[0469] The server automatically sends the patient's condition information and estimated time of arrival to the selected medical institution.

[0470] Specific examples

[0471] For example, consider a scenario in which an ambulance transports a patient to the scene of a traffic accident.

[0472] 1. Data Collection

[0473] The ambulance terminal arrives at the scene of an accident and inputs the patient's blood pressure, pulse, respiratory rate, and injury status, and also scans the patient's insurance card to enter the patient's information into the system.

[0474] The server obtains the patient's past medical history and allergy information from the health insurance card information.

[0475] Medical institutions' terminals send information about congestion and available medical equipment to the server in real time.

[0476] 2. Real-time analysis

[0477] The server inputs all collected data into the generation AI.

[0478] The generative AI analyzes the patient's urgency, the severity of the injury, past medical history, and whether or not they have any allergies, and then lists the most suitable medical institutions to recommend.

[0479] 3. Selection and Notification

[0480] Based on the analysis results of the generating AI, the server determines, for example, that trauma center A is the optimal destination for transport.

[0481] The server automatically notifies trauma center A of the patient's condition and estimated time of arrival.

[0482] The ambulance terminal initiates transport of the patient to the selected trauma center A.

[0483] In this way, the system of the present invention quickly selects the most appropriate medical institution when transporting a patient by ambulance, enabling the patient to receive appropriate medical care promptly.

[0484] The processing flow will be explained below.

[0485] Step 1:

[0486] The terminal (ambulance terminal) arrives at the scene and inputs the patient's vital data (blood pressure, pulse rate, respiratory rate, etc.) and condition information (presence or absence of trauma, description of symptoms, etc.).

[0487] The user (paramedic) scans the patient's insurance card and enters the patient's identifying information into the system.

[0488] Step 2:

[0489] Based on the entered insurance card information, the server queries an internal medical database to obtain the patient's past medical history, allergy information, and prescription drug information.

[0490] Step 3:

[0491] The terminal (the medical institution's terminal) sends information to the server in real time regarding the current admission situation, number of hospital beds, the allocation of specialists, available medical departments, and medical equipment.

[0492] Step 4:

[0493] The server inputs all data sent from the ambulance terminal and the medical institution's terminal into the generative AI.

[0494] Step 5:

[0495] Generative AI analyzes the patient's condition, past medical history, medical institution acceptance status, and available medical capabilities, and creates a list of the most appropriate medical institutions to which the patient should be transported.

[0496] Based on the analysis results, the generative AI ranks the most suitable medical institutions and selects the optimal destination for transport.

[0497] Step 6:

[0498] The server automatically sends the patient's condition information and estimated arrival time to the most appropriate medical institution selected by the generative AI.

[0499] Step 7:

[0500] The terminal (ambulance terminal) transports the patient to the designated medical institution based on the transport instructions sent from the server.

[0501] Step 8:

[0502] The terminal (terminal at the medical institution) checks the information received from the server and begins preparations for admission (preparing the emergency operating room, arranging specialists, etc.).

[0503] In this way, the ambulance destination is selected quickly and accurately through each step, allowing the patient to receive treatment promptly at the appropriate medical institution.

[0504] Example 1

[0505] 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."

[0506] In emergencies such as sudden illness or accidents, it is extremely important to quickly transport patients to the appropriate medical institution. However, it is difficult for on-site medical staff to select the optimal destination with limited information and time. Furthermore, if the medical institution's acceptance status and facility capacity cannot be grasped in real time, the selection of the destination may be delayed, putting the patient's life at risk. A system that can solve these issues is needed.

[0507] 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.

[0508] In this invention, the server includes means for collecting data on the patient's location, urgency, past medical history, medical institution acceptance status, and available medical departments and facilities, means for inputting the collected data into a generative artificial intelligence model and selecting the optimal transport destination taking into consideration the patient's condition and urgency, past medical history, and the response capabilities of nearby medical institutions, means for automatically transmitting the patient's condition information and estimated arrival time to the selected transport destination, means for acquiring data including the patient information from a medical database and using it for analysis, and means for updating the acceptance status of each medical institution in real time and reflecting it in the processing results. This makes it possible to quickly and accurately select the optimal transport destination and ensure that patients receive appropriate medical care promptly.

[0509] "Location" refers to information that indicates the patient's current location or position.

[0510] "Urgency" refers to information that serves as a criterion for assessing the severity of a patient's symptoms and condition and determining the priority of transportation.

[0511] "Medical history" refers to records of medical examinations and treatments that a patient has received at medical institutions in the past.

[0512] "Medical institution acceptance status" refers to information that indicates how many patients a medical institution currently has the capacity to accept.

[0513] "Available medical departments" refers to information indicating the specialized medical services and medical departments that a medical institution can provide.

[0514] "Facilities" refers to information indicating medical equipment and treatment devices owned by a medical institution.

[0515] A "generative artificial intelligence model" is an artificial intelligence that analyzes collected data and selects the optimal destination.

[0516] "Condition information" refers to detailed medical information that indicates a patient's current health condition and symptoms.

[0517] "Estimated arrival time" is information indicating the estimated time when the ambulance is to arrive at the selected medical institution.

[0518] A "medical database" is a database that stores medical data such as a patient's past medical history, allergy information, and prescription drug information.

[0519] This invention is a system that optimizes the process of selecting an ambulance destination. This system collects data on the patient's location, urgency, past medical history, medical institution acceptance status, available medical departments, and facilities, and analyzes it in real time using a generative artificial intelligence model. It then selects the most suitable medical institution and automatically transmits the patient's condition information and estimated arrival time to the selected medical institution.

[0520] Hardware and Software Configuration

[0521] The system consists of the following main components:

[0522] 1. Ambulance terminal

[0523] A tablet or smartphone for paramedics to enter information about the patient's condition.

[0524] A scanner for scanning insurance card information.

[0525] 2. Server

[0526] A high-performance server for running generative artificial intelligence (AI) models.

[0527] An interface for real-time communication with medical databases.

[0528] 3. Medical institution terminals

[0529] A computer terminal for inputting and sending information on current admission status, number of hospital beds, available medical departments, and medical equipment.

[0530] Specific operation of the system

[0531] 1. Data Collection

[0532] When the ambulance arrives at the scene, the ambulance staff enters the patient's condition information (e.g., blood pressure, pulse rate, respiratory rate, and the state of injuries) into the terminal. In addition, the ambulance scans the patient's insurance card to obtain basic information about the patient.

[0533] Based on the insurance card information received, the server retrieves the patient's past medical history, allergy information, prescription drug information, etc. from a medical database.

[0534] The medical institution's terminal sends information on each institution's current admission status, number of beds, available medical departments, and medical equipment to the server in real time.

[0535] 2. Real-time analysis and selection

[0536] The server inputs all collected data into a generative AI model, which analyzes the patient's condition, past medical history, allergy information, and medical institution acceptance status to select the medical institution that best suits the patient.

[0537] 3. Selection and Notification

[0538] The server automatically sends the patient's condition information and estimated time of arrival to the most appropriate medical institution selected by the generative AI model.

[0539] The ambulance's terminal provides emergency personnel with information about the destination medical institution and the optimal route.

[0540] Specific examples

[0541] For example, consider the scenario of an ambulance transporting a patient to the scene of a traffic accident:

[0542] The ambulance terminal arrives at the scene of an accident and inputs the patient's blood pressure, pulse, respiratory rate, and injury status. It also scans the patient's insurance card and inputs the patient's information into the system.

[0543] The server obtains the patient's past medical history and allergy information from the health insurance card information.

[0544] Medical institution terminals provide real-time information to the server about current congestion and available medical equipment.

[0545] Next, prompt the generative AI model with the following sentence:

[0546] "The ambulance has arrived at the scene. Please enter the patient's information. We will provide the following information: blood pressure, pulse rate, respiratory rate, injury status, past medical history, allergy information, and prescription drug information. We will also provide information on the availability and medical facilities of nearby medical institutions. Please select the most appropriate destination."

[0547] Based on this prompt, the generative AI model performs an analysis and selects the optimal transport destination. The system then automatically notifies the destination of the patient's condition and estimated arrival time. By linking systems in this way, the patient can be transported quickly and accurately to the optimal medical institution.

[0548] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0549] Step 1: Data collection

[0550] Ambulance terminal: After arriving at the patient's location, the ambulance crew uses the terminal to input the patient's condition information, including blood pressure, pulse rate, respiratory rate, and the state of injuries. The terminal also scans the insurance card, digitizing the patient's basic information and sending it to the server.

[0551] Input: Patient vital data and insurance card information.

[0552] Output: Digitized patient information.

[0553] Server: After receiving the insurance card information, it accesses the medical database and retrieves the patient's past medical history, allergy information, and prescription drug information.

[0554] Input: Digitized insurance card information.

[0555] Output: Past medical history, allergy information, prescription drug information.

[0556] Medical institution terminals: Each medical institution inputs information on its current admission status, number of beds, available medical departments, and medical equipment, and sends this information to the server in real time.

[0557] Input: Details of admission status, number of beds, available medical specialties, and medical facilities.

[0558] Output: Real-time updated medical institution admission information.

[0559] Step 2: Real-time analysis

[0560] Server: Inputs all collected data into the generative AI model, including patient condition information, past medical history, allergy information, and medical institution acceptance status.

[0561] Input: Patient condition information, past medical history, allergy information, and medical institution acceptance status.

[0562] Output: A consolidated dataset that is fed into the generative AI.

[0563] Generative AI: Analyzes all provided data and selects the most appropriate medical institution for the patient, taking into account the patient's urgency, symptoms, past medical history, and the capacity of nearby medical institutions.

[0564] Data processing and calculation: Urgency assessment, algorithmic optimization calculation, ranking of medical institutions that can accept patients.

[0565] Input: Unified dataset.

[0566] Output: Recommended destination list.

[0567] Step 3: Selection and Notification

[0568] Server: Selects the most suitable medical institution based on the list of recommended destinations received from the generative AI model, taking into account the distance, response capacity, and availability of facilities of each medical institution.

[0569] Input: Recommended Destination List.

[0570] Output: Best destination information.

[0571] Server: Automatically sends the patient's condition information and estimated time of arrival to the selected medical institution.

[0572] Input: Best destination information, patient condition information, estimated time of arrival.

[0573] Output:Notification to healthcare provider.

[0574] Ambulance terminal: Information on selected medical institutions and the optimal route are set and displayed to paramedics.

[0575] Input: Best destination information.

[0576] Output: Navigation information and route instructions.

[0577] In this way, the system of the present invention can optimize the patient transport process and transport patients to the appropriate medical institution quickly and accurately.

[0578] (Application example 1)

[0579] 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."

[0580] In today's medical and logistics fields, efficient resource allocation and rapid response are required, but conventional systems have difficulty providing optimal options in real time. In particular, the process of selecting the destination for emergency medical care and optimizing product placement and picking routes in logistics warehouses rely on human judgment, which is time-consuming and labor-intensive, and prone to judgment errors and delays. New and efficient systems are needed to solve these problems.

[0581] 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.

[0582] In this invention, the server includes means for collecting data on the patient's location, urgency, past medical history, medical institution acceptance status, and available medical departments and facilities, means for inputting the collected data into a generative artificial intelligence (AI) for analysis and selecting an optimal destination, means for automatically transmitting the patient's condition information and estimated arrival time to the selected destination, means for collecting the type, quantity, location information, and in / out data of products in the warehouse, means for inputting the collected product data into a generative AI for analysis and proposing optimal product placement locations and picking routes, and means for notifying warehouse workers of the proposed product placement locations and picking routes. This enables quick and accurate selection of a destination in emergency medical care and optimal product placement and efficient picking in logistics warehouses.

[0583] "Location" refers to the location information of a patient or product, and includes the patient's location in emergency medical care and the location where the product is stored in logistics.

[0584] "Urgency" is an index showing the urgency of a patient's condition, and indicates the degree to which prompt medical response is required.

[0585] "Medical history" is a general term for the medical records of a patient's past medical visits, including medical history and past treatment details.

[0586] "Acceptance status of medical institution" is information indicating whether the medical institution is in a state where it can accept new patients based on the current number of patients and the status of its facilities.

[0587] "Medical departments available" refers to the specialized medical care that a medical institution can provide, including medical departments that deal with specific diseases or conditions.

[0588] "Facilities" refers to physical facilities and equipment, including medical instruments and logistics equipment, owned by medical institutions and logistics warehouses.

[0589] "Means for collection" refers to devices or systems for collecting patient or product-related data, including sensors and input devices.

[0590] "Generative artificial intelligence" refers to an AI model that analyzes collected data and derives optimal decisions.

[0591] "Means for analyzing and selecting the optimal destination" refers to the process by which generative artificial intelligence analyzes data and, based on the results, determines the optimal medical institution or optimal location within a warehouse.

[0592] "Means for automatic transmission" refers to a system or method for automatically transmitting analysis results and necessary information to a designated recipient.

[0593] "Product data" refers to data including product type, quantity, location, and related inventory information.

[0594] "Location" refers to the location where the goods are stored within the warehouse, taking into consideration the most appropriate storage method.

[0595] A "picking route" refers to the optimal route for collecting products within a warehouse, including an efficient route for retrieving products.

[0596] "Means of notification" refers to the methods and devices used to communicate analysis results and work instructions to workers or personnel in charge.

[0597] As an embodiment of the present invention, we provide a system that includes both a process for selecting a destination for emergency medical care and a process for optimizing the placement of goods in a logistics warehouse. The specific configuration and operation procedure are described below.

[0598] 1. Emergency medical system configuration and operation procedures

[0599] Hardware and software:

[0600] Ambulance terminal: A mobile device for entering patient location, condition, and insurance card information.

[0601] Server: A central computer that aggregates collected data and analyzes it using generative artificial intelligence (e.g., OpenAI's GPT-4).

[0602] Medical institution terminal: A device that transmits real-time information on current congestion status, number of hospital beds, available medical departments, and medical equipment to a server.

[0603] Examples:

[0604] When an ambulance arrives at the scene of a traffic accident, the patient's blood pressure, pulse rate, respiratory rate, and the state of any injuries are entered into a terminal in the ambulance. The patient's insurance card is also scanned to obtain information about the patient. The server inputs the collected data into generative artificial intelligence. The AI ​​performs analysis, selects the optimal transport destination, and automatically notifies the selected medical institution of this information.

[0605] 2. Logistics Warehouse System Configuration and Operation Procedures

[0606] Hardware and software:

[0607] Warehouse worker's smartphone: A device used to scan product barcodes and enter information such as product type, quantity, and time of receipt.

[0608] Logistics system server: A central computer that aggregates collected product data and analyzes it using generative artificial intelligence.

[0609] GPS device: A device used to track the location of goods within a warehouse.

[0610] Examples:

[0611] Warehouse workers scan product barcodes and input the product type, quantity, and location. The server then inputs the collected data into generative artificial intelligence. The AI ​​then suggests product placement locations and optimal picking routes, and sends this information to the workers' smartphones in real time.

[0612] Example prompt sentence:

[0613] "Please suggest the optimal product placement and picking route based on the following warehouse data: {"items": [{"id": "A001", "quantity": 10, "location": "Shelf 1"}, {"id": "B002", "quantity": 15, "location": "Shelf 2"}], "orders": [{"id": "O123", "items": ["A001", "B002"], "priority": "high"}]}"

[0614] In this way, the system of the present invention enables rapid and accurate selection of destinations for emergency medical care, and enables efficient product placement and picking in logistics warehouses.

[0615] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0616] Step 1:

[0617] Data collection

[0618] Input: The ambulance terminal inputs the patient's location, condition information (e.g., blood pressure, pulse, respiratory rate, and injury status), and insurance card information. The warehouse worker's smartphone scans the product barcode and inputs the product type, quantity, and time of arrival.

[0619] How it works: The ambulance crew manually enters the patient's condition data into a dedicated terminal and scans the insurance card, while the warehouse worker scans the product's barcode with a smartphone app and enters the product information.

[0620] Output: The collected data is sent to a server.

[0621] Step 2:

[0622] Data Aggregation

[0623] Input: Patient and product data collected in step 1.

[0624] Specific operation: The server automatically receives data sent from the ambulance terminal and the warehouse worker's smartphone and stores it in a database.

[0625] Output: The stored data is prepared to be input into a generative AI model.

[0626] Step 3:

[0627] Real-time analytics

[0628] Input: Curated patient and product data.

[0629] Specific operation: The server inputs the stored data into the generation AI, which analyzes the data and calculates the optimal delivery destination, product placement location, and picking route.

[0630] Output: The analysis results provide the optimal delivery destination, product placement location, and picking route.

[0631] Step 4:

[0632] Selection and Notification

[0633] Input: The optimal delivery destination obtained as a result of the analysis, as well as the product placement location and picking route.

[0634] Specific operation: Based on the analysis results of the generation AI, the server automatically selects the optimal destination medical institution, product placement location, and picking route. The selection results are then sent to the medical institution's terminal and the warehouse worker's smartphone.

[0635] Output: The medical institution's terminal is notified of the patient's condition and estimated arrival time, and the warehouse worker's smartphone is notified of the product location and picking route.

[0636] Step 5:

[0637] execution

[0638] Input: Information notified by the server.

[0639] Specific operations: The medical institution prepares to accept the patient at the selected destination, and the ambulance heads to the designated medical institution. The warehouse worker moves the product to the notified location and works according to the designated picking route.

[0640] Output: Ambulances safely transport patients to the most appropriate medical facility, and warehouse workers efficiently place and pick products.

[0641] 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.

[0642] This invention is a system for optimizing the process of selecting ambulance destinations, and also combines it with an emotion engine that recognizes user emotions. This system collects data such as the patient's location, urgency, past medical history, medical institution acceptance status, available medical departments, and facilities, and analyzes it in real time using generative artificial intelligence (AI) to select and contact the most appropriate medical institution. In addition, collecting and analyzing user emotion data can improve the quality of medical care.

[0643] Program processing

[0644] 1. Data Collection

[0645] When the ambulance arrives at the scene, it inputs the patient's vital signs (blood pressure, pulse rate, respiratory rate, etc.) and condition information (presence or absence of external injuries, description of symptoms, etc.) into the terminal in the ambulance. It also inputs insurance card information to identify the patient.

[0646] The user (paramedic) scans the patient's insurance card and enters the patient's identifying information into the system.

[0647] The emotion engine collects and analyzes paramedic emotional data (voice tone, facial recognition data, etc.).

[0648] 2. Data acquisition and analysis by the server

[0649] Based on the entered insurance card information, the server queries an internal medical database to obtain the patient's past medical history, allergy information, and prescription drug information.

[0650] The medical institution's terminal sends information to the server in real time about the current admission situation, number of beds, specialist allocation, available medical departments, and medical equipment.

[0651] The emotion engine collects and analyzes emotional data (stress levels, fatigue levels, etc.) of medical institution staff.

[0652] 3. Real-time analysis

[0653] The server inputs all data sent from the ambulance terminal, the medical institution's terminal, and the emotion engine into the generative AI.

[0654] Generative AI comprehensively analyzes the patient's condition, past medical history, medical institution acceptance status, available medical capabilities, and the user's emotional data to create a list of the most suitable medical institutions to which the patient should be transported.

[0655] Based on the analysis results, the generative AI ranks the most suitable medical institutions and selects the optimal destination for transport.

[0656] 4. Selection and Notification

[0657] The server automatically sends the patient's condition information and estimated arrival time to the most appropriate medical institution selected by the generative AI.

[0658] The ambulance terminal transports the patient to the designated medical institution based on the transport instructions sent from the server.

[0659] Specific examples

[0660] For example, consider a scenario in which an ambulance transports a patient to the scene of a traffic accident.

[0661] 1. Data Collection

[0662] When the ambulance arrives at the scene of an accident, it inputs the patient's blood pressure, pulse, respiratory rate, and injury status, and also scans the patient's insurance card to enter the patient's information into the system.

[0663] The user (paramedic) scans their insurance card and inputs the patient's specific information into the system. At the same time, the emotion engine collects and analyzes the paramedic's emotional data (voice tone and facial expression data).

[0664] 2. Data acquisition and analysis by the server

[0665] The server obtains the patient's past medical history and allergy information based on the insurance card information.

[0666] The medical institution's terminal sends information about the current congestion situation and available medical equipment to the server in real time.

[0667] The emotion engine collects and analyzes emotional data (e.g., stress levels and fatigue levels) of medical staff.

[0668] 3. Real-time analysis

[0669] The server inputs all collected data into a generative AI.

[0670] The generated AI comprehensively analyzes the patient's urgency, the severity of the injury, past medical history, whether or not they have allergies, the acceptance status of surrounding medical institutions, and staff emotional data to select the most suitable medical institution.

[0671] 4. Selection and Notification

[0672] Based on the analysis results of the generating AI, the server determines, for example, that trauma center A is the optimal destination for transport.

[0673] The server automatically notifies trauma center A of the patient's condition and estimated time of arrival.

[0674] The ambulance terminal transports the patient to the selected trauma center A.

[0675] In this way, not only is the ambulance's destination selected quickly and accurately at each step, but the emotion engine can also analyze the emotions of emergency medical technicians and medical facility staff, further improving the selection of the most appropriate medical facility and the quality of patient care.

[0676] The processing flow will be explained below.

[0677] Step 1:

[0678] The terminal (ambulance terminal) arrives at the scene and inputs the patient's vital data (blood pressure, pulse rate, respiratory rate, etc.) and condition information (presence or absence of external injuries, description of symptoms, etc.). It also inputs insurance card information to identify the patient.

[0679] The user (paramedic) scans the patient's insurance card and enters the patient's identifying information into the system.

[0680] The emotion engine collects emotional data such as the paramedic's voice tone and facial expression data and analyzes it in real time.

[0681] Step 2:

[0682] Based on the entered insurance card information, the server queries an internal medical database to obtain the patient's past medical history, allergy information, and prescription drug information.

[0683] Step 3:

[0684] The terminal (the medical institution's terminal) sends information to the server in real time regarding the current admission situation, number of hospital beds, the allocation of specialists, available medical departments, and medical equipment.

[0685] The emotion engine collects and analyzes emotional data such as stress levels and fatigue levels of medical institution staff.

[0686] Step 4:

[0687] The server inputs data from the ambulance terminal, the medical institution's terminal, and emotion data from the emotion engine into the generative AI.

[0688] Step 5:

[0689] Generative AI comprehensively analyzes the patient's condition, past medical history, the medical institution's acceptance status, available medical capabilities, and the user's emotional data.

[0690] Based on the analysis results, the generative AI ranks the most suitable medical institutions and determines the optimal destination for transport.

[0691] Step 6:

[0692] The server automatically sends the patient's condition information and estimated arrival time to the most appropriate medical institution determined by the generative AI.

[0693] Step 7:

[0694] The terminal (ambulance terminal) transports the patient to the designated medical institution based on the transport instructions sent from the server.

[0695] Step 8:

[0696] The terminal (terminal at the medical institution) checks the information received from the server and begins preparations for admission (preparing the emergency operating room, arranging specialists, etc.).

[0697] By executing each step in this way, the ambulance's destination can be selected quickly and accurately, and the emotion engine can also analyze the emotions of emergency medical technicians and medical institution staff, further improving the quality of overall medical response.

[0698] Example 2

[0699] 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."

[0700] Conventional emergency transport systems select the optimal destination based on information such as the patient's location, urgency, past medical history, and the medical institution's acceptance status. However, this does not take into account the emotional state of the emergency medical technicians and medical institution staff, which means that the quality of emergency response is not sufficiently improved.

[0701] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting data on the patient's location, urgency, past medical history, medical institution acceptance status, and available medical departments and facilities; means including an emotion engine for collecting and analyzing emotion data; means for inputting the collected data and emotion data into a generative artificial intelligence for analysis and selecting the optimal destination; and means for automatically transmitting information on the patient's condition and estimated time of arrival to the destination selected from the analysis results. This enables quick and accurate selection of the destination taking into account the emotional states of paramedics and medical institution staff.

[0702] "Patient location" is information indicating the location where the ambulance or paramedic found the patient.

[0703] "Urgency" is an index that evaluates the urgency of the health condition that the patient is currently facing.

[0704] "Past medical history" refers to information that includes records of medical services and treatments that a patient has received.

[0705] "Acceptance status of medical institutions" is information indicating the capacity of a particular medical institution to accept patients and its current congestion status.

[0706] "Available medical departments and facilities" refers to information about the types of medical treatments that a medical institution can provide and the medical equipment and facilities that can be used.

[0707] "Emotional data" refers to data that indicates the current emotional state of emergency medical technicians and medical institution staff, including stress levels and fatigue levels.

[0708] The "emotion engine" is a technology that analyzes collected voice and facial expression data and generates emotional data.

[0709] "Generative artificial intelligence" refers to machine learning models and algorithms that analyze collected data and generate appropriate results.

[0710] "Destination" refers to the most appropriate medical facility to which the patient should be transported for treatment.

[0711] "Condition information" is data related to the patient's current health condition and vital signs.

[0712] The "estimated arrival time" is information indicating the time when the ambulance is scheduled to arrive at the medical institution to which the patient is being transported.

[0713] An "ambulance terminal" is an electronic device installed in an ambulance for inputting and transmitting patient data on-site.

[0714] "Medical institution terminal" refers to an electronic device used within a medical institution to update admission status and medical treatment information in real time.

[0715] The system of the present invention optimizes the ambulance destination selection process and improves the quality of emergency response. The hardware used includes mobile devices installed in ambulances, cloud-based servers, and computers at medical institutions. The software includes an emotion engine and a generative artificial intelligence (AI) model. A specific embodiment of this system is described below.

[0716] Data collection

[0717] After arriving at the scene of an accident, the ambulance terminal inputs the patient's vital data (blood pressure, pulse rate, respiratory rate) and condition information (presence or absence of external injuries, description of symptoms), and also scans the patient's insurance card to transmit the patient's identifying information to the system.

[0718] The user (paramedic) scans the patient's insurance card and inputs specific information. At the same time, the emotion engine analyzes the paramedic's voice tone and facial expression data to collect emotional data.

[0719] Data acquisition and analysis by the server

[0720] Based on the entered health insurance card information, the server retrieves the patient's past medical history, allergy information, and prescription drug information from an internal medical database. It also receives real-time information from the medical institution's terminal about the current admission situation, number of hospital beds, allocation of specialists, available medical departments, and medical equipment.

[0721] The emotion engine collects and analyzes emotional data (e.g., stress levels, fatigue levels) of medical institution staff.

[0722] Real-time analytics

[0723] The server aggregates all data sent from the ambulance terminals, medical institution terminals, and emotion engines, and inputs it into a generative AI (e.g., GPT-4).

[0724] Generative AI comprehensively analyzes the patient's condition, past medical history, medical institution acceptance status, available medical capabilities, and the user's emotional data, and then lists and ranks the most suitable medical institutions to which the patient should be transported.

[0725] Selection and Notification

[0726] The server automatically sends the patient's condition information and estimated arrival time to the most appropriate medical institution selected by the generative AI.

[0727] The ambulance terminal transports the patient to the designated medical institution based on the transport instructions sent from the server.

[0728] Specific examples

[0729] For example, consider a scenario in which an ambulance transports a patient to the scene of a traffic accident.

[0730] When the ambulance arrives at the scene of an accident, it inputs the patient's blood pressure, pulse, respiratory rate, and injury status, and also scans the patient's insurance card to enter the patient's information into the system.

[0731] The user (paramedic) scans their insurance card and inputs the patient's specific information. At the same time, the emotion engine collects and analyzes the paramedic's emotional data (voice tone and facial expression data).

[0732] The server obtains the patient's past medical history and allergy information based on the health insurance card information.

[0733] The medical institution's terminal sends information about the current congestion situation and available medical equipment to the server in real time.

[0734] The emotion engine collects and analyzes emotional data (e.g., stress levels and fatigue levels) of medical staff.

[0735] The server inputs the various collected data into the generative AI.

[0736] The generative AI comprehensively analyzes the patient's urgency, the severity of the injury, past medical history, whether or not they have allergies, the acceptance status of surrounding medical institutions, and staff emotional data to select the most suitable medical institution.

[0737] The server determines that "the best destination is Trauma Center A, and the reason is that it has surgeons and all the necessary medical equipment," and automatically notifies Trauma Center A of the details.

[0738] The ambulance terminal follows the notified instructions and transports the patient to trauma center A.

[0739] Prompt Sentence Examples

[0740] "Comprehensively analyze the patient's past medical history, current vital signs, the acceptance status of the nearest medical institution, and the emotional data of the paramedics and medical institution staff to determine the optimal destination."

[0741] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0742] Step 1: Data collection

[0743] After arriving at the scene, the ambulance terminal inputs the patient's vital signs (blood pressure 130 / 80, pulse rate 90, respiratory rate 20) and condition information (presence or absence of trauma and description of symptoms). The input data is sent to the server. In addition, the insurance card is scanned and the patient's specific information is entered into the system.

[0744] Input: Patient's vital data, condition information, insurance card information

[0745] Output: Sending the entered data to the server

[0746] The user (paramedic) scans their insurance card and inputs the patient's specific information. At the same time, the emotion engine analyzes the paramedic's voice tone and facial expression data to collect emotional data.

[0747] Input: Insurance card information, voice tone, facial expression data

[0748] Output: Sending collected specific information and emotion data to the server

[0749] Step 2: Data acquisition and analysis by the server

[0750] The server accesses an internal medical database based on the entered health insurance card information to obtain the patient's past medical history, allergy information, and prescription drug information. It also receives real-time information from the medical institution's terminal on the current admission status, number of hospital beds, allocation of specialists, available medical departments, and medical equipment.

[0751] Input: Insurance card information

[0752] Output: Obtaining past medical history, allergy information, and prescription drug information

[0753] The emotion engine collects voice and facial expression data from medical institution staff and analyzes emotional data (stress levels, fatigue levels).

[0754] Input: Voice data, facial expression data

[0755] Output: Emotion data generation and transmission to the server

[0756] Step 3: Real-time analysis

[0757] The server aggregates all data sent from the ambulance terminal, medical institution terminals, and emotion engine, and inputs it into a generative AI (e.g., GPT-4). The generative AI comprehensively analyzes the patient's condition, past medical history, medical institution acceptance status, available medical capabilities, and the user's emotional data, and lists and ranks the most suitable medical institutions to which the patient should be transported.

[0758] Input: Patient condition information, past medical history, medical institution acceptance status, available medical capabilities, emotional data

[0759] Output: List of optimal destinations and ranking

[0760] Step 4: Selection and Notification

[0761] The server automatically sends the patient's condition information and estimated arrival time to the most appropriate medical institution selected by the generative AI, including detailed information on the patient's health, necessary medical equipment, and the doctor in charge.

[0762] Input: List of optimal destinations and patient status information

[0763] Output: Notification to selected medical institutions

[0764] The ambulance terminal receives the transport instructions sent from the server and transports the patient to the designated medical institution.

[0765] Input: Transport instructions from the server

[0766] Output: Patient transport

[0767] As described above, by analyzing the data collected at each step in real time and selecting the optimal destination, it is possible to significantly improve the efficiency and accuracy of emergency transport.

[0768] (Application example 2)

[0769] 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."

[0770] In the conventional process for selecting ambulance destinations, data collection was often done manually, making it difficult to select a destination quickly and accurately. Furthermore, because information on medical institution acceptance status, available medical departments, and medical equipment could not be obtained in real time, it was not possible to quickly select an appropriate destination. Furthermore, in emergency response for security services, there was no established method for analyzing various data, including staff emotional data, in real time to select the optimal response, which increased the risk of inappropriate responses.

[0771] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting data on the patient's location, urgency, past medical history, medical institution acceptance status, and available medical departments and facilities; means for inputting the collected data into a generative artificial intelligence for analysis and selecting the optimal transport destination; means for automatically transmitting information on the patient's condition and estimated arrival time to the selected transport destination; means for collecting information on the on-site situation, past security history, security staff deployment status, and the status of available equipment; and means for collecting and analyzing emotional data of the security staff. This enables quick and accurate selection of the transport destination and optimal emergency response.

[0772] "Location" refers to the place or position where a particular person or thing exists.

[0773] "Urgency" is a measure of the level of danger of an event and the need for immediate action.

[0774] "Past medical history" refers to records of medical services and treatments a patient has received.

[0775] A "medical institution" is a facility or hospital that provides medical services.

[0776] "Acceptance status" refers to a medical institution's ability and readiness to accept patients.

[0777] "Available medical specialties" refers to the specialties and subjects of treatment that a medical institution can provide.

[0778] "Equipment" refers to medical equipment and facilities owned by a medical institution.

[0779] "Data collection means" refers to the methods and devices used to obtain the required data.

[0780] "Generative AI" is AI that analyzes input data and generates optimal results for a specific purpose.

[0781] "Analysis" is the process of examining input data in detail to understand its meaning and structure.

[0782] "Destination" refers to the destination or medical facility to which the patient is being transported by ambulance or other means of transportation.

[0783] "Condition information" refers to information about a patient's health condition and symptoms.

[0784] "Estimated time of arrival" refers to the time the patient is expected to arrive at the designated location.

[0785] "Situation on the ground" refers to the current state or environment at a particular location or event.

[0786] "Security history" refers to a record of past security-related events and responses.

[0787] "Security staff" refers to professional employees in charge of security operations.

[0788] "Staffing" refers to how staff are deployed in a particular location or shift.

[0789] "Equipment" refers to tools and devices used for a particular task or operation.

[0790] "Emotional data" is information that indicates an individual's emotional state, and includes data such as voice tone, facial expression, and heart rate.

[0791] "Real-time" refers to processing and responding to events in the real world almost as they occur.

[0792] The following is a detailed description of an embodiment of the present invention. First, an overview of the entire system will be given, followed by a detailed description of the specific roles and operations of each element.

[0793] System Overview

[0794] The present invention is a system for optimizing the ambulance routing process and the security services emergency response process. The system consists of three main components:

[0795] 1. Data collection terminal: A terminal in the ambulance that inputs information on the patient's location and condition, as well as a means of collecting information on the situation at the scene, past security history, security staff deployment status, and the status of available equipment.

[0796] 2. Server: A means of inputting collected data into generative artificial intelligence for analysis and selecting the optimal destination and emergency response.

[0797] 3. Notification Device: A means of automatically transmitting patient status information and estimated time of arrival to selected destinations.

[0798] Hardware and software used

[0799] Hardware

[0800] Ambulance devices: tablets, smartphones, etc.

[0801] Sensor devices: environmental sensors, security cameras, heart rate monitors, etc.

[0802] Server: A cloud server for data collection, analysis, and storage.

[0803] Notification devices: ambulance and security staff's smart devices (e.g. smartphones, tablets, or smart glasses).

[0804] software

[0805] Data collection software: Used to collect data from sensors and cameras.

[0806] Emotion analysis software: Voice analysis and facial expression analysis engine.

[0807] Generative artificial intelligence (AI) models: Used to analyze input data and optimize.

[0808] Notification system: A system for providing immediate notifications to staff.

[0809] Data processing and calculation

[0810] 1. Processing of data collection terminals

[0811] The ambulance terminal inputs the patient's condition information and insurance card information, and enters the patient's specific information into the system.

[0812] The sensor device collects and analyzes the situation on-site and emotional data of security staff (voice tone and facial expression data).

[0813] 2. Server analysis process

[0814] The server queries an internal database based on the entered data to obtain the patient's medical history and allergy information.

[0815] The server inputs all collected data into generative artificial intelligence, which analyzes the patient's condition, past medical history, medical institution acceptance status, available medical capabilities, security history, and emotional data to create a list of the most appropriate medical institutions and countermeasures.

[0816] 3. Notification System Processing

[0817] The server sends the most appropriate medical institution and emergency response measures selected by the generative AI to the notification device.

[0818] The ambulance terminal and the security staff's smart devices will receive notifications and initiate immediate response.

[0819] Specific examples

[0820] For example, in a scenario where a suspicious individual is detected in security operations at a large event venue, the process would proceed as follows:

[0821] 1. Data Collection

[0822] Security cameras collect footage of suspicious people in real time and send the data to a server for analysis.

[0823] The situation on the scene is entered into a smart device, and the emotion engine analyzes the voice tone and facial expression data of the security staff.

[0824] 2. Analysis processing

[0825] -The server inputs the collected video data, on-site conditions, and staff emotional data into generative AI to calculate the optimal response.

[0826] 3. Notification and Response

[0827] Based on the analysis results, the server notifies security staff of the most appropriate response (for example, sealing off a specific area or notifying the police).

[0828] Security staff will be notified and will begin responding immediately.

[0829] Prompt Sentence Examples

[0830] "A suspicious individual has entered the venue. Please propose the best course of action based on the video data from Camera 5, the on-site input data, and the emotional data of Staff A."

[0831] In this way, the system of the present invention automates the data collection, analysis and notification process in emergency situations, enabling a rapid and accurate response.

[0832] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0833] Step 1:

[0834] The server collects data on the patient's location, urgency, past medical history, medical institution acceptance status, and available medical departments and facilities. The ambulance terminal inputs the patient's location and condition information, and security cameras and sensor devices collect data on the situation at the scene and the emotions of security staff. The input data is sent to the server.

[0835] Step 2:

[0836] The server queries its internal database based on the input data to retrieve the patient's medical history, allergy information, and prescription drug information. Specifically, it performs a database search and retrieves the relevant patient information. The patient's medical history and medical information are returned to the server as output.

[0837] Step 3:

[0838] The server collects real-time information from medical institutions on their admission status, number of beds, the allocation of specialists, available medical departments, and medical equipment. It also collects information on the situation on-site and past security history from security agencies. This data is sent from medical institutions' terminals and security systems and stored on the server.

[0839] Step 4:

[0840] The server inputs all collected data into a generative AI model. Specifically, the AI ​​model is passed information on the patient's condition, past medical history, medical institution acceptance status, security history, emotional data, etc. The input data is analyzed by the AI ​​model, and the optimal transport destination and emergency response measures are output.

[0841] Step 5:

[0842] Generative AI analyzes input data and comprehensively evaluates the patient's urgency, the severity of the injury, past medical history, whether they have any allergies, the acceptance status of nearby medical institutions, and the emotional state of the staff. Based on this evaluation, it selects the most suitable medical institution and response measures and lists them in a ranked format. This list is sent to a server.

[0843] Step 6:

[0844] The server considers the optimal transport destination and emergency response measures selected by the generative AI model and selects the most appropriate one from among them. The output is the optimal medical institution to transport the patient to and the response measures.

[0845] Step 7:

[0846] The server sends the selected medical institution and emergency response measures to the notification device. Specifically, the selection results are sent to the ambulance terminal and the security staff's smart device for notification. The input includes the selection result, and the output includes notification completion.

[0847] Step 8:

[0848] Users (e.g., paramedics or security personnel) receive notifications and initiate actions based on the designated medical facility or response plan, such as dispatching an ambulance or implementing an emergency response, so that the patient is transported to the appropriate medical facility or the emergency situation is appropriately addressed.

[0849] 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.

[0850] 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.

[0851] 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.

[0852] [Third embodiment]

[0853] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0854] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0855] 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).

[0856] 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.

[0857] 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.

[0858] 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).

[0859] 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.

[0860] 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.

[0861] 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.

[0862] 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.

[0863] 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.

[0864] 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."

[0865] This invention is a system for optimizing the process of selecting a destination for ambulances. This system collects data such as the patient's location, urgency, past medical history, medical institution acceptance status, available medical departments and facilities, and analyzes it in real time using generative artificial intelligence (AI), selecting the most suitable medical institution and contacting the patient.

[0866] Program processing

[0867] 1. Data Collection

[0868] When the ambulance arrives at the scene, it inputs information about the patient's condition (vital data, symptoms, injury details, etc.) and also inputs insurance card information to identify the patient.

[0869] The server retrieves past medical history, allergy information, and prescription drug information from a medical database based on insurance card information.

[0870] The medical institution's terminal sends information on the current congestion situation, number of beds, available medical departments and medical equipment to the server in real time.

[0871] 2. Real-time analysis

[0872] The server inputs the collected data into generative artificial intelligence, which then analyzes the data based on the patient's condition, medical history, and the medical institution's acceptance status.

[0873] The generative AI considers the following factors to determine the best destination for a patient:

[0874] Patient's condition and urgency

[0875] Past medical history and allergy information

[0876] The congestion level of nearby medical institutions and the capacity of specialized medical departments

[0877] Medical equipment available at each medical institution

[0878] 3. Selection and Notification

[0879] The server then determines the optimal destination based on the results of the generative AI analysis, taking into account factors such as distance, the current capacity of medical institutions, the availability of specialists, and the availability of necessary medical equipment.

[0880] The server automatically sends the patient's condition information and estimated time of arrival to the selected medical institution.

[0881] Specific examples

[0882] For example, consider a scenario in which an ambulance transports a patient to the scene of a traffic accident.

[0883] 1. Data Collection

[0884] The ambulance terminal arrives at the scene of an accident and inputs the patient's blood pressure, pulse, respiratory rate, and injury status, and also scans the patient's insurance card to enter the patient's information into the system.

[0885] The server obtains the patient's past medical history and allergy information from the health insurance card information.

[0886] Medical institutions' terminals send information about congestion and available medical equipment to the server in real time.

[0887] 2. Real-time analysis

[0888] The server inputs all collected data into the generation AI.

[0889] The generative AI analyzes the patient's urgency, the severity of the injury, past medical history, and whether or not they have any allergies, and then lists the most suitable medical institutions to recommend.

[0890] 3. Selection and Notification

[0891] Based on the analysis results of the generating AI, the server determines, for example, that trauma center A is the optimal destination for transport.

[0892] The server automatically notifies trauma center A of the patient's condition and estimated time of arrival.

[0893] The ambulance terminal initiates transport of the patient to the selected trauma center A.

[0894] In this way, the system of the present invention quickly selects the most appropriate medical institution when transporting a patient by ambulance, enabling the patient to receive appropriate medical care promptly.

[0895] The processing flow will be explained below.

[0896] Step 1:

[0897] The terminal (ambulance terminal) arrives at the scene and inputs the patient's vital data (blood pressure, pulse rate, respiratory rate, etc.) and condition information (presence or absence of trauma, description of symptoms, etc.).

[0898] The user (paramedic) scans the patient's insurance card and enters the patient's identifying information into the system.

[0899] Step 2:

[0900] Based on the entered insurance card information, the server queries an internal medical database to obtain the patient's past medical history, allergy information, and prescription drug information.

[0901] Step 3:

[0902] The terminal (the medical institution's terminal) sends information to the server in real time regarding the current admission situation, number of hospital beds, the allocation of specialists, available medical departments, and medical equipment.

[0903] Step 4:

[0904] The server inputs all data sent from the ambulance terminal and the medical institution's terminal into the generative AI.

[0905] Step 5:

[0906] Generative AI analyzes the patient's condition, past medical history, medical institution acceptance status, and available medical capabilities, and creates a list of the most appropriate medical institutions to which the patient should be transported.

[0907] Based on the analysis results, the generative AI ranks the most suitable medical institutions and selects the optimal destination for transport.

[0908] Step 6:

[0909] The server automatically sends the patient's condition information and estimated arrival time to the most appropriate medical institution selected by the generative AI.

[0910] Step 7:

[0911] The terminal (ambulance terminal) transports the patient to the designated medical institution based on the transport instructions sent from the server.

[0912] Step 8:

[0913] The terminal (terminal at the medical institution) checks the information received from the server and begins preparations for admission (preparing the emergency operating room, arranging specialists, etc.).

[0914] In this way, the ambulance destination is selected quickly and accurately through each step, allowing the patient to receive treatment promptly at the appropriate medical institution.

[0915] Example 1

[0916] 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."

[0917] In emergencies such as sudden illness or accidents, it is extremely important to quickly transport patients to the appropriate medical institution. However, it is difficult for on-site medical staff to select the optimal destination with limited information and time. Furthermore, if the medical institution's acceptance status and facility capacity cannot be grasped in real time, the selection of the destination may be delayed, putting the patient's life at risk. A system that can solve these issues is needed.

[0918] 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.

[0919] In this invention, the server includes means for collecting data on the patient's location, urgency, past medical history, medical institution acceptance status, and available medical departments and facilities, means for inputting the collected data into a generative artificial intelligence model and selecting the optimal transport destination taking into consideration the patient's condition and urgency, past medical history, and the response capabilities of nearby medical institutions, means for automatically transmitting the patient's condition information and estimated arrival time to the selected transport destination, means for acquiring data including the patient information from a medical database and using it for analysis, and means for updating the acceptance status of each medical institution in real time and reflecting it in the processing results. This makes it possible to quickly and accurately select the optimal transport destination and ensure that patients receive appropriate medical care promptly.

[0920] "Location" refers to information that indicates the patient's current location or position.

[0921] "Urgency" refers to information that serves as a criterion for assessing the severity of a patient's symptoms and condition and determining the priority of transportation.

[0922] "Medical history" refers to records of medical examinations and treatments that a patient has received at medical institutions in the past.

[0923] "Medical institution acceptance status" refers to information that indicates how many patients a medical institution currently has the capacity to accept.

[0924] "Available medical departments" refers to information indicating the specialized medical services and medical departments that a medical institution can provide.

[0925] "Facilities" refers to information indicating medical equipment and treatment devices owned by a medical institution.

[0926] A "generative artificial intelligence model" is an artificial intelligence that analyzes collected data and selects the optimal destination.

[0927] "Condition information" refers to detailed medical information that indicates a patient's current health condition and symptoms.

[0928] "Estimated arrival time" is information indicating the estimated time when the ambulance is to arrive at the selected medical institution.

[0929] A "medical database" is a database that stores medical data such as a patient's past medical history, allergy information, and prescription drug information.

[0930] This invention is a system that optimizes the process of selecting an ambulance destination. This system collects data on the patient's location, urgency, past medical history, medical institution acceptance status, available medical departments, and facilities, and analyzes it in real time using a generative artificial intelligence model. It then selects the most suitable medical institution and automatically transmits the patient's condition information and estimated arrival time to the selected medical institution.

[0931] Hardware and Software Configuration

[0932] The system consists of the following main components:

[0933] 1. Ambulance terminal

[0934] A tablet or smartphone for paramedics to enter information about the patient's condition.

[0935] A scanner for scanning insurance card information.

[0936] 2. Server

[0937] A high-performance server for running generative artificial intelligence (AI) models.

[0938] An interface for real-time communication with medical databases.

[0939] 3. Medical institution terminals

[0940] A computer terminal for inputting and sending information on current admission status, number of hospital beds, available medical departments, and medical equipment.

[0941] Specific operation of the system

[0942] 1. Data Collection

[0943] When the ambulance arrives at the scene, the ambulance staff enters the patient's condition information (e.g., blood pressure, pulse rate, respiratory rate, and the state of injuries) into the terminal. In addition, the ambulance scans the patient's insurance card to obtain basic information about the patient.

[0944] Based on the insurance card information received, the server retrieves the patient's past medical history, allergy information, prescription drug information, etc. from a medical database.

[0945] The medical institution's terminal sends information on each institution's current admission status, number of beds, available medical departments, and medical equipment to the server in real time.

[0946] 2. Real-time analysis and selection

[0947] The server inputs all collected data into a generative AI model, which analyzes the patient's condition, past medical history, allergy information, and medical institution acceptance status to select the medical institution that best suits the patient.

[0948] 3. Selection and Notification

[0949] The server automatically sends the patient's condition information and estimated time of arrival to the most appropriate medical institution selected by the generative AI model.

[0950] The ambulance's terminal provides emergency personnel with information about the destination medical institution and the optimal route.

[0951] Specific examples

[0952] For example, consider the scenario of an ambulance transporting a patient to the scene of a traffic accident:

[0953] The ambulance terminal arrives at the scene of an accident and inputs the patient's blood pressure, pulse, respiratory rate, and injury status. It also scans the patient's insurance card and inputs the patient's information into the system.

[0954] The server obtains the patient's past medical history and allergy information from the health insurance card information.

[0955] Medical institution terminals provide real-time information to the server about current congestion and available medical equipment.

[0956] Next, prompt the generative AI model with the following sentence:

[0957] "The ambulance has arrived at the scene. Please enter the patient's information. We will provide the following information: blood pressure, pulse rate, respiratory rate, injury status, past medical history, allergy information, and prescription drug information. We will also provide information on the availability and medical facilities of nearby medical institutions. Please select the most appropriate destination."

[0958] Based on this prompt, the generative AI model performs an analysis and selects the optimal transport destination. The system then automatically notifies the destination of the patient's condition and estimated arrival time. By linking systems in this way, the patient can be transported quickly and accurately to the optimal medical institution.

[0959] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0960] Step 1: Data collection

[0961] Ambulance terminal: After arriving at the patient's location, the ambulance crew uses the terminal to input the patient's condition information, including blood pressure, pulse rate, respiratory rate, and the state of injuries. The terminal also scans the insurance card, digitizing the patient's basic information and sending it to the server.

[0962] Input: Patient vital data and insurance card information.

[0963] Output: Digitized patient information.

[0964] Server: After receiving the insurance card information, it accesses the medical database and retrieves the patient's past medical history, allergy information, and prescription drug information.

[0965] Input: Digitized insurance card information.

[0966] Output: Past medical history, allergy information, prescription drug information.

[0967] Medical institution terminals: Each medical institution inputs information on its current admission status, number of beds, available medical departments, and medical equipment, and sends this information to the server in real time.

[0968] Input: Details of admission status, number of beds, available medical specialties, and medical facilities.

[0969] Output: Real-time updated medical institution admission information.

[0970] Step 2: Real-time analysis

[0971] Server: Inputs all collected data into the generative AI model, including patient condition information, past medical history, allergy information, and medical institution acceptance status.

[0972] Input: Patient condition information, past medical history, allergy information, and medical institution acceptance status.

[0973] Output: A consolidated dataset that is fed into the generative AI.

[0974] Generative AI: Analyzes all provided data and selects the most appropriate medical institution for the patient, taking into account the patient's urgency, symptoms, past medical history, and the capacity of nearby medical institutions.

[0975] Data processing and calculation: Urgency assessment, algorithmic optimization calculation, ranking of medical institutions that can accept patients.

[0976] Input: Unified dataset.

[0977] Output: Recommended destination list.

[0978] Step 3: Selection and Notification

[0979] Server: Selects the most suitable medical institution based on the list of recommended destinations received from the generative AI model, taking into account the distance, response capacity, and availability of facilities of each medical institution.

[0980] Input: Recommended Destination List.

[0981] Output: Best destination information.

[0982] Server: Automatically sends the patient's condition information and estimated time of arrival to the selected medical institution.

[0983] Input: Best destination information, patient condition information, estimated time of arrival.

[0984] Output:Notification to healthcare provider.

[0985] Ambulance terminal: Information on selected medical institutions and the optimal route are set and displayed to paramedics.

[0986] Input: Best destination information.

[0987] Output: Navigation information and route instructions.

[0988] In this way, the system of the present invention can optimize the patient transport process and transport patients to the appropriate medical institution quickly and accurately.

[0989] (Application example 1)

[0990] 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."

[0991] In today's medical and logistics fields, efficient resource allocation and rapid response are required, but conventional systems have difficulty providing optimal options in real time. In particular, the process of selecting the destination for emergency medical care and optimizing product placement and picking routes in logistics warehouses rely on human judgment, which is time-consuming and labor-intensive, and prone to judgment errors and delays. New and efficient systems are needed to solve these problems.

[0992] 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.

[0993] In this invention, the server includes means for collecting data on the patient's location, urgency, past medical history, medical institution acceptance status, and available medical departments and facilities, means for inputting the collected data into a generative artificial intelligence (AI) for analysis and selecting an optimal destination, means for automatically transmitting the patient's condition information and estimated arrival time to the selected destination, means for collecting the type, quantity, location information, and in / out data of products in the warehouse, means for inputting the collected product data into a generative AI for analysis and proposing optimal product placement locations and picking routes, and means for notifying warehouse workers of the proposed product placement locations and picking routes. This enables quick and accurate selection of a destination in emergency medical care and optimal product placement and efficient picking in logistics warehouses.

[0994] "Location" refers to the location information of a patient or product, and includes the patient's location in emergency medical care and the location where the product is stored in logistics.

[0995] "Urgency" is an index showing the urgency of a patient's condition, and indicates the degree to which prompt medical response is required.

[0996] "Medical history" is a general term for the medical records of a patient's past medical visits, including medical history and past treatment details.

[0997] "Acceptance status of medical institution" is information indicating whether the medical institution is in a state where it can accept new patients based on the current number of patients and the status of its facilities.

[0998] "Medical departments available" refers to the specialized medical care that a medical institution can provide, including medical departments that deal with specific diseases or conditions.

[0999] "Facilities" refers to physical facilities and equipment, including medical instruments and logistics equipment, owned by medical institutions and logistics warehouses.

[1000] "Means for collection" refers to devices or systems for collecting patient or product-related data, including sensors and input devices.

[1001] "Generative artificial intelligence" refers to an AI model that analyzes collected data and derives optimal decisions.

[1002] "Means for analyzing and selecting the optimal destination" refers to the process by which generative artificial intelligence analyzes data and, based on the results, determines the optimal medical institution or optimal location within a warehouse.

[1003] "Means for automatic transmission" refers to a system or method for automatically transmitting analysis results and necessary information to a designated recipient.

[1004] "Product data" refers to data including product type, quantity, location, and related inventory information.

[1005] "Location" refers to the location where the goods are stored within the warehouse, taking into consideration the most appropriate storage method.

[1006] A "picking route" refers to the optimal route for collecting products within a warehouse, including an efficient route for retrieving products.

[1007] "Means of notification" refers to the methods and devices used to communicate analysis results and work instructions to workers or personnel in charge.

[1008] As an embodiment of the present invention, we provide a system that includes both a process for selecting a destination for emergency medical care and a process for optimizing the placement of goods in a logistics warehouse. The specific configuration and operation procedure are described below.

[1009] 1. Emergency medical system configuration and operation procedures

[1010] Hardware and software:

[1011] Ambulance terminal: A mobile device for entering patient location, condition, and insurance card information.

[1012] Server: A central computer that aggregates collected data and analyzes it using generative artificial intelligence (e.g., OpenAI's GPT-4).

[1013] Medical institution terminal: A device that transmits real-time information on current congestion status, number of hospital beds, available medical departments, and medical equipment to a server.

[1014] Examples:

[1015] When an ambulance arrives at the scene of a traffic accident, the patient's blood pressure, pulse rate, respiratory rate, and the state of any injuries are entered into a terminal in the ambulance. The patient's insurance card is also scanned to obtain information about the patient. The server inputs the collected data into generative artificial intelligence. The AI ​​performs analysis, selects the optimal transport destination, and automatically notifies the selected medical institution of this information.

[1016] 2. Logistics Warehouse System Configuration and Operation Procedures

[1017] Hardware and software:

[1018] Warehouse worker's smartphone: A device used to scan product barcodes and enter information such as product type, quantity, and time of receipt.

[1019] Logistics system server: A central computer that aggregates collected product data and analyzes it using generative artificial intelligence.

[1020] GPS device: A device used to track the location of goods within a warehouse.

[1021] Examples:

[1022] Warehouse workers scan product barcodes and input the product type, quantity, and location. The server then inputs the collected data into generative artificial intelligence. The AI ​​then suggests product placement locations and optimal picking routes, and sends this information to the workers' smartphones in real time.

[1023] Example prompt sentence:

[1024] "Please suggest the optimal product placement and picking route based on the following warehouse data: {"items": [{"id": "A001", "quantity": 10, "location": "Shelf 1"}, {"id": "B002", "quantity": 15, "location": "Shelf 2"}], "orders": [{"id": "O123", "items": ["A001", "B002"], "priority": "high"}]}"

[1025] In this way, the system of the present invention enables rapid and accurate selection of destinations for emergency medical care, and enables efficient product placement and picking in logistics warehouses.

[1026] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1027] Step 1:

[1028] Data collection

[1029] Input: The ambulance terminal inputs the patient's location, condition information (e.g., blood pressure, pulse, respiratory rate, and injury status), and insurance card information. The warehouse worker's smartphone scans the product barcode and inputs the product type, quantity, and time of arrival.

[1030] How it works: The ambulance crew manually enters the patient's condition data into a dedicated terminal and scans the insurance card, while the warehouse worker scans the product's barcode with a smartphone app and enters the product information.

[1031] Output: The collected data is sent to a server.

[1032] Step 2:

[1033] Data Aggregation

[1034] Input: Patient and product data collected in step 1.

[1035] Specific operation: The server automatically receives data sent from the ambulance terminal and the warehouse worker's smartphone and stores it in a database.

[1036] Output: The stored data is prepared to be input into a generative AI model.

[1037] Step 3:

[1038] Real-time analytics

[1039] Input: Curated patient and product data.

[1040] Specific operation: The server inputs the stored data into the generation AI, which analyzes the data and calculates the optimal delivery destination, product placement location, and picking route.

[1041] Output: The analysis results provide the optimal delivery destination, product placement location, and picking route.

[1042] Step 4:

[1043] Selection and Notification

[1044] Input: The optimal delivery destination obtained as a result of the analysis, as well as the product placement location and picking route.

[1045] Specific operation: Based on the analysis results of the generation AI, the server automatically selects the optimal destination medical institution, product placement location, and picking route. The selection results are then sent to the medical institution's terminal and the warehouse worker's smartphone.

[1046] Output: The medical institution's terminal is notified of the patient's condition and estimated arrival time, and the warehouse worker's smartphone is notified of the product location and picking route.

[1047] Step 5:

[1048] execution

[1049] Input: Information notified by the server.

[1050] Specific operations: The medical institution prepares to accept the patient at the selected destination, and the ambulance heads to the designated medical institution. The warehouse worker moves the product to the notified location and works according to the designated picking route.

[1051] Output: Ambulances safely transport patients to the most appropriate medical facility, and warehouse workers efficiently place and pick products.

[1052] 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.

[1053] This invention is a system for optimizing the process of selecting ambulance destinations, and also combines it with an emotion engine that recognizes user emotions. This system collects data such as the patient's location, urgency, past medical history, medical institution acceptance status, available medical departments, and facilities, and analyzes it in real time using generative artificial intelligence (AI) to select and contact the most appropriate medical institution. In addition, collecting and analyzing user emotion data can improve the quality of medical care.

[1054] Program processing

[1055] 1. Data Collection

[1056] When the ambulance arrives at the scene, it inputs the patient's vital signs (blood pressure, pulse rate, respiratory rate, etc.) and condition information (presence or absence of external injuries, description of symptoms, etc.) into the terminal in the ambulance. It also inputs insurance card information to identify the patient.

[1057] The user (paramedic) scans the patient's insurance card and enters the patient's identifying information into the system.

[1058] The emotion engine collects and analyzes paramedic emotional data (voice tone, facial recognition data, etc.).

[1059] 2. Data acquisition and analysis by the server

[1060] Based on the entered insurance card information, the server queries an internal medical database to obtain the patient's past medical history, allergy information, and prescription drug information.

[1061] The medical institution's terminal sends information to the server in real time about the current admission situation, number of beds, specialist allocation, available medical departments, and medical equipment.

[1062] The emotion engine collects and analyzes emotional data (stress levels, fatigue levels, etc.) of medical institution staff.

[1063] 3. Real-time analysis

[1064] The server inputs all data sent from the ambulance terminal, the medical institution's terminal, and the emotion engine into the generative AI.

[1065] Generative AI comprehensively analyzes the patient's condition, past medical history, medical institution acceptance status, available medical capabilities, and the user's emotional data to create a list of the most suitable medical institutions to which the patient should be transported.

[1066] Based on the analysis results, the generative AI ranks the most suitable medical institutions and selects the optimal destination for transport.

[1067] 4. Selection and Notification

[1068] The server automatically sends the patient's condition information and estimated arrival time to the most appropriate medical institution selected by the generative AI.

[1069] The ambulance terminal transports the patient to the designated medical institution based on the transport instructions sent from the server.

[1070] Specific examples

[1071] For example, consider a scenario in which an ambulance transports a patient to the scene of a traffic accident.

[1072] 1. Data Collection

[1073] When the ambulance arrives at the scene of an accident, it inputs the patient's blood pressure, pulse, respiratory rate, and injury status, and also scans the patient's insurance card to enter the patient's information into the system.

[1074] The user (paramedic) scans their insurance card and inputs the patient's specific information into the system. At the same time, the emotion engine collects and analyzes the paramedic's emotional data (voice tone and facial expression data).

[1075] 2. Data acquisition and analysis by the server

[1076] The server obtains the patient's past medical history and allergy information based on the insurance card information.

[1077] The medical institution's terminal sends information about the current congestion situation and available medical equipment to the server in real time.

[1078] The emotion engine collects and analyzes emotional data (e.g., stress levels and fatigue levels) of medical staff.

[1079] 3. Real-time analysis

[1080] The server inputs all collected data into a generative AI.

[1081] The generated AI comprehensively analyzes the patient's urgency, the severity of the injury, past medical history, whether or not they have allergies, the acceptance status of surrounding medical institutions, and staff emotional data to select the most suitable medical institution.

[1082] 4. Selection and Notification

[1083] Based on the analysis results of the generating AI, the server determines, for example, that trauma center A is the optimal destination for transport.

[1084] The server automatically notifies trauma center A of the patient's condition and estimated time of arrival.

[1085] The ambulance terminal transports the patient to the selected trauma center A.

[1086] In this way, not only is the ambulance's destination selected quickly and accurately at each step, but the emotion engine can also analyze the emotions of emergency medical technicians and medical facility staff, further improving the selection of the most appropriate medical facility and the quality of patient care.

[1087] The processing flow will be explained below.

[1088] Step 1:

[1089] The terminal (ambulance terminal) arrives at the scene and inputs the patient's vital data (blood pressure, pulse rate, respiratory rate, etc.) and condition information (presence or absence of external injuries, description of symptoms, etc.). It also inputs insurance card information to identify the patient.

[1090] The user (paramedic) scans the patient's insurance card and enters the patient's identifying information into the system.

[1091] The emotion engine collects emotional data such as the paramedic's voice tone and facial expression data and analyzes it in real time.

[1092] Step 2:

[1093] Based on the entered insurance card information, the server queries an internal medical database to obtain the patient's past medical history, allergy information, and prescription drug information.

[1094] Step 3:

[1095] The terminal (the medical institution's terminal) sends information to the server in real time regarding the current admission situation, number of hospital beds, the allocation of specialists, available medical departments, and medical equipment.

[1096] The emotion engine collects and analyzes emotional data such as stress levels and fatigue levels of medical institution staff.

[1097] Step 4:

[1098] The server inputs data from the ambulance terminal, the medical institution's terminal, and emotion data from the emotion engine into the generative AI.

[1099] Step 5:

[1100] Generative AI comprehensively analyzes the patient's condition, past medical history, the medical institution's acceptance status, available medical capabilities, and the user's emotional data.

[1101] Based on the analysis results, the generative AI ranks the most suitable medical institutions and determines the optimal destination for transport.

[1102] Step 6:

[1103] The server automatically sends the patient's condition information and estimated arrival time to the most appropriate medical institution determined by the generative AI.

[1104] Step 7:

[1105] The terminal (ambulance terminal) transports the patient to the designated medical institution based on the transport instructions sent from the server.

[1106] Step 8:

[1107] The terminal (terminal at the medical institution) checks the information received from the server and begins preparations for admission (preparing the emergency operating room, arranging specialists, etc.).

[1108] By executing each step in this way, the ambulance's destination can be selected quickly and accurately, and the emotion engine can also analyze the emotions of emergency medical technicians and medical institution staff, further improving the quality of overall medical response.

[1109] Example 2

[1110] 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."

[1111] Conventional emergency transport systems select the optimal destination based on information such as the patient's location, urgency, past medical history, and the medical institution's acceptance status. However, this does not take into account the emotional state of the emergency medical technicians and medical institution staff, which means that the quality of emergency response is not sufficiently improved.

[1112] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting data on the patient's location, urgency, past medical history, medical institution acceptance status, and available medical departments and facilities; means including an emotion engine for collecting and analyzing emotion data; means for inputting the collected data and emotion data into a generative artificial intelligence for analysis and selecting the optimal destination; and means for automatically transmitting information on the patient's condition and estimated time of arrival to the destination selected from the analysis results. This enables quick and accurate selection of the destination taking into account the emotional states of paramedics and medical institution staff.

[1113] "Patient location" is information indicating the location where the ambulance or paramedic found the patient.

[1114] "Urgency" is an index that evaluates the urgency of the health condition that the patient is currently facing.

[1115] "Past medical history" refers to information that includes records of medical services and treatments that a patient has received.

[1116] "Acceptance status of medical institutions" is information indicating the capacity of a particular medical institution to accept patients and its current congestion status.

[1117] "Available medical departments and facilities" refers to information about the types of medical treatments that a medical institution can provide and the medical equipment and facilities that can be used.

[1118] "Emotional data" refers to data that indicates the current emotional state of emergency medical technicians and medical institution staff, including stress levels and fatigue levels.

[1119] The "emotion engine" is a technology that analyzes collected voice and facial expression data and generates emotional data.

[1120] "Generative artificial intelligence" refers to machine learning models and algorithms that analyze collected data and generate appropriate results.

[1121] "Destination" refers to the most appropriate medical facility to which the patient should be transported for treatment.

[1122] "Condition information" is data related to the patient's current health condition and vital signs.

[1123] The "estimated arrival time" is information indicating the time when the ambulance is scheduled to arrive at the medical institution to which the patient is being transported.

[1124] An "ambulance terminal" is an electronic device installed in an ambulance for inputting and transmitting patient data on-site.

[1125] "Medical institution terminal" refers to an electronic device used within a medical institution to update admission status and medical treatment information in real time.

[1126] The system of the present invention optimizes the ambulance destination selection process and improves the quality of emergency response. The hardware used includes mobile devices installed in ambulances, cloud-based servers, and computers at medical institutions. The software includes an emotion engine and a generative artificial intelligence (AI) model. A specific embodiment of this system is described below.

[1127] Data collection

[1128] After arriving at the scene of an accident, the ambulance terminal inputs the patient's vital data (blood pressure, pulse rate, respiratory rate) and condition information (presence or absence of external injuries, description of symptoms), and also scans the patient's insurance card to transmit the patient's identifying information to the system.

[1129] The user (paramedic) scans the patient's insurance card and inputs specific information. At the same time, the emotion engine analyzes the paramedic's voice tone and facial expression data to collect emotional data.

[1130] Data acquisition and analysis by the server

[1131] Based on the entered health insurance card information, the server retrieves the patient's past medical history, allergy information, and prescription drug information from an internal medical database. It also receives real-time information from the medical institution's terminal about the current admission situation, number of hospital beds, allocation of specialists, available medical departments, and medical equipment.

[1132] The emotion engine collects and analyzes emotional data (e.g., stress levels, fatigue levels) of medical institution staff.

[1133] Real-time analytics

[1134] The server aggregates all data sent from the ambulance terminals, medical institution terminals, and emotion engines, and inputs it into a generative AI (e.g., GPT-4).

[1135] Generative AI comprehensively analyzes the patient's condition, past medical history, medical institution acceptance status, available medical capabilities, and the user's emotional data, and then lists and ranks the most suitable medical institutions to which the patient should be transported.

[1136] Selection and Notification

[1137] The server automatically sends the patient's condition information and estimated arrival time to the most appropriate medical institution selected by the generative AI.

[1138] The ambulance terminal transports the patient to the designated medical institution based on the transport instructions sent from the server.

[1139] Specific examples

[1140] For example, consider a scenario in which an ambulance transports a patient to the scene of a traffic accident.

[1141] When the ambulance arrives at the scene of an accident, it inputs the patient's blood pressure, pulse, respiratory rate, and injury status, and also scans the patient's insurance card to enter the patient's information into the system.

[1142] The user (paramedic) scans their insurance card and inputs the patient's specific information. At the same time, the emotion engine collects and analyzes the paramedic's emotional data (voice tone and facial expression data).

[1143] The server obtains the patient's past medical history and allergy information based on the health insurance card information.

[1144] The medical institution's terminal sends information about the current congestion situation and available medical equipment to the server in real time.

[1145] The emotion engine collects and analyzes emotional data (e.g., stress levels and fatigue levels) of medical staff.

[1146] The server inputs the various collected data into the generative AI.

[1147] The generative AI comprehensively analyzes the patient's urgency, the severity of the injury, past medical history, whether or not they have allergies, the acceptance status of surrounding medical institutions, and staff emotional data to select the most suitable medical institution.

[1148] The server determines that "the best destination is Trauma Center A, and the reason is that it has surgeons and all the necessary medical equipment," and automatically notifies Trauma Center A of the details.

[1149] The ambulance terminal follows the notified instructions and transports the patient to trauma center A.

[1150] Prompt Sentence Examples

[1151] "Comprehensively analyze the patient's past medical history, current vital signs, the acceptance status of the nearest medical institution, and the emotional data of the paramedics and medical institution staff to determine the optimal destination."

[1152] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1153] Step 1: Data collection

[1154] After arriving at the scene, the ambulance terminal inputs the patient's vital signs (blood pressure 130 / 80, pulse rate 90, respiratory rate 20) and condition information (presence or absence of trauma and description of symptoms). The input data is sent to the server. In addition, the insurance card is scanned and the patient's specific information is entered into the system.

[1155] Input: Patient's vital data, condition information, insurance card information

[1156] Output: Sending the entered data to the server

[1157] The user (paramedic) scans their insurance card and inputs the patient's specific information. At the same time, the emotion engine analyzes the paramedic's voice tone and facial expression data to collect emotional data.

[1158] Input: Insurance card information, voice tone, facial expression data

[1159] Output: Sending collected specific information and emotion data to the server

[1160] Step 2: Data acquisition and analysis by the server

[1161] The server accesses an internal medical database based on the entered health insurance card information to obtain the patient's past medical history, allergy information, and prescription drug information. It also receives real-time information from the medical institution's terminal on the current admission status, number of hospital beds, allocation of specialists, available medical departments, and medical equipment.

[1162] Input: Insurance card information

[1163] Output: Obtaining past medical history, allergy information, and prescription drug information

[1164] The emotion engine collects voice and facial expression data from medical institution staff and analyzes emotional data (stress levels, fatigue levels).

[1165] Input: Voice data, facial expression data

[1166] Output: Emotion data generation and transmission to the server

[1167] Step 3: Real-time analysis

[1168] The server aggregates all data sent from the ambulance terminal, medical institution terminals, and emotion engine, and inputs it into a generative AI (e.g., GPT-4). The generative AI comprehensively analyzes the patient's condition, past medical history, medical institution acceptance status, available medical capabilities, and the user's emotional data, and lists and ranks the most suitable medical institutions to which the patient should be transported.

[1169] Input: Patient condition information, past medical history, medical institution acceptance status, available medical capabilities, emotional data

[1170] Output: List of optimal destinations and ranking

[1171] Step 4: Selection and Notification

[1172] The server automatically sends the patient's condition information and estimated arrival time to the most appropriate medical institution selected by the generative AI, including detailed information on the patient's health, necessary medical equipment, and the doctor in charge.

[1173] Input: List of optimal destinations and patient status information

[1174] Output: Notification to selected medical institutions

[1175] The ambulance terminal receives the transport instructions sent from the server and transports the patient to the designated medical institution.

[1176] Input: Transport instructions from the server

[1177] Output: Patient transport

[1178] As described above, by analyzing the data collected at each step in real time and selecting the optimal destination, it is possible to significantly improve the efficiency and accuracy of emergency transport.

[1179] (Application example 2)

[1180] 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."

[1181] In the conventional process for selecting ambulance destinations, data collection was often done manually, making it difficult to select a destination quickly and accurately. Furthermore, because information on medical institution acceptance status, available medical departments, and medical equipment could not be obtained in real time, it was not possible to quickly select an appropriate destination. Furthermore, in emergency response for security services, there was no established method for analyzing various data, including staff emotional data, in real time to select the optimal response, which increased the risk of inappropriate responses.

[1182] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting data on the patient's location, urgency, past medical history, medical institution acceptance status, and available medical departments and facilities; means for inputting the collected data into a generative artificial intelligence for analysis and selecting the optimal transport destination; means for automatically transmitting information on the patient's condition and estimated arrival time to the selected transport destination; means for collecting information on the on-site situation, past security history, security staff deployment status, and the status of available equipment; and means for collecting and analyzing emotional data of the security staff. This enables quick and accurate selection of the transport destination and optimal emergency response.

[1183] "Location" refers to the place or position where a particular person or thing exists.

[1184] "Urgency" is a measure of the level of danger of an event and the need for immediate action.

[1185] "Past medical history" refers to records of medical services and treatments a patient has received.

[1186] A "medical institution" is a facility or hospital that provides medical services.

[1187] "Acceptance status" refers to a medical institution's ability and readiness to accept patients.

[1188] "Available medical specialties" refers to the specialties and subjects of treatment that a medical institution can provide.

[1189] "Equipment" refers to medical equipment and facilities owned by a medical institution.

[1190] "Data collection means" refers to the methods and devices used to obtain the required data.

[1191] "Generative AI" is AI that analyzes input data and generates optimal results for a specific purpose.

[1192] "Analysis" is the process of examining input data in detail to understand its meaning and structure.

[1193] "Destination" refers to the destination or medical facility to which the patient is being transported by ambulance or other means of transportation.

[1194] "Condition information" refers to information about a patient's health condition and symptoms.

[1195] "Estimated time of arrival" refers to the time the patient is expected to arrive at the designated location.

[1196] "Situation on the ground" refers to the current state or environment at a particular location or event.

[1197] "Security history" refers to a record of past security-related events and responses.

[1198] "Security staff" refers to professional employees in charge of security operations.

[1199] "Staffing" refers to how staff are deployed in a particular location or shift.

[1200] "Equipment" refers to tools and devices used for a particular task or operation.

[1201] "Emotional data" is information that indicates an individual's emotional state, and includes data such as voice tone, facial expression, and heart rate.

[1202] "Real-time" refers to processing and responding to events in the real world almost as they occur.

[1203] The following is a detailed description of an embodiment of the present invention. First, an overview of the entire system will be given, followed by a detailed description of the specific roles and operations of each element.

[1204] System Overview

[1205] The present invention is a system for optimizing the ambulance routing process and the security services emergency response process. The system consists of three main components:

[1206] 1. Data collection terminal: A terminal in the ambulance that inputs information on the patient's location and condition, as well as a means of collecting information on the situation at the scene, past security history, security staff deployment status, and the status of available equipment.

[1207] 2. Server: A means of inputting collected data into generative artificial intelligence for analysis and selecting the optimal destination and emergency response.

[1208] 3. Notification Device: A means of automatically transmitting patient status information and estimated time of arrival to selected destinations.

[1209] Hardware and software used

[1210] Hardware

[1211] Ambulance devices: tablets, smartphones, etc.

[1212] Sensor devices: environmental sensors, security cameras, heart rate monitors, etc.

[1213] Server: A cloud server for data collection, analysis, and storage.

[1214] Notification devices: ambulance and security staff's smart devices (e.g. smartphones, tablets, or smart glasses).

[1215] software

[1216] Data collection software: Used to collect data from sensors and cameras.

[1217] Emotion analysis software: Voice analysis and facial expression analysis engine.

[1218] Generative artificial intelligence (AI) models: Used to analyze input data and optimize.

[1219] Notification system: A system for providing immediate notifications to staff.

[1220] Data processing and calculation

[1221] 1. Processing of data collection terminals

[1222] The ambulance terminal inputs the patient's condition information and insurance card information, and enters the patient's specific information into the system.

[1223] The sensor device collects and analyzes the situation on-site and emotional data of security staff (voice tone and facial expression data).

[1224] 2. Server analysis process

[1225] The server queries an internal database based on the entered data to obtain the patient's medical history and allergy information.

[1226] The server inputs all collected data into generative artificial intelligence, which analyzes the patient's condition, past medical history, medical institution acceptance status, available medical capabilities, security history, and emotional data to create a list of the most appropriate medical institutions and countermeasures.

[1227] 3. Notification System Processing

[1228] The server sends the most appropriate medical institution and emergency response measures selected by the generative AI to the notification device.

[1229] The ambulance terminal and the security staff's smart devices will receive notifications and initiate immediate response.

[1230] Specific examples

[1231] For example, in a scenario where a suspicious individual is detected in security operations at a large event venue, the process would proceed as follows:

[1232] 1. Data Collection

[1233] Security cameras collect footage of suspicious people in real time and send the data to a server for analysis.

[1234] The situation on the scene is entered into a smart device, and the emotion engine analyzes the voice tone and facial expression data of the security staff.

[1235] 2. Analysis processing

[1236] -The server inputs the collected video data, on-site conditions, and staff emotional data into generative AI to calculate the optimal response.

[1237] 3. Notification and Response

[1238] Based on the analysis results, the server notifies security staff of the most appropriate response (for example, sealing off a specific area or notifying the police).

[1239] Security staff will be notified and will begin responding immediately.

[1240] Prompt Sentence Examples

[1241] "A suspicious individual has entered the venue. Please propose the best course of action based on the video data from Camera 5, the on-site input data, and the emotional data of Staff A."

[1242] In this way, the system of the present invention automates the data collection, analysis and notification process in emergency situations, enabling a rapid and accurate response.

[1243] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1244] Step 1:

[1245] The server collects data on the patient's location, urgency, past medical history, medical institution acceptance status, and available medical departments and facilities. The ambulance terminal inputs the patient's location and condition information, and security cameras and sensor devices collect data on the situation at the scene and the emotions of security staff. The input data is sent to the server.

[1246] Step 2:

[1247] The server queries its internal database based on the input data to retrieve the patient's medical history, allergy information, and prescription drug information. Specifically, it performs a database search and retrieves the relevant patient information. The patient's medical history and medical information are returned to the server as output.

[1248] Step 3:

[1249] The server collects real-time information from medical institutions on their admission status, number of beds, the allocation of specialists, available medical departments, and medical equipment. It also collects information on the situation on-site and past security history from security agencies. This data is sent from medical institutions' terminals and security systems and stored on the server.

[1250] Step 4:

[1251] The server inputs all collected data into a generative AI model. Specifically, the AI ​​model is passed information on the patient's condition, past medical history, medical institution acceptance status, security history, emotional data, etc. The input data is analyzed by the AI ​​model, and the optimal transport destination and emergency response measures are output.

[1252] Step 5:

[1253] Generative AI analyzes input data and comprehensively evaluates the patient's urgency, the severity of the injury, past medical history, whether they have any allergies, the acceptance status of nearby medical institutions, and the emotional state of the staff. Based on this evaluation, it selects the most suitable medical institution and response measures and lists them in a ranked format. This list is sent to a server.

[1254] Step 6:

[1255] The server considers the optimal transport destination and emergency response measures selected by the generative AI model and selects the most appropriate one from among them. The output is the optimal medical institution to transport the patient to and the response measures.

[1256] Step 7:

[1257] The server sends the selected medical institution and emergency response measures to the notification device. Specifically, the selection results are sent to the ambulance terminal and the security staff's smart device for notification. The input includes the selection result, and the output includes notification completion.

[1258] Step 8:

[1259] Users (e.g., paramedics or security personnel) receive notifications and initiate actions based on the designated medical facility or response plan, such as dispatching an ambulance or implementing an emergency response, so that the patient is transported to the appropriate medical facility or the emergency situation is appropriately addressed.

[1260] 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.

[1261] 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.

[1262] 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.

[1263] [Fourth embodiment]

[1264] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1265] 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.

[1266] 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).

[1267] 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.

[1268] 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.

[1269] 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).

[1270] 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.

[1271] 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.

[1272] 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.

[1273] 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.

[1274] 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.

[1275] 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.

[1276] 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."

[1277] This invention is a system for optimizing the process of selecting a destination for ambulances. This system collects data such as the patient's location, urgency, past medical history, medical institution acceptance status, available medical departments and facilities, and analyzes it in real time using generative artificial intelligence (AI), selecting the most suitable medical institution and contacting the patient.

[1278] Program processing

[1279] 1. Data Collection

[1280] When the ambulance arrives at the scene, it inputs information about the patient's condition (vital data, symptoms, injury details, etc.) and also inputs insurance card information to identify the patient.

[1281] The server retrieves past medical history, allergy information, and prescription drug information from a medical database based on insurance card information.

[1282] The medical institution's terminal sends information on the current congestion situation, number of beds, available medical departments and medical equipment to the server in real time.

[1283] 2. Real-time analysis

[1284] The server inputs the collected data into generative artificial intelligence, which then analyzes the data based on the patient's condition, medical history, and the medical institution's acceptance status.

[1285] The generative AI considers the following factors to determine the best destination for a patient:

[1286] Patient's condition and urgency

[1287] Past medical history and allergy information

[1288] The congestion level of nearby medical institutions and the capacity of specialized medical departments

[1289] Medical equipment available at each medical institution

[1290] 3. Selection and Notification

[1291] The server then determines the optimal destination based on the results of the generative AI analysis, taking into account factors such as distance, the current capacity of medical institutions, the availability of specialists, and the availability of necessary medical equipment.

[1292] The server automatically sends the patient's condition information and estimated time of arrival to the selected medical institution.

[1293] Specific examples

[1294] For example, consider a scenario in which an ambulance transports a patient to the scene of a traffic accident.

[1295] 1. Data Collection

[1296] The ambulance terminal arrives at the scene of an accident and inputs the patient's blood pressure, pulse, respiratory rate, and injury status, and also scans the patient's insurance card to enter the patient's information into the system.

[1297] The server obtains the patient's past medical history and allergy information from the health insurance card information.

[1298] Medical institutions' terminals send information about congestion and available medical equipment to the server in real time.

[1299] 2. Real-time analysis

[1300] The server inputs all collected data into the generation AI.

[1301] The generative AI analyzes the patient's urgency, the severity of the injury, past medical history, and whether or not they have any allergies, and then lists the most suitable medical institutions to recommend.

[1302] 3. Selection and Notification

[1303] Based on the analysis results of the generating AI, the server determines, for example, that trauma center A is the optimal destination for transport.

[1304] The server automatically notifies trauma center A of the patient's condition and estimated time of arrival.

[1305] The ambulance terminal initiates transport of the patient to the selected trauma center A.

[1306] In this way, the system of the present invention quickly selects the most appropriate medical institution when transporting a patient by ambulance, enabling the patient to receive appropriate medical care promptly.

[1307] The processing flow will be explained below.

[1308] Step 1:

[1309] The terminal (ambulance terminal) arrives at the scene and inputs the patient's vital data (blood pressure, pulse rate, respiratory rate, etc.) and condition information (presence or absence of trauma, description of symptoms, etc.).

[1310] The user (paramedic) scans the patient's insurance card and enters the patient's identifying information into the system.

[1311] Step 2:

[1312] Based on the entered insurance card information, the server queries an internal medical database to obtain the patient's past medical history, allergy information, and prescription drug information.

[1313] Step 3:

[1314] The terminal (the medical institution's terminal) sends information to the server in real time regarding the current admission situation, number of hospital beds, the allocation of specialists, available medical departments, and medical equipment.

[1315] Step 4:

[1316] The server inputs all data sent from the ambulance terminal and the medical institution's terminal into the generative AI.

[1317] Step 5:

[1318] Generative AI analyzes the patient's condition, past medical history, medical institution acceptance status, and available medical capabilities, and creates a list of the most appropriate medical institutions to which the patient should be transported.

[1319] Based on the analysis results, the generative AI ranks the most suitable medical institutions and selects the optimal destination for transport.

[1320] Step 6:

[1321] The server automatically sends the patient's condition information and estimated arrival time to the most appropriate medical institution selected by the generative AI.

[1322] Step 7:

[1323] The terminal (ambulance terminal) transports the patient to the designated medical institution based on the transport instructions sent from the server.

[1324] Step 8:

[1325] The terminal (terminal at the medical institution) checks the information received from the server and begins preparations for admission (preparing the emergency operating room, arranging specialists, etc.).

[1326] In this way, the ambulance destination is selected quickly and accurately through each step, allowing the patient to receive treatment promptly at the appropriate medical institution.

[1327] Example 1

[1328] 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."

[1329] In emergencies such as sudden illness or accidents, it is extremely important to quickly transport patients to the appropriate medical institution. However, it is difficult for on-site medical staff to select the optimal destination with limited information and time. Furthermore, if the medical institution's acceptance status and facility capacity cannot be grasped in real time, the selection of the destination may be delayed, putting the patient's life at risk. A system that can solve these issues is needed.

[1330] 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.

[1331] In this invention, the server includes means for collecting data on the patient's location, urgency, past medical history, medical institution acceptance status, and available medical departments and facilities, means for inputting the collected data into a generative artificial intelligence model and selecting the optimal transport destination taking into consideration the patient's condition and urgency, past medical history, and the response capabilities of nearby medical institutions, means for automatically transmitting the patient's condition information and estimated arrival time to the selected transport destination, means for acquiring data including the patient information from a medical database and using it for analysis, and means for updating the acceptance status of each medical institution in real time and reflecting it in the processing results. This makes it possible to quickly and accurately select the optimal transport destination and ensure that patients receive appropriate medical care promptly.

[1332] "Location" refers to information that indicates the patient's current location or position.

[1333] "Urgency" refers to information that serves as a criterion for assessing the severity of a patient's symptoms and condition and determining the priority of transportation.

[1334] "Medical history" refers to records of medical examinations and treatments that a patient has received at medical institutions in the past.

[1335] "Medical institution acceptance status" refers to information that indicates how many patients a medical institution currently has the capacity to accept.

[1336] "Available medical departments" refers to information indicating the specialized medical services and medical departments that a medical institution can provide.

[1337] "Facilities" refers to information indicating medical equipment and treatment devices owned by a medical institution.

[1338] A "generative artificial intelligence model" is an artificial intelligence that analyzes collected data and selects the optimal destination.

[1339] "Condition information" refers to detailed medical information that indicates a patient's current health condition and symptoms.

[1340] "Estimated arrival time" is information indicating the estimated time when the ambulance is to arrive at the selected medical institution.

[1341] A "medical database" is a database that stores medical data such as a patient's past medical history, allergy information, and prescription drug information.

[1342] This invention is a system that optimizes the process of selecting an ambulance destination. This system collects data on the patient's location, urgency, past medical history, medical institution acceptance status, available medical departments, and facilities, and analyzes it in real time using a generative artificial intelligence model. It then selects the most suitable medical institution and automatically transmits the patient's condition information and estimated arrival time to the selected medical institution.

[1343] Hardware and Software Configuration

[1344] The system consists of the following main components:

[1345] 1. Ambulance terminal

[1346] A tablet or smartphone for paramedics to enter information about the patient's condition.

[1347] A scanner for scanning insurance card information.

[1348] 2. Server

[1349] A high-performance server for running generative artificial intelligence (AI) models.

[1350] An interface for real-time communication with medical databases.

[1351] 3. Medical institution terminals

[1352] A computer terminal for inputting and sending information on current admission status, number of hospital beds, available medical departments, and medical equipment.

[1353] Specific operation of the system

[1354] 1. Data Collection

[1355] When the ambulance arrives at the scene, the ambulance staff enters the patient's condition information (e.g., blood pressure, pulse rate, respiratory rate, and the state of injuries) into the terminal. In addition, the ambulance scans the patient's insurance card to obtain basic information about the patient.

[1356] Based on the insurance card information received, the server retrieves the patient's past medical history, allergy information, prescription drug information, etc. from a medical database.

[1357] The medical institution's terminal sends information on each institution's current admission status, number of beds, available medical departments, and medical equipment to the server in real time.

[1358] 2. Real-time analysis and selection

[1359] The server inputs all collected data into a generative AI model, which analyzes the patient's condition, past medical history, allergy information, and medical institution acceptance status to select the medical institution that best suits the patient.

[1360] 3. Selection and Notification

[1361] The server automatically sends the patient's condition information and estimated time of arrival to the most appropriate medical institution selected by the generative AI model.

[1362] The ambulance's terminal provides emergency personnel with information about the destination medical institution and the optimal route.

[1363] Specific examples

[1364] For example, consider the scenario of an ambulance transporting a patient to the scene of a traffic accident:

[1365] The ambulance terminal arrives at the scene of an accident and inputs the patient's blood pressure, pulse, respiratory rate, and injury status. It also scans the patient's insurance card and inputs the patient's information into the system.

[1366] The server obtains the patient's past medical history and allergy information from the health insurance card information.

[1367] Medical institution terminals provide real-time information to the server about current congestion and available medical equipment.

[1368] Next, prompt the generative AI model with the following sentence:

[1369] "The ambulance has arrived at the scene. Please enter the patient's information. We will provide the following information: blood pressure, pulse rate, respiratory rate, injury status, past medical history, allergy information, and prescription drug information. We will also provide information on the availability and medical facilities of nearby medical institutions. Please select the most appropriate destination."

[1370] Based on this prompt, the generative AI model performs an analysis and selects the optimal transport destination. The system then automatically notifies the destination of the patient's condition and estimated arrival time. By linking systems in this way, the patient can be transported quickly and accurately to the optimal medical institution.

[1371] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1372] Step 1: Data collection

[1373] Ambulance terminal: After arriving at the patient's location, the ambulance crew uses the terminal to input the patient's condition information, including blood pressure, pulse rate, respiratory rate, and the state of injuries. The terminal also scans the insurance card, digitizing the patient's basic information and sending it to the server.

[1374] Input: Patient vital data and insurance card information.

[1375] Output: Digitized patient information.

[1376] Server: After receiving the insurance card information, it accesses the medical database and retrieves the patient's past medical history, allergy information, and prescription drug information.

[1377] Input: Digitized insurance card information.

[1378] Output: Past medical history, allergy information, prescription drug information.

[1379] Medical institution terminals: Each medical institution inputs information on its current admission status, number of beds, available medical departments, and medical equipment, and sends this information to the server in real time.

[1380] Input: Details of admission status, number of beds, available medical specialties, and medical facilities.

[1381] Output: Real-time updated medical institution admission information.

[1382] Step 2: Real-time analysis

[1383] Server: Inputs all collected data into the generative AI model, including patient condition information, past medical history, allergy information, and medical institution acceptance status.

[1384] Input: Patient condition information, past medical history, allergy information, and medical institution acceptance status.

[1385] Output: A consolidated dataset that is fed into the generative AI.

[1386] Generative AI: Analyzes all provided data and selects the most appropriate medical institution for the patient, taking into account the patient's urgency, symptoms, past medical history, and the capacity of nearby medical institutions.

[1387] Data processing and calculation: Urgency assessment, algorithmic optimization calculation, ranking of medical institutions that can accept patients.

[1388] Input: Unified dataset.

[1389] Output: Recommended destination list.

[1390] Step 3: Selection and Notification

[1391] Server: Selects the most suitable medical institution based on the list of recommended destinations received from the generative AI model, taking into account the distance, response capacity, and availability of facilities of each medical institution.

[1392] Input: Recommended Destination List.

[1393] Output: Best destination information.

[1394] Server: Automatically sends the patient's condition information and estimated time of arrival to the selected medical institution.

[1395] Input: Best destination information, patient condition information, estimated time of arrival.

[1396] Output:Notification to healthcare provider.

[1397] Ambulance terminal: Information on selected medical institutions and the optimal route are set and displayed to paramedics.

[1398] Input: Best destination information.

[1399] Output: Navigation information and route instructions.

[1400] In this way, the system of the present invention can optimize the patient transport process and transport patients to the appropriate medical institution quickly and accurately.

[1401] (Application example 1)

[1402] 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."

[1403] In today's medical and logistics fields, efficient resource allocation and rapid response are required, but conventional systems have difficulty providing optimal options in real time. In particular, the process of selecting the destination for emergency medical care and optimizing product placement and picking routes in logistics warehouses rely on human judgment, which is time-consuming and labor-intensive, and prone to judgment errors and delays. New and efficient systems are needed to solve these problems.

[1404] 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.

[1405] In this invention, the server includes means for collecting data on the patient's location, urgency, past medical history, medical institution acceptance status, and available medical departments and facilities, means for inputting the collected data into a generative artificial intelligence (AI) for analysis and selecting an optimal destination, means for automatically transmitting the patient's condition information and estimated arrival time to the selected destination, means for collecting the type, quantity, location information, and in / out data of products in the warehouse, means for inputting the collected product data into a generative AI for analysis and proposing optimal product placement locations and picking routes, and means for notifying warehouse workers of the proposed product placement locations and picking routes. This enables quick and accurate selection of a destination in emergency medical care and optimal product placement and efficient picking in logistics warehouses.

[1406] "Location" refers to the location information of a patient or product, and includes the patient's location in emergency medical care and the location where the product is stored in logistics.

[1407] "Urgency" is an index showing the urgency of a patient's condition, and indicates the degree to which prompt medical response is required.

[1408] "Medical history" is a general term for the medical records of a patient's past medical visits, including medical history and past treatment details.

[1409] "Acceptance status of medical institution" is information indicating whether the medical institution is in a state where it can accept new patients based on the current number of patients and the status of its facilities.

[1410] "Medical departments available" refers to the specialized medical care that a medical institution can provide, including medical departments that deal with specific diseases or conditions.

[1411] "Facilities" refers to physical facilities and equipment, including medical instruments and logistics equipment, owned by medical institutions and logistics warehouses.

[1412] "Means for collection" refers to devices or systems for collecting patient or product-related data, including sensors and input devices.

[1413] "Generative artificial intelligence" refers to an AI model that analyzes collected data and derives optimal decisions.

[1414] "Means for analyzing and selecting the optimal destination" refers to the process by which generative artificial intelligence analyzes data and, based on the results, determines the optimal medical institution or optimal location within a warehouse.

[1415] "Means for automatic transmission" refers to a system or method for automatically transmitting analysis results and necessary information to a designated recipient.

[1416] "Product data" refers to data including product type, quantity, location, and related inventory information.

[1417] "Location" refers to the location where the goods are stored within the warehouse, taking into consideration the most appropriate storage method.

[1418] A "picking route" refers to the optimal route for collecting products within a warehouse, including an efficient route for retrieving products.

[1419] "Means of notification" refers to the methods and devices used to communicate analysis results and work instructions to workers or personnel in charge.

[1420] As an embodiment of the present invention, we provide a system that includes both a process for selecting a destination for emergency medical care and a process for optimizing the placement of goods in a logistics warehouse. The specific configuration and operation procedure are described below.

[1421] 1. Emergency medical system configuration and operation procedures

[1422] Hardware and software:

[1423] Ambulance terminal: A mobile device for entering patient location, condition, and insurance card information.

[1424] Server: A central computer that aggregates collected data and analyzes it using generative artificial intelligence (e.g., OpenAI's GPT-4).

[1425] Medical institution terminal: A device that transmits real-time information on current congestion status, number of hospital beds, available medical departments, and medical equipment to a server.

[1426] Examples:

[1427] When an ambulance arrives at the scene of a traffic accident, the patient's blood pressure, pulse rate, respiratory rate, and the state of any injuries are entered into a terminal in the ambulance. The patient's insurance card is also scanned to obtain information about the patient. The server inputs the collected data into generative artificial intelligence. The AI ​​performs analysis, selects the optimal transport destination, and automatically notifies the selected medical institution of this information.

[1428] 2. Logistics Warehouse System Configuration and Operation Procedures

[1429] Hardware and software:

[1430] Warehouse worker's smartphone: A device used to scan product barcodes and enter information such as product type, quantity, and time of receipt.

[1431] Logistics system server: A central computer that aggregates collected product data and analyzes it using generative artificial intelligence.

[1432] GPS device: A device used to track the location of goods within a warehouse.

[1433] Examples:

[1434] Warehouse workers scan product barcodes and input the product type, quantity, and location. The server then inputs the collected data into generative artificial intelligence. The AI ​​then suggests product placement locations and optimal picking routes, and sends this information to the workers' smartphones in real time.

[1435] Example prompt sentence:

[1436] "Please suggest the optimal product placement and picking route based on the following warehouse data: {"items": [{"id": "A001", "quantity": 10, "location": "Shelf 1"}, {"id": "B002", "quantity": 15, "location": "Shelf 2"}], "orders": [{"id": "O123", "items": ["A001", "B002"], "priority": "high"}]}"

[1437] In this way, the system of the present invention enables rapid and accurate selection of destinations for emergency medical care, and enables efficient product placement and picking in logistics warehouses.

[1438] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1439] Step 1:

[1440] Data collection

[1441] Input: The ambulance terminal inputs the patient's location, condition information (e.g., blood pressure, pulse, respiratory rate, and injury status), and insurance card information. The warehouse worker's smartphone scans the product barcode and inputs the product type, quantity, and time of arrival.

[1442] How it works: The ambulance crew manually enters the patient's condition data into a dedicated terminal and scans the insurance card, while the warehouse worker scans the product's barcode with a smartphone app and enters the product information.

[1443] Output: The collected data is sent to a server.

[1444] Step 2:

[1445] Data Aggregation

[1446] Input: Patient and product data collected in step 1.

[1447] Specific operation: The server automatically receives data sent from the ambulance terminal and the warehouse worker's smartphone and stores it in a database.

[1448] Output: The stored data is prepared to be input into a generative AI model.

[1449] Step 3:

[1450] Real-time analytics

[1451] Input: Curated patient and product data.

[1452] Specific operation: The server inputs the stored data into the generation AI, which analyzes the data and calculates the optimal delivery destination, product placement location, and picking route.

[1453] Output: The analysis results provide the optimal delivery destination, product placement location, and picking route.

[1454] Step 4:

[1455] Selection and Notification

[1456] Input: The optimal delivery destination obtained as a result of the analysis, as well as the product placement location and picking route.

[1457] Specific operation: Based on the analysis results of the generation AI, the server automatically selects the optimal destination medical institution, product placement location, and picking route. The selection results are then sent to the medical institution's terminal and the warehouse worker's smartphone.

[1458] Output: The medical institution's terminal is notified of the patient's condition and estimated arrival time, and the warehouse worker's smartphone is notified of the product location and picking route.

[1459] Step 5:

[1460] execution

[1461] Input: Information notified by the server.

[1462] Specific operations: The medical institution prepares to accept the patient at the selected destination, and the ambulance heads to the designated medical institution. The warehouse worker moves the product to the notified location and works according to the designated picking route.

[1463] Output: Ambulances safely transport patients to the most appropriate medical facility, and warehouse workers efficiently place and pick products.

[1464] 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.

[1465] This invention is a system for optimizing the process of selecting ambulance destinations, and also combines it with an emotion engine that recognizes user emotions. This system collects data such as the patient's location, urgency, past medical history, medical institution acceptance status, available medical departments, and facilities, and analyzes it in real time using generative artificial intelligence (AI) to select and contact the most appropriate medical institution. In addition, collecting and analyzing user emotion data can improve the quality of medical care.

[1466] Program processing

[1467] 1. Data Collection

[1468] When the ambulance arrives at the scene, it inputs the patient's vital signs (blood pressure, pulse rate, respiratory rate, etc.) and condition information (presence or absence of external injuries, description of symptoms, etc.) into the terminal in the ambulance. It also inputs insurance card information to identify the patient.

[1469] The user (paramedic) scans the patient's insurance card and enters the patient's identifying information into the system.

[1470] The emotion engine collects and analyzes paramedic emotional data (voice tone, facial recognition data, etc.).

[1471] 2. Data acquisition and analysis by the server

[1472] Based on the entered insurance card information, the server queries an internal medical database to obtain the patient's past medical history, allergy information, and prescription drug information.

[1473] The medical institution's terminal sends information to the server in real time about the current admission situation, number of beds, specialist allocation, available medical departments, and medical equipment.

[1474] The emotion engine collects and analyzes emotional data (stress levels, fatigue levels, etc.) of medical institution staff.

[1475] 3. Real-time analysis

[1476] The server inputs all data sent from the ambulance terminal, the medical institution's terminal, and the emotion engine into the generative AI.

[1477] Generative AI comprehensively analyzes the patient's condition, past medical history, medical institution acceptance status, available medical capabilities, and the user's emotional data to create a list of the most suitable medical institutions to which the patient should be transported.

[1478] Based on the analysis results, the generative AI ranks the most suitable medical institutions and selects the optimal destination for transport.

[1479] 4. Selection and Notification

[1480] The server automatically sends the patient's condition information and estimated arrival time to the most appropriate medical institution selected by the generative AI.

[1481] The ambulance terminal transports the patient to the designated medical institution based on the transport instructions sent from the server.

[1482] Specific examples

[1483] For example, consider a scenario in which an ambulance transports a patient to the scene of a traffic accident.

[1484] 1. Data Collection

[1485] When the ambulance arrives at the scene of an accident, it inputs the patient's blood pressure, pulse, respiratory rate, and injury status, and also scans the patient's insurance card to enter the patient's information into the system.

[1486] The user (paramedic) scans their insurance card and inputs the patient's specific information into the system. At the same time, the emotion engine collects and analyzes the paramedic's emotional data (voice tone and facial expression data).

[1487] 2. Data acquisition and analysis by the server

[1488] The server obtains the patient's past medical history and allergy information based on the insurance card information.

[1489] The medical institution's terminal sends information about the current congestion situation and available medical equipment to the server in real time.

[1490] The emotion engine collects and analyzes emotional data (e.g., stress levels and fatigue levels) of medical staff.

[1491] 3. Real-time analysis

[1492] The server inputs all collected data into a generative AI.

[1493] The generated AI comprehensively analyzes the patient's urgency, the severity of the injury, past medical history, whether or not they have allergies, the acceptance status of surrounding medical institutions, and staff emotional data to select the most suitable medical institution.

[1494] 4. Selection and Notification

[1495] Based on the analysis results of the generating AI, the server determines, for example, that trauma center A is the optimal destination for transport.

[1496] The server automatically notifies trauma center A of the patient's condition and estimated time of arrival.

[1497] The ambulance terminal transports the patient to the selected trauma center A.

[1498] In this way, not only is the ambulance's destination selected quickly and accurately at each step, but the emotion engine can also analyze the emotions of emergency medical technicians and medical facility staff, further improving the selection of the most appropriate medical facility and the quality of patient care.

[1499] The processing flow will be explained below.

[1500] Step 1:

[1501] The terminal (ambulance terminal) arrives at the scene and inputs the patient's vital data (blood pressure, pulse rate, respiratory rate, etc.) and condition information (presence or absence of external injuries, description of symptoms, etc.). It also inputs insurance card information to identify the patient.

[1502] The user (paramedic) scans the patient's insurance card and enters the patient's identifying information into the system.

[1503] The emotion engine collects emotional data such as the paramedic's voice tone and facial expression data and analyzes it in real time.

[1504] Step 2:

[1505] Based on the entered insurance card information, the server queries an internal medical database to obtain the patient's past medical history, allergy information, and prescription drug information.

[1506] Step 3:

[1507] The terminal (the medical institution's terminal) sends information to the server in real time regarding the current admission situation, number of hospital beds, the allocation of specialists, available medical departments, and medical equipment.

[1508] The emotion engine collects and analyzes emotional data such as stress levels and fatigue levels of medical institution staff.

[1509] Step 4:

[1510] The server inputs data from the ambulance terminal, the medical institution's terminal, and emotion data from the emotion engine into the generative AI.

[1511] Step 5:

[1512] Generative AI comprehensively analyzes the patient's condition, past medical history, the medical institution's acceptance status, available medical capabilities, and the user's emotional data.

[1513] Based on the analysis results, the generative AI ranks the most suitable medical institutions and determines the optimal destination for transport.

[1514] Step 6:

[1515] The server automatically sends the patient's condition information and estimated arrival time to the most appropriate medical institution determined by the generative AI.

[1516] Step 7:

[1517] The terminal (ambulance terminal) transports the patient to the designated medical institution based on the transport instructions sent from the server.

[1518] Step 8:

[1519] The terminal (terminal at the medical institution) checks the information received from the server and begins preparations for admission (preparing the emergency operating room, arranging specialists, etc.).

[1520] By executing each step in this way, the ambulance's destination can be selected quickly and accurately, and the emotion engine can also analyze the emotions of emergency medical technicians and medical institution staff, further improving the quality of overall medical response.

[1521] Example 2

[1522] 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."

[1523] Conventional emergency transport systems select the optimal destination based on information such as the patient's location, urgency, past medical history, and the medical institution's acceptance status. However, this does not take into account the emotional state of the emergency medical technicians and medical institution staff, which means that the quality of emergency response is not sufficiently improved.

[1524] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting data on the patient's location, urgency, past medical history, medical institution acceptance status, and available medical departments and facilities; means including an emotion engine for collecting and analyzing emotion data; means for inputting the collected data and emotion data into a generative artificial intelligence for analysis and selecting the optimal destination; and means for automatically transmitting information on the patient's condition and estimated time of arrival to the destination selected from the analysis results. This enables quick and accurate selection of the destination taking into account the emotional states of paramedics and medical institution staff.

[1525] "Patient location" is information indicating the location where the ambulance or paramedic found the patient.

[1526] "Urgency" is an index that evaluates the urgency of the health condition that the patient is currently facing.

[1527] "Past medical history" refers to information that includes records of medical services and treatments that a patient has received.

[1528] "Acceptance status of medical institutions" is information indicating the capacity of a particular medical institution to accept patients and its current congestion status.

[1529] "Available medical departments and facilities" refers to information about the types of medical treatments that a medical institution can provide and the medical equipment and facilities that can be used.

[1530] "Emotional data" refers to data that indicates the current emotional state of emergency medical technicians and medical institution staff, including stress levels and fatigue levels.

[1531] The "emotion engine" is a technology that analyzes collected voice and facial expression data and generates emotional data.

[1532] "Generative artificial intelligence" refers to machine learning models and algorithms that analyze collected data and generate appropriate results.

[1533] "Destination" refers to the most appropriate medical facility to which the patient should be transported for treatment.

[1534] "Condition information" is data related to the patient's current health condition and vital signs.

[1535] The "estimated arrival time" is information indicating the time when the ambulance is scheduled to arrive at the medical institution to which the patient is being transported.

[1536] An "ambulance terminal" is an electronic device installed in an ambulance for inputting and transmitting patient data on-site.

[1537] "Medical institution terminal" refers to an electronic device used within a medical institution to update admission status and medical treatment information in real time.

[1538] The system of the present invention optimizes the ambulance destination selection process and improves the quality of emergency response. The hardware used includes mobile devices installed in ambulances, cloud-based servers, and computers at medical institutions. The software includes an emotion engine and a generative artificial intelligence (AI) model. A specific embodiment of this system is described below.

[1539] Data collection

[1540] After arriving at the scene of an accident, the ambulance terminal inputs the patient's vital data (blood pressure, pulse rate, respiratory rate) and condition information (presence or absence of external injuries, description of symptoms), and also scans the patient's insurance card to transmit the patient's identifying information to the system.

[1541] The user (paramedic) scans the patient's insurance card and inputs specific information. At the same time, the emotion engine analyzes the paramedic's voice tone and facial expression data to collect emotional data.

[1542] Data acquisition and analysis by the server

[1543] Based on the entered health insurance card information, the server retrieves the patient's past medical history, allergy information, and prescription drug information from an internal medical database. It also receives real-time information from the medical institution's terminal about the current admission situation, number of hospital beds, allocation of specialists, available medical departments, and medical equipment.

[1544] The emotion engine collects and analyzes emotional data (e.g., stress levels, fatigue levels) of medical institution staff.

[1545] Real-time analytics

[1546] The server aggregates all data sent from the ambulance terminals, medical institution terminals, and emotion engines, and inputs it into a generative AI (e.g., GPT-4).

[1547] Generative AI comprehensively analyzes the patient's condition, past medical history, medical institution acceptance status, available medical capabilities, and the user's emotional data, and then lists and ranks the most suitable medical institutions to which the patient should be transported.

[1548] Selection and Notification

[1549] The server automatically sends the patient's condition information and estimated arrival time to the most appropriate medical institution selected by the generative AI.

[1550] The ambulance terminal transports the patient to the designated medical institution based on the transport instructions sent from the server.

[1551] Specific examples

[1552] For example, consider a scenario in which an ambulance transports a patient to the scene of a traffic accident.

[1553] When the ambulance arrives at the scene of an accident, it inputs the patient's blood pressure, pulse, respiratory rate, and injury status, and also scans the patient's insurance card to enter the patient's information into the system.

[1554] The user (paramedic) scans their insurance card and inputs the patient's specific information. At the same time, the emotion engine collects and analyzes the paramedic's emotional data (voice tone and facial expression data).

[1555] The server obtains the patient's past medical history and allergy information based on the health insurance card information.

[1556] The medical institution's terminal sends information about the current congestion situation and available medical equipment to the server in real time.

[1557] The emotion engine collects and analyzes emotional data (e.g., stress levels and fatigue levels) of medical staff.

[1558] The server inputs the various collected data into the generative AI.

[1559] The generative AI comprehensively analyzes the patient's urgency, the severity of the injury, past medical history, whether or not they have allergies, the acceptance status of surrounding medical institutions, and staff emotional data to select the most suitable medical institution.

[1560] The server determines that "the best destination is Trauma Center A, and the reason is that it has surgeons and all the necessary medical equipment," and automatically notifies Trauma Center A of the details.

[1561] The ambulance terminal follows the notified instructions and transports the patient to trauma center A.

[1562] Prompt Sentence Examples

[1563] "Comprehensively analyze the patient's past medical history, current vital signs, the acceptance status of the nearest medical institution, and the emotional data of the paramedics and medical institution staff to determine the optimal destination."

[1564] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1565] Step 1: Data collection

[1566] After arriving at the scene, the ambulance terminal inputs the patient's vital signs (blood pressure 130 / 80, pulse rate 90, respiratory rate 20) and condition information (presence or absence of trauma and description of symptoms). The input data is sent to the server. In addition, the insurance card is scanned and the patient's specific information is entered into the system.

[1567] Input: Patient's vital data, condition information, insurance card information

[1568] Output: Sending the entered data to the server

[1569] The user (paramedic) scans their insurance card and inputs the patient's specific information. At the same time, the emotion engine analyzes the paramedic's voice tone and facial expression data to collect emotional data.

[1570] Input: Insurance card information, voice tone, facial expression data

[1571] Output: Sending collected specific information and emotion data to the server

[1572] Step 2: Data acquisition and analysis by the server

[1573] The server accesses an internal medical database based on the entered health insurance card information to obtain the patient's past medical history, allergy information, and prescription drug information. It also receives real-time information from the medical institution's terminal on the current admission status, number of hospital beds, allocation of specialists, available medical departments, and medical equipment.

[1574] Input: Insurance card information

[1575] Output: Obtaining past medical history, allergy information, and prescription drug information

[1576] The emotion engine collects voice and facial expression data from medical institution staff and analyzes emotional data (stress levels, fatigue levels).

[1577] Input: Voice data, facial expression data

[1578] Output: Emotion data generation and transmission to the server

[1579] Step 3: Real-time analysis

[1580] The server aggregates all data sent from the ambulance terminal, medical institution terminals, and emotion engine, and inputs it into a generative AI (e.g., GPT-4). The generative AI comprehensively analyzes the patient's condition, past medical history, medical institution acceptance status, available medical capabilities, and the user's emotional data, and lists and ranks the most suitable medical institutions to which the patient should be transported.

[1581] Input: Patient condition information, past medical history, medical institution acceptance status, available medical capabilities, emotional data

[1582] Output: List of optimal destinations and ranking

[1583] Step 4: Selection and Notification

[1584] The server automatically sends the patient's condition information and estimated arrival time to the most appropriate medical institution selected by the generative AI, including detailed information on the patient's health, necessary medical equipment, and the doctor in charge.

[1585] Input: List of optimal destinations and patient status information

[1586] Output: Notification to selected medical institutions

[1587] The ambulance terminal receives the transport instructions sent from the server and transports the patient to the designated medical institution.

[1588] Input: Transport instructions from the server

[1589] Output: Patient transport

[1590] As described above, by analyzing the data collected at each step in real time and selecting the optimal destination, it is possible to significantly improve the efficiency and accuracy of emergency transport.

[1591] (Application example 2)

[1592] 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."

[1593] In the conventional process for selecting ambulance destinations, data collection was often done manually, making it difficult to select a destination quickly and accurately. Furthermore, because information on medical institution acceptance status, available medical departments, and medical equipment could not be obtained in real time, it was not possible to quickly select an appropriate destination. Furthermore, in emergency response for security services, there was no established method for analyzing various data, including staff emotional data, in real time to select the optimal response, which increased the risk of inappropriate responses.

[1594] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting data on the patient's location, urgency, past medical history, medical institution acceptance status, and available medical departments and facilities; means for inputting the collected data into a generative artificial intelligence for analysis and selecting the optimal transport destination; means for automatically transmitting information on the patient's condition and estimated arrival time to the selected transport destination; means for collecting information on the on-site situation, past security history, security staff deployment status, and the status of available equipment; and means for collecting and analyzing emotional data of the security staff. This enables quick and accurate selection of the transport destination and optimal emergency response.

[1595] "Location" refers to the place or position where a particular person or thing exists.

[1596] "Urgency" is a measure of the level of danger of an event and the need for immediate action.

[1597] "Past medical history" refers to records of medical services and treatments a patient has received.

[1598] A "medical institution" is a facility or hospital that provides medical services.

[1599] "Acceptance status" refers to a medical institution's ability and readiness to accept patients.

[1600] "Available medical specialties" refers to the specialties and subjects of treatment that a medical institution can provide.

[1601] "Equipment" refers to medical equipment and facilities owned by a medical institution.

[1602] "Data collection means" refers to the methods and devices used to obtain the required data.

[1603] "Generative AI" is AI that analyzes input data and generates optimal results for a specific purpose.

[1604] "Analysis" is the process of examining input data in detail to understand its meaning and structure.

[1605] "Destination" refers to the destination or medical facility to which the patient is being transported by ambulance or other means of transportation.

[1606] "Condition information" refers to information about a patient's health condition and symptoms.

[1607] "Estimated time of arrival" refers to the time the patient is expected to arrive at the designated location.

[1608] "Situation on the ground" refers to the current state or environment at a particular location or event.

[1609] "Security history" refers to a record of past security-related events and responses.

[1610] "Security staff" refers to professional employees in charge of security operations.

[1611] "Staffing" refers to how staff are deployed in a particular location or shift.

[1612] "Equipment" refers to tools and devices used for a particular task or operation.

[1613] "Emotional data" is information that indicates an individual's emotional state, and includes data such as voice tone, facial expression, and heart rate.

[1614] "Real-time" refers to processing and responding to events in the real world almost as they occur.

[1615] The following is a detailed description of an embodiment of the present invention. First, an overview of the entire system will be given, followed by a detailed description of the specific roles and operations of each element.

[1616] System Overview

[1617] The present invention is a system for optimizing the ambulance routing process and the security services emergency response process. The system consists of three main components:

[1618] 1. Data collection terminal: A terminal in the ambulance that inputs information on the patient's location and condition, as well as a means of collecting information on the situation at the scene, past security history, security staff deployment status, and the status of available equipment.

[1619] 2. Server: A means of inputting collected data into generative artificial intelligence for analysis and selecting the optimal destination and emergency response.

[1620] 3. Notification Device: A means of automatically transmitting patient status information and estimated time of arrival to selected destinations.

[1621] Hardware and software used

[1622] Hardware

[1623] Ambulance devices: tablets, smartphones, etc.

[1624] Sensor devices: environmental sensors, security cameras, heart rate monitors, etc.

[1625] Server: A cloud server for data collection, analysis, and storage.

[1626] Notification devices: ambulance and security staff's smart devices (e.g. smartphones, tablets, or smart glasses).

[1627] software

[1628] Data collection software: Used to collect data from sensors and cameras.

[1629] Emotion analysis software: Voice analysis and facial expression analysis engine.

[1630] Generative artificial intelligence (AI) models: Used to analyze input data and optimize.

[1631] Notification system: A system for providing immediate notifications to staff.

[1632] Data processing and calculation

[1633] 1. Processing of data collection terminals

[1634] The ambulance terminal inputs the patient's condition information and insurance card information, and enters the patient's specific information into the system.

[1635] The sensor device collects and analyzes the situation on-site and emotional data of security staff (voice tone and facial expression data).

[1636] 2. Server analysis process

[1637] The server queries an internal database based on the entered data to obtain the patient's medical history and allergy information.

[1638] The server inputs all collected data into generative artificial intelligence, which analyzes the patient's condition, past medical history, medical institution acceptance status, available medical capabilities, security history, and emotional data to create a list of the most appropriate medical institutions and countermeasures.

[1639] 3. Notification System Processing

[1640] The server sends the most appropriate medical institution and emergency response measures selected by the generative AI to the notification device.

[1641] The ambulance terminal and the security staff's smart devices will receive notifications and initiate immediate response.

[1642] Specific examples

[1643] For example, in a scenario where a suspicious individual is detected in security operations at a large event venue, the process would proceed as follows:

[1644] 1. Data Collection

[1645] Security cameras collect footage of suspicious people in real time and send the data to a server for analysis.

[1646] The situation on the scene is entered into a smart device, and the emotion engine analyzes the voice tone and facial expression data of the security staff.

[1647] 2. Analysis processing

[1648] -The server inputs the collected video data, on-site conditions, and staff emotional data into generative AI to calculate the optimal response.

[1649] 3. Notification and Response

[1650] Based on the analysis results, the server notifies security staff of the most appropriate response (for example, sealing off a specific area or notifying the police).

[1651] Security staff will be notified and will begin responding immediately.

[1652] Prompt Sentence Examples

[1653] "A suspicious individual has entered the venue. Please propose the best course of action based on the video data from Camera 5, the on-site input data, and the emotional data of Staff A."

[1654] In this way, the system of the present invention automates the data collection, analysis and notification process in emergency situations, enabling a rapid and accurate response.

[1655] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1656] Step 1:

[1657] The server collects data on the patient's location, urgency, past medical history, medical institution acceptance status, and available medical departments and facilities. The ambulance terminal inputs the patient's location and condition information, and security cameras and sensor devices collect data on the situation at the scene and the emotions of security staff. The input data is sent to the server.

[1658] Step 2:

[1659] The server queries its internal database based on the input data to retrieve the patient's medical history, allergy information, and prescription drug information. Specifically, it performs a database search and retrieves the relevant patient information. The patient's medical history and medical information are returned to the server as output.

[1660] Step 3:

[1661] The server collects real-time information from medical institutions on their admission status, number of beds, the allocation of specialists, available medical departments, and medical equipment. It also collects information on the situation on-site and past security history from security agencies. This data is sent from medical institutions' terminals and security systems and stored on the server.

[1662] Step 4:

[1663] The server inputs all collected data into a generative AI model. Specifically, the AI ​​model is passed information on the patient's condition, past medical history, medical institution acceptance status, security history, emotional data, etc. The input data is analyzed by the AI ​​model, and the optimal transport destination and emergency response measures are output.

[1664] Step 5:

[1665] Generative AI analyzes input data and comprehensively evaluates the patient's urgency, the severity of the injury, past medical history, whether they have any allergies, the acceptance status of nearby medical institutions, and the emotional state of the staff. Based on this evaluation, it selects the most suitable medical institution and response measures and lists them in a ranked format. This list is sent to a server.

[1666] Step 6:

[1667] The server considers the optimal transport destination and emergency response measures selected by the generative AI model and selects the most appropriate one from among them. The output is the optimal medical institution to transport the patient to and the response measures.

[1668] Step 7:

[1669] The server sends the selected medical institution and emergency response measures to the notification device. Specifically, the selection results are sent to the ambulance terminal and the security staff's smart device for notification. The input includes the selection result, and the output includes notification completion.

[1670] Step 8:

[1671] Users (e.g., paramedics or security personnel) receive notifications and initiate actions based on the designated medical facility or response plan, such as dispatching an ambulance or implementing an emergency response, so that the patient is transported to the appropriate medical facility or the emergency situation is appropriately addressed.

[1672] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1673] 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.

[1674] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1675] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1676] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1677] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1678] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1679] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1680] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1681] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1682] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1683] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1684] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1685] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1686] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1687] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1688] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1689] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1690] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1691] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1692] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1693] The following is further disclosed regarding the above embodiment.

[1694] (Claim 1)

[1695] A means of collecting data on the patient's location, urgency, past medical history, medical institution acceptance status, available medical departments and facilities, and

[1696] A means for inputting the collected data into a generative artificial intelligence for analysis and selecting an optimal destination;

[1697] means for automatically transmitting patient condition information and estimated arrival time to the selected destination;

[1698] A system including:

[1699] (Claim 2)

[1700] 2. The system according to claim 1, further comprising a terminal means for an ambulance to input the location and condition information of the patient.

[1701] (Claim 3)

[1702] 2. The system according to claim 1, further comprising a terminal means of the medical institution for updating information on the acceptance status and range of medical treatment available at the medical institution in real time.

[1703] "Example 1"

[1704] (Claim 1)

[1705] A means of collecting data on the patient's location, urgency, past medical history, medical institution acceptance status, available medical departments and facilities, and

[1706] A means for inputting the collected data into a generative artificial intelligence model and selecting the optimal destination for transportation, taking into consideration the patient's condition and urgency, past medical history, and the response capabilities of nearby medical institutions;

[1707] means for automatically transmitting patient condition information and estimated arrival time to the selected destination;

[1708] a means for acquiring data including the patient information from a medical database and using the data for analysis;

[1709] A means to update the acceptance status of each medical institution in real time and reflect it in the processing results,

[1710] A system including:

[1711] (Claim 2)

[1712] 2. The system according to claim 1, further comprising a terminal means in an ambulance for inputting the patient's location and condition information and scanning the insurance card information to identify the patient.

[1713] (Claim 3)

[1714] 2. The system according to claim 1, further comprising a terminal means of the medical institution that updates information on the medical institution's acceptance status and available facilities in real time and transmits the information to the server.

[1715] "Application Example 1"

[1716] (Claim 1)

[1717] A means of collecting data on the patient's location, urgency, past medical history, medical institution acceptance status, available medical departments and facilities, and

[1718] A means for inputting the collected data into a generative artificial intelligence for analysis and selecting an optimal destination;

[1719] means for automatically transmitting patient condition information and estimated arrival time to the selected destination;

[1720] A means for collecting the type, quantity, location information, and inbound and outbound data of goods in a warehouse;

[1721] a means for inputting the collected product data into a generative artificial intelligence for analysis and proposing an optimal product placement location and picking route;

[1722] means for notifying a warehouse worker of the proposed product placement location and picking route;

[1723] A system including:

[1724] (Claim 2)

[1725] 2. The system according to claim 1, further comprising a terminal means for an ambulance to input the location and condition information of the patient.

[1726] (Claim 3)

[1727] 2. The system according to claim 1, further comprising a terminal means of the medical institution for updating information on the acceptance status and range of medical treatment available at the medical institution in real time.

[1728] "Example 2: Combining Emotion Engines"

[1729] (Claim 1)

[1730] A means of collecting data on the patient's location, urgency, past medical history, medical institution acceptance status, available medical departments and facilities, and

[1731] means for collecting and analyzing emotion data, the means including an emotion engine;

[1732] a means for inputting the collected data and emotion data into a generative artificial intelligence for analysis and selecting an optimal destination;

[1733] means for automatically transmitting the patient's condition information and estimated arrival time to the destination selected from the analysis results;

[1734] A system including:

[1735] (Claim 2)

[1736] 2. The system according to claim 1, further comprising a terminal means in the ambulance for inputting information on the patient's location and condition.

[1737] (Claim 3)

[1738] 2. The system according to claim 1, further comprising a terminal means at the medical institution for updating information on the medical institution's acceptance status and scope of medical treatment in real time.

[1739] "Application example 2 when combining emotion engines"

[1740] (Claim 1)

[1741] A means of collecting data on the patient's location, urgency, past medical history, medical institution acceptance status, available medical departments and facilities, and

[1742] A means for inputting the collected data into a generative artificial intelligence for analysis and selecting an optimal destination;

[1743] means for automatically transmitting patient condition information and estimated arrival time to the selected destination;

[1744] A means of selecting the most appropriate emergency response by collecting information on the situation at the scene, past security history, the deployment status of security staff, and the status of available equipment, as well as a means of collecting and analyzing emotional data from security staff;

[1745] A system including:

[1746] (Claim 2)

[1747] 2. The system according to claim 1, further comprising a terminal means in the ambulance for inputting information on the patient's location and condition.

[1748] (Claim 3)

[1749] 2. The system according to claim 1, further comprising a terminal means of the medical institution for updating information on the acceptance status and range of medical treatment available at the medical institution in real time. [Explanation of symbols]

[1750] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting data on the patient's location, urgency, past medical history, medical institution acceptance status, available medical departments and facilities, and A means for inputting the collected data into a generative artificial intelligence for analysis and selecting an optimal destination; means for automatically transmitting patient condition information and estimated arrival time to the selected destination; A system including:

2. 2. The system according to claim 1, further comprising a terminal means for an ambulance to input information about the patient's location and condition.

3. 2. The system according to claim 1, further comprising a terminal means of the medical institution for updating information on the acceptance status and range of medical treatment available at the medical institution in real time.

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