System

Abstract

A system is provided.SOLUTION: This system is provided with a means for inputting information related to the condition and symptom of a patient, a means for collecting information related to the medical care ability of a medical institution, available facilities and the number of vacant beds and storing them in a database, and a means for collecting information related to traffic conditions in an area. The system includes a means for accumulating information in a database, a means for selecting an optimum medical institution corresponding to the symptom of a patient in real time by using an AI algorithm based on the accumulated information, a means for displaying a list of the selected medical institution, a means for notifying a user of arrival schedule information of the patient to the selected medical institution, and a means for guiding an optimum route to the selected medical institution.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] During the COVID-19 pandemic, ambulance dispatch rates have remained high, exacerbating problems such as "ambulances not coming even when called" and "nowhere to take patients." This not only lengthens the time it takes for patients to be transported to appropriate medical institutions, but also increases the burden on ambulance teams coordinating where to take patients. Under the current system, there are often situations where it is difficult to respond to patients quickly due to a shortage of hospital beds, a lack of medical staff capacity, and the inability to find hospitals that can handle specialized medical conditions. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems with a system including: means for inputting information about a patient's condition and symptoms; means for collecting and storing in a database information about medical institutions' medical treatment capabilities, available facilities, and number of available beds; means for collecting and storing in a database information about local traffic conditions; means for selecting in real time the medical institution most suitable for treating the patient's symptoms using an AI algorithm based on the accumulated information; means for displaying a list of selected medical institutions; means for notifying the medical institution selected by the user of the patient's expected arrival information; and means for guiding the user to the selected medical institution on the optimal route. This system enables emergency teams to transport patients quickly and appropriately, reducing the burden on medical institutions and ensuring prompt treatment for patients.

[0006] "Patient" means a person who requires treatment at a medical institution.

[0007] "Condition" refers to the physical and mental state of a patient's health.

[0008] "Symptoms" refer to specific signs or sensations experienced by a patient that are related to an illness or physical abnormality.

[0009] "Medical institution" means a facility that provides medical services, such as a hospital, medical office, or clinic.

[0010] "Medical capacity" refers to the range and quality of medical services that a medical institution can provide.

[0011] "Available facilities" refers to the equipment and instruments that a medical institution can use for treatment or diagnosis.

[0012] "Available beds" refers to the number of patient beds available at a medical institution.

[0013] "Traffic conditions within a region" refers to traffic flow, congestion, and road conditions in a specific region.

[0014] "Database" refers to a structured data storage and management system.

[0015] An "AI algorithm" is a calculation procedure that uses artificial intelligence to process data and make optimal decisions.

[0016] "Real-time" refers to data collection and processing occurring immediately, without delay.

[0017] "User" refers to anyone who uses the system, especially emergency personnel.

[0018] The "optimal medical institution" refers to a medical institution that has the medical capabilities and facilities to respond most appropriately and quickly to a patient's symptoms.

[0019] A "list" is a list of multiple options arranged by item.

[0020] "Notification" refers to the act or means of informing others of information.

[0021] A "route" refers to a path from a particular point to another point.

[0022] "Guidance" refers to the act of providing instructions and information to reach a destination. [Brief explanation of the drawings]

[0023] [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

[0024] 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.

[0025] First, the terms used in the following description will be explained.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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."

[0031] [First embodiment]

[0032] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0033] 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.

[0034] 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).

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0040] 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.

[0041] 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.

[0042] 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.

[0043] 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."

[0044] The present invention is a system for quickly selecting the most suitable medical institution based on the condition and symptoms of a patient, and a specific embodiment for implementing this system will be described.

[0045] First, the user (emergency responder) enters basic information about the patient and details of their symptoms into a terminal at the scene. The terminal is a tablet or smartphone, and can access the server via an internet connection. The information entered includes the patient's age, gender, specific symptoms (e.g., chest pain, shortness of breath), and vital signs (heart rate, blood pressure, oxygen saturation).

[0046] The server then collects real-time data on medical institutions' medical capacity, available facilities, and the number of available beds, and stores it in a database. This information is periodically retrieved from each hospital's system via an API. The server also collects traffic situation data from traffic management centers and public transportation databases. This data is also stored in the database.

[0047] After receiving the patient information entered by the user, the server uses an AI algorithm to integrate it with information from the accumulated database and select a medical institution that best suits the patient's symptoms in real time. The AI ​​algorithm estimates the specialty corresponding to the patient's symptoms and lists hospitals with doctors with that specialty as candidates. In addition, it takes into account traffic data to analyze the route that will allow the patient to be transported quickly.

[0048] A list of selected medical institutions is displayed on the user's device along with the optimal route. The list also includes detailed information such as the number of available beds at each hospital, whether or not specialists are available, and travel time. The user can refer to this list to select the medical institution that will be the final destination for the patient.

[0049] When a user selects a hospital, the selection information is sent from the terminal to the server, and the server notifies the selected medical institution of the patient's expected arrival information. The notification includes the patient's symptoms, vital signs, estimated arrival time, etc., allowing the hospital to quickly prepare to accept the patient.

[0050] Finally, the server provides the user with optimal route information to the selected medical institution. The device receives this information and provides visual navigation to the user, ensuring that the ambulance arrives at the hospital in the shortest time possible and that the patient receives appropriate treatment promptly.

[0051] As a concrete example, consider the case of a 50-year-old man complaining of chest pain. The user (ambulance crew member) enters the necessary information into the terminal and sends it to the server. The server determines that cardiac surgery is necessary and lists the most suitable hospitals from multiple hospitals in the area. Finally, the user selects the recommended Hospital A, and upon receiving a notification from the server, Hospital A prepares to accept the patient. At the same time, the user transports the patient to Hospital A via the optimal route, and prompt treatment is provided.

[0052] As a result, the present invention streamlines the process of selecting a destination for emergency medical care, enabling patients to be seen and treated quickly.

[0053] The processing flow will be explained below.

[0054] Step 1: User enters patient information

[0055] On-site, users enter information into the terminal, such as the patient's age, gender, specific symptoms (e.g., chest pain, shortness of breath), and vital signs (heart rate, blood pressure, oxygen saturation).

[0056] The device stores the entered information and sends the data to the server using a communication protocol (such as HTTPS).

[0057] Step 2: Data collection by the server

[0058] The server periodically collects data on each medical institution's medical capacity, available facilities, and number of available beds. This data is obtained through the hospital system's API.

[0059] The server collects real-time traffic situation data (e.g., congestion information and road conditions) from traffic management centers and public transportation databases.

[0060] The server stores the collected data in a database.

[0061] Step 3: AI analysis by server

[0062] The server integrates the patient information received from the user with existing medical institution and transportation data stored in a database.

[0063] The server uses AI algorithms to predict the specialty (e.g., cardiac surgery, internal medicine) that a patient needs to see based on their symptoms.

[0064] The server lists medical institutions according to the estimated specialty and calculates the optimal route taking into account traffic data.

[0065] Step 4: Present a list of medical institutions

[0066] Based on the analysis, the server generates a list of recommended medical institutions, including details such as the number of available beds, availability of specialists, and travel time.

[0067] The server transmits the generated list to the user terminal.

[0068] The terminal displays the received list of medical institutions to the user.

[0069] Step 5: User selects medical institution

[0070] The user selects a hospital to which the patient will be transported from the displayed list of medical institutions.

[0071] The terminal transmits the selected medical institution information to the server.

[0072] Step 6: Server Sends Notification

[0073] The server notifies the selected medical institution of the patient's expected arrival information, including the patient's symptoms, vital signs, and estimated time of arrival.

[0074] Medical institutions will receive notification and prepare to accept the patient.

[0075] Step 7: Optimal route guidance

[0076] The server transmits information on the optimal route to the selected medical institution to the user terminal.

[0077] The device then displays the received route guidance information to the user and provides navigation, allowing the ambulance to arrive at the hospital in the shortest time possible and ensuring prompt treatment for the patient.

[0078] Example 1

[0079] 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."

[0080] Conventional emergency medical systems have had difficulty quickly selecting the appropriate medical institution based on the patient's condition and symptoms. As a result, it took time to determine where to transport the patient, which could delay appropriate treatment. In addition, it was not possible to collect and comprehensively analyze information such as the medical capacity, number of available beds, and traffic conditions of each medical institution in real time, which led to issues with selecting the optimal transport route. There is a need to solve these problems.

[0081] 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.

[0082] In this invention, the server includes means for inputting information on the patient's condition and symptoms, means for collecting information on medical institutions' medical treatment capabilities, available facilities, and number of available beds and storing it in a database, means for collecting information on local traffic conditions and storing it in a database, means for selecting the medical institution most suitable for the patient's symptoms in real time using an AI algorithm based on the accumulated information, means for displaying a list of the selected medical institutions, means for notifying the medical institution selected by the user of the patient's expected arrival information, means for guiding the user on the optimal route to the selected medical institution, means for estimating medical institutions with specialized fields based on patient information using an AI algorithm, and means for providing information on the optimal route for patient transport in real time. This enables the rapid selection of an appropriate medical institution and guidance on the optimal transport route.

[0083] "Means for inputting information about a patient's condition and symptoms" refers to a device or interface that allows a user to input basic information about a patient and details of the patient's symptoms, such as the patient's age, gender, specific symptoms, and vital signs.

[0084] "Means of collecting information on medical institutions' medical treatment capabilities, available facilities, and number of available beds, and storing it in a database" refers to the functions and systems that obtain information on medical treatment capabilities, available facilities, and number of available beds from each medical institution and store that data in a database.

[0085] "Means of collecting information on traffic conditions within an area and storing it in a database" refers to functions and systems that collect information on traffic conditions obtained from traffic management centers and public transportation databases and store that data in a database.

[0086] "Means of using AI algorithms to select the most appropriate medical institution to address a patient's symptoms in real time" refers to a function or system that uses AI algorithms based on collected data to instantly select the medical institution that is most suitable for the patient's symptoms.

[0087] "Means for displaying a list of selected medical institutions" refers to a device or system that displays to the user a list of candidate medical institutions selected by an AI algorithm.

[0088] "Means for notifying the medical institution selected by the user of the patient's expected arrival information" refers to a function or system that sends the patient's expected arrival information to the medical institution selected by the user, prompting the hospital to prepare for the patient's arrival.

[0089] "Means for providing guidance on the optimal route to the selected medical institution" refers to a device or system that provides route information for the shortest route to the medical institution selected by the user and performs navigation.

[0090] "Means of using an AI algorithm to estimate the appropriate specialized medical institution based on patient information" refers to a function or system that analyzes patient information and uses an AI algorithm to estimate the appropriate specialized medical institution that can respond to the patient's symptoms.

[0091] "Means for providing real-time information on optimal patient transport routes" refers to functions and systems that calculate and provide optimal routes for quickly transporting patients based on real-time traffic information.

[0092] The present invention provides a system for quickly selecting the most appropriate medical institution based on the patient's condition and symptoms. This section describes a specific embodiment of the system. The system includes a user (an emergency medical technician), a terminal (a tablet or a smartphone), and a server.

[0093] First, the user enters basic information about the patient and details of their symptoms into the device on-site. The information entered includes the patient's age, gender, specific symptoms (e.g., chest pain, shortness of breath), and vital signs (heart rate, blood pressure, oxygen saturation). The device is connected to the Internet, and the entered data is sent to a server. The device used is a tablet or smartphone, and data entry and transmission are performed via a dedicated application.

[0094] The server then collects data on medical capacity, available facilities, and the number of available beds from multiple medical institutions. This information is periodically obtained through each medical institution's API. The server also collects traffic status data from traffic management centers and public transportation databases and stores it in a database. This ensures that the server always has the latest medical and transportation information.

[0095] Based on the received patient information, the server uses an AI algorithm to select the most appropriate medical institution. The AI ​​algorithm estimates the specialty corresponding to the patient's symptoms and lists medical institutions with that specialist on-site as candidates. It also takes into account traffic data to analyze the optimal transport route. The AI ​​algorithm uses a machine learning model to make optimal decisions based on past data.

[0096] A list of selected medical institutions is displayed on the user's terminal along with detailed information (e.g., number of available beds, availability of specialists, travel time). The user refers to this list to select the final destination. Once the user makes a selection, the information is sent from the terminal to the server, which then notifies the selected medical institution of the patient's expected arrival information. The notification includes the patient's symptoms, vital signs, and estimated arrival time.

[0097] The server then provides the user's device with optimal route information to the selected medical institution. Based on this information, the device performs visual navigation and guides the user to the medical institution in the shortest time possible. This allows the ambulance to arrive at the hospital quickly and the patient to receive appropriate treatment promptly.

[0098] As a concrete example, consider the case of a 50-year-old man complaining of chest pain. The user (paramedic) enters the necessary information into the device and sends it. The server uses an AI algorithm to determine that cardiac surgery is necessary and lists the most suitable hospitals from multiple hospitals in the area. The user selects Hospital A from the displayed list, and upon receiving a notification from the server, Hospital A prepares to accept the patient. At the same time, the user transports the patient to Hospital A via the optimal route, allowing for prompt treatment.

[0099] Example prompt sentence:

[0100] "A 50-year-old man complains of chest pain. The necessary information is entered and sent to the server, which uses an AI algorithm to list the most suitable hospitals. Finally, Hospital A is selected, a notification is sent, and the patient is transported via the optimal route."

[0101] This system will streamline the process of selecting a destination for emergency medical care, enabling patients to be seen and treated quickly.

[0102] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0103] Step 1:

[0104] User input of patient information

[0105] On-site, the user enters the patient's basic information (age, gender, specific symptoms) and vital signs (heart rate, blood pressure, oxygen saturation) into the terminal. The terminal is connected to the Internet and sends this information to the server. Specifically, the user enters the necessary information into the input form displayed on the tablet screen and presses the "Submit" button.

[0106] Input: Patient's age, gender, symptoms, vital signs

[0107] Output: Patient information transferred to the server

[0108] Step 2:

[0109] Server collects data and updates the database

[0110] The server collects data on medical capacity, available facilities, and number of available beds from each medical institution via API. It also collects data on traffic conditions from the traffic management center and public transportation database. This data is periodically saved in the database. Specifically, the server calls the API and writes the acquired data to the database.

[0111] Input: Real-time data obtained from medical institutions and traffic control centers

[0112] Output: Latest medical and traffic information stored in a database

[0113] Step 3:

[0114] Server analyzes patient information and selects medical institutions

[0115] The server analyzes the patient information received from the user and uses an AI algorithm to select the most suitable medical institution. The AI ​​algorithm estimates the specialty corresponding to the patient's symptoms and lists medical institutions with that specialist. Specifically, the server launches the AI ​​model, compares the patient information with the contents of the database, and lists the most suitable medical institutions.

[0116] Input: Patient information received from the user, medical institution information stored in the database

[0117] Output: A list of the best medical institutions

[0118] Step 4:

[0119] Optimal route analysis by the server

[0120] The server analyzes the traffic data to find the optimal route for quickly transporting the patient. This route information is provided to the user's device along with a list of candidate medical institutions. Specifically, the server analyzes the traffic data to calculate the shortest route and adds the result to the list.

[0121] Input: Traffic information stored in the database

[0122] Output: A list of medical institutions with optimal routes

[0123] Step 5:

[0124] User selection of medical institution

[0125] The user selects the most suitable hospital from a list of medical institutions displayed on the terminal. Once the selection is confirmed, the information is sent from the terminal to the server. Specifically, the user selects a hospital from the list and presses the "Select" button.

[0126] Input: A list of candidate medical institutions provided by the server

[0127] Output: The information of the selected medical institution is sent to the server.

[0128] Step 6:

[0129] Notification to medical institutions by the server

[0130] The server notifies the selected medical institution of the patient's expected arrival information. The notification includes the patient's symptoms, vital signs, and estimated arrival time. Specifically, the server sends the notification to the selected hospital's system via an API.

[0131] Input: Information about the medical institution selected by the user

[0132] Output: Medical institutions to be notified

[0133] Step 7:

[0134] Server provides optimal route information

[0135] The server provides the user's device with optimal route information to the selected medical institution. The device uses this information to perform visual navigation. Specifically, the device's map application begins navigation based on the route data sent from the server.

[0136] Input: Optimal route data to selected medical institutions

[0137] Output: User terminal that displays the optimal route

[0138] Step 8:

[0139] User transports patient and starts treatment

[0140] The user follows the navigation on the device to transport the patient to the selected medical institution. After arriving at the hospital, the patient can receive appropriate treatment quickly. Specifically, the user follows the navigation to proceed along the monitored route, and upon arriving at the hospital, the patient is handed over to medical staff.

[0141] Input: Optimal route information provided by the server

[0142] Output: Patients who arrived at the selected medical institution

[0143] Through the above processing steps, this system streamlines the process of selecting a destination for emergency medical care, enabling patients to be seen and treated quickly.

[0144] (Application example 1)

[0145] 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."

[0146] Conventional food delivery systems lack efficient methods for delivery personnel to select the optimal restaurant and deliver meals quickly. This leads to delivery delays and selection errors, resulting in lower customer satisfaction. Furthermore, there are challenges, such as taking into account real-time traffic conditions and appropriately selecting restaurants based on their availability. To solve these challenges, a system is needed to improve delivery efficiency and accuracy.

[0147] 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.

[0148] In this invention, the server includes a means for inputting information about the user's status and needs, a means for collecting information about providers' service capabilities, available facilities, and availability and storing it in a database, and a means for collecting information about local traffic and resource conditions and storing it in a database. This allows the server to select the optimal provider in real time based on the collected information using an AI algorithm, enabling the delivery of services in response to the user's request. By utilizing a generative AI model, the collected information and the optimal provider and route can be displayed, notified, and guided, improving delivery efficiency and accuracy and customer satisfaction.

[0149] "Patient or User" refers to the individual receiving the service, i.e., the patient, delivery recipient, or orderer.

[0150] "Condition and needs" refers to information including the current health condition, symptoms, requests, and content of requests of the person receiving the service.

[0151] "Provider" refers to a medical institution, restaurant, or other business that provides a service.

[0152] "Service capabilities" refers to the provider's technical capabilities, staffing, response capabilities, and equipment availability.

[0153] "Available Facilities" refers to the equipment, tools, and resources available to the Provider to provide the Service.

[0154] "Availability" refers to reservations for the provider's services, available beds, delivery slots, availability of counters, etc.

[0155] "Traffic conditions" refers to the state of road congestion in the area, whether there is traffic congestion, and operation status.

[0156] "Resource status" refers to the status of personnel, equipment, vehicles, and other material resources available to the provider.

[0157] "AI algorithm" refers to an algorithm that analyzes input data and automatically makes the optimal selection.

[0158] "Real-time" means that information is acquired, processed, and displayed almost instantly.

[0159] "Generative AI model" refers to the AI ​​model used to analyze, display, notify, and guide collected information.

[0160] "Visual navigation" refers to a function that displays maps and directions to help delivery personnel and others reach their destination via the optimal route.

[0161] "Database" refers to a system in which accumulated information is managed and stored.

[0162] This invention is a "Smart Delivery Assistant" system for food delivery services that enables delivery personnel to select the most suitable restaurant and deliver food efficiently. This system consists of a server, a user terminal, and a provider terminal.

[0163] First, the delivery person (user) inputs the order details, delivery address, and delivery time into their smartphone. The server receives this information and collects the real-time data described below.

[0164] The server collects real-time data on the business status of partner restaurants, food preparation times, and traffic conditions. This data is obtained from APIs in the restaurant management system and traffic information APIs (e.g., Google Maps API) and stored in a database.

[0165] Based on this collected data, the server uses an AI algorithm, which is built using a deep learning framework such as TENSORFLOW®, to comprehensively analyze the order details, the supplier's situation, and traffic information to select the most suitable restaurant.

[0166] The selected restaurant and the optimal delivery route information are visually displayed on the delivery person's smartphone. When the delivery person sends information about their expected arrival time to the selected restaurant to the server, the server notifies the restaurant's device of the order and the estimated arrival time. This allows the restaurant to prepare the food efficiently.

[0167] Furthermore, the server uses the Google Maps API to provide delivery staff with detailed route information to the delivery destination, allowing them to deliver food in the shortest time possible, leading to increased customer satisfaction.

[0168] As a concrete example, if a customer at 1-2-3, Shibuya-ku, Tokyo, orders pizza and pasta at 6:00 p.m., the delivery person enters this information into their smartphone. The server collects and analyzes the availability and traffic information of partner restaurants in real time to select the most suitable restaurant. The selected restaurant and delivery route are then visually displayed, and the delivery person notifies the restaurant of their estimated arrival time. Finally, the food is delivered to the customer via the shortest route using the Google Maps API.

[0169] An example of a prompt sentence is as follows:

[0170] "I'd like to deliver pizza and pasta to 1-2-3, Shibuya-ku, Tokyo by 6:00 PM. Please tell me the best restaurant and delivery route."

[0171] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0172] Step 1:

[0173] The user inputs the order details, delivery address, and delivery time into their smartphone. The input information is sent to the server. The input data may include, for example, "Deliver pizza and pasta to 1-2-3, Shibuya-ku, Tokyo by 6:00 PM." This provides the server with basic delivery information.

[0174] Step 2:

[0175] Based on the received order information, the server collects data on the business status of partner restaurants, food preparation time, and traffic conditions in real time. The server sends requests to the restaurant's management system API and traffic information API (e.g., Google Maps API), and receives restaurant business status and traffic information in response. This data is stored in a database.

[0176] Step 3:

[0177] The server uses the accumulated order details and real-time data as input data and applies them to an AI algorithm. The AI ​​algorithm uses TensorFlow's deep learning model to select the most suitable restaurant. The data is processed to perform a comprehensive evaluation that takes into account the restaurant's level of congestion, food preparation time, and traffic conditions. The output is the ID of the most suitable restaurant and a rating score.

[0178] Step 4:

[0179] The server then sends the extracted information about the optimal restaurant and the optimal route to that restaurant to the user's smartphone. The selected restaurant and detailed route are visually displayed on the user's device, allowing the delivery person to understand which restaurant to go to and which route to take.

[0180] Step 5:

[0181] The user confirms the expected arrival information at the selected restaurant and sends it to the server. Input includes the expected arrival time at the restaurant and confirmation of the restaurant selection. The server receives this information and notifies the provider terminal (restaurant) of the order details and expected arrival time. This allows the restaurant to prepare the food efficiently.

[0182] Step 6:

[0183] The server uses the Google Maps API to provide the user with the optimal route from the selected restaurant to the delivery destination. The route information is displayed in a turn-by-turn navigation format, allowing the user to receive specific guidance on how to reach the delivery destination in the shortest time.

[0184] These steps will enable the "Smart Delivery Assistant" system to improve delivery efficiency and customer satisfaction.

[0185] 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.

[0186] The present invention combines an emotion engine with a system that quickly selects the most appropriate medical institution based on the patient's condition and symptoms, thereby realizing efficient transportation while reducing the user's mental burden. Specific embodiments of the present invention are described below.

[0187] First, the user (emergency responder) enters basic information about the patient and details of their symptoms into a terminal at the scene. The terminal is a tablet or smartphone, and can access the server via an internet connection. The information entered includes the patient's age, gender, specific symptoms (e.g., chest pain, shortness of breath), and vital signs (heart rate, blood pressure, oxygen saturation).

[0188] The server collects real-time data on medical institutions' medical capacity, available facilities, and number of available beds, and stores it in a database. This information is obtained from each medical institution's system via an API. In addition, the server collects traffic situation data from traffic management centers and public transportation databases, and stores this data in the database.

[0189] Next, after receiving the patient information entered by the user, the server uses an AI algorithm to integrate it with information from the accumulated database and select a medical institution suitable for the patient's symptoms in real time. The AI ​​algorithm estimates the specialty corresponding to the patient's symptoms and lists medical institutions with specialists on-site as candidates. It also takes into account traffic data to analyze the route that will allow the patient to be transported quickly.

[0190] A list of selected medical institutions is displayed on the user's device along with the optimal route. This list includes detailed information such as the number of available beds at each institution, whether they have specialists, and travel time. The emotion engine then recognizes the user's emotions in real time and adjusts the interface based on those emotions. For example, if the user is feeling stressed, the interface will be simplified and important information will be highlighted.

[0191] When the user selects the medical institution to which the patient will be transported, that information is sent to the server. The server then notifies the selected medical institution of the patient's expected arrival information. The notification includes the patient's symptoms, vital signs, and estimated arrival time, allowing the medical institution to quickly prepare to accept the patient.

[0192] The server also uses an emotion engine to provide alerts and guidance based on the user's emotional state. For example, if the user is feeling anxious, the server will display an alert to help them regain their composure and provide instructions on the appropriate next steps. This makes it possible to transport the patient to a medical facility quickly and appropriately while reducing the user's mental burden.

[0193] Finally, the server provides the user with optimal route information to the selected medical institution. The device receives this information and provides visual navigation to the user, ensuring that the ambulance arrives at the hospital in the shortest time possible and that the patient receives appropriate treatment promptly.

[0194] As a concrete example, consider the case of a 50-year-old man complaining of chest pain. The user (paramedic) enters the necessary information into the terminal and sends it to the server. The server determines that cardiac surgery is necessary and lists the most suitable hospitals from multiple hospitals in the area. The emotion engine recognizes the user's stress, simplifies the interface, and highlights important information. Finally, the user selects the recommended Hospital A, and Hospital A, upon receiving a notification from the server, prepares to accept the patient. At the same time, the user transports the patient to Hospital A via the optimal route, ensuring prompt treatment.

[0195] The present invention improves the efficiency of the process for selecting a destination for emergency medical care and reduces the mental burden on users, thereby enabling patients to be seen and treated quickly.

[0196] The processing flow will be explained below.

[0197] Step 1: User enters patient information

[0198] On-site, users enter information into the terminal, such as the patient's age, gender, specific symptoms (e.g., chest pain, shortness of breath), and vital signs (heart rate, blood pressure, oxygen saturation).

[0199] The device stores this entered information and transmits the data to the server using a secure communication protocol (e.g., HTTPS).

[0200] Step 2: Data collection by the server

[0201] The server periodically collects data from each medical institution regarding their medical capacity, available facilities, and number of available beds, and stores it in a database. This information is automatically retrieved from each medical institution's system via an API.

[0202] The server collects traffic situation data in real time from traffic management centers and public transportation databases, and also stores this data in the database.

[0203] Step 3: AI analysis by server

[0204] The server integrates the patient information received from the user with medical institution data and transportation data stored in the database.

[0205] The server uses AI algorithms to analyze the patient's symptoms and estimate the required specialty (e.g., cardiac surgery, internal medicine).

[0206] The server lists medical institutions that correspond to the estimated specialty and calculates the optimal transport route taking into account traffic data.

[0207] Step 4: Present a list of medical institutions

[0208] Based on the results of the AI ​​analysis, the server generates a list of recommended medical institutions, including details such as the number of available beds, availability of specialists, and travel time.

[0209] The server transmits the generated list to the user terminal.

[0210] The terminal displays the received list of medical institutions to the user.

[0211] Step 5: Emotion Recognition with the Emotion Engine

[0212] The device uses a built-in emotion engine to recognize the user's emotions (e.g., stress, impatience, calmness) in real time.

[0213] The emotion engine adjusts the interface based on the user's emotional state: for example, if the user is stressed, it simplifies the interface and highlights important information.

[0214] Step 6: User selects medical institution

[0215] The user selects a hospital to which the patient will be transported from the displayed list of medical institutions.

[0216] The terminal transmits the selected medical institution information to the server.

[0217] Step 7: Server Sends Notification

[0218] The server notifies the selected medical institution of the patient's expected arrival information, including the patient's symptoms, vital signs, and estimated time of arrival.

[0219] Medical institutions will receive notification and prepare to accept the patient.

[0220] Step 8: Emotion Engine provides guidance

[0221] The server uses an emotion engine to provide alerts and guidance based on the user's emotional state: if the user is panicking, an alert will be displayed to help them regain their composure and provide appropriate next steps.

[0222] Step 9: Optimal Route Guidance

[0223] The server transmits information on the optimal route to the selected medical institution to the user terminal.

[0224] The device then displays the received route guidance information to the user and provides navigation, allowing the ambulance to arrive at the hospital in the shortest time possible and allowing the patient to receive appropriate treatment promptly.

[0225] Example 2

[0226] 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."

[0227] In emergency medical care, it is necessary to select and transport a patient to the appropriate medical institution quickly and appropriately according to the patient's condition. However, the current system takes time to gather information, select a medical institution, and provide transport route guidance, and this places a heavy mental burden on emergency medical personnel. Furthermore, there is a lack of real-time transport route guidance that responds to changes in traffic conditions. This is an obstacle to providing prompt medical care to patients.

[0228] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0229] In this invention, the server includes means for inputting information on the patient's condition and symptoms, means for collecting information on medical institutions' medical treatment capabilities, available facilities, and number of available beds and storing it in a database, means for collecting information on local traffic conditions and storing it in a database, means for selecting the medical institution most suitable for the patient's symptoms in real time using an AI algorithm based on the accumulated information, means for displaying a list of the selected medical institutions, means for notifying the medical institution selected by the user of the patient's expected arrival information, means for guiding the user to the selected medical institution along the optimal route, and an emotion engine for recognizing the user's emotional state in real time and adjusting the interface. This enables faster emergency medical care and reduces the mental burden on emergency medical personnel.

[0230] "Information about the patient's condition and symptoms" includes information such as the patient's age, sex, specific symptoms (e.g., chest pain or shortness of breath), and vital signs (heart rate, blood pressure, oxygen saturation).

[0231] "Medical capabilities of medical institutions" is information indicating the medical departments and specialties of medical institutions, whether or not they have specialists, and the types of treatments and procedures they can handle.

[0232] "Available facilities" refers to information including the types of medical equipment that can be used within the medical institution, their operating status, and available testing facilities.

[0233] The "number of available beds" is information indicating the number of hospital beds currently available within a medical institution.

[0234] "Traffic conditions within the area" refers to information about the transportation route when a patient is transported, including the effects of traffic congestion, road construction, accidents, and the like.

[0235] An "AI algorithm" is a program or model that uses machine learning and artificial intelligence techniques to analyze data and select the most appropriate medical institution to address a patient's symptoms.

[0236] The "list of selected medical institutions" is a list of medical institutions that are most suitable for the patient's symptoms, selected by an AI algorithm.

[0237] "Patient arrival schedule information" is information provided to the selected medical institution, including the patient's current condition, vital signs, and expected arrival time.

[0238] The "optimal route" is a recommended route for transporting a patient to a selected medical institution in the shortest time possible, taking into account real-time traffic conditions.

[0239] An "emotion engine" is software or algorithms that recognize a user's emotional state in real time and adjust the interface based on that emotion.

[0240] This invention relates to a system for emergency medical care that quickly selects the most appropriate medical institution based on the patient's condition and symptoms, and realizes efficient transportation while reducing the user's mental burden. Specific embodiments for carrying out the invention are described below.

[0241] Hardware and software used

[0242] 1. User device (tablet, smartphone, etc.)

[0243] Server is accessible via an internet connection

[0244] Provides an input interface (a form for entering patient information)

[0245] 2. Server

[0246] Database: Accumulates information on medical institutions (treatment capacity, available facilities, number of available beds) and traffic conditions

[0247] AI algorithms: Models using machine learning frameworks such as TensorFlow and PyTorch

[0248] Emotion engine: an algorithm that recognizes user emotions and adjusts the interface

[0249] 3. Software

[0250] Traffic management system: Real-time traffic situation

[0251] API: Obtaining medical institution information

[0252] Google Maps and Waze: Providing routes

[0253] System Description

[0254] 1. Enter patient information

[0255] At the scene, the user (emergency responder) enters the patient's age, gender, specific symptoms (e.g., chest pain, difficulty breathing), and vital signs (heart rate, blood pressure, oxygen saturation) into the terminal.

[0256] The terminal formats the data entered by the user and immediately transmits it to the server.

[0257] 2. Data collection

[0258] The device transmits the entered patient information to a server in real time via an internet connection.

[0259] The server collects data via API about each medical institution's medical capacity, available facilities, number of available beds, and traffic conditions within the area, and stores the data in a database.

[0260] 3. Selection of medical institution

[0261] Once the server receives the patient information entered by the user, it uses an AI algorithm to integrate the information from the database and select the most appropriate medical institution to address the patient's symptoms in real time.

[0262] The AI ​​algorithm estimates the specialty that corresponds to the patient's symptoms and lists medical institutions where that specialist is on-site.

[0263] The server also takes into account traffic data to evaluate and analyze the fastest possible transportation routes.

[0264] 4. Use of Emotion Engine

[0265] The device is equipped with an emotion engine that recognizes the user's emotions in real time, specifically by measuring the user's emotions from facial expressions and voice via a camera and microphone.

[0266] The emotion engine simplifies the interface and highlights important information when the user is feeling stressed.

[0267] 5. Transportation support

[0268] The user selects a recommended medical institution on the terminal, and the terminal transmits the information to the server.

[0269] The server notifies the selected medical institution of the patient's expected arrival information, and the medical institution promptly begins preparations to accept the patient.

[0270] The server provides the user's terminal with optimal route information to the selected medical institution, and the user receives visual navigation while transporting the patient.

[0271] Specific examples

[0272] As a concrete example, consider the case where a 50-year-old man complains of chest pain. The processing procedure in this case is as follows:

[0273] 1. The user (emergency medical personnel) enters the necessary information into the terminal (e.g., 50-year-old male, chest pain).

[0274] 2. The server receives this information, determines that a cardiac specialist is needed, and lists multiple hospitals in the area (e.g., Hospitals A, B, and C).

[0275] 3. The server calculates the optimal route to each hospital based on traffic data.

[0276] 4. The emotion engine recognizes the user's stress, simplifies the interface and highlights important information.

[0277] 5. The user selects the recommended Hospital A, and the information is sent to Hospital A via the server.

[0278] 6. The server notifies Hospital A of the expected arrival information, and the optimal transport route is displayed on the user's terminal.

[0279] Example prompts for generative AI models

[0280] "A 50-year-old man complains of chest pain. The paramedic inputs the necessary information into the terminal. Please explain the specific steps involved in the system, which uses an AI algorithm to select the appropriate medical institution and an emotion engine to adjust the interface to quickly transport the patient via the optimal route."

[0281] This invention combines advanced AI technology with human emotion recognition to simultaneously speed up emergency medical care and reduce the mental burden on users.

[0282] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0283] Step 1:

[0284] Entering patient information

[0285] At the scene, the user (emergency medical personnel) enters the patient's age, gender, specific symptoms (e.g., chest pain, difficulty breathing), and vital signs (heart rate, blood pressure, oxygen saturation) into a device such as a tablet or smartphone.

[0286] Input: Information about the patient's condition and symptoms.

[0287] Output: The input patient information is formatted and converted into a format that can be sent to the server.

[0288] What happens: The device validates the entered data (e.g., checking for required fields) and converts it into the required format. It then sends this information to the server via an internet connection.

[0289] Step 2:

[0290] Data collection

[0291] The terminal transmits the entered patient information to a server in real time via an internet connection.

[0292] The server collects data on each medical institution's medical capabilities, available facilities, number of available beds, and local traffic conditions via API and stores it in a database.

[0293] Input: Patient information, medical institution information, traffic information.

[0294] Output: Consolidated information stored in a database.

[0295] Specific operation: The server uses an API that is updated every minute to collect necessary data from medical institutions and traffic management centers, and stores this data sequentially in a database.

[0296] Step 3:

[0297] Selection of medical institutions

[0298] When the server receives the patient information entered by the user, it uses an AI algorithm to integrate the information from the accumulated database and selects the most appropriate medical institution to address the patient's symptoms in real time.

[0299] Input: Patient information, medical institution and transportation information stored in the database.

[0300] Output: A list of the best medical institutions.

[0301] Specific operation: An AI algorithm (built using TensorFlow and PyTorch) is executed to evaluate and list each medical institution, taking into account their medical capabilities to respond to the patient's symptoms, the availability of specialists, the number of available beds, and traffic conditions.

[0302] Step 4:

[0303] Displaying results and using the emotion engine

[0304] The server transmits a list of selected medical institutions and the optimal route to the user terminal.

[0305] The device is equipped with an emotion engine that recognizes the user's emotional state in real time and adjusts the interface accordingly.

[0306] Input: list of optimal medical institutions, user sentiment.

[0307] Output: A list of the best medical institutions with a user-friendly interface.

[0308] Specific operation: The device recognizes the user's emotions through the camera and microphone (e.g., facial expressions, tone of voice), and simplifies the interface and highlights important information if the user is feeling stressed.

[0309] Step 5:

[0310] Delivery destination selection and notification

[0311] The user selects a destination from a list of medical institutions displayed on the terminal.

[0312] The terminal transmits information about the selected medical institution to the server.

[0313] The server notifies the selected medical institution of the patient's expected arrival information.

[0314] Input: User selected medical institution, patient information.

[0315] Output: Notification of expected arrival information to medical institution.

[0316] Specific operation: The user selects a medical institution using the terminal interface, and the selection information is sent to the server. The server then notifies the selected medical institution of the patient's symptoms, vital signs, and estimated arrival time.

[0317] Step 6:

[0318] Transportation assistance

[0319] The server provides the user terminal with optimal route information to the selected medical institution.

[0320] The device integrates this route information with map data to provide visual navigation to the user.

[0321] Input: Selected medical institutions, transportation data.

[0322] Output: Visual navigation information.

[0323] Specific operation: The server calculates the optimal route using Google Maps or Waze and provides it to the user's device. The device displays the route guidance on a map, updates the route in real time, and guides the emergency responder.

[0324] This system enables prompt and appropriate transport of patients and reduces the mental burden on users.

[0325] (Application example 2)

[0326] 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."

[0327] In modern emergency medical care, it is important to respond quickly to a patient's condition and select the most appropriate medical institution. In particular, understanding traffic congestion and medical institution availability in real time and determining the appropriate route are essential to improving patient survival rates. However, emergency medical personnel are often under high stress, which makes it difficult for them to make optimal decisions. The present invention aims to solve these issues and achieve efficient and rapid patient transport by evaluating the user's emotional state in real time and providing an appropriate interface.

[0328] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0329] In this invention, the server includes a means for inputting information about the patient's condition and symptoms, a means for collecting information about medical institutions' medical treatment capabilities, available facilities, and the number of available beds and storing it in a database, and a means for collecting information about local traffic conditions and storing it in a database. This makes it possible to select the most appropriate medical institution to address the patient's symptoms in real time using an AI algorithm based on the accumulated information. In addition, by evaluating the user's emotional state in real time and adjusting the interface, it is possible to reduce the user's mental burden and enable quick and appropriate patient transport.

[0330] The "means for inputting information about a patient's condition and symptoms" refers to a system that allows emergency personnel and medical professionals to input a patient's vital signs and specific symptoms into an electronic device.

[0331] "Means of collecting information on medical institutions' medical treatment capabilities, available facilities, and number of available beds, and storing it in a database" refers to a system that collects data on medical treatment capabilities, facility status, and number of available beds from each medical institution in real time and centrally manages it.

[0332] "Means for collecting information on traffic conditions within the region and storing it in a database" refers to a system for collecting traffic information from traffic management centers and public transportation databases and storing it in a database in real time.

[0333] "Means for using AI algorithms to select the most appropriate medical institution to address a patient's symptoms in real time" is a system that uses AI technology to analyze collected data and select the medical institution that is most suitable for the patient's symptoms in real time.

[0334] The "means for displaying a list of selected medical institutions" is a system for displaying a list of medical institutions selected by the AI ​​algorithm on the user's device.

[0335] The "means for notifying the medical institution selected by the user of the patient's expected arrival information" is a communication system that allows emergency personnel to notify the medical institution selected by the emergency personnel of the patient's symptoms, vital signs, and expected arrival time.

[0336] The "means for providing guidance on the optimal route to the selected medical institution" is a system that analyzes the optimal route for an ambulance to quickly reach the selected medical institution and provides it as navigation information.

[0337] "Means for evaluating the user's emotional state in real time and adjusting the interface" refers to a system that evaluates the emotional state of emergency personnel in real time using an emotion engine or similar, and simplifies the user interface or highlights important information according to their stress level.

[0338] This system allows emergency personnel and medical professionals to input information about a patient's condition and symptoms, selects the most appropriate medical institution based on that information, and transports the patient quickly. This system uses an emotion engine to reduce the user's mental burden, enabling efficient and prompt response.

[0339] Specifically, paramedics use electronic devices such as smartphones to input basic information about the patient (age, gender, symptoms, etc.). The devices communicate with a server via the internet and send the input information to the server. The server then stores information on medical institutions' medical capabilities, available facilities, number of available beds, and local traffic conditions in a database, and uses an AI algorithm to select the most suitable medical institution.

[0340] The hardware and software used in this system are as follows:

[0341] Hardware: Smartphones, server machines

[0342] Software: Flask (a Python micro web framework), Emotion API, Navigation API

[0343] The server uses an AI algorithm to integrate the received patient information with medical institution and transportation data in the database and select the most suitable medical institution in real time. A list of selected medical institutions is displayed on the terminal, and the user can select the one they consider most suitable. After selection, the server notifies the medical institution of the patient's expected arrival information and provides the terminal with optimal route information, and transportation begins.

[0344] The emotion engine assesses the emotional state of the paramedic in real time and dynamically adjusts the user interface based on the results: for example, if the paramedic is in a high-stress state, the interface will be simplified and important information will be highlighted.

[0345] As a concrete example, consider a 50-year-old man complaining of chest pain. The paramedic uses his smartphone to enter the following information:

[0346] Patient's age: 50 years

[0347] Gender: Male

[0348] Symptoms: Chest pain

[0349] Vital signs: Heart rate 100, blood pressure 150 / 95, oxygen saturation 92

[0350] This information is sent to a server, where an AI algorithm determines that cardiac surgery is required and lists the most appropriate hospitals from multiple hospitals in the area. An emotion engine recognizes the paramedic's stress level and simplifies the interface to highlight important information. Finally, the paramedic selects a recommended hospital, and the information is sent to the server, which notifies the hospital. The user receives navigation information guiding them to the optimal route, ensuring a speedy transport.

[0351] Below are some example prompts to input to a generative AI model:

[0352] "A 50-year-old man complains of chest pain. The paramedic enters the information into a smartphone and sends it to a server. Please explain what data and process are used to suggest the most appropriate medical institution."

[0353] As described above, the present invention automates the process of selecting a transport destination for emergency medical care, thereby realizing a prompt and appropriate response while reducing the mental burden on the user.

[0354] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0355] Step 1:

[0356] The user (emergency medical personnel) uses a device such as a smartphone to input basic patient information (age, gender, symptoms) and vital signs (heart rate, blood pressure, oxygen saturation). By inputting this data, the device prepares information on the patient's condition.

[0357] input:

[0358] Patient's age, sex, and symptoms

[0359] Vital signs (heart rate, blood pressure, oxygen saturation)

[0360] output:

[0361] Prepared patient status information

[0362] Step 2:

[0363] Using an internet connection, the device sends patient status information to a server, which receives the information and stores it in a database.

[0364] input:

[0365] Prepared patient status information

[0366] output:

[0367] Patient information sent to the server

[0368] Step 3:

[0369] The server collects data from medical institutions regarding their medical capacity, available facilities, and the number of available beds via API and stores it in a database. It also collects traffic data from the traffic management center in real time.

[0370] input:

[0371] Medical data from medical institutions

[0372] Traffic data from the Traffic Management Center

[0373] output:

[0374] Medical institution and transportation data stored in the database

[0375] Step 4:

[0376] The server inputs the accumulated information into an AI algorithm that selects the medical institution that best suits the patient's symptoms in real time. The AI ​​algorithm integrates and analyzes the patient's condition information, medical institution and transportation data.

[0377] input:

[0378] Patient status information

[0379] Medical institution and transportation data stored in the database

[0380] output:

[0381] A list of selected optimal medical institutions

[0382] Step 5:

[0383] The server sends a list of selected medical institutions to the terminal, which then displays it to the user, who then selects an appropriate medical institution from the list.

[0384] input:

[0385] A list of selected optimal medical institutions

[0386] output:

[0387] List of medical institutions displayed to users

[0388] Step 6:

[0389] The terminal sends the information about the medical institution selected by the user to the server, and the server notifies the selected medical institution of the patient's expected arrival information.

[0390] input:

[0391] Medical institution information selected by the user

[0392] output:

[0393] Arrival information sent to selected medical institutions

[0394] Step 7:

[0395] The server analyzes the optimal route to the selected medical institution and sends the information to the terminal, which then uses the received route information to provide navigation.

[0396] input:

[0397] Selected medical institution information

[0398] Traffic Data

[0399] output:

[0400] Optimal route information for navigation

[0401] Step 8:

[0402] The device uses an emotion engine to assess the emotional state of the paramedic in real time, and automatically adjusts the interface based on that emotional state, highlighting important information and simplifying operation even when the paramedic is in a high-stress situation.

[0403] input:

[0404] Data on the emotional state of emergency personnel

[0405] output:

[0406] Adjusted User Interface

[0407] This step will streamline the entire system and allow emergency responses to be carried out quickly and accurately.

[0408] 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.

[0409] 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.

[0410] 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.

[0411] [Second embodiment]

[0412] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0413] 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.

[0414] 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).

[0415] 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.

[0416] 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.

[0417] 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).

[0418] 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.

[0419] 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.

[0420] 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.

[0421] 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.

[0422] 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.

[0423] 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."

[0424] The present invention is a system for quickly selecting the most suitable medical institution based on the condition and symptoms of a patient, and a specific embodiment for implementing this system will be described.

[0425] First, the user (emergency responder) enters basic information about the patient and details of their symptoms into a terminal at the scene. The terminal is a tablet or smartphone, and can access the server via an internet connection. The information entered includes the patient's age, gender, specific symptoms (e.g., chest pain, shortness of breath), and vital signs (heart rate, blood pressure, oxygen saturation).

[0426] The server then collects real-time data on medical institutions' medical capacity, available facilities, and the number of available beds, and stores it in a database. This information is periodically retrieved from each hospital's system via an API. The server also collects traffic situation data from traffic management centers and public transportation databases. This data is also stored in the database.

[0427] After receiving the patient information entered by the user, the server uses an AI algorithm to integrate it with information from the accumulated database and select a medical institution that best suits the patient's symptoms in real time. The AI ​​algorithm estimates the specialty corresponding to the patient's symptoms and lists hospitals with doctors with that specialty as candidates. In addition, it takes into account traffic data to analyze the route that will allow the patient to be transported quickly.

[0428] A list of selected medical institutions is displayed on the user's device along with the optimal route. The list also includes detailed information such as the number of available beds at each hospital, whether or not specialists are available, and travel time. The user can refer to this list to select the medical institution that will be the final destination for the patient.

[0429] When a user selects a hospital, the selection information is sent from the terminal to the server, and the server notifies the selected medical institution of the patient's expected arrival information. The notification includes the patient's symptoms, vital signs, estimated arrival time, etc., allowing the hospital to quickly prepare to accept the patient.

[0430] Finally, the server provides the user with optimal route information to the selected medical institution. The device receives this information and provides visual navigation to the user, ensuring that the ambulance arrives at the hospital in the shortest time possible and that the patient receives appropriate treatment promptly.

[0431] As a concrete example, consider the case of a 50-year-old man complaining of chest pain. The user (ambulance crew member) enters the necessary information into the terminal and sends it to the server. The server determines that cardiac surgery is necessary and lists the most suitable hospitals from multiple hospitals in the area. Finally, the user selects the recommended Hospital A, and upon receiving a notification from the server, Hospital A prepares to accept the patient. At the same time, the user transports the patient to Hospital A via the optimal route, and prompt treatment is provided.

[0432] As a result, the present invention streamlines the process of selecting a destination for emergency medical care, enabling patients to be seen and treated quickly.

[0433] The processing flow will be explained below.

[0434] Step 1: User enters patient information

[0435] On-site, users enter information into the terminal, such as the patient's age, gender, specific symptoms (e.g., chest pain, shortness of breath), and vital signs (heart rate, blood pressure, oxygen saturation).

[0436] The device stores the entered information and sends the data to the server using a communication protocol (such as HTTPS).

[0437] Step 2: Data collection by the server

[0438] The server periodically collects data on each medical institution's medical capacity, available facilities, and number of available beds. This data is obtained through the hospital system's API.

[0439] The server collects real-time traffic situation data (e.g., congestion information and road conditions) from traffic management centers and public transportation databases.

[0440] The server stores the collected data in a database.

[0441] Step 3: AI analysis by server

[0442] The server integrates the patient information received from the user with existing medical institution and transportation data stored in a database.

[0443] The server uses AI algorithms to predict the specialty (e.g., cardiac surgery, internal medicine) that a patient needs to see based on their symptoms.

[0444] The server lists medical institutions according to the estimated specialty and calculates the optimal route taking into account traffic data.

[0445] Step 4: Present a list of medical institutions

[0446] Based on the analysis, the server generates a list of recommended medical institutions, including details such as the number of available beds, availability of specialists, and travel time.

[0447] The server transmits the generated list to the user terminal.

[0448] The terminal displays the received list of medical institutions to the user.

[0449] Step 5: User selects medical institution

[0450] The user selects a hospital to which the patient will be transported from the displayed list of medical institutions.

[0451] The terminal transmits the selected medical institution information to the server.

[0452] Step 6: Server Sends Notification

[0453] The server notifies the selected medical institution of the patient's expected arrival information, including the patient's symptoms, vital signs, and estimated time of arrival.

[0454] Medical institutions will receive notification and prepare to accept the patient.

[0455] Step 7: Optimal route guidance

[0456] The server transmits information on the optimal route to the selected medical institution to the user terminal.

[0457] The device then displays the received route guidance information to the user and provides navigation, allowing the ambulance to arrive at the hospital in the shortest time possible and ensuring prompt treatment for the patient.

[0458] Example 1

[0459] 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."

[0460] Conventional emergency medical systems have had difficulty quickly selecting the appropriate medical institution based on the patient's condition and symptoms. As a result, it took time to determine where to transport the patient, which could delay appropriate treatment. In addition, it was not possible to collect and comprehensively analyze information such as the medical capacity, number of available beds, and traffic conditions of each medical institution in real time, which led to issues with selecting the optimal transport route. There is a need to solve these problems.

[0461] 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.

[0462] In this invention, the server includes means for inputting information on the patient's condition and symptoms, means for collecting information on medical institutions' medical treatment capabilities, available facilities, and number of available beds and storing it in a database, means for collecting information on local traffic conditions and storing it in a database, means for selecting the medical institution most suitable for the patient's symptoms in real time using an AI algorithm based on the accumulated information, means for displaying a list of the selected medical institutions, means for notifying the medical institution selected by the user of the patient's expected arrival information, means for guiding the user on the optimal route to the selected medical institution, means for estimating medical institutions with specialized fields based on patient information using an AI algorithm, and means for providing information on the optimal route for patient transport in real time. This enables the rapid selection of an appropriate medical institution and guidance on the optimal transport route.

[0463] "Means for inputting information about a patient's condition and symptoms" refers to a device or interface that allows a user to input basic information about a patient and details of the patient's symptoms, such as the patient's age, gender, specific symptoms, and vital signs.

[0464] "Means of collecting information on medical institutions' medical treatment capabilities, available facilities, and number of available beds, and storing it in a database" refers to the functions and systems that obtain information on medical treatment capabilities, available facilities, and number of available beds from each medical institution and store that data in a database.

[0465] "Means of collecting information on traffic conditions within an area and storing it in a database" refers to functions and systems that collect information on traffic conditions obtained from traffic management centers and public transportation databases and store that data in a database.

[0466] "Means of using AI algorithms to select the most appropriate medical institution to address a patient's symptoms in real time" refers to a function or system that uses AI algorithms based on collected data to instantly select the medical institution that is most suitable for the patient's symptoms.

[0467] "Means for displaying a list of selected medical institutions" refers to a device or system that displays to the user a list of candidate medical institutions selected by an AI algorithm.

[0468] "Means for notifying the medical institution selected by the user of the patient's expected arrival information" refers to a function or system that sends the patient's expected arrival information to the medical institution selected by the user, prompting the hospital to prepare for the patient's arrival.

[0469] "Means for providing guidance on the optimal route to the selected medical institution" refers to a device or system that provides route information for the shortest route to the medical institution selected by the user and performs navigation.

[0470] "Means of using an AI algorithm to estimate the appropriate specialized medical institution based on patient information" refers to a function or system that analyzes patient information and uses an AI algorithm to estimate the appropriate specialized medical institution that can respond to the patient's symptoms.

[0471] "Means for providing real-time information on optimal patient transport routes" refers to functions and systems that calculate and provide optimal routes for quickly transporting patients based on real-time traffic information.

[0472] The present invention provides a system for quickly selecting the most appropriate medical institution based on the patient's condition and symptoms. This section describes a specific embodiment of the system. The system includes a user (an emergency medical technician), a terminal (a tablet or a smartphone), and a server.

[0473] First, the user enters basic information about the patient and details of their symptoms into the device on-site. The information entered includes the patient's age, gender, specific symptoms (e.g., chest pain, shortness of breath), and vital signs (heart rate, blood pressure, oxygen saturation). The device is connected to the Internet, and the entered data is sent to a server. The device used is a tablet or smartphone, and data entry and transmission are performed via a dedicated application.

[0474] The server then collects data on medical capacity, available facilities, and the number of available beds from multiple medical institutions. This information is periodically obtained through each medical institution's API. The server also collects traffic status data from traffic management centers and public transportation databases and stores it in a database. This ensures that the server always has the latest medical and transportation information.

[0475] Based on the received patient information, the server uses an AI algorithm to select the most appropriate medical institution. The AI ​​algorithm estimates the specialty corresponding to the patient's symptoms and lists medical institutions with that specialist on-site as candidates. It also takes into account traffic data to analyze the optimal transport route. The AI ​​algorithm uses a machine learning model to make optimal decisions based on past data.

[0476] A list of selected medical institutions is displayed on the user's terminal along with detailed information (e.g., number of available beds, availability of specialists, travel time). The user refers to this list to select the final destination. Once the user makes a selection, the information is sent from the terminal to the server, which then notifies the selected medical institution of the patient's expected arrival information. The notification includes the patient's symptoms, vital signs, and estimated arrival time.

[0477] The server then provides the user's device with optimal route information to the selected medical institution. Based on this information, the device performs visual navigation and guides the user to the medical institution in the shortest time possible. This allows the ambulance to arrive at the hospital quickly and the patient to receive appropriate treatment promptly.

[0478] As a concrete example, consider the case of a 50-year-old man complaining of chest pain. The user (paramedic) enters the necessary information into the device and sends it. The server uses an AI algorithm to determine that cardiac surgery is necessary and lists the most suitable hospitals from multiple hospitals in the area. The user selects Hospital A from the displayed list, and upon receiving a notification from the server, Hospital A prepares to accept the patient. At the same time, the user transports the patient to Hospital A via the optimal route, allowing for prompt treatment.

[0479] Example prompt sentence:

[0480] "A 50-year-old man complains of chest pain. The necessary information is entered and sent to the server, which uses an AI algorithm to list the most suitable hospitals. Finally, Hospital A is selected, a notification is sent, and the patient is transported via the optimal route."

[0481] This system will streamline the process of selecting a destination for emergency medical care, enabling patients to be seen and treated quickly.

[0482] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0483] Step 1:

[0484] User input of patient information

[0485] On-site, the user enters the patient's basic information (age, gender, specific symptoms) and vital signs (heart rate, blood pressure, oxygen saturation) into the terminal. The terminal is connected to the Internet and sends this information to the server. Specifically, the user enters the necessary information into the input form displayed on the tablet screen and presses the "Submit" button.

[0486] Input: Patient's age, gender, symptoms, vital signs

[0487] Output: Patient information transferred to the server

[0488] Step 2:

[0489] Server collects data and updates the database

[0490] The server collects data on medical capacity, available facilities, and number of available beds from each medical institution via API. It also collects data on traffic conditions from the traffic management center and public transportation database. This data is periodically saved in the database. Specifically, the server calls the API and writes the acquired data to the database.

[0491] Input: Real-time data obtained from medical institutions and traffic control centers

[0492] Output: Latest medical and traffic information stored in a database

[0493] Step 3:

[0494] Server analyzes patient information and selects medical institutions

[0495] The server analyzes the patient information received from the user and uses an AI algorithm to select the most suitable medical institution. The AI ​​algorithm estimates the specialty corresponding to the patient's symptoms and lists medical institutions with that specialist. Specifically, the server launches the AI ​​model, compares the patient information with the contents of the database, and lists the most suitable medical institutions.

[0496] Input: Patient information received from the user, medical institution information stored in the database

[0497] Output: A list of the best medical institutions

[0498] Step 4:

[0499] Optimal route analysis by the server

[0500] The server analyzes the traffic data to find the optimal route for quickly transporting the patient. This route information is provided to the user's device along with a list of candidate medical institutions. Specifically, the server analyzes the traffic data to calculate the shortest route and adds the result to the list.

[0501] Input: Traffic information stored in the database

[0502] Output: A list of medical institutions with optimal routes

[0503] Step 5:

[0504] User selection of medical institution

[0505] The user selects the most suitable hospital from a list of medical institutions displayed on the terminal. Once the selection is confirmed, the information is sent from the terminal to the server. Specifically, the user selects a hospital from the list and presses the "Select" button.

[0506] Input: A list of candidate medical institutions provided by the server

[0507] Output: The information of the selected medical institution is sent to the server.

[0508] Step 6:

[0509] Notification to medical institutions by the server

[0510] The server notifies the selected medical institution of the patient's expected arrival information. The notification includes the patient's symptoms, vital signs, and estimated arrival time. Specifically, the server sends the notification to the selected hospital's system via an API.

[0511] Input: Information about the medical institution selected by the user

[0512] Output: Medical institutions to be notified

[0513] Step 7:

[0514] Server provides optimal route information

[0515] The server provides the user's device with optimal route information to the selected medical institution. The device uses this information to perform visual navigation. Specifically, the device's map application begins navigation based on the route data sent from the server.

[0516] Input: Optimal route data to selected medical institutions

[0517] Output: User terminal that displays the optimal route

[0518] Step 8:

[0519] User transports patient and starts treatment

[0520] The user follows the navigation on the device to transport the patient to the selected medical institution. After arriving at the hospital, the patient can receive appropriate treatment quickly. Specifically, the user follows the navigation to proceed along the monitored route, and upon arriving at the hospital, the patient is handed over to medical staff.

[0521] Input: Optimal route information provided by the server

[0522] Output: Patients who arrived at the selected medical institution

[0523] Through the above processing steps, this system streamlines the process of selecting a destination for emergency medical care, enabling patients to be seen and treated quickly.

[0524] (Application example 1)

[0525] 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."

[0526] Conventional food delivery systems lack efficient methods for delivery personnel to select the optimal restaurant and deliver meals quickly. This leads to delivery delays and selection errors, resulting in lower customer satisfaction. Furthermore, there are challenges, such as taking into account real-time traffic conditions and appropriately selecting restaurants based on their availability. To solve these challenges, a system is needed to improve delivery efficiency and accuracy.

[0527] 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.

[0528] In this invention, the server includes a means for inputting information about the user's status and needs, a means for collecting information about providers' service capabilities, available facilities, and availability and storing it in a database, and a means for collecting information about local traffic and resource conditions and storing it in a database. This allows the server to select the optimal provider in real time based on the collected information using an AI algorithm, enabling the delivery of services in response to the user's request. By utilizing a generative AI model, the collected information and the optimal provider and route can be displayed, notified, and guided, improving delivery efficiency and accuracy and customer satisfaction.

[0529] "Patient or User" refers to the individual receiving the service, i.e., the patient, delivery recipient, or orderer.

[0530] "Condition and needs" refers to information including the current health condition, symptoms, requests, and content of requests of the person receiving the service.

[0531] "Provider" refers to a medical institution, restaurant, or other business that provides a service.

[0532] "Service capabilities" refers to the provider's technical capabilities, staffing, response capabilities, and equipment availability.

[0533] "Available Facilities" refers to the equipment, tools, and resources available to the Provider to provide the Service.

[0534] "Availability" refers to reservations for the provider's services, available beds, delivery slots, availability of counters, etc.

[0535] "Traffic conditions" refers to the state of road congestion in the area, whether there is traffic congestion, and operation status.

[0536] "Resource status" refers to the status of personnel, equipment, vehicles, and other material resources available to the provider.

[0537] "AI algorithm" refers to an algorithm that analyzes input data and automatically makes the optimal selection.

[0538] "Real-time" means that information is acquired, processed, and displayed almost instantly.

[0539] "Generative AI model" refers to the AI ​​model used to analyze, display, notify, and guide collected information.

[0540] "Visual navigation" refers to a function that displays maps and directions to help delivery personnel and others reach their destination via the optimal route.

[0541] "Database" refers to a system in which accumulated information is managed and stored.

[0542] This invention is a "Smart Delivery Assistant" system for food delivery services that enables delivery personnel to select the most suitable restaurant and deliver food efficiently. This system consists of a server, a user terminal, and a provider terminal.

[0543] First, the delivery person (user) inputs the order details, delivery address, and delivery time into their smartphone. The server receives this information and collects the real-time data described below.

[0544] The server collects real-time data on the business status of partner restaurants, food preparation times, and traffic conditions. This data is obtained from APIs of restaurant management systems and traffic information APIs (e.g., Google Maps API) and stored in a database.

[0545] Based on this collected data, the server uses an AI algorithm, built using a deep learning framework such as TensorFlow, to comprehensively analyze the order details, the supplier's situation, and traffic information to select the most suitable restaurant.

[0546] The selected restaurant and the optimal delivery route information are visually displayed on the delivery person's smartphone. When the delivery person sends information about their expected arrival time to the selected restaurant to the server, the server notifies the restaurant's device of the order and the estimated arrival time. This allows the restaurant to prepare the food efficiently.

[0547] Furthermore, the server uses the Google Maps API to provide delivery staff with detailed route information to the delivery destination, allowing them to deliver food in the shortest time possible, leading to increased customer satisfaction.

[0548] As a concrete example, if a customer at 1-2-3, Shibuya-ku, Tokyo, orders pizza and pasta at 6:00 p.m., the delivery person enters this information into their smartphone. The server collects and analyzes the availability and traffic information of partner restaurants in real time to select the most suitable restaurant. The selected restaurant and delivery route are then visually displayed, and the delivery person notifies the restaurant of their estimated arrival time. Finally, the food is delivered to the customer via the shortest route using the Google Maps API.

[0549] An example of a prompt sentence is as follows:

[0550] "I'd like to deliver pizza and pasta to 1-2-3, Shibuya-ku, Tokyo by 6:00 PM. Please tell me the best restaurant and delivery route."

[0551] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0552] Step 1:

[0553] The user inputs the order details, delivery address, and delivery time into their smartphone. The input information is sent to the server. The input data may include, for example, "Deliver pizza and pasta to 1-2-3, Shibuya-ku, Tokyo by 6:00 PM." This provides the server with basic delivery information.

[0554] Step 2:

[0555] Based on the received order information, the server collects data on the business status of partner restaurants, food preparation time, and traffic conditions in real time. The server sends requests to the restaurant's management system API and traffic information API (e.g., Google Maps API), and receives restaurant business status and traffic information in response. This data is stored in a database.

[0556] Step 3:

[0557] The server uses the accumulated order details and real-time data as input data and applies them to an AI algorithm. The AI ​​algorithm uses TensorFlow's deep learning model to select the most suitable restaurant. The data is processed to perform a comprehensive evaluation that takes into account the restaurant's level of congestion, food preparation time, and traffic conditions. The output is the ID of the most suitable restaurant and a rating score.

[0558] Step 4:

[0559] The server then sends the extracted information about the optimal restaurant and the optimal route to that restaurant to the user's smartphone. The selected restaurant and detailed route are visually displayed on the user's device, allowing the delivery person to understand which restaurant to go to and which route to take.

[0560] Step 5:

[0561] The user confirms the expected arrival information at the selected restaurant and sends it to the server. Input includes the expected arrival time at the restaurant and confirmation of the restaurant selection. The server receives this information and notifies the provider terminal (restaurant) of the order details and expected arrival time. This allows the restaurant to prepare the food efficiently.

[0562] Step 6:

[0563] The server uses the Google Maps API to provide the user with the optimal route from the selected restaurant to the delivery destination. The route information is displayed in a turn-by-turn navigation format, allowing the user to receive specific guidance on how to reach the delivery destination in the shortest time.

[0564] These steps will enable the "Smart Delivery Assistant" system to improve delivery efficiency and customer satisfaction.

[0565] 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.

[0566] The present invention combines an emotion engine with a system that quickly selects the most appropriate medical institution based on the patient's condition and symptoms, thereby realizing efficient transportation while reducing the user's mental burden. Specific embodiments of the present invention are described below.

[0567] First, the user (emergency responder) enters basic information about the patient and details of their symptoms into a terminal at the scene. The terminal is a tablet or smartphone, and can access the server via an internet connection. The information entered includes the patient's age, gender, specific symptoms (e.g., chest pain, shortness of breath), and vital signs (heart rate, blood pressure, oxygen saturation).

[0568] The server collects real-time data on medical institutions' medical capacity, available facilities, and number of available beds, and stores it in a database. This information is obtained from each medical institution's system via an API. In addition, the server collects traffic situation data from traffic management centers and public transportation databases, and stores this data in the database.

[0569] Next, after receiving the patient information entered by the user, the server uses an AI algorithm to integrate it with information from the accumulated database and select a medical institution suitable for the patient's symptoms in real time. The AI ​​algorithm estimates the specialty corresponding to the patient's symptoms and lists medical institutions with specialists on-site as candidates. It also takes into account traffic data to analyze the route that will allow the patient to be transported quickly.

[0570] A list of selected medical institutions is displayed on the user's device along with the optimal route. This list includes detailed information such as the number of available beds at each institution, whether they have specialists, and travel time. The emotion engine then recognizes the user's emotions in real time and adjusts the interface based on those emotions. For example, if the user is feeling stressed, the interface will be simplified and important information will be highlighted.

[0571] When the user selects the medical institution to which the patient will be transported, that information is sent to the server. The server then notifies the selected medical institution of the patient's expected arrival information. The notification includes the patient's symptoms, vital signs, and estimated arrival time, allowing the medical institution to quickly prepare to accept the patient.

[0572] The server also uses an emotion engine to provide alerts and guidance based on the user's emotional state. For example, if the user is feeling anxious, the server will display an alert to help them regain their composure and provide instructions on the appropriate next steps. This makes it possible to transport the patient to a medical facility quickly and appropriately while reducing the user's mental burden.

[0573] Finally, the server provides the user with optimal route information to the selected medical institution. The device receives this information and provides visual navigation to the user, ensuring that the ambulance arrives at the hospital in the shortest time possible and that the patient receives appropriate treatment promptly.

[0574] As a concrete example, consider the case of a 50-year-old man complaining of chest pain. The user (paramedic) enters the necessary information into the terminal and sends it to the server. The server determines that cardiac surgery is necessary and lists the most suitable hospitals from multiple hospitals in the area. The emotion engine recognizes the user's stress, simplifies the interface, and highlights important information. Finally, the user selects the recommended Hospital A, and Hospital A, upon receiving a notification from the server, prepares to accept the patient. At the same time, the user transports the patient to Hospital A via the optimal route, ensuring prompt treatment.

[0575] The present invention improves the efficiency of the process for selecting a destination for emergency medical care and reduces the mental burden on users, thereby enabling patients to be seen and treated quickly.

[0576] The processing flow will be explained below.

[0577] Step 1: User enters patient information

[0578] On-site, users enter information into the terminal, such as the patient's age, gender, specific symptoms (e.g., chest pain, shortness of breath), and vital signs (heart rate, blood pressure, oxygen saturation).

[0579] The device stores this entered information and transmits the data to the server using a secure communication protocol (e.g., HTTPS).

[0580] Step 2: Data collection by the server

[0581] The server periodically collects data from each medical institution regarding their medical capacity, available facilities, and number of available beds, and stores it in a database. This information is automatically retrieved from each medical institution's system via an API.

[0582] The server collects traffic situation data in real time from traffic management centers and public transportation databases, and also stores this data in the database.

[0583] Step 3: AI analysis by server

[0584] The server integrates the patient information received from the user with medical institution data and transportation data stored in the database.

[0585] The server uses AI algorithms to analyze the patient's symptoms and estimate the required specialty (e.g., cardiac surgery, internal medicine).

[0586] The server lists medical institutions that correspond to the estimated specialty and calculates the optimal transport route taking into account traffic data.

[0587] Step 4: Present a list of medical institutions

[0588] Based on the results of the AI ​​analysis, the server generates a list of recommended medical institutions, including details such as the number of available beds, availability of specialists, and travel time.

[0589] The server transmits the generated list to the user terminal.

[0590] The terminal displays the received list of medical institutions to the user.

[0591] Step 5: Emotion Recognition with the Emotion Engine

[0592] The device uses a built-in emotion engine to recognize the user's emotions (e.g., stress, impatience, calmness) in real time.

[0593] The emotion engine adjusts the interface based on the user's emotional state: for example, if the user is stressed, it simplifies the interface and highlights important information.

[0594] Step 6: User selects medical institution

[0595] The user selects a hospital to which the patient will be transported from the displayed list of medical institutions.

[0596] The terminal transmits the selected medical institution information to the server.

[0597] Step 7: Server Sends Notification

[0598] The server notifies the selected medical institution of the patient's expected arrival information, including the patient's symptoms, vital signs, and estimated time of arrival.

[0599] Medical institutions will receive notification and prepare to accept the patient.

[0600] Step 8: Emotion Engine provides guidance

[0601] The server uses an emotion engine to provide alerts and guidance based on the user's emotional state: if the user is panicking, an alert will be displayed to help them regain their composure and provide appropriate next steps.

[0602] Step 9: Optimal Route Guidance

[0603] The server transmits information on the optimal route to the selected medical institution to the user terminal.

[0604] The device then displays the received route guidance information to the user and provides navigation, allowing the ambulance to arrive at the hospital in the shortest time possible and allowing the patient to receive appropriate treatment promptly.

[0605] Example 2

[0606] 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."

[0607] In emergency medical care, it is necessary to select and transport a patient to the appropriate medical institution quickly and appropriately according to the patient's condition. However, the current system takes time to gather information, select a medical institution, and provide transport route guidance, and this places a heavy mental burden on emergency medical personnel. Furthermore, there is a lack of real-time transport route guidance that responds to changes in traffic conditions. This is an obstacle to providing prompt medical care to patients.

[0608] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0609] In this invention, the server includes means for inputting information on the patient's condition and symptoms, means for collecting information on medical institutions' medical treatment capabilities, available facilities, and number of available beds and storing it in a database, means for collecting information on local traffic conditions and storing it in a database, means for selecting the medical institution most suitable for the patient's symptoms in real time using an AI algorithm based on the accumulated information, means for displaying a list of the selected medical institutions, means for notifying the medical institution selected by the user of the patient's expected arrival information, means for guiding the user to the selected medical institution along the optimal route, and an emotion engine for recognizing the user's emotional state in real time and adjusting the interface. This enables faster emergency medical care and reduces the mental burden on emergency medical personnel.

[0610] "Information about the patient's condition and symptoms" includes information such as the patient's age, sex, specific symptoms (e.g., chest pain or shortness of breath), and vital signs (heart rate, blood pressure, oxygen saturation).

[0611] "Medical capabilities of medical institutions" is information indicating the medical departments and specialties of medical institutions, whether or not they have specialists, and the types of treatments and procedures they can handle.

[0612] "Available facilities" refers to information including the types of medical equipment that can be used within the medical institution, their operating status, and available testing facilities.

[0613] The "number of available beds" is information indicating the number of hospital beds currently available within a medical institution.

[0614] "Traffic conditions within the area" refers to information about the transportation route when a patient is transported, including the effects of traffic congestion, road construction, accidents, and the like.

[0615] An "AI algorithm" is a program or model that uses machine learning and artificial intelligence techniques to analyze data and select the most appropriate medical institution to address a patient's symptoms.

[0616] The "list of selected medical institutions" is a list of medical institutions that are most suitable for the patient's symptoms, selected by an AI algorithm.

[0617] "Patient arrival schedule information" is information provided to the selected medical institution, including the patient's current condition, vital signs, and expected arrival time.

[0618] The "optimal route" is a recommended route for transporting a patient to a selected medical institution in the shortest time possible, taking into account real-time traffic conditions.

[0619] An "emotion engine" is software or algorithms that recognize a user's emotional state in real time and adjust the interface based on that emotion.

[0620] This invention relates to a system for emergency medical care that quickly selects the most appropriate medical institution based on the patient's condition and symptoms, and realizes efficient transportation while reducing the user's mental burden. Specific embodiments for carrying out the invention are described below.

[0621] Hardware and software used

[0622] 1. User device (tablet, smartphone, etc.)

[0623] Server is accessible via an internet connection

[0624] Provides an input interface (a form for entering patient information)

[0625] 2. Server

[0626] Database: Accumulates information on medical institutions (treatment capacity, available facilities, number of available beds) and traffic conditions

[0627] AI algorithms: Models using machine learning frameworks such as TensorFlow and PyTorch

[0628] Emotion engine: an algorithm that recognizes user emotions and adjusts the interface

[0629] 3. Software

[0630] Traffic management system: Real-time traffic situation

[0631] API: Obtaining medical institution information

[0632] Google Maps and Waze: Providing routes

[0633] System Description

[0634] 1. Enter patient information

[0635] At the scene, the user (emergency responder) enters the patient's age, gender, specific symptoms (e.g., chest pain, difficulty breathing), and vital signs (heart rate, blood pressure, oxygen saturation) into the terminal.

[0636] The terminal formats the data entered by the user and immediately transmits it to the server.

[0637] 2. Data collection

[0638] The device transmits the entered patient information to a server in real time via an internet connection.

[0639] The server collects data via API about each medical institution's medical capacity, available facilities, number of available beds, and traffic conditions within the area, and stores the data in a database.

[0640] 3. Selection of medical institution

[0641] Once the server receives the patient information entered by the user, it uses an AI algorithm to integrate the information from the database and select the most appropriate medical institution to address the patient's symptoms in real time.

[0642] The AI ​​algorithm estimates the specialty that corresponds to the patient's symptoms and lists medical institutions where that specialist is on-site.

[0643] The server also takes into account traffic data to evaluate and analyze the fastest possible transportation routes.

[0644] 4. Use of Emotion Engine

[0645] The device is equipped with an emotion engine that recognizes the user's emotions in real time, specifically by measuring the user's emotions from facial expressions and voice via a camera and microphone.

[0646] The emotion engine simplifies the interface and highlights important information when the user is feeling stressed.

[0647] 5. Transportation support

[0648] The user selects a recommended medical institution on the terminal, and the terminal transmits the information to the server.

[0649] The server notifies the selected medical institution of the patient's expected arrival information, and the medical institution promptly begins preparations to accept the patient.

[0650] The server provides the user's terminal with optimal route information to the selected medical institution, and the user receives visual navigation while transporting the patient.

[0651] Specific examples

[0652] As a concrete example, consider the case where a 50-year-old man complains of chest pain. The processing procedure in this case is as follows:

[0653] 1. The user (emergency medical personnel) enters the necessary information into the terminal (e.g., 50-year-old male, chest pain).

[0654] 2. The server receives this information, determines that a cardiac specialist is needed, and lists multiple hospitals in the area (e.g., Hospitals A, B, and C).

[0655] 3. The server calculates the optimal route to each hospital based on traffic data.

[0656] 4. The emotion engine recognizes the user's stress, simplifies the interface and highlights important information.

[0657] 5. The user selects the recommended Hospital A, and the information is sent to Hospital A via the server.

[0658] 6. The server notifies Hospital A of the expected arrival information, and the optimal transport route is displayed on the user's terminal.

[0659] Example prompts for generative AI models

[0660] "A 50-year-old man complains of chest pain. The paramedic inputs the necessary information into the terminal. Please explain the specific steps involved in the system, which uses an AI algorithm to select the appropriate medical institution and an emotion engine to adjust the interface to quickly transport the patient via the optimal route."

[0661] This invention combines advanced AI technology with human emotion recognition to simultaneously speed up emergency medical care and reduce the mental burden on users.

[0662] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0663] Step 1:

[0664] Entering patient information

[0665] At the scene, the user (emergency medical personnel) enters the patient's age, gender, specific symptoms (e.g., chest pain, difficulty breathing), and vital signs (heart rate, blood pressure, oxygen saturation) into a device such as a tablet or smartphone.

[0666] Input: Information about the patient's condition and symptoms.

[0667] Output: The input patient information is formatted and converted into a format that can be sent to the server.

[0668] What happens: The device validates the entered data (e.g., checking for required fields) and converts it into the required format. It then sends this information to the server via an internet connection.

[0669] Step 2:

[0670] Data collection

[0671] The terminal transmits the entered patient information to a server in real time via an internet connection.

[0672] The server collects data on each medical institution's medical capabilities, available facilities, number of available beds, and local traffic conditions via API and stores it in a database.

[0673] Input: Patient information, medical institution information, traffic information.

[0674] Output: Consolidated information stored in a database.

[0675] Specific operation: The server uses an API that is updated every minute to collect necessary data from medical institutions and traffic management centers, and stores this data sequentially in a database.

[0676] Step 3:

[0677] Selection of medical institutions

[0678] When the server receives the patient information entered by the user, it uses an AI algorithm to integrate the information from the accumulated database and selects the most appropriate medical institution to address the patient's symptoms in real time.

[0679] Input: Patient information, medical institution and transportation information stored in the database.

[0680] Output: A list of the best medical institutions.

[0681] Specific operation: An AI algorithm (built using TensorFlow and PyTorch) is executed to evaluate and list each medical institution, taking into account their medical capabilities to respond to the patient's symptoms, the availability of specialists, the number of available beds, and traffic conditions.

[0682] Step 4:

[0683] Displaying results and using the emotion engine

[0684] The server transmits a list of selected medical institutions and the optimal route to the user terminal.

[0685] The device is equipped with an emotion engine that recognizes the user's emotional state in real time and adjusts the interface accordingly.

[0686] Input: list of optimal medical institutions, user sentiment.

[0687] Output: A list of the best medical institutions with a user-friendly interface.

[0688] Specific operation: The device recognizes the user's emotions through the camera and microphone (e.g., facial expressions, tone of voice), and simplifies the interface and highlights important information if the user is feeling stressed.

[0689] Step 5:

[0690] Delivery destination selection and notification

[0691] The user selects a destination from a list of medical institutions displayed on the terminal.

[0692] The terminal transmits information about the selected medical institution to the server.

[0693] The server notifies the selected medical institution of the patient's expected arrival information.

[0694] Input: User selected medical institution, patient information.

[0695] Output: Notification of expected arrival information to medical institution.

[0696] Specific operation: The user selects a medical institution using the terminal interface, and the selection information is sent to the server. The server then notifies the selected medical institution of the patient's symptoms, vital signs, and estimated arrival time.

[0697] Step 6:

[0698] Transportation assistance

[0699] The server provides the user terminal with optimal route information to the selected medical institution.

[0700] The device integrates this route information with map data to provide visual navigation to the user.

[0701] Input: Selected medical institutions, transportation data.

[0702] Output: Visual navigation information.

[0703] Specific operation: The server calculates the optimal route using Google Maps or Waze and provides it to the user's device. The device displays the route guidance on a map, updates the route in real time, and guides the emergency responder.

[0704] This system enables prompt and appropriate transport of patients and reduces the mental burden on users.

[0705] (Application example 2)

[0706] 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."

[0707] In modern emergency medical care, it is important to respond quickly to a patient's condition and select the most appropriate medical institution. In particular, understanding traffic congestion and medical institution availability in real time and determining the appropriate route are essential to improving patient survival rates. However, emergency medical personnel are often under high stress, which makes it difficult for them to make optimal decisions. The present invention aims to solve these issues and achieve efficient and rapid patient transport by evaluating the user's emotional state in real time and providing an appropriate interface.

[0708] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0709] In this invention, the server includes a means for inputting information about the patient's condition and symptoms, a means for collecting information about medical institutions' medical treatment capabilities, available facilities, and the number of available beds and storing it in a database, and a means for collecting information about local traffic conditions and storing it in a database. This makes it possible to select the most appropriate medical institution to address the patient's symptoms in real time using an AI algorithm based on the accumulated information. In addition, by evaluating the user's emotional state in real time and adjusting the interface, it is possible to reduce the user's mental burden and enable quick and appropriate patient transport.

[0710] The "means for inputting information about a patient's condition and symptoms" refers to a system that allows emergency personnel and medical professionals to input a patient's vital signs and specific symptoms into an electronic device.

[0711] "Means of collecting information on medical institutions' medical treatment capabilities, available facilities, and number of available beds, and storing it in a database" refers to a system that collects data on medical treatment capabilities, facility status, and number of available beds from each medical institution in real time and centrally manages it.

[0712] "Means for collecting information on traffic conditions within the region and storing it in a database" refers to a system for collecting traffic information from traffic management centers and public transportation databases and storing it in a database in real time.

[0713] "Means for using AI algorithms to select the most appropriate medical institution to address a patient's symptoms in real time" is a system that uses AI technology to analyze collected data and select the medical institution that is most suitable for the patient's symptoms in real time.

[0714] The "means for displaying a list of selected medical institutions" is a system for displaying a list of medical institutions selected by the AI ​​algorithm on the user's device.

[0715] The "means for notifying the medical institution selected by the user of the patient's expected arrival information" is a communication system that allows emergency personnel to notify the medical institution selected by the emergency personnel of the patient's symptoms, vital signs, and expected arrival time.

[0716] The "means for providing guidance on the optimal route to the selected medical institution" is a system that analyzes the optimal route for an ambulance to quickly reach the selected medical institution and provides it as navigation information.

[0717] "Means for evaluating the user's emotional state in real time and adjusting the interface" refers to a system that evaluates the emotional state of emergency personnel in real time using an emotion engine or similar, and simplifies the user interface or highlights important information according to their stress level.

[0718] This system allows emergency personnel and medical professionals to input information about a patient's condition and symptoms, selects the most appropriate medical institution based on that information, and transports the patient quickly. This system uses an emotion engine to reduce the user's mental burden, enabling efficient and prompt response.

[0719] Specifically, paramedics use electronic devices such as smartphones to input basic information about the patient (age, gender, symptoms, etc.). The devices communicate with a server via the internet and send the input information to the server. The server then stores information on medical institutions' medical capabilities, available facilities, number of available beds, and local traffic conditions in a database, and uses an AI algorithm to select the most suitable medical institution.

[0720] The hardware and software used in this system are as follows:

[0721] Hardware: Smartphones, server machines

[0722] Software: Flask (a Python micro web framework), Emotion API, Navigation API

[0723] The server uses an AI algorithm to integrate the received patient information with medical institution and transportation data in the database and select the most suitable medical institution in real time. A list of selected medical institutions is displayed on the terminal, and the user can select the one they consider most suitable. After selection, the server notifies the medical institution of the patient's expected arrival information and provides the terminal with optimal route information, and transportation begins.

[0724] The emotion engine assesses the emotional state of the paramedic in real time and dynamically adjusts the user interface based on the results: for example, if the paramedic is in a high-stress state, the interface will be simplified and important information will be highlighted.

[0725] As a concrete example, consider a 50-year-old man complaining of chest pain. The paramedic uses his smartphone to enter the following information:

[0726] Patient's age: 50 years

[0727] Gender: Male

[0728] Symptoms: Chest pain

[0729] Vital signs: Heart rate 100, blood pressure 150 / 95, oxygen saturation 92

[0730] This information is sent to a server, where an AI algorithm determines that cardiac surgery is required and lists the most appropriate hospitals from multiple hospitals in the area. An emotion engine recognizes the paramedic's stress level and simplifies the interface to highlight important information. Finally, the paramedic selects a recommended hospital, and the information is sent to the server, which notifies the hospital. The user receives navigation information guiding them to the optimal route, ensuring a speedy transport.

[0731] Below are some example prompts to input to a generative AI model:

[0732] "A 50-year-old man complains of chest pain. The paramedic enters the information into a smartphone and sends it to a server. Please explain what data and process are used to suggest the most appropriate medical institution."

[0733] As described above, the present invention automates the process of selecting a transport destination for emergency medical care, thereby realizing a prompt and appropriate response while reducing the mental burden on the user.

[0734] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0735] Step 1:

[0736] The user (emergency medical personnel) uses a device such as a smartphone to input basic patient information (age, gender, symptoms) and vital signs (heart rate, blood pressure, oxygen saturation). By inputting this data, the device prepares information on the patient's condition.

[0737] input:

[0738] Patient's age, sex, and symptoms

[0739] Vital signs (heart rate, blood pressure, oxygen saturation)

[0740] output:

[0741] Prepared patient status information

[0742] Step 2:

[0743] Using an internet connection, the device sends patient status information to a server, which receives the information and stores it in a database.

[0744] input:

[0745] Prepared patient status information

[0746] output:

[0747] Patient information sent to the server

[0748] Step 3:

[0749] The server collects data from medical institutions regarding their medical capacity, available facilities, and the number of available beds via API and stores it in a database. It also collects traffic data from the traffic management center in real time.

[0750] input:

[0751] Medical data from medical institutions

[0752] Traffic data from the Traffic Management Center

[0753] output:

[0754] Medical institution and transportation data stored in the database

[0755] Step 4:

[0756] The server inputs the accumulated information into an AI algorithm that selects the medical institution that best suits the patient's symptoms in real time. The AI ​​algorithm integrates and analyzes the patient's condition information, medical institution and transportation data.

[0757] input:

[0758] Patient status information

[0759] Medical institution and transportation data stored in the database

[0760] output:

[0761] A list of selected optimal medical institutions

[0762] Step 5:

[0763] The server sends a list of selected medical institutions to the terminal, which then displays it to the user, who then selects an appropriate medical institution from the list.

[0764] input:

[0765] A list of selected optimal medical institutions

[0766] output:

[0767] List of medical institutions displayed to users

[0768] Step 6:

[0769] The terminal sends the information about the medical institution selected by the user to the server, and the server notifies the selected medical institution of the patient's expected arrival information.

[0770] input:

[0771] Medical institution information selected by the user

[0772] output:

[0773] Arrival information sent to selected medical institutions

[0774] Step 7:

[0775] The server analyzes the optimal route to the selected medical institution and sends the information to the terminal, which then uses the received route information to provide navigation.

[0776] input:

[0777] Selected medical institution information

[0778] Traffic Data

[0779] output:

[0780] Optimal route information for navigation

[0781] Step 8:

[0782] The device uses an emotion engine to assess the emotional state of the paramedic in real time, and automatically adjusts the interface based on that emotional state, highlighting important information and simplifying operation even when the paramedic is in a high-stress situation.

[0783] input:

[0784] Data on the emotional state of emergency personnel

[0785] output:

[0786] Adjusted User Interface

[0787] This step will streamline the entire system and allow emergency responses to be carried out quickly and accurately.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] [Third embodiment]

[0792] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0793] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0794] 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).

[0795] 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.

[0796] 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.

[0797] 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).

[0798] 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.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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."

[0804] The present invention is a system for quickly selecting the most suitable medical institution based on the condition and symptoms of a patient, and a specific embodiment for implementing this system will be described.

[0805] First, the user (emergency responder) enters basic information about the patient and details of their symptoms into a terminal at the scene. The terminal is a tablet or smartphone, and can access the server via an internet connection. The information entered includes the patient's age, gender, specific symptoms (e.g., chest pain, shortness of breath), and vital signs (heart rate, blood pressure, oxygen saturation).

[0806] The server then collects real-time data on medical institutions' medical capacity, available facilities, and the number of available beds, and stores it in a database. This information is periodically retrieved from each hospital's system via an API. The server also collects traffic situation data from traffic management centers and public transportation databases. This data is also stored in the database.

[0807] After receiving the patient information entered by the user, the server uses an AI algorithm to integrate it with information from the accumulated database and select a medical institution that best suits the patient's symptoms in real time. The AI ​​algorithm estimates the specialty corresponding to the patient's symptoms and lists hospitals with doctors with that specialty as candidates. In addition, it takes into account traffic data to analyze the route that will allow the patient to be transported quickly.

[0808] A list of selected medical institutions is displayed on the user's device along with the optimal route. The list also includes detailed information such as the number of available beds at each hospital, whether or not specialists are available, and travel time. The user can refer to this list to select the medical institution that will be the final destination for the patient.

[0809] When a user selects a hospital, the selection information is sent from the terminal to the server, and the server notifies the selected medical institution of the patient's expected arrival information. The notification includes the patient's symptoms, vital signs, estimated arrival time, etc., allowing the hospital to quickly prepare to accept the patient.

[0810] Finally, the server provides the user with optimal route information to the selected medical institution. The device receives this information and provides visual navigation to the user, ensuring that the ambulance arrives at the hospital in the shortest time possible and that the patient receives appropriate treatment promptly.

[0811] As a concrete example, consider the case of a 50-year-old man complaining of chest pain. The user (ambulance crew member) enters the necessary information into the terminal and sends it to the server. The server determines that cardiac surgery is necessary and lists the most suitable hospitals from multiple hospitals in the area. Finally, the user selects the recommended Hospital A, and upon receiving a notification from the server, Hospital A prepares to accept the patient. At the same time, the user transports the patient to Hospital A via the optimal route, and prompt treatment is provided.

[0812] As a result, the present invention streamlines the process of selecting a destination for emergency medical care, enabling patients to be seen and treated quickly.

[0813] The processing flow will be explained below.

[0814] Step 1: User enters patient information

[0815] On-site, users enter information into the terminal, such as the patient's age, gender, specific symptoms (e.g., chest pain, shortness of breath), and vital signs (heart rate, blood pressure, oxygen saturation).

[0816] The device stores the entered information and sends the data to the server using a communication protocol (such as HTTPS).

[0817] Step 2: Data collection by the server

[0818] The server periodically collects data on each medical institution's medical capacity, available facilities, and number of available beds. This data is obtained through the hospital system's API.

[0819] The server collects real-time traffic situation data (e.g., congestion information and road conditions) from traffic management centers and public transportation databases.

[0820] The server stores the collected data in a database.

[0821] Step 3: AI analysis by server

[0822] The server integrates the patient information received from the user with existing medical institution and transportation data stored in a database.

[0823] The server uses AI algorithms to predict the specialty (e.g., cardiac surgery, internal medicine) that a patient needs to see based on their symptoms.

[0824] The server lists medical institutions according to the estimated specialty and calculates the optimal route taking into account traffic data.

[0825] Step 4: Present a list of medical institutions

[0826] Based on the analysis, the server generates a list of recommended medical institutions, including details such as the number of available beds, availability of specialists, and travel time.

[0827] The server transmits the generated list to the user terminal.

[0828] The terminal displays the received list of medical institutions to the user.

[0829] Step 5: User selects medical institution

[0830] The user selects a hospital to which the patient will be transported from the displayed list of medical institutions.

[0831] The terminal transmits the selected medical institution information to the server.

[0832] Step 6: Server Sends Notification

[0833] The server notifies the selected medical institution of the patient's expected arrival information, including the patient's symptoms, vital signs, and estimated time of arrival.

[0834] Medical institutions will receive notification and prepare to accept the patient.

[0835] Step 7: Optimal route guidance

[0836] The server transmits information on the optimal route to the selected medical institution to the user terminal.

[0837] The device then displays the received route guidance information to the user and provides navigation, allowing the ambulance to arrive at the hospital in the shortest time possible and ensuring prompt treatment for the patient.

[0838] Example 1

[0839] 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."

[0840] Conventional emergency medical systems have had difficulty quickly selecting the appropriate medical institution based on the patient's condition and symptoms. As a result, it took time to determine where to transport the patient, which could delay appropriate treatment. In addition, it was not possible to collect and comprehensively analyze information such as the medical capacity, number of available beds, and traffic conditions of each medical institution in real time, which led to issues with selecting the optimal transport route. There is a need to solve these problems.

[0841] 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.

[0842] In this invention, the server includes means for inputting information on the patient's condition and symptoms, means for collecting information on medical institutions' medical treatment capabilities, available facilities, and number of available beds and storing it in a database, means for collecting information on local traffic conditions and storing it in a database, means for selecting the medical institution most suitable for the patient's symptoms in real time using an AI algorithm based on the accumulated information, means for displaying a list of the selected medical institutions, means for notifying the medical institution selected by the user of the patient's expected arrival information, means for guiding the user on the optimal route to the selected medical institution, means for estimating medical institutions with specialized fields based on patient information using an AI algorithm, and means for providing information on the optimal route for patient transport in real time. This enables the rapid selection of an appropriate medical institution and guidance on the optimal transport route.

[0843] "Means for inputting information about a patient's condition and symptoms" refers to a device or interface that allows a user to input basic information about a patient and details of the patient's symptoms, such as the patient's age, gender, specific symptoms, and vital signs.

[0844] "Means of collecting information on medical institutions' medical treatment capabilities, available facilities, and number of available beds, and storing it in a database" refers to the functions and systems that obtain information on medical treatment capabilities, available facilities, and number of available beds from each medical institution and store that data in a database.

[0845] "Means of collecting information on traffic conditions within an area and storing it in a database" refers to functions and systems that collect information on traffic conditions obtained from traffic management centers and public transportation databases and store that data in a database.

[0846] "Means of using AI algorithms to select the most appropriate medical institution to address a patient's symptoms in real time" refers to a function or system that uses AI algorithms based on collected data to instantly select the medical institution that is most suitable for the patient's symptoms.

[0847] "Means for displaying a list of selected medical institutions" refers to a device or system that displays to the user a list of candidate medical institutions selected by an AI algorithm.

[0848] "Means for notifying the medical institution selected by the user of the patient's expected arrival information" refers to a function or system that sends the patient's expected arrival information to the medical institution selected by the user, prompting the hospital to prepare for the patient's arrival.

[0849] "Means for providing guidance on the optimal route to the selected medical institution" refers to a device or system that provides route information for the shortest route to the medical institution selected by the user and performs navigation.

[0850] "Means of using an AI algorithm to estimate the appropriate specialized medical institution based on patient information" refers to a function or system that analyzes patient information and uses an AI algorithm to estimate the appropriate specialized medical institution that can respond to the patient's symptoms.

[0851] "Means for providing real-time information on optimal patient transport routes" refers to functions and systems that calculate and provide optimal routes for quickly transporting patients based on real-time traffic information.

[0852] The present invention provides a system for quickly selecting the most appropriate medical institution based on the patient's condition and symptoms. This section describes a specific embodiment of the system. The system includes a user (an emergency medical technician), a terminal (a tablet or a smartphone), and a server.

[0853] First, the user enters basic information about the patient and details of their symptoms into the device on-site. The information entered includes the patient's age, gender, specific symptoms (e.g., chest pain, shortness of breath), and vital signs (heart rate, blood pressure, oxygen saturation). The device is connected to the Internet, and the entered data is sent to a server. The device used is a tablet or smartphone, and data entry and transmission are performed via a dedicated application.

[0854] The server then collects data on medical capacity, available facilities, and the number of available beds from multiple medical institutions. This information is periodically obtained through each medical institution's API. The server also collects traffic status data from traffic management centers and public transportation databases and stores it in a database. This ensures that the server always has the latest medical and transportation information.

[0855] Based on the received patient information, the server uses an AI algorithm to select the most appropriate medical institution. The AI ​​algorithm estimates the specialty corresponding to the patient's symptoms and lists medical institutions with that specialist on-site as candidates. It also takes into account traffic data to analyze the optimal transport route. The AI ​​algorithm uses a machine learning model to make optimal decisions based on past data.

[0856] A list of selected medical institutions is displayed on the user's terminal along with detailed information (e.g., number of available beds, availability of specialists, travel time). The user refers to this list to select the final destination. Once the user makes a selection, the information is sent from the terminal to the server, which then notifies the selected medical institution of the patient's expected arrival information. The notification includes the patient's symptoms, vital signs, and estimated arrival time.

[0857] The server then provides the user's device with optimal route information to the selected medical institution. Based on this information, the device performs visual navigation and guides the user to the medical institution in the shortest time possible. This allows the ambulance to arrive at the hospital quickly and the patient to receive appropriate treatment promptly.

[0858] As a concrete example, consider the case of a 50-year-old man complaining of chest pain. The user (paramedic) enters the necessary information into the device and sends it. The server uses an AI algorithm to determine that cardiac surgery is necessary and lists the most suitable hospitals from multiple hospitals in the area. The user selects Hospital A from the displayed list, and upon receiving a notification from the server, Hospital A prepares to accept the patient. At the same time, the user transports the patient to Hospital A via the optimal route, allowing for prompt treatment.

[0859] Example prompt sentence:

[0860] "A 50-year-old man complains of chest pain. The necessary information is entered and sent to the server, which uses an AI algorithm to list the most suitable hospitals. Finally, Hospital A is selected, a notification is sent, and the patient is transported via the optimal route."

[0861] This system will streamline the process of selecting a destination for emergency medical care, enabling patients to be seen and treated quickly.

[0862] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0863] Step 1:

[0864] User input of patient information

[0865] On-site, the user enters the patient's basic information (age, gender, specific symptoms) and vital signs (heart rate, blood pressure, oxygen saturation) into the terminal. The terminal is connected to the Internet and sends this information to the server. Specifically, the user enters the necessary information into the input form displayed on the tablet screen and presses the "Submit" button.

[0866] Input: Patient's age, gender, symptoms, vital signs

[0867] Output: Patient information transferred to the server

[0868] Step 2:

[0869] Server collects data and updates the database

[0870] The server collects data on medical capacity, available facilities, and number of available beds from each medical institution via API. It also collects data on traffic conditions from the traffic management center and public transportation database. This data is periodically saved in the database. Specifically, the server calls the API and writes the acquired data to the database.

[0871] Input: Real-time data obtained from medical institutions and traffic control centers

[0872] Output: Latest medical and traffic information stored in a database

[0873] Step 3:

[0874] Server analyzes patient information and selects medical institutions

[0875] The server analyzes the patient information received from the user and uses an AI algorithm to select the most suitable medical institution. The AI ​​algorithm estimates the specialty corresponding to the patient's symptoms and lists medical institutions with that specialist. Specifically, the server launches the AI ​​model, compares the patient information with the contents of the database, and lists the most suitable medical institutions.

[0876] Input: Patient information received from the user, medical institution information stored in the database

[0877] Output: A list of the best medical institutions

[0878] Step 4:

[0879] Optimal route analysis by the server

[0880] The server analyzes the traffic data to find the optimal route for quickly transporting the patient. This route information is provided to the user's device along with a list of candidate medical institutions. Specifically, the server analyzes the traffic data to calculate the shortest route and adds the result to the list.

[0881] Input: Traffic information stored in the database

[0882] Output: A list of medical institutions with optimal routes

[0883] Step 5:

[0884] User selection of medical institution

[0885] The user selects the most suitable hospital from a list of medical institutions displayed on the terminal. Once the selection is confirmed, the information is sent from the terminal to the server. Specifically, the user selects a hospital from the list and presses the "Select" button.

[0886] Input: A list of candidate medical institutions provided by the server

[0887] Output: The information of the selected medical institution is sent to the server.

[0888] Step 6:

[0889] Notification to medical institutions by the server

[0890] The server notifies the selected medical institution of the patient's expected arrival information. The notification includes the patient's symptoms, vital signs, and estimated arrival time. Specifically, the server sends the notification to the selected hospital's system via an API.

[0891] Input: Information about the medical institution selected by the user

[0892] Output: Medical institutions to be notified

[0893] Step 7:

[0894] Server provides optimal route information

[0895] The server provides the user's device with optimal route information to the selected medical institution. The device uses this information to perform visual navigation. Specifically, the device's map application begins navigation based on the route data sent from the server.

[0896] Input: Optimal route data to selected medical institutions

[0897] Output: User terminal that displays the optimal route

[0898] Step 8:

[0899] User transports patient and starts treatment

[0900] The user follows the navigation on the device to transport the patient to the selected medical institution. After arriving at the hospital, the patient can receive appropriate treatment quickly. Specifically, the user follows the navigation to proceed along the monitored route, and upon arriving at the hospital, the patient is handed over to medical staff.

[0901] Input: Optimal route information provided by the server

[0902] Output: Patients who arrived at the selected medical institution

[0903] Through the above processing steps, this system streamlines the process of selecting a destination for emergency medical care, enabling patients to be seen and treated quickly.

[0904] (Application example 1)

[0905] 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."

[0906] Conventional food delivery systems lack efficient methods for delivery personnel to select the optimal restaurant and deliver meals quickly. This leads to delivery delays and selection errors, resulting in lower customer satisfaction. Furthermore, there are challenges, such as taking into account real-time traffic conditions and appropriately selecting restaurants based on their availability. To solve these challenges, a system is needed to improve delivery efficiency and accuracy.

[0907] 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.

[0908] In this invention, the server includes a means for inputting information about the user's status and needs, a means for collecting information about providers' service capabilities, available facilities, and availability and storing it in a database, and a means for collecting information about local traffic and resource conditions and storing it in a database. This allows the server to select the optimal provider in real time based on the collected information using an AI algorithm, enabling the delivery of services in response to the user's request. By utilizing a generative AI model, the collected information and the optimal provider and route can be displayed, notified, and guided, improving delivery efficiency and accuracy and customer satisfaction.

[0909] "Patient or User" refers to the individual receiving the service, i.e., the patient, delivery recipient, or orderer.

[0910] "Condition and needs" refers to information including the current health condition, symptoms, requests, and content of requests of the person receiving the service.

[0911] "Provider" refers to a medical institution, restaurant, or other business that provides a service.

[0912] "Service capabilities" refers to the provider's technical capabilities, staffing, response capabilities, and equipment availability.

[0913] "Available Facilities" refers to the equipment, tools, and resources available to the Provider to provide the Service.

[0914] "Availability" refers to reservations for the provider's services, available beds, delivery slots, availability of counters, etc.

[0915] "Traffic conditions" refers to the state of road congestion in the area, whether there is traffic congestion, and operation status.

[0916] "Resource status" refers to the status of personnel, equipment, vehicles, and other material resources available to the provider.

[0917] "AI algorithm" refers to an algorithm that analyzes input data and automatically makes the optimal selection.

[0918] "Real-time" means that information is acquired, processed, and displayed almost instantly.

[0919] "Generative AI model" refers to the AI ​​model used to analyze, display, notify, and guide collected information.

[0920] "Visual navigation" refers to a function that displays maps and directions to help delivery personnel and others reach their destination via the optimal route.

[0921] "Database" refers to a system in which accumulated information is managed and stored.

[0922] This invention is a "Smart Delivery Assistant" system for food delivery services that enables delivery personnel to select the most suitable restaurant and deliver food efficiently. This system consists of a server, a user terminal, and a provider terminal.

[0923] First, the delivery person (user) inputs the order details, delivery address, and delivery time into their smartphone. The server receives this information and collects the real-time data described below.

[0924] The server collects real-time data on the business status of partner restaurants, food preparation times, and traffic conditions. This data is obtained from APIs of restaurant management systems and traffic information APIs (e.g., Google Maps API) and stored in a database.

[0925] Based on this collected data, the server uses an AI algorithm, built using a deep learning framework such as TensorFlow, to comprehensively analyze the order details, the supplier's situation, and traffic information to select the most suitable restaurant.

[0926] The selected restaurant and the optimal delivery route information are visually displayed on the delivery person's smartphone. When the delivery person sends information about their expected arrival time to the selected restaurant to the server, the server notifies the restaurant's device of the order and the estimated arrival time. This allows the restaurant to prepare the food efficiently.

[0927] Furthermore, the server uses the Google Maps API to provide delivery staff with detailed route information to the delivery destination, allowing them to deliver food in the shortest time possible, leading to increased customer satisfaction.

[0928] As a concrete example, if a customer at 1-2-3, Shibuya-ku, Tokyo, orders pizza and pasta at 6:00 p.m., the delivery person enters this information into their smartphone. The server collects and analyzes the availability and traffic information of partner restaurants in real time to select the most suitable restaurant. The selected restaurant and delivery route are then visually displayed, and the delivery person notifies the restaurant of their estimated arrival time. Finally, the food is delivered to the customer via the shortest route using the Google Maps API.

[0929] An example of a prompt sentence is as follows:

[0930] "I'd like to deliver pizza and pasta to 1-2-3, Shibuya-ku, Tokyo by 6:00 PM. Please tell me the best restaurant and delivery route."

[0931] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0932] Step 1:

[0933] The user inputs the order details, delivery address, and delivery time into their smartphone. The input information is sent to the server. The input data may include, for example, "Deliver pizza and pasta to 1-2-3, Shibuya-ku, Tokyo by 6:00 PM." This provides the server with basic delivery information.

[0934] Step 2:

[0935] Based on the received order information, the server collects data on the business status of partner restaurants, food preparation time, and traffic conditions in real time. The server sends requests to the restaurant's management system API and traffic information API (e.g., Google Maps API), and receives restaurant business status and traffic information in response. This data is stored in a database.

[0936] Step 3:

[0937] The server uses the accumulated order details and real-time data as input data and applies them to an AI algorithm. The AI ​​algorithm uses TensorFlow's deep learning model to select the most suitable restaurant. The data is processed to perform a comprehensive evaluation that takes into account the restaurant's level of congestion, food preparation time, and traffic conditions. The output is the ID of the most suitable restaurant and a rating score.

[0938] Step 4:

[0939] The server then sends the extracted information about the optimal restaurant and the optimal route to that restaurant to the user's smartphone. The selected restaurant and detailed route are visually displayed on the user's device, allowing the delivery person to understand which restaurant to go to and which route to take.

[0940] Step 5:

[0941] The user confirms the expected arrival information at the selected restaurant and sends it to the server. Input includes the expected arrival time at the restaurant and confirmation of the restaurant selection. The server receives this information and notifies the provider terminal (restaurant) of the order details and expected arrival time. This allows the restaurant to prepare the food efficiently.

[0942] Step 6:

[0943] The server uses the Google Maps API to provide the user with the optimal route from the selected restaurant to the delivery destination. The route information is displayed in a turn-by-turn navigation format, allowing the user to receive specific guidance on how to reach the delivery destination in the shortest time.

[0944] These steps will enable the "Smart Delivery Assistant" system to improve delivery efficiency and customer satisfaction.

[0945] 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.

[0946] The present invention combines an emotion engine with a system that quickly selects the most appropriate medical institution based on the patient's condition and symptoms, thereby realizing efficient transportation while reducing the user's mental burden. Specific embodiments of the present invention are described below.

[0947] First, the user (emergency responder) enters basic information about the patient and details of their symptoms into a terminal at the scene. The terminal is a tablet or smartphone, and can access the server via an internet connection. The information entered includes the patient's age, gender, specific symptoms (e.g., chest pain, shortness of breath), and vital signs (heart rate, blood pressure, oxygen saturation).

[0948] The server collects real-time data on medical institutions' medical capacity, available facilities, and number of available beds, and stores it in a database. This information is obtained from each medical institution's system via an API. In addition, the server collects traffic situation data from traffic management centers and public transportation databases, and stores this data in the database.

[0949] Next, after receiving the patient information entered by the user, the server uses an AI algorithm to integrate it with information from the accumulated database and select a medical institution suitable for the patient's symptoms in real time. The AI ​​algorithm estimates the specialty corresponding to the patient's symptoms and lists medical institutions with specialists on-site as candidates. It also takes into account traffic data to analyze the route that will allow the patient to be transported quickly.

[0950] A list of selected medical institutions is displayed on the user's device along with the optimal route. This list includes detailed information such as the number of available beds at each institution, whether they have specialists, and travel time. The emotion engine then recognizes the user's emotions in real time and adjusts the interface based on those emotions. For example, if the user is feeling stressed, the interface will be simplified and important information will be highlighted.

[0951] When the user selects the medical institution to which the patient will be transported, that information is sent to the server. The server then notifies the selected medical institution of the patient's expected arrival information. The notification includes the patient's symptoms, vital signs, and estimated arrival time, allowing the medical institution to quickly prepare to accept the patient.

[0952] The server also uses an emotion engine to provide alerts and guidance based on the user's emotional state. For example, if the user is feeling anxious, the server will display an alert to help them regain their composure and provide instructions on the appropriate next steps. This makes it possible to transport the patient to a medical facility quickly and appropriately while reducing the user's mental burden.

[0953] Finally, the server provides the user with optimal route information to the selected medical institution. The device receives this information and provides visual navigation to the user, ensuring that the ambulance arrives at the hospital in the shortest time possible and that the patient receives appropriate treatment promptly.

[0954] As a concrete example, consider the case of a 50-year-old man complaining of chest pain. The user (paramedic) enters the necessary information into the terminal and sends it to the server. The server determines that cardiac surgery is necessary and lists the most suitable hospitals from multiple hospitals in the area. The emotion engine recognizes the user's stress, simplifies the interface, and highlights important information. Finally, the user selects the recommended Hospital A, and Hospital A, upon receiving a notification from the server, prepares to accept the patient. At the same time, the user transports the patient to Hospital A via the optimal route, ensuring prompt treatment.

[0955] The present invention improves the efficiency of the process for selecting a destination for emergency medical care and reduces the mental burden on users, thereby enabling patients to be seen and treated quickly.

[0956] The processing flow will be explained below.

[0957] Step 1: User enters patient information

[0958] On-site, users enter information into the terminal, such as the patient's age, gender, specific symptoms (e.g., chest pain, shortness of breath), and vital signs (heart rate, blood pressure, oxygen saturation).

[0959] The device stores this entered information and transmits the data to the server using a secure communication protocol (e.g., HTTPS).

[0960] Step 2: Data collection by the server

[0961] The server periodically collects data from each medical institution regarding their medical capacity, available facilities, and number of available beds, and stores it in a database. This information is automatically retrieved from each medical institution's system via an API.

[0962] The server collects traffic situation data in real time from traffic management centers and public transportation databases, and also stores this data in the database.

[0963] Step 3: AI analysis by server

[0964] The server integrates the patient information received from the user with medical institution data and transportation data stored in the database.

[0965] The server uses AI algorithms to analyze the patient's symptoms and estimate the required specialty (e.g., cardiac surgery, internal medicine).

[0966] The server lists medical institutions that correspond to the estimated specialty and calculates the optimal transport route taking into account traffic data.

[0967] Step 4: Present a list of medical institutions

[0968] Based on the results of the AI ​​analysis, the server generates a list of recommended medical institutions, including details such as the number of available beds, availability of specialists, and travel time.

[0969] The server transmits the generated list to the user terminal.

[0970] The terminal displays the received list of medical institutions to the user.

[0971] Step 5: Emotion Recognition with the Emotion Engine

[0972] The device uses a built-in emotion engine to recognize the user's emotions (e.g., stress, impatience, calmness) in real time.

[0973] The emotion engine adjusts the interface based on the user's emotional state: for example, if the user is stressed, it simplifies the interface and highlights important information.

[0974] Step 6: User selects medical institution

[0975] The user selects a hospital to which the patient will be transported from the displayed list of medical institutions.

[0976] The terminal transmits the selected medical institution information to the server.

[0977] Step 7: Server Sends Notification

[0978] The server notifies the selected medical institution of the patient's expected arrival information, including the patient's symptoms, vital signs, and estimated time of arrival.

[0979] Medical institutions will receive notification and prepare to accept the patient.

[0980] Step 8: Emotion Engine provides guidance

[0981] The server uses an emotion engine to provide alerts and guidance based on the user's emotional state: if the user is panicking, an alert will be displayed to help them regain their composure and provide appropriate next steps.

[0982] Step 9: Optimal Route Guidance

[0983] The server transmits information on the optimal route to the selected medical institution to the user terminal.

[0984] The device then displays the received route guidance information to the user and provides navigation, allowing the ambulance to arrive at the hospital in the shortest time possible and allowing the patient to receive appropriate treatment promptly.

[0985] Example 2

[0986] 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."

[0987] In emergency medical care, it is necessary to select and transport a patient to the appropriate medical institution quickly and appropriately according to the patient's condition. However, the current system takes time to gather information, select a medical institution, and provide transport route guidance, and this places a heavy mental burden on emergency medical personnel. Furthermore, there is a lack of real-time transport route guidance that responds to changes in traffic conditions. This is an obstacle to providing prompt medical care to patients.

[0988] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0989] In this invention, the server includes means for inputting information on the patient's condition and symptoms, means for collecting information on medical institutions' medical treatment capabilities, available facilities, and number of available beds and storing it in a database, means for collecting information on local traffic conditions and storing it in a database, means for selecting the medical institution most suitable for the patient's symptoms in real time using an AI algorithm based on the accumulated information, means for displaying a list of the selected medical institutions, means for notifying the medical institution selected by the user of the patient's expected arrival information, means for guiding the user to the selected medical institution along the optimal route, and an emotion engine for recognizing the user's emotional state in real time and adjusting the interface. This enables faster emergency medical care and reduces the mental burden on emergency medical personnel.

[0990] "Information about the patient's condition and symptoms" includes information such as the patient's age, sex, specific symptoms (e.g., chest pain or shortness of breath), and vital signs (heart rate, blood pressure, oxygen saturation).

[0991] "Medical capabilities of medical institutions" is information indicating the medical departments and specialties of medical institutions, whether or not they have specialists, and the types of treatments and procedures they can handle.

[0992] "Available facilities" refers to information including the types of medical equipment that can be used within the medical institution, their operating status, and available testing facilities.

[0993] The "number of available beds" is information indicating the number of hospital beds currently available within a medical institution.

[0994] "Traffic conditions within the area" refers to information about the transportation route when a patient is transported, including the effects of traffic congestion, road construction, accidents, and the like.

[0995] An "AI algorithm" is a program or model that uses machine learning and artificial intelligence techniques to analyze data and select the most appropriate medical institution to address a patient's symptoms.

[0996] The "list of selected medical institutions" is a list of medical institutions that are most suitable for the patient's symptoms, selected by an AI algorithm.

[0997] "Patient arrival schedule information" is information provided to the selected medical institution, including the patient's current condition, vital signs, and expected arrival time.

[0998] The "optimal route" is a recommended route for transporting a patient to a selected medical institution in the shortest time possible, taking into account real-time traffic conditions.

[0999] An "emotion engine" is software or algorithms that recognize a user's emotional state in real time and adjust the interface based on that emotion.

[1000] This invention relates to a system for emergency medical care that quickly selects the most appropriate medical institution based on the patient's condition and symptoms, and realizes efficient transportation while reducing the user's mental burden. Specific embodiments for carrying out the invention are described below.

[1001] Hardware and software used

[1002] 1. User device (tablet, smartphone, etc.)

[1003] Server is accessible via an internet connection

[1004] Provides an input interface (a form for entering patient information)

[1005] 2. Server

[1006] Database: Accumulates information on medical institutions (treatment capacity, available facilities, number of available beds) and traffic conditions

[1007] AI algorithms: Models using machine learning frameworks such as TensorFlow and PyTorch

[1008] Emotion engine: an algorithm that recognizes user emotions and adjusts the interface

[1009] 3. Software

[1010] Traffic management system: Real-time traffic situation

[1011] API: Obtaining medical institution information

[1012] Google Maps and Waze: Providing routes

[1013] System Description

[1014] 1. Enter patient information

[1015] At the scene, the user (emergency responder) enters the patient's age, gender, specific symptoms (e.g., chest pain, difficulty breathing), and vital signs (heart rate, blood pressure, oxygen saturation) into the terminal.

[1016] The terminal formats the data entered by the user and immediately transmits it to the server.

[1017] 2. Data collection

[1018] The device transmits the entered patient information to a server in real time via an internet connection.

[1019] The server collects data via API about each medical institution's medical capacity, available facilities, number of available beds, and traffic conditions within the area, and stores the data in a database.

[1020] 3. Selection of medical institution

[1021] Once the server receives the patient information entered by the user, it uses an AI algorithm to integrate the information from the database and select the most appropriate medical institution to address the patient's symptoms in real time.

[1022] The AI ​​algorithm estimates the specialty that corresponds to the patient's symptoms and lists medical institutions where that specialist is on-site.

[1023] The server also takes into account traffic data to evaluate and analyze the fastest possible transportation routes.

[1024] 4. Use of Emotion Engine

[1025] The device is equipped with an emotion engine that recognizes the user's emotions in real time, specifically by measuring the user's emotions from facial expressions and voice via a camera and microphone.

[1026] The emotion engine simplifies the interface and highlights important information when the user is feeling stressed.

[1027] 5. Transportation support

[1028] The user selects a recommended medical institution on the terminal, and the terminal transmits the information to the server.

[1029] The server notifies the selected medical institution of the patient's expected arrival information, and the medical institution promptly begins preparations to accept the patient.

[1030] The server provides the user's terminal with optimal route information to the selected medical institution, and the user receives visual navigation while transporting the patient.

[1031] Specific examples

[1032] As a concrete example, consider the case where a 50-year-old man complains of chest pain. The processing procedure in this case is as follows:

[1033] 1. The user (emergency medical personnel) enters the necessary information into the terminal (e.g., 50-year-old male, chest pain).

[1034] 2. The server receives this information, determines that a cardiac specialist is needed, and lists multiple hospitals in the area (e.g., Hospitals A, B, and C).

[1035] 3. The server calculates the optimal route to each hospital based on traffic data.

[1036] 4. The emotion engine recognizes the user's stress, simplifies the interface and highlights important information.

[1037] 5. The user selects the recommended Hospital A, and the information is sent to Hospital A via the server.

[1038] 6. The server notifies Hospital A of the expected arrival information, and the optimal transport route is displayed on the user's terminal.

[1039] Example prompts for generative AI models

[1040] "A 50-year-old man complains of chest pain. The paramedic inputs the necessary information into the terminal. Please explain the specific steps involved in the system, which uses an AI algorithm to select the appropriate medical institution and an emotion engine to adjust the interface to quickly transport the patient via the optimal route."

[1041] This invention combines advanced AI technology with human emotion recognition to simultaneously speed up emergency medical care and reduce the mental burden on users.

[1042] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1043] Step 1:

[1044] Entering patient information

[1045] At the scene, the user (emergency medical personnel) enters the patient's age, gender, specific symptoms (e.g., chest pain, difficulty breathing), and vital signs (heart rate, blood pressure, oxygen saturation) into a device such as a tablet or smartphone.

[1046] Input: Information about the patient's condition and symptoms.

[1047] Output: The input patient information is formatted and converted into a format that can be sent to the server.

[1048] What happens: The device validates the entered data (e.g., checking for required fields) and converts it into the required format. It then sends this information to the server via an internet connection.

[1049] Step 2:

[1050] Data collection

[1051] The terminal transmits the entered patient information to a server in real time via an internet connection.

[1052] The server collects data on each medical institution's medical capabilities, available facilities, number of available beds, and local traffic conditions via API and stores it in a database.

[1053] Input: Patient information, medical institution information, traffic information.

[1054] Output: Consolidated information stored in a database.

[1055] Specific operation: The server uses an API that is updated every minute to collect necessary data from medical institutions and traffic management centers, and stores this data sequentially in a database.

[1056] Step 3:

[1057] Selection of medical institutions

[1058] When the server receives the patient information entered by the user, it uses an AI algorithm to integrate the information from the accumulated database and selects the most appropriate medical institution to address the patient's symptoms in real time.

[1059] Input: Patient information, medical institution and transportation information stored in the database.

[1060] Output: A list of the best medical institutions.

[1061] Specific operation: An AI algorithm (built using TensorFlow and PyTorch) is executed to evaluate and list each medical institution, taking into account their medical capabilities to respond to the patient's symptoms, the availability of specialists, the number of available beds, and traffic conditions.

[1062] Step 4:

[1063] Displaying results and using the emotion engine

[1064] The server transmits a list of selected medical institutions and the optimal route to the user terminal.

[1065] The device is equipped with an emotion engine that recognizes the user's emotional state in real time and adjusts the interface accordingly.

[1066] Input: list of optimal medical institutions, user sentiment.

[1067] Output: A list of the best medical institutions with a user-friendly interface.

[1068] Specific operation: The device recognizes the user's emotions through the camera and microphone (e.g., facial expressions, tone of voice), and simplifies the interface and highlights important information if the user is feeling stressed.

[1069] Step 5:

[1070] Delivery destination selection and notification

[1071] The user selects a destination from a list of medical institutions displayed on the terminal.

[1072] The terminal transmits information about the selected medical institution to the server.

[1073] The server notifies the selected medical institution of the patient's expected arrival information.

[1074] Input: User selected medical institution, patient information.

[1075] Output: Notification of expected arrival information to medical institution.

[1076] Specific operation: The user selects a medical institution using the terminal interface, and the selection information is sent to the server. The server then notifies the selected medical institution of the patient's symptoms, vital signs, and estimated arrival time.

[1077] Step 6:

[1078] Transportation assistance

[1079] The server provides the user terminal with optimal route information to the selected medical institution.

[1080] The device integrates this route information with map data to provide visual navigation to the user.

[1081] Input: Selected medical institutions, transportation data.

[1082] Output: Visual navigation information.

[1083] Specific operation: The server calculates the optimal route using Google Maps or Waze and provides it to the user's device. The device displays the route guidance on a map, updates the route in real time, and guides the emergency responder.

[1084] This system enables prompt and appropriate transport of patients and reduces the mental burden on users.

[1085] (Application example 2)

[1086] 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."

[1087] In modern emergency medical care, it is important to respond quickly to a patient's condition and select the most appropriate medical institution. In particular, understanding traffic congestion and medical institution availability in real time and determining the appropriate route are essential to improving patient survival rates. However, emergency medical personnel are often under high stress, which makes it difficult for them to make optimal decisions. The present invention aims to solve these issues and achieve efficient and rapid patient transport by evaluating the user's emotional state in real time and providing an appropriate interface.

[1088] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1089] In this invention, the server includes a means for inputting information about the patient's condition and symptoms, a means for collecting information about medical institutions' medical treatment capabilities, available facilities, and the number of available beds and storing it in a database, and a means for collecting information about local traffic conditions and storing it in a database. This makes it possible to select the most appropriate medical institution to address the patient's symptoms in real time using an AI algorithm based on the accumulated information. In addition, by evaluating the user's emotional state in real time and adjusting the interface, it is possible to reduce the user's mental burden and enable quick and appropriate patient transport.

[1090] The "means for inputting information about a patient's condition and symptoms" refers to a system that allows emergency personnel and medical professionals to input a patient's vital signs and specific symptoms into an electronic device.

[1091] "Means of collecting information on medical institutions' medical treatment capabilities, available facilities, and number of available beds, and storing it in a database" refers to a system that collects data on medical treatment capabilities, facility status, and number of available beds from each medical institution in real time and centrally manages it.

[1092] "Means for collecting information on traffic conditions within the region and storing it in a database" refers to a system for collecting traffic information from traffic management centers and public transportation databases and storing it in a database in real time.

[1093] "Means for using AI algorithms to select the most appropriate medical institution to address a patient's symptoms in real time" is a system that uses AI technology to analyze collected data and select the medical institution that is most suitable for the patient's symptoms in real time.

[1094] The "means for displaying a list of selected medical institutions" is a system for displaying a list of medical institutions selected by the AI ​​algorithm on the user's device.

[1095] The "means for notifying the medical institution selected by the user of the patient's expected arrival information" is a communication system that allows emergency personnel to notify the medical institution selected by the emergency personnel of the patient's symptoms, vital signs, and expected arrival time.

[1096] The "means for providing guidance on the optimal route to the selected medical institution" is a system that analyzes the optimal route for an ambulance to quickly reach the selected medical institution and provides it as navigation information.

[1097] "Means for evaluating the user's emotional state in real time and adjusting the interface" refers to a system that evaluates the emotional state of emergency personnel in real time using an emotion engine or similar, and simplifies the user interface or highlights important information according to their stress level.

[1098] This system allows emergency personnel and medical professionals to input information about a patient's condition and symptoms, selects the most appropriate medical institution based on that information, and transports the patient quickly. This system uses an emotion engine to reduce the user's mental burden, enabling efficient and prompt response.

[1099] Specifically, paramedics use electronic devices such as smartphones to input basic information about the patient (age, gender, symptoms, etc.). The devices communicate with a server via the internet and send the input information to the server. The server then stores information on medical institutions' medical capabilities, available facilities, number of available beds, and local traffic conditions in a database, and uses an AI algorithm to select the most suitable medical institution.

[1100] The hardware and software used in this system are as follows:

[1101] Hardware: Smartphones, server machines

[1102] Software: Flask (a Python micro web framework), Emotion API, Navigation API

[1103] The server uses an AI algorithm to integrate the received patient information with medical institution and transportation data in the database and select the most suitable medical institution in real time. A list of selected medical institutions is displayed on the terminal, and the user can select the one they consider most suitable. After selection, the server notifies the medical institution of the patient's expected arrival information and provides the terminal with optimal route information, and transportation begins.

[1104] The emotion engine assesses the emotional state of the paramedic in real time and dynamically adjusts the user interface based on the results: for example, if the paramedic is in a high-stress state, the interface will be simplified and important information will be highlighted.

[1105] As a concrete example, consider a 50-year-old man complaining of chest pain. The paramedic uses his smartphone to enter the following information:

[1106] Patient's age: 50 years

[1107] Gender: Male

[1108] Symptoms: Chest pain

[1109] Vital signs: Heart rate 100, blood pressure 150 / 95, oxygen saturation 92

[1110] This information is sent to a server, where an AI algorithm determines that cardiac surgery is required and lists the most appropriate hospitals from multiple hospitals in the area. An emotion engine recognizes the paramedic's stress level and simplifies the interface to highlight important information. Finally, the paramedic selects a recommended hospital, and the information is sent to the server, which notifies the hospital. The user receives navigation information guiding them to the optimal route, ensuring a speedy transport.

[1111] Below are some example prompts to input to a generative AI model:

[1112] "A 50-year-old man complains of chest pain. The paramedic enters the information into a smartphone and sends it to a server. Please explain what data and process are used to suggest the most appropriate medical institution."

[1113] As described above, the present invention automates the process of selecting a transport destination for emergency medical care, thereby realizing a prompt and appropriate response while reducing the mental burden on the user.

[1114] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1115] Step 1:

[1116] The user (emergency medical personnel) uses a device such as a smartphone to input basic patient information (age, gender, symptoms) and vital signs (heart rate, blood pressure, oxygen saturation). By inputting this data, the device prepares information on the patient's condition.

[1117] input:

[1118] Patient's age, sex, and symptoms

[1119] Vital signs (heart rate, blood pressure, oxygen saturation)

[1120] output:

[1121] Prepared patient status information

[1122] Step 2:

[1123] Using an internet connection, the device sends patient status information to a server, which receives the information and stores it in a database.

[1124] input:

[1125] Prepared patient status information

[1126] output:

[1127] Patient information sent to the server

[1128] Step 3:

[1129] The server collects data from medical institutions regarding their medical capacity, available facilities, and the number of available beds via API and stores it in a database. It also collects traffic data from the traffic management center in real time.

[1130] input:

[1131] Medical data from medical institutions

[1132] Traffic data from the Traffic Management Center

[1133] output:

[1134] Medical institution and transportation data stored in the database

[1135] Step 4:

[1136] The server inputs the accumulated information into an AI algorithm that selects the medical institution that best suits the patient's symptoms in real time. The AI ​​algorithm integrates and analyzes the patient's condition information, medical institution and transportation data.

[1137] input:

[1138] Patient status information

[1139] Medical institution and transportation data stored in the database

[1140] output:

[1141] A list of selected optimal medical institutions

[1142] Step 5:

[1143] The server sends a list of selected medical institutions to the terminal, which then displays it to the user, who then selects an appropriate medical institution from the list.

[1144] input:

[1145] A list of selected optimal medical institutions

[1146] output:

[1147] List of medical institutions displayed to users

[1148] Step 6:

[1149] The terminal sends the information about the medical institution selected by the user to the server, and the server notifies the selected medical institution of the patient's expected arrival information.

[1150] input:

[1151] Medical institution information selected by the user

[1152] output:

[1153] Arrival information sent to selected medical institutions

[1154] Step 7:

[1155] The server analyzes the optimal route to the selected medical institution and sends the information to the terminal, which then uses the received route information to provide navigation.

[1156] input:

[1157] Selected medical institution information

[1158] Traffic Data

[1159] output:

[1160] Optimal route information for navigation

[1161] Step 8:

[1162] The device uses an emotion engine to assess the emotional state of the paramedic in real time, and automatically adjusts the interface based on that emotional state, highlighting important information and simplifying operation even when the paramedic is in a high-stress situation.

[1163] input:

[1164] Data on the emotional state of emergency personnel

[1165] output:

[1166] Adjusted User Interface

[1167] This step will streamline the entire system and allow emergency responses to be carried out quickly and accurately.

[1168] 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.

[1169] 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.

[1170] 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.

[1171] [Fourth embodiment]

[1172] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1173] 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.

[1174] 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).

[1175] 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.

[1176] 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.

[1177] 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).

[1178] 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.

[1179] 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.

[1180] 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.

[1181] 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.

[1182] 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.

[1183] 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.

[1184] 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."

[1185] The present invention is a system for quickly selecting the most suitable medical institution based on the condition and symptoms of a patient, and a specific embodiment for implementing this system will be described.

[1186] First, the user (emergency responder) enters basic information about the patient and details of their symptoms into a terminal at the scene. The terminal is a tablet or smartphone, and can access the server via an internet connection. The information entered includes the patient's age, gender, specific symptoms (e.g., chest pain, shortness of breath), and vital signs (heart rate, blood pressure, oxygen saturation).

[1187] The server then collects real-time data on medical institutions' medical capacity, available facilities, and the number of available beds, and stores it in a database. This information is periodically retrieved from each hospital's system via an API. The server also collects traffic situation data from traffic management centers and public transportation databases. This data is also stored in the database.

[1188] After receiving the patient information entered by the user, the server uses an AI algorithm to integrate it with information from the accumulated database and select a medical institution that best suits the patient's symptoms in real time. The AI ​​algorithm estimates the specialty corresponding to the patient's symptoms and lists hospitals with doctors with that specialty as candidates. In addition, it takes into account traffic data to analyze the route that will allow the patient to be transported quickly.

[1189] A list of selected medical institutions is displayed on the user's device along with the optimal route. The list also includes detailed information such as the number of available beds at each hospital, whether or not specialists are available, and travel time. The user can refer to this list to select the medical institution that will be the final destination for the patient.

[1190] When a user selects a hospital, the selection information is sent from the terminal to the server, and the server notifies the selected medical institution of the patient's expected arrival information. The notification includes the patient's symptoms, vital signs, estimated arrival time, etc., allowing the hospital to quickly prepare to accept the patient.

[1191] Finally, the server provides the user with optimal route information to the selected medical institution. The device receives this information and provides visual navigation to the user, ensuring that the ambulance arrives at the hospital in the shortest time possible and that the patient receives appropriate treatment promptly.

[1192] As a concrete example, consider the case of a 50-year-old man complaining of chest pain. The user (ambulance crew member) enters the necessary information into the terminal and sends it to the server. The server determines that cardiac surgery is necessary and lists the most suitable hospitals from multiple hospitals in the area. Finally, the user selects the recommended Hospital A, and upon receiving a notification from the server, Hospital A prepares to accept the patient. At the same time, the user transports the patient to Hospital A via the optimal route, and prompt treatment is provided.

[1193] As a result, the present invention streamlines the process of selecting a destination for emergency medical care, enabling patients to be seen and treated quickly.

[1194] The processing flow will be explained below.

[1195] Step 1: User enters patient information

[1196] On-site, users enter information into the terminal, such as the patient's age, gender, specific symptoms (e.g., chest pain, shortness of breath), and vital signs (heart rate, blood pressure, oxygen saturation).

[1197] The device stores the entered information and sends the data to the server using a communication protocol (such as HTTPS).

[1198] Step 2: Data collection by the server

[1199] The server periodically collects data on each medical institution's medical capacity, available facilities, and number of available beds. This data is obtained through the hospital system's API.

[1200] The server collects real-time traffic situation data (e.g., congestion information and road conditions) from traffic management centers and public transportation databases.

[1201] The server stores the collected data in a database.

[1202] Step 3: AI analysis by server

[1203] The server integrates the patient information received from the user with existing medical institution and transportation data stored in a database.

[1204] The server uses AI algorithms to predict the specialty (e.g., cardiac surgery, internal medicine) that a patient needs to see based on their symptoms.

[1205] The server lists medical institutions according to the estimated specialty and calculates the optimal route taking into account traffic data.

[1206] Step 4: Present a list of medical institutions

[1207] Based on the analysis, the server generates a list of recommended medical institutions, including details such as the number of available beds, availability of specialists, and travel time.

[1208] The server transmits the generated list to the user terminal.

[1209] The terminal displays the received list of medical institutions to the user.

[1210] Step 5: User selects medical institution

[1211] The user selects a hospital to which the patient will be transported from the displayed list of medical institutions.

[1212] The terminal transmits the selected medical institution information to the server.

[1213] Step 6: Server Sends Notification

[1214] The server notifies the selected medical institution of the patient's expected arrival information, including the patient's symptoms, vital signs, and estimated time of arrival.

[1215] Medical institutions will receive notification and prepare to accept the patient.

[1216] Step 7: Optimal route guidance

[1217] The server transmits information on the optimal route to the selected medical institution to the user terminal.

[1218] The device then displays the received route guidance information to the user and provides navigation, allowing the ambulance to arrive at the hospital in the shortest time possible and ensuring prompt treatment for the patient.

[1219] Example 1

[1220] 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."

[1221] Conventional emergency medical systems have had difficulty quickly selecting the appropriate medical institution based on the patient's condition and symptoms. As a result, it took time to determine where to transport the patient, which could delay appropriate treatment. In addition, it was not possible to collect and comprehensively analyze information such as the medical capacity, number of available beds, and traffic conditions of each medical institution in real time, which led to issues with selecting the optimal transport route. There is a need to solve these problems.

[1222] 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.

[1223] In this invention, the server includes means for inputting information on the patient's condition and symptoms, means for collecting information on medical institutions' medical treatment capabilities, available facilities, and number of available beds and storing it in a database, means for collecting information on local traffic conditions and storing it in a database, means for selecting the medical institution most suitable for the patient's symptoms in real time using an AI algorithm based on the accumulated information, means for displaying a list of the selected medical institutions, means for notifying the medical institution selected by the user of the patient's expected arrival information, means for guiding the user on the optimal route to the selected medical institution, means for estimating medical institutions with specialized fields based on patient information using an AI algorithm, and means for providing information on the optimal route for patient transport in real time. This enables the rapid selection of an appropriate medical institution and guidance on the optimal transport route.

[1224] "Means for inputting information about a patient's condition and symptoms" refers to a device or interface that allows a user to input basic information about a patient and details of the patient's symptoms, such as the patient's age, gender, specific symptoms, and vital signs.

[1225] "Means of collecting information on medical institutions' medical treatment capabilities, available facilities, and number of available beds, and storing it in a database" refers to the functions and systems that obtain information on medical treatment capabilities, available facilities, and number of available beds from each medical institution and store that data in a database.

[1226] "Means of collecting information on traffic conditions within an area and storing it in a database" refers to functions and systems that collect information on traffic conditions obtained from traffic management centers and public transportation databases and store that data in a database.

[1227] "Means of using AI algorithms to select the most appropriate medical institution to address a patient's symptoms in real time" refers to a function or system that uses AI algorithms based on collected data to instantly select the medical institution that is most suitable for the patient's symptoms.

[1228] "Means for displaying a list of selected medical institutions" refers to a device or system that displays to the user a list of candidate medical institutions selected by an AI algorithm.

[1229] "Means for notifying the medical institution selected by the user of the patient's expected arrival information" refers to a function or system that sends the patient's expected arrival information to the medical institution selected by the user, prompting the hospital to prepare for the patient's arrival.

[1230] "Means for providing guidance on the optimal route to the selected medical institution" refers to a device or system that provides route information for the shortest route to the medical institution selected by the user and performs navigation.

[1231] "Means of using an AI algorithm to estimate the appropriate specialized medical institution based on patient information" refers to a function or system that analyzes patient information and uses an AI algorithm to estimate the appropriate specialized medical institution that can respond to the patient's symptoms.

[1232] "Means for providing real-time information on optimal patient transport routes" refers to functions and systems that calculate and provide optimal routes for quickly transporting patients based on real-time traffic information.

[1233] The present invention provides a system for quickly selecting the most appropriate medical institution based on the patient's condition and symptoms. This section describes a specific embodiment of the system. The system includes a user (an emergency medical technician), a terminal (a tablet or a smartphone), and a server.

[1234] First, the user enters basic information about the patient and details of their symptoms into the device on-site. The information entered includes the patient's age, gender, specific symptoms (e.g., chest pain, shortness of breath), and vital signs (heart rate, blood pressure, oxygen saturation). The device is connected to the Internet, and the entered data is sent to a server. The device used is a tablet or smartphone, and data entry and transmission are performed via a dedicated application.

[1235] The server then collects data on medical capacity, available facilities, and the number of available beds from multiple medical institutions. This information is periodically obtained through each medical institution's API. The server also collects traffic status data from traffic management centers and public transportation databases and stores it in a database. This ensures that the server always has the latest medical and transportation information.

[1236] Based on the received patient information, the server uses an AI algorithm to select the most appropriate medical institution. The AI ​​algorithm estimates the specialty corresponding to the patient's symptoms and lists medical institutions with that specialist on-site as candidates. It also takes into account traffic data to analyze the optimal transport route. The AI ​​algorithm uses a machine learning model to make optimal decisions based on past data.

[1237] A list of selected medical institutions is displayed on the user's terminal along with detailed information (e.g., number of available beds, availability of specialists, travel time). The user refers to this list to select the final destination. Once the user makes a selection, the information is sent from the terminal to the server, which then notifies the selected medical institution of the patient's expected arrival information. The notification includes the patient's symptoms, vital signs, and estimated arrival time.

[1238] The server then provides the user's device with optimal route information to the selected medical institution. Based on this information, the device performs visual navigation and guides the user to the medical institution in the shortest time possible. This allows the ambulance to arrive at the hospital quickly and the patient to receive appropriate treatment promptly.

[1239] As a concrete example, consider the case of a 50-year-old man complaining of chest pain. The user (paramedic) enters the necessary information into the device and sends it. The server uses an AI algorithm to determine that cardiac surgery is necessary and lists the most suitable hospitals from multiple hospitals in the area. The user selects Hospital A from the displayed list, and upon receiving a notification from the server, Hospital A prepares to accept the patient. At the same time, the user transports the patient to Hospital A via the optimal route, allowing for prompt treatment.

[1240] Example prompt sentence:

[1241] "A 50-year-old man complains of chest pain. The necessary information is entered and sent to the server, which uses an AI algorithm to list the most suitable hospitals. Finally, Hospital A is selected, a notification is sent, and the patient is transported via the optimal route."

[1242] This system will streamline the process of selecting a destination for emergency medical care, enabling patients to be seen and treated quickly.

[1243] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1244] Step 1:

[1245] User input of patient information

[1246] On-site, the user enters the patient's basic information (age, gender, specific symptoms) and vital signs (heart rate, blood pressure, oxygen saturation) into the terminal. The terminal is connected to the Internet and sends this information to the server. Specifically, the user enters the necessary information into the input form displayed on the tablet screen and presses the "Submit" button.

[1247] Input: Patient's age, gender, symptoms, vital signs

[1248] Output: Patient information transferred to the server

[1249] Step 2:

[1250] Server collects data and updates the database

[1251] The server collects data on medical capacity, available facilities, and number of available beds from each medical institution via API. It also collects data on traffic conditions from the traffic management center and public transportation database. This data is periodically saved in the database. Specifically, the server calls the API and writes the acquired data to the database.

[1252] Input: Real-time data obtained from medical institutions and traffic control centers

[1253] Output: Latest medical and traffic information stored in a database

[1254] Step 3:

[1255] Server analyzes patient information and selects medical institutions

[1256] The server analyzes the patient information received from the user and uses an AI algorithm to select the most suitable medical institution. The AI ​​algorithm estimates the specialty corresponding to the patient's symptoms and lists medical institutions with that specialist. Specifically, the server launches the AI ​​model, compares the patient information with the contents of the database, and lists the most suitable medical institutions.

[1257] Input: Patient information received from the user, medical institution information stored in the database

[1258] Output: A list of the best medical institutions

[1259] Step 4:

[1260] Optimal route analysis by the server

[1261] The server analyzes the traffic data to find the optimal route for quickly transporting the patient. This route information is provided to the user's device along with a list of candidate medical institutions. Specifically, the server analyzes the traffic data to calculate the shortest route and adds the result to the list.

[1262] Input: Traffic information stored in the database

[1263] Output: A list of medical institutions with optimal routes

[1264] Step 5:

[1265] User selection of medical institution

[1266] The user selects the most suitable hospital from a list of medical institutions displayed on the terminal. Once the selection is confirmed, the information is sent from the terminal to the server. Specifically, the user selects a hospital from the list and presses the "Select" button.

[1267] Input: A list of candidate medical institutions provided by the server

[1268] Output: The information of the selected medical institution is sent to the server.

[1269] Step 6:

[1270] Notification to medical institutions by the server

[1271] The server notifies the selected medical institution of the patient's expected arrival information. The notification includes the patient's symptoms, vital signs, and estimated arrival time. Specifically, the server sends the notification to the selected hospital's system via an API.

[1272] Input: Information about the medical institution selected by the user

[1273] Output: Medical institutions to be notified

[1274] Step 7:

[1275] Server provides optimal route information

[1276] The server provides the user's device with optimal route information to the selected medical institution. The device uses this information to perform visual navigation. Specifically, the device's map application begins navigation based on the route data sent from the server.

[1277] Input: Optimal route data to selected medical institutions

[1278] Output: User terminal that displays the optimal route

[1279] Step 8:

[1280] User transports patient and starts treatment

[1281] The user follows the navigation on the device to transport the patient to the selected medical institution. After arriving at the hospital, the patient can receive appropriate treatment quickly. Specifically, the user follows the navigation to proceed along the monitored route, and upon arriving at the hospital, the patient is handed over to medical staff.

[1282] Input: Optimal route information provided by the server

[1283] Output: Patients who arrived at the selected medical institution

[1284] Through the above processing steps, this system streamlines the process of selecting a destination for emergency medical care, enabling patients to be seen and treated quickly.

[1285] (Application example 1)

[1286] 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."

[1287] Conventional food delivery systems lack efficient methods for delivery personnel to select the optimal restaurant and deliver meals quickly. This leads to delivery delays and selection errors, resulting in lower customer satisfaction. Furthermore, there are challenges, such as taking into account real-time traffic conditions and appropriately selecting restaurants based on their availability. To solve these challenges, a system is needed to improve delivery efficiency and accuracy.

[1288] 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.

[1289] In this invention, the server includes a means for inputting information about the user's status and needs, a means for collecting information about providers' service capabilities, available facilities, and availability and storing it in a database, and a means for collecting information about local traffic and resource conditions and storing it in a database. This allows the server to select the optimal provider in real time based on the collected information using an AI algorithm, enabling the delivery of services in response to the user's request. By utilizing a generative AI model, the collected information and the optimal provider and route can be displayed, notified, and guided, improving delivery efficiency and accuracy and customer satisfaction.

[1290] "Patient or User" refers to the individual receiving the service, i.e., the patient, delivery recipient, or orderer.

[1291] "Condition and needs" refers to information including the current health condition, symptoms, requests, and content of requests of the person receiving the service.

[1292] "Provider" refers to a medical institution, restaurant, or other business that provides a service.

[1293] "Service capabilities" refers to the provider's technical capabilities, staffing, response capabilities, and equipment availability.

[1294] "Available Facilities" refers to the equipment, tools, and resources available to the Provider to provide the Service.

[1295] "Availability" refers to reservations for the provider's services, available beds, delivery slots, availability of counters, etc.

[1296] "Traffic conditions" refers to the state of road congestion in the area, whether there is traffic congestion, and operation status.

[1297] "Resource status" refers to the status of personnel, equipment, vehicles, and other material resources available to the provider.

[1298] "AI algorithm" refers to an algorithm that analyzes input data and automatically makes the optimal selection.

[1299] "Real-time" means that information is acquired, processed, and displayed almost instantly.

[1300] "Generative AI model" refers to the AI ​​model used to analyze, display, notify, and guide collected information.

[1301] "Visual navigation" refers to a function that displays maps and directions to help delivery personnel and others reach their destination via the optimal route.

[1302] "Database" refers to a system in which accumulated information is managed and stored.

[1303] This invention is a "Smart Delivery Assistant" system for food delivery services that enables delivery personnel to select the most suitable restaurant and deliver food efficiently. This system consists of a server, a user terminal, and a provider terminal.

[1304] First, the delivery person (user) inputs the order details, delivery address, and delivery time into their smartphone. The server receives this information and collects the real-time data described below.

[1305] The server collects real-time data on the business status of partner restaurants, food preparation times, and traffic conditions. This data is obtained from APIs of restaurant management systems and traffic information APIs (e.g., Google Maps API) and stored in a database.

[1306] Based on this collected data, the server uses an AI algorithm, built using a deep learning framework such as TensorFlow, to comprehensively analyze the order details, the supplier's situation, and traffic information to select the most suitable restaurant.

[1307] The selected restaurant and the optimal delivery route information are visually displayed on the delivery person's smartphone. When the delivery person sends information about their expected arrival time to the selected restaurant to the server, the server notifies the restaurant's device of the order and the estimated arrival time. This allows the restaurant to prepare the food efficiently.

[1308] Furthermore, the server uses the Google Maps API to provide delivery staff with detailed route information to the delivery destination, allowing them to deliver food in the shortest time possible, leading to increased customer satisfaction.

[1309] As a concrete example, if a customer at 1-2-3, Shibuya-ku, Tokyo, orders pizza and pasta at 6:00 p.m., the delivery person enters this information into their smartphone. The server collects and analyzes the availability and traffic information of partner restaurants in real time to select the most suitable restaurant. The selected restaurant and delivery route are then visually displayed, and the delivery person notifies the restaurant of their estimated arrival time. Finally, the food is delivered to the customer via the shortest route using the Google Maps API.

[1310] An example of a prompt sentence is as follows:

[1311] "I'd like to deliver pizza and pasta to 1-2-3, Shibuya-ku, Tokyo by 6:00 PM. Please tell me the best restaurant and delivery route."

[1312] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1313] Step 1:

[1314] The user inputs the order details, delivery address, and delivery time into their smartphone. The input information is sent to the server. The input data may include, for example, "Deliver pizza and pasta to 1-2-3, Shibuya-ku, Tokyo by 6:00 PM." This provides the server with basic delivery information.

[1315] Step 2:

[1316] Based on the received order information, the server collects data on the business status of partner restaurants, food preparation time, and traffic conditions in real time. The server sends requests to the restaurant's management system API and traffic information API (e.g., Google Maps API), and receives restaurant business status and traffic information in response. This data is stored in a database.

[1317] Step 3:

[1318] The server uses the accumulated order details and real-time data as input data and applies them to an AI algorithm. The AI ​​algorithm uses TensorFlow's deep learning model to select the most suitable restaurant. The data is processed to perform a comprehensive evaluation that takes into account the restaurant's level of congestion, food preparation time, and traffic conditions. The output is the ID of the most suitable restaurant and a rating score.

[1319] Step 4:

[1320] The server then sends the extracted information about the optimal restaurant and the optimal route to that restaurant to the user's smartphone. The selected restaurant and detailed route are visually displayed on the user's device, allowing the delivery person to understand which restaurant to go to and which route to take.

[1321] Step 5:

[1322] The user confirms the expected arrival information at the selected restaurant and sends it to the server. Input includes the expected arrival time at the restaurant and confirmation of the restaurant selection. The server receives this information and notifies the provider terminal (restaurant) of the order details and expected arrival time. This allows the restaurant to prepare the food efficiently.

[1323] Step 6:

[1324] The server uses the Google Maps API to provide the user with the optimal route from the selected restaurant to the delivery destination. The route information is displayed in a turn-by-turn navigation format, allowing the user to receive specific guidance on how to reach the delivery destination in the shortest time.

[1325] These steps will enable the "Smart Delivery Assistant" system to improve delivery efficiency and customer satisfaction.

[1326] 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.

[1327] The present invention combines an emotion engine with a system that quickly selects the most appropriate medical institution based on the patient's condition and symptoms, thereby realizing efficient transportation while reducing the user's mental burden. Specific embodiments of the present invention are described below.

[1328] First, the user (emergency responder) enters basic information about the patient and details of their symptoms into a terminal at the scene. The terminal is a tablet or smartphone, and can access the server via an internet connection. The information entered includes the patient's age, gender, specific symptoms (e.g., chest pain, shortness of breath), and vital signs (heart rate, blood pressure, oxygen saturation).

[1329] The server collects real-time data on medical institutions' medical capacity, available facilities, and number of available beds, and stores it in a database. This information is obtained from each medical institution's system via an API. In addition, the server collects traffic situation data from traffic management centers and public transportation databases, and stores this data in the database.

[1330] Next, after receiving the patient information entered by the user, the server uses an AI algorithm to integrate it with information from the accumulated database and select a medical institution suitable for the patient's symptoms in real time. The AI ​​algorithm estimates the specialty corresponding to the patient's symptoms and lists medical institutions with specialists on-site as candidates. It also takes into account traffic data to analyze the route that will allow the patient to be transported quickly.

[1331] A list of selected medical institutions is displayed on the user's device along with the optimal route. This list includes detailed information such as the number of available beds at each institution, whether they have specialists, and travel time. The emotion engine then recognizes the user's emotions in real time and adjusts the interface based on those emotions. For example, if the user is feeling stressed, the interface will be simplified and important information will be highlighted.

[1332] When the user selects the medical institution to which the patient will be transported, that information is sent to the server. The server then notifies the selected medical institution of the patient's expected arrival information. The notification includes the patient's symptoms, vital signs, and estimated arrival time, allowing the medical institution to quickly prepare to accept the patient.

[1333] The server also uses an emotion engine to provide alerts and guidance based on the user's emotional state. For example, if the user is feeling anxious, the server will display an alert to help them regain their composure and provide instructions on the appropriate next steps. This makes it possible to transport the patient to a medical facility quickly and appropriately while reducing the user's mental burden.

[1334] Finally, the server provides the user with optimal route information to the selected medical institution. The device receives this information and provides visual navigation to the user, ensuring that the ambulance arrives at the hospital in the shortest time possible and that the patient receives appropriate treatment promptly.

[1335] As a concrete example, consider the case of a 50-year-old man complaining of chest pain. The user (paramedic) enters the necessary information into the terminal and sends it to the server. The server determines that cardiac surgery is necessary and lists the most suitable hospitals from multiple hospitals in the area. The emotion engine recognizes the user's stress, simplifies the interface, and highlights important information. Finally, the user selects the recommended Hospital A, and Hospital A, upon receiving a notification from the server, prepares to accept the patient. At the same time, the user transports the patient to Hospital A via the optimal route, ensuring prompt treatment.

[1336] The present invention improves the efficiency of the process for selecting a destination for emergency medical care and reduces the mental burden on users, thereby enabling patients to be seen and treated quickly.

[1337] The processing flow will be explained below.

[1338] Step 1: User enters patient information

[1339] On-site, users enter information into the terminal, such as the patient's age, gender, specific symptoms (e.g., chest pain, shortness of breath), and vital signs (heart rate, blood pressure, oxygen saturation).

[1340] The device stores this entered information and transmits the data to the server using a secure communication protocol (e.g., HTTPS).

[1341] Step 2: Data collection by the server

[1342] The server periodically collects data from each medical institution regarding their medical capacity, available facilities, and number of available beds, and stores it in a database. This information is automatically retrieved from each medical institution's system via an API.

[1343] The server collects traffic situation data in real time from traffic management centers and public transportation databases, and also stores this data in the database.

[1344] Step 3: AI analysis by server

[1345] The server integrates the patient information received from the user with medical institution data and transportation data stored in the database.

[1346] The server uses AI algorithms to analyze the patient's symptoms and estimate the required specialty (e.g., cardiac surgery, internal medicine).

[1347] The server lists medical institutions that correspond to the estimated specialty and calculates the optimal transport route taking into account traffic data.

[1348] Step 4: Present a list of medical institutions

[1349] Based on the results of the AI ​​analysis, the server generates a list of recommended medical institutions, including details such as the number of available beds, availability of specialists, and travel time.

[1350] The server transmits the generated list to the user terminal.

[1351] The terminal displays the received list of medical institutions to the user.

[1352] Step 5: Emotion Recognition with the Emotion Engine

[1353] The device uses a built-in emotion engine to recognize the user's emotions (e.g., stress, impatience, calmness) in real time.

[1354] The emotion engine adjusts the interface based on the user's emotional state: for example, if the user is stressed, it simplifies the interface and highlights important information.

[1355] Step 6: User selects medical institution

[1356] The user selects a hospital to which the patient will be transported from the displayed list of medical institutions.

[1357] The terminal transmits the selected medical institution information to the server.

[1358] Step 7: Server Sends Notification

[1359] The server notifies the selected medical institution of the patient's expected arrival information, including the patient's symptoms, vital signs, and estimated time of arrival.

[1360] Medical institutions will receive notification and prepare to accept the patient.

[1361] Step 8: Emotion Engine provides guidance

[1362] The server uses an emotion engine to provide alerts and guidance based on the user's emotional state: if the user is panicking, an alert will be displayed to help them regain their composure and provide appropriate next steps.

[1363] Step 9: Optimal Route Guidance

[1364] The server transmits information on the optimal route to the selected medical institution to the user terminal.

[1365] The device then displays the received route guidance information to the user and provides navigation, allowing the ambulance to arrive at the hospital in the shortest time possible and allowing the patient to receive appropriate treatment promptly.

[1366] Example 2

[1367] 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."

[1368] In emergency medical care, it is necessary to select and transport a patient to the appropriate medical institution quickly and appropriately according to the patient's condition. However, the current system takes time to gather information, select a medical institution, and provide transport route guidance, and this places a heavy mental burden on emergency medical personnel. Furthermore, there is a lack of real-time transport route guidance that responds to changes in traffic conditions. This is an obstacle to providing prompt medical care to patients.

[1369] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1370] In this invention, the server includes means for inputting information on the patient's condition and symptoms, means for collecting information on medical institutions' medical treatment capabilities, available facilities, and number of available beds and storing it in a database, means for collecting information on local traffic conditions and storing it in a database, means for selecting the medical institution most suitable for the patient's symptoms in real time using an AI algorithm based on the accumulated information, means for displaying a list of the selected medical institutions, means for notifying the medical institution selected by the user of the patient's expected arrival information, means for guiding the user to the selected medical institution along the optimal route, and an emotion engine for recognizing the user's emotional state in real time and adjusting the interface. This enables faster emergency medical care and reduces the mental burden on emergency medical personnel.

[1371] "Information about the patient's condition and symptoms" includes information such as the patient's age, sex, specific symptoms (e.g., chest pain or shortness of breath), and vital signs (heart rate, blood pressure, oxygen saturation).

[1372] "Medical capabilities of medical institutions" is information indicating the medical departments and specialties of medical institutions, whether or not they have specialists, and the types of treatments and procedures they can handle.

[1373] "Available facilities" refers to information including the types of medical equipment that can be used within the medical institution, their operating status, and available testing facilities.

[1374] The "number of available beds" is information indicating the number of hospital beds currently available within a medical institution.

[1375] "Traffic conditions within the area" refers to information about the transportation route when a patient is transported, including the effects of traffic congestion, road construction, accidents, and the like.

[1376] An "AI algorithm" is a program or model that uses machine learning and artificial intelligence techniques to analyze data and select the most appropriate medical institution to address a patient's symptoms.

[1377] The "list of selected medical institutions" is a list of medical institutions that are most suitable for the patient's symptoms, selected by an AI algorithm.

[1378] "Patient arrival schedule information" is information provided to the selected medical institution, including the patient's current condition, vital signs, and expected arrival time.

[1379] The "optimal route" is a recommended route for transporting a patient to a selected medical institution in the shortest time possible, taking into account real-time traffic conditions.

[1380] An "emotion engine" is software or algorithms that recognize a user's emotional state in real time and adjust the interface based on that emotion.

[1381] This invention relates to a system for emergency medical care that quickly selects the most appropriate medical institution based on the patient's condition and symptoms, and realizes efficient transportation while reducing the user's mental burden. Specific embodiments for carrying out the invention are described below.

[1382] Hardware and software used

[1383] 1. User device (tablet, smartphone, etc.)

[1384] Server is accessible via an internet connection

[1385] Provides an input interface (a form for entering patient information)

[1386] 2. Server

[1387] Database: Accumulates information on medical institutions (treatment capacity, available facilities, number of available beds) and traffic conditions

[1388] AI algorithms: Models using machine learning frameworks such as TensorFlow and PyTorch

[1389] Emotion engine: an algorithm that recognizes user emotions and adjusts the interface

[1390] 3. Software

[1391] Traffic management system: Real-time traffic situation

[1392] API: Obtaining medical institution information

[1393] Google Maps and Waze: Providing routes

[1394] System Description

[1395] 1. Enter patient information

[1396] At the scene, the user (emergency responder) enters the patient's age, gender, specific symptoms (e.g., chest pain, difficulty breathing), and vital signs (heart rate, blood pressure, oxygen saturation) into the terminal.

[1397] The terminal formats the data entered by the user and immediately transmits it to the server.

[1398] 2. Data collection

[1399] The device transmits the entered patient information to a server in real time via an internet connection.

[1400] The server collects data via API about each medical institution's medical capacity, available facilities, number of available beds, and traffic conditions within the area, and stores the data in a database.

[1401] 3. Selection of medical institution

[1402] Once the server receives the patient information entered by the user, it uses an AI algorithm to integrate the information from the database and select the most appropriate medical institution to address the patient's symptoms in real time.

[1403] The AI ​​algorithm estimates the specialty that corresponds to the patient's symptoms and lists medical institutions where that specialist is on-site.

[1404] The server also takes into account traffic data to evaluate and analyze the fastest possible transportation routes.

[1405] 4. Use of Emotion Engine

[1406] The device is equipped with an emotion engine that recognizes the user's emotions in real time, specifically by measuring the user's emotions from facial expressions and voice via a camera and microphone.

[1407] The emotion engine simplifies the interface and highlights important information when the user is feeling stressed.

[1408] 5. Transportation support

[1409] The user selects a recommended medical institution on the terminal, and the terminal transmits the information to the server.

[1410] The server notifies the selected medical institution of the patient's expected arrival information, and the medical institution promptly begins preparations to accept the patient.

[1411] The server provides the user's terminal with optimal route information to the selected medical institution, and the user receives visual navigation while transporting the patient.

[1412] Specific examples

[1413] As a concrete example, consider the case where a 50-year-old man complains of chest pain. The processing procedure in this case is as follows:

[1414] 1. The user (emergency medical personnel) enters the necessary information into the terminal (e.g., 50-year-old male, chest pain).

[1415] 2. The server receives this information, determines that a cardiac specialist is needed, and lists multiple hospitals in the area (e.g., Hospitals A, B, and C).

[1416] 3. The server calculates the optimal route to each hospital based on traffic data.

[1417] 4. The emotion engine recognizes the user's stress, simplifies the interface and highlights important information.

[1418] 5. The user selects the recommended Hospital A, and the information is sent to Hospital A via the server.

[1419] 6. The server notifies Hospital A of the expected arrival information, and the optimal transport route is displayed on the user's terminal.

[1420] Example prompts for generative AI models

[1421] "A 50-year-old man complains of chest pain. The paramedic inputs the necessary information into the terminal. Please explain the specific steps involved in the system, which uses an AI algorithm to select the appropriate medical institution and an emotion engine to adjust the interface to quickly transport the patient via the optimal route."

[1422] This invention combines advanced AI technology with human emotion recognition to simultaneously speed up emergency medical care and reduce the mental burden on users.

[1423] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1424] Step 1:

[1425] Entering patient information

[1426] At the scene, the user (emergency medical personnel) enters the patient's age, gender, specific symptoms (e.g., chest pain, difficulty breathing), and vital signs (heart rate, blood pressure, oxygen saturation) into a device such as a tablet or smartphone.

[1427] Input: Information about the patient's condition and symptoms.

[1428] Output: The input patient information is formatted and converted into a format that can be sent to the server.

[1429] What happens: The device validates the entered data (e.g., checking for required fields) and converts it into the required format. It then sends this information to the server via an internet connection.

[1430] Step 2:

[1431] Data collection

[1432] The terminal transmits the entered patient information to a server in real time via an internet connection.

[1433] The server collects data on each medical institution's medical capabilities, available facilities, number of available beds, and local traffic conditions via API and stores it in a database.

[1434] Input: Patient information, medical institution information, traffic information.

[1435] Output: Consolidated information stored in a database.

[1436] Specific operation: The server uses an API that is updated every minute to collect necessary data from medical institutions and traffic management centers, and stores this data sequentially in a database.

[1437] Step 3:

[1438] Selection of medical institutions

[1439] When the server receives the patient information entered by the user, it uses an AI algorithm to integrate the information from the accumulated database and selects the most appropriate medical institution to address the patient's symptoms in real time.

[1440] Input: Patient information, medical institution and transportation information stored in the database.

[1441] Output: A list of the best medical institutions.

[1442] Specific operation: An AI algorithm (built using TensorFlow and PyTorch) is executed to evaluate and list each medical institution, taking into account their medical capabilities to respond to the patient's symptoms, the availability of specialists, the number of available beds, and traffic conditions.

[1443] Step 4:

[1444] Displaying results and using the emotion engine

[1445] The server transmits a list of selected medical institutions and the optimal route to the user terminal.

[1446] The device is equipped with an emotion engine that recognizes the user's emotional state in real time and adjusts the interface accordingly.

[1447] Input: list of optimal medical institutions, user sentiment.

[1448] Output: A list of the best medical institutions with a user-friendly interface.

[1449] Specific operation: The device recognizes the user's emotions through the camera and microphone (e.g., facial expressions, tone of voice), and simplifies the interface and highlights important information if the user is feeling stressed.

[1450] Step 5:

[1451] Delivery destination selection and notification

[1452] The user selects a destination from a list of medical institutions displayed on the terminal.

[1453] The terminal transmits information about the selected medical institution to the server.

[1454] The server notifies the selected medical institution of the patient's expected arrival information.

[1455] Input: User selected medical institution, patient information.

[1456] Output: Notification of expected arrival information to medical institution.

[1457] Specific operation: The user selects a medical institution using the terminal interface, and the selection information is sent to the server. The server then notifies the selected medical institution of the patient's symptoms, vital signs, and estimated arrival time.

[1458] Step 6:

[1459] Transportation assistance

[1460] The server provides the user terminal with optimal route information to the selected medical institution.

[1461] The device integrates this route information with map data to provide visual navigation to the user.

[1462] Input: Selected medical institutions, transportation data.

[1463] Output: Visual navigation information.

[1464] Specific operation: The server calculates the optimal route using Google Maps or Waze and provides it to the user's device. The device displays the route guidance on a map, updates the route in real time, and guides the emergency responder.

[1465] This system enables prompt and appropriate transport of patients and reduces the mental burden on users.

[1466] (Application example 2)

[1467] 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."

[1468] In modern emergency medical care, it is important to respond quickly to a patient's condition and select the most appropriate medical institution. In particular, understanding traffic congestion and medical institution availability in real time and determining the appropriate route are essential to improving patient survival rates. However, emergency medical personnel are often under high stress, which makes it difficult for them to make optimal decisions. The present invention aims to solve these issues and achieve efficient and rapid patient transport by evaluating the user's emotional state in real time and providing an appropriate interface.

[1469] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1470] In this invention, the server includes a means for inputting information about the patient's condition and symptoms, a means for collecting information about medical institutions' medical treatment capabilities, available facilities, and the number of available beds and storing it in a database, and a means for collecting information about local traffic conditions and storing it in a database. This makes it possible to select the most appropriate medical institution to address the patient's symptoms in real time using an AI algorithm based on the accumulated information. In addition, by evaluating the user's emotional state in real time and adjusting the interface, it is possible to reduce the user's mental burden and enable quick and appropriate patient transport.

[1471] The "means for inputting information about a patient's condition and symptoms" refers to a system that allows emergency personnel and medical professionals to input a patient's vital signs and specific symptoms into an electronic device.

[1472] "Means of collecting information on medical institutions' medical treatment capabilities, available facilities, and number of available beds, and storing it in a database" refers to a system that collects data on medical treatment capabilities, facility status, and number of available beds from each medical institution in real time and centrally manages it.

[1473] "Means for collecting information on traffic conditions within the region and storing it in a database" refers to a system for collecting traffic information from traffic management centers and public transportation databases and storing it in a database in real time.

[1474] "Means for using AI algorithms to select the most appropriate medical institution to address a patient's symptoms in real time" is a system that uses AI technology to analyze collected data and select the medical institution that is most suitable for the patient's symptoms in real time.

[1475] The "means for displaying a list of selected medical institutions" is a system for displaying a list of medical institutions selected by the AI ​​algorithm on the user's device.

[1476] The "means for notifying the medical institution selected by the user of the patient's expected arrival information" is a communication system that allows emergency personnel to notify the medical institution selected by the emergency personnel of the patient's symptoms, vital signs, and expected arrival time.

[1477] The "means for providing guidance on the optimal route to the selected medical institution" is a system that analyzes the optimal route for an ambulance to quickly reach the selected medical institution and provides it as navigation information.

[1478] "Means for evaluating the user's emotional state in real time and adjusting the interface" refers to a system that evaluates the emotional state of emergency personnel in real time using an emotion engine or similar, and simplifies the user interface or highlights important information according to their stress level.

[1479] This system allows emergency personnel and medical professionals to input information about a patient's condition and symptoms, selects the most appropriate medical institution based on that information, and transports the patient quickly. This system uses an emotion engine to reduce the user's mental burden, enabling efficient and prompt response.

[1480] Specifically, paramedics use electronic devices such as smartphones to input basic information about the patient (age, gender, symptoms, etc.). The devices communicate with a server via the internet and send the input information to the server. The server then stores information on medical institutions' medical capabilities, available facilities, number of available beds, and local traffic conditions in a database, and uses an AI algorithm to select the most suitable medical institution.

[1481] The hardware and software used in this system are as follows:

[1482] Hardware: Smartphones, server machines

[1483] Software: Flask (a Python micro web framework), Emotion API, Navigation API

[1484] The server uses an AI algorithm to integrate the received patient information with medical institution and transportation data in the database and select the most suitable medical institution in real time. A list of selected medical institutions is displayed on the terminal, and the user can select the one they consider most suitable. After selection, the server notifies the medical institution of the patient's expected arrival information and provides the terminal with optimal route information, and transportation begins.

[1485] The emotion engine assesses the emotional state of the paramedic in real time and dynamically adjusts the user interface based on the results: for example, if the paramedic is in a high-stress state, the interface will be simplified and important information will be highlighted.

[1486] As a concrete example, consider a 50-year-old man complaining of chest pain. The paramedic uses his smartphone to enter the following information:

[1487] Patient's age: 50 years

[1488] Gender: Male

[1489] Symptoms: Chest pain

[1490] Vital signs: Heart rate 100, blood pressure 150 / 95, oxygen saturation 92

[1491] This information is sent to a server, where an AI algorithm determines that cardiac surgery is required and lists the most appropriate hospitals from multiple hospitals in the area. An emotion engine recognizes the paramedic's stress level and simplifies the interface to highlight important information. Finally, the paramedic selects a recommended hospital, and the information is sent to the server, which notifies the hospital. The user receives navigation information guiding them to the optimal route, ensuring a speedy transport.

[1492] Below are some example prompts to input to a generative AI model:

[1493] "A 50-year-old man complains of chest pain. The paramedic enters the information into a smartphone and sends it to a server. Please explain what data and process are used to suggest the most appropriate medical institution."

[1494] As described above, the present invention automates the process of selecting a transport destination for emergency medical care, thereby realizing a prompt and appropriate response while reducing the mental burden on the user.

[1495] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1496] Step 1:

[1497] The user (emergency medical personnel) uses a device such as a smartphone to input basic patient information (age, gender, symptoms) and vital signs (heart rate, blood pressure, oxygen saturation). By inputting this data, the device prepares information on the patient's condition.

[1498] input:

[1499] Patient's age, sex, and symptoms

[1500] Vital signs (heart rate, blood pressure, oxygen saturation)

[1501] output:

[1502] Prepared patient status information

[1503] Step 2:

[1504] Using an internet connection, the device sends patient status information to a server, which receives the information and stores it in a database.

[1505] input:

[1506] Prepared patient status information

[1507] output:

[1508] Patient information sent to the server

[1509] Step 3:

[1510] The server collects data from medical institutions regarding their medical capacity, available facilities, and the number of available beds via API and stores it in a database. It also collects traffic data from the traffic management center in real time.

[1511] input:

[1512] Medical data from medical institutions

[1513] Traffic data from the Traffic Management Center

[1514] output:

[1515] Medical institution and transportation data stored in the database

[1516] Step 4:

[1517] The server inputs the accumulated information into an AI algorithm that selects the medical institution that best suits the patient's symptoms in real time. The AI ​​algorithm integrates and analyzes the patient's condition information, medical institution and transportation data.

[1518] input:

[1519] Patient status information

[1520] Medical institution and transportation data stored in the database

[1521] output:

[1522] A list of selected optimal medical institutions

[1523] Step 5:

[1524] The server sends a list of selected medical institutions to the terminal, which then displays it to the user, who then selects an appropriate medical institution from the list.

[1525] input:

[1526] A list of selected optimal medical institutions

[1527] output:

[1528] List of medical institutions displayed to users

[1529] Step 6:

[1530] The terminal sends the information about the medical institution selected by the user to the server, and the server notifies the selected medical institution of the patient's expected arrival information.

[1531] input:

[1532] Medical institution information selected by the user

[1533] output:

[1534] Arrival information sent to selected medical institutions

[1535] Step 7:

[1536] The server analyzes the optimal route to the selected medical institution and sends the information to the terminal, which then uses the received route information to provide navigation.

[1537] input:

[1538] Selected medical institution information

[1539] Traffic Data

[1540] output:

[1541] Optimal route information for navigation

[1542] Step 8:

[1543] The device uses an emotion engine to assess the emotional state of the paramedic in real time, and automatically adjusts the interface based on that emotional state, highlighting important information and simplifying operation even when the paramedic is in a high-stress situation.

[1544] input:

[1545] Data on the emotional state of emergency personnel

[1546] output:

[1547] Adjusted User Interface

[1548] This step will streamline the entire system and allow emergency responses to be carried out quickly and accurately.

[1549] 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.

[1550] 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.

[1551] 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.

[1552] 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.

[1553] 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.

[1554] 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.

[1555] 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).

[1556] 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.

[1557] 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."

[1558] 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.

[1559] 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).

[1560] 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.

[1561] 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.

[1562] 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.

[1563] 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.

[1564] 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.

[1565] 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.

[1566] 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.

[1567] 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.

[1568] 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.

[1569] 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.

[1570] The following is further disclosed regarding the above embodiment.

[1571] (Claim 1)

[1572] a means for inputting information about the patient's condition and symptoms;

[1573] A means of collecting and storing information on medical institutions' medical capacity, available facilities, and number of available beds in a database;

[1574] A means for collecting information on traffic conditions within the area and storing it in a database;

[1575] Based on the accumulated information, an AI algorithm is used to select the most appropriate medical institution in real time to respond to the patient's symptoms.

[1576] a means for displaying a list of selected medical institutions;

[1577] means for notifying a medical institution selected by the user of the patient's expected arrival information;

[1578] A means for providing guidance on the optimal route to the selected medical institution;

[1579] A system including:

[1580] (Claim 2)

[1581] 2. The system according to claim 1, which integrates and analyzes information on the patient's condition and symptoms, information on the medical institution's medical treatment capacity, available facilities, number of available beds, and information on traffic conditions within the area.

[1582] (Claim 3)

[1583] The system of claim 1, including an AI algorithm that estimates and selects a medical institution with the expertise to address a patient's symptoms based on information collected in real time.

[1584] "Example 1"

[1585] (Claim 1)

[1586] a means for inputting information about the patient's condition and symptoms;

[1587] A means of collecting and storing information on medical institutions' medical capacity, available facilities, and number of available beds in a database;

[1588] A means for collecting information on traffic conditions within the area and storing it in a database;

[1589] Based on the accumulated information, an AI algorithm is used to select the most appropriate medical institution in real time to respond to the patient's symptoms.

[1590] a means for displaying a list of selected medical institutions;

[1591] means for notifying a medical institution selected by the user of the patient's expected arrival information;

[1592] A means for providing guidance on the optimal route to the selected medical institution;

[1593] A method to estimate medical institutions specializing in specific fields using AI algorithms based on patient information, and

[1594] A means of providing real-time information on optimal patient transport routes;

[1595] A system including:

[1596] (Claim 2)

[1597] 2. The system according to claim 1, which integrates and analyzes information on the patient's condition and symptoms, information on the medical institution's medical treatment capacity, available facilities, number of available beds, and information on traffic conditions within the area.

[1598] (Claim 3)

[1599] The system of claim 1, including an AI algorithm that estimates and selects a medical institution with the expertise to address a patient's symptoms based on information collected in real time.

[1600] "Application Example 1"

[1601] (Claim 1)

[1602] a means for inputting information regarding the patient's or user's condition and needs;

[1603] a means for collecting and storing in a database information about the service capabilities, available facilities, and availability of providers;

[1604] A means of collecting information on traffic conditions and resource conditions within the region and storing it in a database;

[1605] Based on the accumulated information, an AI algorithm is used to select the most suitable provider in real time to respond to the situation.

[1606] a means for displaying a list of selected providers;

[1607] means for notifying a provider selected by the user of the estimated arrival information;

[1608] A means for guiding the optimal route to the selected provider;

[1609] A system including:

[1610] (Claim 2)

[1611] 2. The system according to claim 1, which integrates and ...

Claims

1. a means for inputting information about the patient's condition and symptoms; A means of collecting and storing information on medical institutions' medical capacity, available facilities, and number of available beds in a database; A means for collecting information on traffic conditions within the area and storing it in a database; Based on the accumulated information, an AI algorithm is used to select the most appropriate medical institution in real time to respond to the patient's symptoms. a means for displaying a list of selected medical institutions; means for notifying a medical institution selected by the user of the patient's expected arrival information; A means for providing guidance on the optimal route to the selected medical institution; A system including:

2. 2. The system according to claim 1, which integrates and analyzes information on the patient's condition and symptoms, information on the medical treatment capacity of medical institutions, available facilities, number of available beds, and information on traffic conditions within the area.

3. The system of claim 1, including an AI algorithm that estimates and selects a medical institution with a specialty that corresponds to the patient's symptoms based on information collected in real time.

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