system

Abstract

We provide the system. [Solution] A means of analyzing traffic information using a generative model and deriving the optimal transport route, A means of evaluating the patient acceptance situation at medical institutions and selecting appropriate medical facilities, A means of monitoring a patient's vital signs in real time and issuing a warning signal if an abnormality is detected, A means of controlling transportation methods utilizing autonomous driving technology and optimizing transportation efficiency, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In emergency medicine, it is an important issue to quickly and safely transport patients to appropriate medical institutions in an emergency. However, there is a problem that the transportation time is delayed due to traffic congestion and insufficient grasp of the acceptance status of medical institutions. In addition, it is difficult to monitor the patient's condition in real time and prepare appropriate medical treatments in advance. Therefore, there is a need for a system that solves these problems, improves the efficiency of emergency transportation and the quality of medical care, and increases the survival rate.

Means for Solving the Problems

[0005] This invention provides a means for analyzing traffic information in real time using a generative model and automatically selecting the optimal transport route. Furthermore, it includes a means for evaluating the patient acceptance status of medical institutions using a linked system and selecting the most suitable medical facility for transport. This system incorporates a means for constantly monitoring the patient's vital signs and issuing warnings to medical personnel when abnormalities are detected. In addition, by using autonomous driving technology to control transport means that enhance the efficiency of transport, rapid and safe emergency transport is realized. This invention shortens transport time and optimizes medical preparation time, thereby improving the quality of emergency medical care.

[0006] A "generative model" is an algorithm that analyzes vast amounts of data to find specific patterns or trends within it.

[0007] "Traffic information" refers to data on the movement of vehicles on roads, congestion levels, and other traffic-related information.

[0008] A "transportation route" refers to a path from one point to another.

[0009] A "medical institution" is a facility that provides medical services to patients.

[0010] "Patient acceptance status" refers to information indicating a medical institution's capacity to accommodate patients and its ability to provide prompt treatment.

[0011] "Biometric information" refers to indicators that show a patient's health status, such as heart rate, blood pressure, and body temperature.

[0012] "Autonomous driving technology" refers to technology that enables vehicles to operate automatically without human intervention.

[0013] "Transportation means" refers to methods or devices used to move goods or people to a specific location. [Brief explanation of the drawing]

[0014] [Figure 1]It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0020] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and 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), or Bluetooth (registered trademark), etc.

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] As shown in Figure 2, in the data processing device 12, specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] In this embodiment of the invention, a system that analyzes traffic information and the availability of medical facilities in real time is used to achieve rapid and appropriate patient transport in emergencies. The main components of the system and their operation are described in detail below.

[0036] First, when the terminal receives an emergency call, paramedics enter detailed information about the patient and use sensors to obtain biometric data. This includes the patient's heart rate, blood pressure, and body temperature. The terminal then sends this data to a server.

[0037] The server uses a generative model to analyze real-time traffic data and determine the optimal transport route. The server also checks the availability of medical facilities, evaluating information such as whether necessary specialists are available and whether there are vacant beds. Based on this information, it sends recommended medical facilities and route information back to the terminal.

[0038] The terminal uses autonomous driving technology to control the ambulance and guide it to its destination based on the optimal route. During this time, the terminal continuously collects the patient's biometric information and transmits the data to a server in real time. The server analyzes this information and immediately alerts paramedics if any abnormalities are detected.

[0039] As a concrete example, consider a case where a patient is found in a state close to cardiac arrest. In this case, the terminal detects a rapid change in heart rate and reports this abnormality to the server. Based on this information, the server quickly selects the nearest medical facility equipped with specialized cardiac equipment and transmits that information to the terminal. The terminal then carries out necessary stabilization measures and rapid transport to the medical facility.

[0040] Furthermore, the server transmits patient information to the systems of pre-selected medical institutions, prompting them to prepare for immediate treatment upon arrival. In this way, it is possible to shorten transport times, optimize medical preparations, and improve survival rates.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The user receives an emergency call and enters the patient's basic information and location into the terminal. They also attach vital signs sensors to the patient and begin acquiring biometric data.

[0044] Step 2:

[0045] The terminal transmits the patient's basic information, location information, and biometric information to the server.

[0046] Step 3:

[0047] The server uses a generative model to analyze real-time traffic data and calculate the optimal transport route, taking into account information on traffic congestion and road closures.

[0048] Step 4:

[0049] The server checks the availability of medical facilities, confirming the presence of necessary specialists and bed vacancies. Based on this information, it selects the most suitable medical facility.

[0050] Step 5:

[0051] The server sends information about the optimal route and medical facilities back to the terminal.

[0052] Step 6:

[0053] The terminal uses autonomous driving technology to automatically set a route and dispatch the ambulance.

[0054] Step 7:

[0055] The device continuously transmits biometric information to the server in real time.

[0056] Step 8:

[0057] The server continuously analyzes biometric information and issues a warning to the user if an anomaly is detected.

[0058] Step 9:

[0059] Based on newly acquired traffic information, the server recalculates the route as needed and sends the updated information to the terminal.

[0060] Step 10:

[0061] The device transmits patient information to the selected medical institution before arrival, allowing the doctor to prepare.

[0062] Step 11:

[0063] The device arrives at its destination, and the patient is smoothly handed over to the medical facility.

[0064] (Example 1)

[0065] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0066] Modern emergency medical transport demands improved time efficiency and rapid patient transfer to appropriate medical facilities. However, many obstacles exist, including traffic congestion, fluctuations in the availability of medical facilities, and the difficulty of real-time monitoring of vital signs. Overcoming these obstacles is essential to further reduce transport time and waiting times before treatment begins.

[0067] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0068] In this invention, the server includes means for a generative model to analyze traffic information acquired through the vehicle's control device and calculate the optimal transport route; means for evaluating the acceptance capacity of medical facilities and selecting appropriate facilities; and means for monitoring biometric data in real time and issuing warnings when abnormalities occur. This enables efficient and rapid emergency transport.

[0069] A "generative model" is an algorithm used to analyze data and make predictions or decisions based on specific conditions.

[0070] "Traffic information" refers to data on congestion levels and travel times on roads and public transportation.

[0071] A "transportation route" is the optimal path for a vehicle to travel from its starting point to its destination.

[0072] "Acceptance capacity" refers to the immediate ability of a healthcare facility to admit patients and begin treatment.

[0073] "Biometric data" refers to information that indicates a patient's physical condition, such as heart rate, blood pressure, and body temperature.

[0074] "Autonomous driving technology" refers to technology that enables vehicles to operate automatically without human intervention.

[0075] A "warning" is a signal or notification used to alert relevant parties to danger when an abnormal situation occurs.

[0076] This invention provides a system for optimizing emergency medical transport by analyzing traffic information and monitoring the availability of medical facilities in real time. The following describes a specific implementation of this system.

[0077] The server uses a generative AI model to analyze real-time traffic information and calculate the optimal transport route. This analysis utilizes a traffic API to obtain the latest traffic conditions and road information. Based on the analysis results, the server determines the optimal route and transmits it to the terminal.

[0078] The terminal transmits patient information entered by paramedics and biometric data acquired using sensors to a server. The terminal also features autonomous driving technology and controls the ambulance according to the server's instructions, following the optimal route. Inside the ambulance, the patient's heart rate, blood pressure, and body temperature are monitored in real time, and any abnormalities are immediately reported to the server.

[0079] The user (in this case, an emergency medical technician) receives information from the server via a terminal and verifies whether the medical facility to which the patient is being transported is appropriate. In addition, the user uses the terminal to monitor the patient's condition and perform any necessary medical procedures during transport.

[0080] As a specific example, in the emergency transport of a patient suspected of having a heart condition, if the terminal detects a sudden change in heart rate from the acquired biometric data, the server calculates the shortest route to a cardiac specialist facility and transmits that information to the terminal. As a result, the ambulance can quickly reach its destination using autonomous driving technology.

[0081] An example of a prompt message might be, "Request the optimal route and recommended medical facilities for emergency transport of a patient with cardiac disease." This allows for prior contact with the receiving medical institution, ensuring that treatment can begin immediately upon arrival.

[0082] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0083] Step 1:

[0084] When the terminal receives an emergency call, the user enters patient information. Detailed information such as the patient's name, age, gender, and symptoms are manually registered on the terminal's interface. The terminal also acquires biometric data such as heart rate, blood pressure, and body temperature through sensors. The entered information, along with this biometric data, is transmitted to the server in packet format.

[0085] Step 2:

[0086] The server uses patient information and biometric data received from the terminal as input to analyze real-time traffic information using a generated AI model. It collects data on current congestion and the shortest route from a traffic API. By analyzing this data, it calculates the optimal route and outputs the result to the terminal. The analysis results include predicted arrival time and intermediate stop information.

[0087] Step 3:

[0088] The server evaluates the capacity of healthcare facilities. Based on the patient's symptoms, it generates a list of potential healthcare facilities and checks the availability of beds and specialists at each facility. It retrieves data through an interface with the healthcare institution's information system and selects the most suitable facility. The facility selection information is transmitted to the terminal.

[0089] Step 4:

[0090] The terminal controls the ambulance using autonomous driving technology based on the optimal route and medical facility information received from the server. It activates the automatic navigation system and heads towards the destination according to the recommended route. If new traffic information arises along the way, the terminal automatically recalculates and adjusts the route based on that information.

[0091] Step 5:

[0092] The terminal continuously collects biometric data during transport and transmits it to the server in real time. This information is monitored by the server to enable a rapid response if an anomaly is detected. The server sends a warning signal back to the terminal if an anomaly is detected, and also warns the user.

[0093] Step 6:

[0094] The server pre-transmits patient information to the designated healthcare facility. This data transmission allows medical staff to prepare for treatment, enabling efficient treatment upon the patient's arrival. The server integrates with the facility's system and notifies the terminal of the progress once the necessary preparations are complete.

[0095] (Application Example 1)

[0096] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0097] Rapid and accurate patient transport in emergencies is a critical challenge in healthcare settings. However, delays can occur due to traffic congestion and uncertainty regarding the availability of medical facilities. In addition, paramedics must perform numerous tasks during patient transport, which can impact patient care. To address these issues, a system is needed that optimizes routes and medical conditions in real time, enabling smooth transport.

[0098] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0099] In this invention, the server includes means for analyzing traffic information using a generative model and deriving the optimal transport route; means for evaluating the patient acceptance status at medical institutions and selecting an appropriate medical facility; means for monitoring the patient's vital signs in real time and issuing a warning signal if an abnormality is detected; means for providing information to emergency service operators and personnel and optimizing the state during transport; and means for checking the readiness status of the destination facility and enabling immediate treatment upon arrival. This enables rapid and appropriate patient transport even in emergency situations.

[0100] A "generative model" is an artificial intelligence technology used to derive optimal solutions based on data collected in real time.

[0101] "Traffic information" refers to data related to driving conditions such as road congestion and traffic jams.

[0102] A "transportation route" is information that indicates the optimal route from a certain point to a destination.

[0103] A "medical institution" refers to a facility that provides medical care and treatment to patients.

[0104] "Patient acceptance status" refers to the state of whether a medical institution is ready to examine or treat patients.

[0105] "Biometric information" refers to data about a person's physical condition, such as heart rate, blood pressure, and body temperature.

[0106] A "warning signal" is a notification issued to alert relevant parties when an anomaly is detected.

[0107] "Autonomous driving technology" refers to the ability of a vehicle to drive automatically without human intervention.

[0108] An "emergency service operator" is a professional responsible for communication and directing during emergencies.

[0109] "Team members" refers to personnel who have been specially trained to carry out emergency medical services.

[0110] "Information provision" refers to the act of conveying necessary data and knowledge to users.

[0111] "The receiving facility" refers to the medical institution that will accept and treat the patient.

[0112] "Preparation" refers to a state in which everything necessary for a particular action or activity is in place.

[0113] In implementing this invention, the server processes real-time data using a generative model to analyze traffic information and the availability status of medical facilities. The server uses Python and utilizes the TENSORFLOW® library to build an AI model. In addition, real-time driving data collected through publicly available APIs is used for traffic information, and the availability status of medical facilities is obtained through OpenAPI.

[0114] The smartphone application allows emergency medical personnel to monitor a patient's biometric information and transmit data to a server as needed. The application utilizes an Android® device and runs programs in Java® or Kotlin. Patient information is collected from sensors via Bluetooth or Wi-Fi. This enables emergency personnel to constantly monitor the patient's condition, obtain route information, and transport the patient to the appropriate medical facility.

[0115] The autonomous vehicles utilize ROS (Robot Operating System) as their control system, automatically navigating the optimal route based on traffic conditions. This allows personnel to focus on patient care and information gathering.

[0116] As a concrete example, consider an operation to accept injured persons in transit during an emergency response. The server analyzes the injured person's biological condition and suggests the optimal medical facility and transport route. The input prompt to the generated AI model is, "Based on the injured person's location information, identify the optimal medical facility and route in real time."

[0117] In this way, rapid and accurate patient transport is achieved through the cooperation of servers, terminals, and users.

[0118] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0119] Step 1:

[0120] The terminal receives the emergency call. The user, an emergency medical technician, begins entering the patient's biometric information, while simultaneously collecting data through biosensors. The entered data includes heart rate, blood pressure, and body temperature, which are transmitted to the terminal via Bluetooth or Wi-Fi. The terminal receives this information and prepares it for transmission to the server.

[0121] Step 2:

[0122] The device sends collected biometric information to the server. The server receives this data in real time and compares it to normal values ​​using a generative AI model. The server performs data calculations, and if an abnormal value is detected, it immediately sends a warning signal to the device and generates further warning information regarding transportation options and healthcare selection.

[0123] Step 3:

[0124] The server obtains the latest traffic information via a public API and calculates the optimal transportation route using a generated AI model. This input includes current location and destination information. The model outputs the optimal route considering traffic conditions, which is then sent to the terminal. Furthermore, it uses OpenAPI to check the readiness status of each medical institution and selects the most suitable facility.

[0125] Step 4:

[0126] The server calculates the optimal route and transmits information about selected medical facilities to the terminal. The user, an emergency medical technician, transports the patient to the designated medical facility according to the instructions from the terminal. During transport, the terminal automatically provides operational instructions to the autonomous vehicle using ROS. The terminal dynamically changes the route according to the traffic conditions at the time, optimizing the transport to the destination.

[0127] Step 5:

[0128] The terminal continuously monitors the patient's vital signs during transport and sends the analysis results to the server. The server continues to monitor for any new abnormalities and sends additional instructions or warnings to the user as needed. This information is also shared before arrival at the medical facility to facilitate preparation for admission.

[0129] Step 6:

[0130] The user arrives at the designated medical facility and begins transporting the patient. Based on the patient information received from the server in advance, the medical facility can immediately begin treatment. The information sent from the server to the medical facility includes the patient's latest biological status and suggested necessary procedures.

[0131] Through the above series of processing steps, rapid and accurate patient transport in emergencies is achieved. The generating AI model performs data processing that takes into account traffic conditions and the status of medical facilities, based on the prompt message "Identify the optimal medical facility and route in real time based on the location information of the injured person."

[0132] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0133] In this embodiment of the invention, a system is provided that combines emotion recognition technology with rapid and safe patient transport in emergencies. This system is effective by integrating an emotion engine that analyzes the user's emotions, in addition to traffic information analysis using a generative model, real-time biometric information monitoring, and optimization of transport efficiency through autonomous driving technology.

[0134] First, the terminal receives an emergency call, and paramedics input the patient's basic information and location. Based on this information, the terminal activates an emotion engine to sense the emotions of the user during the ride. This emotion engine evaluates the user's emotional state in real time using voice tone, facial expressions, sweating, and heart rate data.

[0135] The server uses a generative model to analyze traffic data and determine the optimal transport route. It also queries the availability of medical facilities and selects the necessary medical facilities. The results of this processing are then sent to the terminal.

[0136] Based on the route information and medical facility information it receives, the terminal uses autonomous driving technology to accurately guide the ambulance to its destination. During the journey, the emotion engine continuously monitors the user's stress level and emotional changes, and if an abnormality is detected, the terminal sends an alert to the emergency medical staff via the server.

[0137] For example, if a patient is experiencing severe pain, the emotion engine recognizes this as a high stress level, and the server automatically adjusts transportation methods to ensure a safe and rapid response. It can also provide the user with voice feedback and relaxation instructions that correspond to specific emotional states.

[0138] Finally, the terminal transmits patient information to a pre-selected medical institution, allowing medical staff to prepare for arrival. In this way, time is reduced, the quality of medical care is improved, and the user's emotional well-being is simultaneously addressed, contributing to an increase in survival rates.

[0139] The following describes the processing flow.

[0140] Step 1:

[0141] The user receives an emergency call and enters the patient's basic information and location into the terminal. The terminal then begins acquiring biometric information from sensors attached to the patient.

[0142] Step 2:

[0143] The terminal transmits basic patient information, location information, biometric information, and user emotion data determined using an emotion engine to the server.

[0144] Step 3:

[0145] The server uses a generative model to analyze real-time traffic data and calculate the optimal transport route. This process takes into account traffic congestion and road closures.

[0146] Step 4:

[0147] The server investigates the acceptance status of each medical institution and selects facilities that can provide the necessary medical services. It then transmits the selected route and medical facility information to the terminal.

[0148] Step 5:

[0149] The terminal activates the autonomous driving system and dispatches the ambulance according to the received route information.

[0150] Step 6:

[0151] The device continuously transmits the patient's biometric information to the server, and the emotion engine also continuously monitors the user's emotions.

[0152] Step 7:

[0153] The server analyzes biometric and emotional data, provides feedback to the user if an anomaly is detected, and adjusts the autonomous driving route and speed as needed. It also issues warnings to emergency medical staff if deemed necessary.

[0154] Step 8:

[0155] The device will pre-send patient information to the medical institution where it is scheduled to arrive, facilitating treatment preparation.

[0156] Step 9:

[0157] The terminal arrives at its destination, the patient is safely handed over to medical facility staff, and medical treatment is initiated quickly.

[0158] (Example 2)

[0159] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0160] To ensure the rapid and safe transport of patients in emergencies, it is necessary to comprehensively evaluate multiple factors, such as traffic conditions, the capacity of medical facilities, and real-time monitoring of patients' emotions and biometric information, in order to provide the optimal transport approach. However, conventional systems can only address these factors individually, making integrated and efficient transport difficult.

[0161] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0162] In this invention, the server includes means for analyzing traffic information using a generative model and deriving the optimal transport route, means for evaluating the patient acceptance status at medical institutions and selecting an appropriate medical facility, and means for analyzing the patient's emotional state in real time and detecting changes in stress levels. This enables safe and rapid transport while considering the multifaceted condition of the patient.

[0163] A "generative model" is a mathematical model used to generate new information based on input data, and is particularly used in the analysis of traffic information.

[0164] "Traffic information" refers to real-time data on road and transportation systems, such as congestion levels and traffic restrictions.

[0165] A "transportation route" refers to the route selected to optimize travel from a certain point to a destination.

[0166] A "medical institution" is a facility that provides treatment and diagnosis for patients, and includes hospitals and clinics.

[0167] "Acceptance status" refers to the number of patients and the status of cases that a medical institution can currently handle.

[0168] "Emotional state" refers to the state of an individual's psychological and physiological emotional responses as observed in real time.

[0169] "Stress level" is a numerical representation of the degree of psychological and physiological pressure or tension an individual is experiencing.

[0170] "Autonomous driving technology" refers to technology that enables vehicles to operate automatically without human intervention.

[0171] "Biometric information" refers to data related to human vital signs such as heart rate, body temperature, and sweating.

[0172] A "warning signal" refers to a signal issued by a system or device to alert the user when it detects an abnormality.

[0173] This invention is a system that combines generative AI models, emotion recognition technology, and autonomous driving technology to optimize patient transport in emergencies. The system mainly consists of servers and terminals, each performing processing according to its respective role.

[0174] The server analyzes traffic information using a generative AI model. This AI model is based on algorithms learned from a wide range of datasets and analyzes data collected through Google Maps and similar real-time traffic information services. The server then uses this to derive the optimal transportation route to the destination. Additionally, the server references a database of medical facilities, evaluates the patient acceptance status of each facility, and selects the most suitable medical facility.

[0175] The terminal receives emergency calls and activates emotion recognition technology based on the patient's basic information entered by paramedics. This emotion recognition is performed using the terminal's built-in camera and microphone, as well as a wearable device capable of heart rate monitoring, for example, using Microsoft's (registered trademark) emotion recognition API. This technology allows for real-time analysis of the patient's emotional state from their voice tone, facial expressions, heart rate, etc. Based on the analysis results, the system suggests the most appropriate response if the stress level is high.

[0176] Autonomous driving technology, such as that used by Waymo, automatically operates ambulances based on safe transport routes transmitted from terminals. During operation, emotion recognition technology continuously monitors the patient's stress and emotional state, immediately reporting any abnormalities to the server and issuing warnings to medical staff if necessary.

[0177] For example, if a patient is experiencing severe pain, emotion recognition technology can detect this pain as a high stress level, and the server will prioritize recalculating the shortest and most optimal route to ensure safe and rapid transport. Furthermore, if necessary, the terminal can provide voice guidance to the patient to help them relax.

[0178] An example of a prompt message would be: "An emergency call has been received. Please enter patient information. The server will calculate the optimal transport route based on the received data, monitor the patient's emotional state in real time, and issue alerts to medical staff as needed. Please notify us when the patient has been transported to the appropriate medical facility."

[0179] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0180] Step 1:

[0181] The terminal receives an emergency call. Using a dedicated application installed on the terminal, paramedics input the patient's basic information and location. Based on this information, the terminal sends data to the server. The input includes the patient's name, age, symptoms, and location, and the output is a secure transmission of data to the server.

[0182] Step 2:

[0183] The device activates emotion recognition technology. Its camera and microphone capture the patient's voice tone and facial expressions, while a wearable device monitors their heart rate. This data is input into an emotion recognition API, which analyzes the user's state in real time. Inputs include voice, video, and biometric information, while output is an evaluation of the patient's emotional state.

[0184] Step 3:

[0185] The server analyzes received data using a generating AI model and processes traffic information. The server obtains real-time traffic information from traffic data providers and calculates the optimal transport route. The input is location information and real-time traffic conditions, and the output is the determination of the optimal transport route.

[0186] Step 4:

[0187] The server checks the acceptance status of medical institutions. It refers to its database of medical institutions, evaluates each institution's capacity and facilities, and selects the most appropriate medical facility. Inputs include the number of patients, symptoms, and acceptance status, while the output is the selection of the appropriate medical institution.

[0188] Step 5:

[0189] The terminal transmits route and medical facility information received from the server to the autonomous driving technology. The autonomous driving system operates the ambulance based on this information, adjusting the route in real time. The input is the optimal route and destination, and the output is the control of the autonomous driving state.

[0190] Step 6:

[0191] The terminal continuously monitors the patient's emotional state during operation. If the patient's stress level increases or an abnormality is detected, the terminal notifies medical staff via the server. The input is the result of emotion recognition, and the output is a warning signal in case of an abnormality.

[0192] Step 7:

[0193] The terminal transmits patient information to the medical institution before arrival. The information is sent to the medical institution via a server in advance, allowing medical staff to prepare. Inputs include patient symptoms and estimated arrival time, while output is the notification of information to the medical institution.

[0194] (Application Example 2)

[0195] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0196] In emergency patient transport, not only is rapid and safe transportation important, but the psychological care of patients and their caregivers during the journey is also crucial. However, conventional systems do not adequately monitor the user's mental state based on emotion recognition or provide appropriate feedback based on that, posing challenges in reducing anxiety and stress during transport. Furthermore, optimizing the transport route in response to changes in traffic conditions and the patient's condition is also difficult.

[0197] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0198] In this invention, the server includes means for analyzing traffic information using a generative model and deriving the optimal transport route, means for monitoring the patient's biometric information and emotional state in real time and issuing a warning signal when an abnormality is detected, and means for providing emotion-based voice feedback to stabilize the user's mental state. This makes it possible to transport the user quickly and efficiently while reducing stress and providing a sense of security.

[0199] A "generative model" is a form of machine learning that learns patterns from data and generates new information.

[0200] "Traffic information" refers to information about the condition of roads and transportation systems, including traffic congestion and road closures.

[0201] A "transportation route" refers to the optimal path to a destination, enabling efficient travel.

[0202] A "medical institution" is a facility that provides medical services, and includes hospitals and clinics.

[0203] "Patient acceptance status" refers to the current number of patients at a medical institution and the number of patients who can be treated.

[0204] "Biological information" refers to biological information obtained from the human body, such as heart rate, sweating, and body temperature.

[0205] "Emotional state" refers to an individual's emotional state, including stress, anxiety, and feelings of security.

[0206] A "warning signal" is a notification issued when a system detects an anomaly, indicating a situation that requires immediate attention.

[0207] "Autonomous driving technology" refers to technology that allows vehicles and equipment to move or perform tasks automatically without human intervention.

[0208] "Transportation means" refers to the means of moving goods or people from one place to another, and includes vehicles and machinery.

[0209] "Stabilizing one's mental state" refers to the process of calming an individual's emotions and reducing anxiety and stress.

[0210] "Voice feedback" is a technology that uses voice to provide information and instructions to users.

[0211] This invention provides a system for efficiently and safely transporting patients in emergencies. The server analyzes traffic information using a generative AI model to determine the optimal transport route. In doing so, the server receives real-time updated traffic data and constantly calculates the best route. Furthermore, the server has the function of evaluating the patient acceptance status of medical facilities and selecting an appropriate medical facility.

[0212] The device monitors the user's biometric information and emotional state 24 hours a day. It uses an emotion recognition engine to analyze voice tone, facial expressions, and vital data. If an abnormality is detected, the device immediately sends a warning signal to the server and medical staff. Furthermore, the user receives immediate voice feedback based on their emotional state, providing emotional support. This helps alleviate user stress and provide a sense of security.

[0213] Autonomous driving technology-based transportation systems travel along the most efficient route to their destination, following route information received from a server. This technology minimizes interference during operation and maximizes the efficiency and safety of transportation.

[0214] As a concrete example, a patient involved in an accident in the mountains could wear smart glasses and utilize this system to be safely transported to a medical facility in the shortest possible time. Furthermore, an example of a prompt message would be, "Use the emotion engine to detect if the patient is experiencing high levels of anxiety and suggest ways to provide reassurance." This input would allow the system to quickly generate appropriate feedback and support the patient.

[0215] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0216] Step 1:

[0217] The server collects traffic information in real time and analyzes it using a generative AI model. This analysis derives the optimal transportation route. It receives current traffic data as input and sends information about the optimal route to the terminal as output. In this process, the server utilizes the generative AI model to perform pattern recognition on the data.

[0218] Step 2:

[0219] The device acquires the patient's biometric information and emotional state, and analyzes it using an emotion engine. It uses heart rate, voice tone, and facial expression data as input, and outputs an evaluation of the emotional state. Throughout this process, the device collects the necessary data through sensor devices and processes it in real time.

[0220] Step 3:

[0221] The terminal sends a warning signal to the server based on the anomaly detection results obtained from the emotion engine. The input is the evaluation result of the emotional state, and the output is a warning signal. In this step, the terminal automatically detects an anomaly and immediately issues a signal.

[0222] Step 4:

[0223] The terminal generates and provides voice feedback to the user based on the patient's condition. It uses the results of an emotional state assessment as input and generates voice messages as output. In this process, it selects an appropriate message based on instructions from the emotion engine and plays it back via a voice control device.

[0224] Step 5:

[0225] Based on autonomous driving technology, the system uses optimal route information to operate the transport vehicle. The input is optimal route information transmitted from the server, and the output is the efficient movement to the destination. In this step, a route-based motion plan is generated, and actuator control is performed for autonomous driving.

[0226] Step 6:

[0227] Before the user arrives at their destination, the terminal transmits patient information to the medical institution. Inputs include basic patient information and current status data, while output is the transmission of data to the medical institution. Here, the terminal ensures that the information is organized beforehand and transmitted via a secure communication method.

[0228] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0229] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0230] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0231] [Second Embodiment]

[0232] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0233] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0234] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0235] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0236] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0237] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0238] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0239] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0240] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0241] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0242] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0243] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0244] In this embodiment of the invention, a system that analyzes traffic information and the availability of medical facilities in real time is used to achieve rapid and appropriate patient transport in emergencies. The main components of the system and their operation are described in detail below.

[0245] First, when the terminal receives an emergency call, paramedics enter detailed information about the patient and use sensors to obtain biometric data. This includes the patient's heart rate, blood pressure, and body temperature. The terminal then sends this data to a server.

[0246] The server uses a generative model to analyze real-time traffic data and determine the optimal transport route. The server also checks the availability of medical facilities, evaluating information such as whether necessary specialists are available and whether there are vacant beds. Based on this information, it sends recommended medical facilities and route information back to the terminal.

[0247] The terminal uses autonomous driving technology to control the ambulance and guide it to its destination based on the optimal route. During this time, the terminal continuously collects the patient's biometric information and transmits the data to a server in real time. The server analyzes this information and immediately alerts paramedics if any abnormalities are detected.

[0248] As a concrete example, consider a case where a patient is found in a state close to cardiac arrest. In this case, the terminal detects a rapid change in heart rate and reports this abnormality to the server. Based on this information, the server quickly selects the nearest medical facility equipped with specialized cardiac equipment and transmits that information to the terminal. The terminal then carries out necessary stabilization measures and rapid transport to the medical facility.

[0249] Furthermore, the server transmits patient information to the systems of pre-selected medical institutions, prompting them to prepare for immediate treatment upon arrival. In this way, it is possible to shorten transport times, optimize medical preparations, and improve survival rates.

[0250] The following describes the processing flow.

[0251] Step 1:

[0252] The user receives an emergency call and enters the patient's basic information and location into the terminal. They also attach vital signs sensors to the patient and begin acquiring biometric data.

[0253] Step 2:

[0254] The terminal transmits the patient's basic information, location information, and biometric information to the server.

[0255] Step 3:

[0256] The server uses a generative model to analyze real-time traffic data and calculate the optimal transport route, taking into account information on traffic congestion and road closures.

[0257] Step 4:

[0258] The server checks the availability of medical facilities, confirming the presence of necessary specialists and bed vacancies. Based on this information, it selects the most suitable medical facility.

[0259] Step 5:

[0260] The server sends information about the optimal route and medical facilities back to the terminal.

[0261] Step 6:

[0262] The terminal uses autonomous driving technology to automatically set a route and dispatch the ambulance.

[0263] Step 7:

[0264] The device continuously transmits biometric information to the server in real time.

[0265] Step 8:

[0266] The server continuously analyzes biometric information and issues a warning to the user if an anomaly is detected.

[0267] Step 9:

[0268] Based on newly acquired traffic information, the server recalculates the route as needed and sends the updated information to the terminal.

[0269] Step 10:

[0270] The device transmits patient information to the selected medical institution before arrival, allowing the doctor to prepare.

[0271] Step 11:

[0272] The device arrives at its destination, and the patient is smoothly handed over to the medical facility.

[0273] (Example 1)

[0274] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0275] Modern emergency medical transport demands improved time efficiency and rapid patient transfer to appropriate medical facilities. However, many obstacles exist, including traffic congestion, fluctuations in the availability of medical facilities, and the difficulty of real-time monitoring of vital signs. Overcoming these obstacles is essential to further reduce transport time and waiting times before treatment begins.

[0276] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0277] In this invention, the server includes means for a generative model to analyze traffic information acquired through the vehicle's control device and calculate the optimal transport route; means for evaluating the acceptance capacity of medical facilities and selecting appropriate facilities; and means for monitoring biometric data in real time and issuing warnings when abnormalities occur. This enables efficient and rapid emergency transport.

[0278] A "generative model" is an algorithm used to analyze data and make predictions or decisions based on specific conditions.

[0279] "Traffic information" refers to data on congestion levels and travel times on roads and public transportation.

[0280] A "transportation route" is the optimal path for a vehicle to travel from its starting point to its destination.

[0281] "Acceptance capacity" refers to the immediate response ability of a medical facility to accommodate patients and initiate treatment.

[0282] "Biological data" refers to information indicating the physical state of a patient, such as heart rate, blood pressure, body temperature, etc.

[0283] "Autonomous driving technology" refers to the technology for a vehicle to perform driving operations automatically without human operation.

[0284] "Warning" refers to a signal or notification to inform relevant parties of danger when an abnormal situation occurs.

[0285] This invention provides a system for grasping traffic information analysis and the acceptance status of medical institutions in real time in order to optimize emergency medical transportation. The following shows specific forms for implementing this system.

[0286] The server analyzes real-time traffic information using a generated AI model and calculates an optimal transportation route. For this analysis, the latest traffic conditions and road information are obtained using a traffic API. The server determines an optimal route based on the analysis results and transmits it to the terminal.

[0287] The terminal transmits patient information input by emergency responders and biological data obtained using sensors to the server. In addition, the terminal has a control function implementing autonomous driving technology and drives the ambulance along the optimal route according to instructions from the server. Inside the ambulance, the patient's heart rate, blood pressure, body temperature, etc. are monitored in real time, and if there is an abnormality, it is immediately reported to the server.

[0288] The user (here the user is an emergency responder) receives information from the server via the terminal and checks whether the medical institution as the patient's destination is appropriate. In addition, the user monitors the patient's condition using the terminal and implements necessary medical measures during transportation.

[0289] As a specific example, in the emergency transport of a patient suspected of having a heart condition, if the terminal detects a sudden change in heart rate from the acquired biometric data, the server calculates the shortest route to a cardiac specialist facility and transmits that information to the terminal. As a result, the ambulance can quickly reach its destination using autonomous driving technology.

[0290] An example of a prompt message might be, "Request the optimal route and recommended medical facilities for emergency transport of a patient with cardiac disease." This allows for prior contact with the receiving medical institution, ensuring that treatment can begin immediately upon arrival.

[0291] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0292] Step 1:

[0293] When the terminal receives an emergency call, the user enters patient information. Detailed information such as the patient's name, age, gender, and symptoms are manually registered on the terminal's interface. The terminal also acquires biometric data such as heart rate, blood pressure, and body temperature through sensors. The entered information, along with this biometric data, is transmitted to the server in packet format.

[0294] Step 2:

[0295] The server uses patient information and biometric data received from the terminal as input to analyze real-time traffic information using a generated AI model. It collects data on current congestion and the shortest route from a traffic API. By analyzing this data, it calculates the optimal route and outputs the result to the terminal. The analysis results include predicted arrival time and intermediate stop information.

[0296] Step 3:

[0297] The server evaluates the capacity of healthcare facilities. Based on the patient's symptoms, it generates a list of potential healthcare facilities and checks the availability of beds and specialists at each facility. It retrieves data through an interface with the healthcare institution's information system and selects the most suitable facility. The facility selection information is transmitted to the terminal.

[0298] Step 4:

[0299] The terminal controls the ambulance using autonomous driving technology based on the optimal route and medical facility information received from the server. It activates the automatic navigation system and heads towards the destination according to the recommended route. If new traffic information arises along the way, the terminal automatically recalculates and adjusts the route based on that information.

[0300] Step 5:

[0301] The terminal continuously collects biometric data during transport and transmits it to the server in real time. This information is monitored by the server to enable a rapid response if an anomaly is detected. The server sends a warning signal back to the terminal if an anomaly is detected, and also warns the user.

[0302] Step 6:

[0303] The server pre-transmits patient information to the designated healthcare facility. This data transmission allows medical staff to prepare for treatment, enabling efficient treatment upon the patient's arrival. The server integrates with the facility's system and notifies the terminal of the progress once the necessary preparations are complete.

[0304] (Application Example 1)

[0305] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0306] Quick and accurate patient transportation in emergencies is an important issue in the medical field. However, due to traffic congestion and the opacity of the acceptance situation of medical institutions, delays in transportation may occur. In addition, emergency team members have to perform many operations during patient transportation, which may affect patient care. To solve these problems, a system that optimizes routes and medical situations in real time and enables smooth transportation is needed.

[0307] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0308] In this invention, the server includes means for analyzing traffic information using a generative model to derive an optimal transportation route, means for evaluating the patient acceptance situation in a medical institution and selecting an appropriate medical facility, means for monitoring the patient's biological information in real time and issuing a warning signal when an abnormality is detected, means for providing information for emergency service operators and team members and optimizing the moving state, and means for checking the preparation status of the facility at the destination and enabling immediate treatment upon arrival. As a result, quick and appropriate patient transportation is possible even in an emergency.

[0309] The "generative model" is an artificial intelligence technology used to derive an optimal solution based on data collected in real time.

[0310] "Traffic information" refers to data related to driving conditions such as road congestion and traffic jams.

[0311] The "transportation route" is information indicating the optimal route from a certain point to the destination.

[0312] A "medical institution" refers to a facility that provides medical treatment and care to patients.

[0313] The "patient acceptance situation" is a state indicating whether a medical institution is prepared to diagnose or treat a patient.

[0314] "Biometric information" refers to data about a person's physical condition, such as heart rate, blood pressure, and body temperature.

[0315] A "warning signal" is a notification issued to alert relevant parties when an anomaly is detected.

[0316] "Autonomous driving technology" refers to the ability of a vehicle to drive automatically without human intervention.

[0317] An "emergency service operator" is a professional responsible for communication and directing during emergencies.

[0318] "Team members" refers to personnel who have been specially trained to carry out emergency medical services.

[0319] "Information provision" refers to the act of conveying necessary data and knowledge to users.

[0320] "The receiving facility" refers to the medical institution that will accept and treat the patient.

[0321] "Preparation" refers to a state in which everything necessary for a particular action or activity is in place.

[0322] In implementing this invention, the server processes real-time data using a generative model to analyze traffic information and the availability status of medical facilities. The server uses Python and utilizes the TensorFlow library to build the AI ​​model. Real-time driving data collected through publicly available APIs is used for traffic information, and the availability status of medical facilities is obtained through OpenAPI.

[0323] The smartphone application allows emergency medical personnel to monitor patients' biometric information and transmit data to a server as needed. Android devices are used, and the program runs in Java or Kotlin. Patient information is collected from sensors via Bluetooth or Wi-Fi. This allows emergency personnel to constantly monitor the patient's condition, obtain route information, and transport the patient to the appropriate medical facility.

[0324] The autonomous vehicles utilize ROS (Robot Operating System) as their control system, automatically navigating the optimal route based on traffic conditions. This allows personnel to focus on patient care and information gathering.

[0325] As a concrete example, consider an operation to accept injured persons in transit during an emergency response. The server analyzes the injured person's biological condition and suggests the optimal medical facility and transport route. The input prompt to the generated AI model is, "Based on the injured person's location information, identify the optimal medical facility and route in real time."

[0326] In this way, rapid and accurate patient transport is achieved through the cooperation of servers, terminals, and users.

[0327] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0328] Step 1:

[0329] The terminal receives the emergency call. The user, an emergency medical technician, begins entering the patient's biometric information, while simultaneously collecting data through biosensors. The entered data includes heart rate, blood pressure, and body temperature, which are transmitted to the terminal via Bluetooth or Wi-Fi. The terminal receives this information and prepares it for transmission to the server.

[0330] Step 2:

[0331] The device sends collected biometric information to the server. The server receives this data in real time and compares it to normal values ​​using a generative AI model. The server performs data calculations, and if an abnormal value is detected, it immediately sends a warning signal to the device and generates further warning information regarding transportation options and healthcare selection.

[0332] Step 3:

[0333] The server obtains the latest traffic information via a public API and calculates the optimal transportation route using a generated AI model. This input includes current location and destination information. The model outputs the optimal route considering traffic conditions, which is then sent to the terminal. Furthermore, it uses OpenAPI to check the readiness status of each medical institution and selects the most suitable facility.

[0334] Step 4:

[0335] The server calculates the optimal route and transmits information about selected medical facilities to the terminal. The user, an emergency medical technician, transports the patient to the designated medical facility according to the instructions from the terminal. During transport, the terminal automatically provides operational instructions to the autonomous vehicle using ROS. The terminal dynamically changes the route according to the traffic conditions at the time, optimizing the transport to the destination.

[0336] Step 5:

[0337] The terminal continuously monitors the patient's vital signs during transport and sends the analysis results to the server. The server continues to monitor for any new abnormalities and sends additional instructions or warnings to the user as needed. This information is also shared before arrival at the medical facility to facilitate preparation for admission.

[0338] Step 6:

[0339] The user arrives at the designated medical facility and begins transporting the patient. Based on the patient information received from the server in advance, the medical facility can immediately begin treatment. The information sent from the server to the medical facility includes the patient's latest biological status and suggested necessary procedures.

[0340] Through the above series of processing steps, rapid and accurate patient transport in emergencies is achieved. The generating AI model performs data processing that takes into account traffic conditions and the status of medical facilities, based on the prompt message "Identify the optimal medical facility and route in real time based on the location information of the injured person."

[0341] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0342] In this embodiment of the invention, a system is provided that combines emotion recognition technology with rapid and safe patient transport in emergencies. This system is effective by integrating an emotion engine that analyzes the user's emotions, in addition to traffic information analysis using a generative model, real-time biometric information monitoring, and optimization of transport efficiency through autonomous driving technology.

[0343] First, the terminal receives an emergency call, and paramedics input the patient's basic information and location. Based on this information, the terminal activates an emotion engine to sense the emotions of the user during the ride. This emotion engine evaluates the user's emotional state in real time using voice tone, facial expressions, sweating, and heart rate data.

[0344] The server uses a generative model to analyze traffic data and determine the optimal transport route. It also queries the availability of medical facilities and selects the necessary medical facilities. The results of this processing are then sent to the terminal.

[0345] Based on the route information and medical facility information it receives, the terminal uses autonomous driving technology to accurately guide the ambulance to its destination. During the journey, the emotion engine continuously monitors the user's stress level and emotional changes, and if an abnormality is detected, the terminal sends an alert to the emergency medical staff via the server.

[0346] For example, if a patient is experiencing severe pain, the emotion engine recognizes this as a high stress level, and the server automatically adjusts transportation methods to ensure a safe and rapid response. It can also provide the user with voice feedback and relaxation instructions that correspond to specific emotional states.

[0347] Finally, the terminal transmits patient information to a pre-selected medical institution, allowing medical staff to prepare for arrival. In this way, time is reduced, the quality of medical care is improved, and the user's emotional well-being is simultaneously addressed, contributing to an increase in survival rates.

[0348] The following describes the processing flow.

[0349] Step 1:

[0350] The user receives an emergency call and enters the patient's basic information and location into the terminal. The terminal then begins acquiring biometric information from sensors attached to the patient.

[0351] Step 2:

[0352] The terminal transmits basic patient information, location information, biometric information, and user emotion data determined using an emotion engine to the server.

[0353] Step 3:

[0354] The server uses a generative model to analyze real-time traffic data and calculate the optimal transport route. This process takes into account traffic congestion and road closures.

[0355] Step 4:

[0356] The server investigates the acceptance status of each medical institution and selects facilities that can provide the necessary medical services. It then transmits the selected route and medical facility information to the terminal.

[0357] Step 5:

[0358] The terminal activates the autonomous driving system and dispatches the ambulance according to the received route information.

[0359] Step 6:

[0360] The device continuously transmits the patient's biometric information to the server, and the emotion engine also continuously monitors the user's emotions.

[0361] Step 7:

[0362] The server analyzes biometric and emotional data, provides feedback to the user if an anomaly is detected, and adjusts the autonomous driving route and speed as needed. It also issues warnings to emergency medical staff if deemed necessary.

[0363] Step 8:

[0364] The device will pre-send patient information to the medical institution where it is scheduled to arrive, facilitating treatment preparation.

[0365] Step 9:

[0366] The terminal arrives at its destination, the patient is safely handed over to medical facility staff, and medical treatment is initiated quickly.

[0367] (Example 2)

[0368] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0369] To ensure the rapid and safe transport of patients in emergencies, it is necessary to comprehensively evaluate multiple factors, such as traffic conditions, the capacity of medical facilities, and real-time monitoring of patients' emotions and biometric information, in order to provide the optimal transport approach. However, conventional systems can only address these factors individually, making integrated and efficient transport difficult.

[0370] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0371] In this invention, the server includes means for analyzing traffic information using a generative model and deriving the optimal transport route, means for evaluating the patient acceptance status at medical institutions and selecting an appropriate medical facility, and means for analyzing the patient's emotional state in real time and detecting changes in stress levels. This enables safe and rapid transport while considering the multifaceted condition of the patient.

[0372] A "generative model" is a mathematical model used to generate new information based on input data, and is particularly used in the analysis of traffic information.

[0373] "Traffic information" refers to real-time data on road and transportation systems, such as congestion levels and traffic restrictions.

[0374] A "transportation route" refers to the route selected to optimize travel from a certain point to a destination.

[0375] A "medical institution" is a facility that provides treatment and diagnosis for patients, and includes hospitals and clinics.

[0376] "Acceptance status" refers to the number of patients and the status of cases that a medical institution can currently handle.

[0377] "Emotional state" refers to the state of an individual's psychological and physiological emotional responses as observed in real time.

[0378] "Stress level" is a numerical representation of the degree of psychological and physiological pressure or tension an individual is experiencing.

[0379] "Autonomous driving technology" refers to technology that enables vehicles to operate automatically without human intervention.

[0380] "Biometric information" refers to data related to human vital signs such as heart rate, body temperature, and sweating.

[0381] A "warning signal" refers to a signal issued by a system or device to alert the user when it detects an abnormality.

[0382] This invention is a system that combines generative AI models, emotion recognition technology, and autonomous driving technology to optimize patient transport in emergencies. The system mainly consists of servers and terminals, each performing processing according to its respective role.

[0383] The server analyzes traffic information using a generative AI model. This AI model is based on algorithms learned from a wide range of datasets and analyzes data collected through Google Maps and similar real-time traffic information services. The server then uses this to derive the optimal transportation route to the destination. Additionally, the server references a database of healthcare facilities, evaluates the patient acceptance status of each facility, and selects the most suitable medical facility.

[0384] The terminal receives emergency calls and activates emotion recognition technology based on the patient's basic information entered by paramedics. This emotion recognition is performed using the terminal's built-in camera and microphone, as well as a wearable device capable of heart rate monitoring, for example, by using Microsoft's emotion recognition API. This technology allows for real-time analysis of the patient's emotional state from their voice tone, facial expressions, heart rate, etc. Based on the analysis results, the system suggests the most appropriate response if the stress level is high.

[0385] Autonomous driving technology, such as that used by Waymo, automatically operates ambulances based on safe transport routes transmitted from terminals. During operation, emotion recognition technology continuously monitors the patient's stress and emotional state, immediately reporting any abnormalities to the server and issuing warnings to medical staff if necessary.

[0386] For example, if a patient is experiencing severe pain, emotion recognition technology can detect this pain as a high stress level, and the server will prioritize recalculating the shortest and most optimal route to ensure safe and rapid transport. Furthermore, if necessary, the terminal can provide voice guidance to the patient to help them relax.

[0387] An example of a prompt message would be: "An emergency call has been received. Please enter patient information. The server will calculate the optimal transport route based on the received data, monitor the patient's emotional state in real time, and issue alerts to medical staff as needed. Please notify us when the patient has been transported to the appropriate medical facility."

[0388] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0389] Step 1:

[0390] The terminal receives an emergency call. Using a dedicated application installed on the terminal, paramedics input the patient's basic information and location. Based on this information, the terminal sends data to the server. The input includes the patient's name, age, symptoms, and location, and the output is a secure transmission of data to the server.

[0391] Step 2:

[0392] The device activates emotion recognition technology. Its camera and microphone capture the patient's voice tone and facial expressions, while a wearable device monitors their heart rate. This data is input into an emotion recognition API, which analyzes the user's state in real time. Inputs include voice, video, and biometric information, while output is an evaluation of the patient's emotional state.

[0393] Step 3:

[0394] The server analyzes received data using a generating AI model and processes traffic information. The server obtains real-time traffic information from traffic data providers and calculates the optimal transport route. The input is location information and real-time traffic conditions, and the output is the determination of the optimal transport route.

[0395] Step 4:

[0396] The server checks the acceptance status of medical institutions. It refers to its database of medical institutions, evaluates each institution's capacity and facilities, and selects the most appropriate medical facility. Inputs include the number of patients, symptoms, and acceptance status, while the output is the selection of the appropriate medical institution.

[0397] Step 5:

[0398] The terminal transmits route and medical facility information received from the server to the autonomous driving technology. The autonomous driving system operates the ambulance based on this information, adjusting the route in real time. The input is the optimal route and destination, and the output is the control of the autonomous driving state.

[0399] Step 6:

[0400] The terminal continuously monitors the patient's emotional state during operation. If the patient's stress level increases or an abnormality is detected, the terminal notifies medical staff via the server. The input is the result of emotion recognition, and the output is a warning signal in case of an abnormality.

[0401] Step 7:

[0402] The terminal transmits patient information to the medical institution before arrival. The information is sent to the medical institution via a server in advance, allowing medical staff to prepare. Inputs include patient symptoms and estimated arrival time, while output is the notification of information to the medical institution.

[0403] (Application Example 2)

[0404] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0405] In emergency patient transport, not only is rapid and safe transportation important, but the psychological care of patients and their caregivers during the journey is also crucial. However, conventional systems do not adequately monitor the user's mental state based on emotion recognition or provide appropriate feedback based on that, posing challenges in reducing anxiety and stress during transport. Furthermore, optimizing the transport route in response to changes in traffic conditions and the patient's condition is also difficult.

[0406] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0407] In this invention, the server includes means for analyzing traffic information using a generative model and deriving the optimal transport route, means for monitoring the patient's biometric information and emotional state in real time and issuing a warning signal when an abnormality is detected, and means for providing emotion-based voice feedback to stabilize the user's mental state. This makes it possible to transport the user quickly and efficiently while reducing stress and providing a sense of security.

[0408] A "generative model" is a form of machine learning that learns patterns from data and generates new information.

[0409] "Traffic information" refers to information about the condition of roads and transportation systems, including traffic congestion and road closures.

[0410] A "transportation route" refers to the optimal path to a destination, enabling efficient travel.

[0411] A "medical institution" is a facility that provides medical services, and includes hospitals and clinics.

[0412] "Patient acceptance status" refers to the current number of patients at a medical institution and the number of patients who can be treated.

[0413] "Biological information" refers to biological information obtained from the human body, such as heart rate, sweating, and body temperature.

[0414] "Emotional state" refers to an individual's emotional state, including stress, anxiety, and feelings of security.

[0415] A "warning signal" is a notification issued when a system detects an anomaly, indicating a situation that requires immediate attention.

[0416] "Autonomous driving technology" refers to technology that allows vehicles and equipment to move or perform tasks automatically without human intervention.

[0417] "Transportation means" refers to the means of moving goods or people from one place to another, and includes vehicles and machinery.

[0418] "Stabilizing one's mental state" refers to the process of calming an individual's emotions and reducing anxiety and stress.

[0419] "Voice feedback" is a technology that uses voice to provide information and instructions to users.

[0420] This invention provides a system for efficiently and safely transporting patients in emergencies. The server analyzes traffic information using a generative AI model to determine the optimal transport route. In doing so, the server receives real-time updated traffic data and constantly calculates the best route. Furthermore, the server has the function of evaluating the patient acceptance status of medical facilities and selecting an appropriate medical facility.

[0421] The device monitors the user's biometric information and emotional state 24 hours a day. It uses an emotion recognition engine to analyze voice tone, facial expressions, and vital data. If an abnormality is detected, the device immediately sends a warning signal to the server and medical staff. Furthermore, the user receives immediate voice feedback based on their emotional state, providing emotional support. This helps alleviate user stress and provide a sense of security.

[0422] Autonomous driving technology-based transportation systems travel along the most efficient route to their destination, following route information received from a server. This technology minimizes interference during operation and maximizes the efficiency and safety of transportation.

[0423] As a concrete example, a patient involved in an accident in the mountains could wear smart glasses and utilize this system to be safely transported to a medical facility in the shortest possible time. Furthermore, an example of a prompt message would be, "Use the emotion engine to detect if the patient is experiencing high levels of anxiety and suggest ways to provide reassurance." This input would allow the system to quickly generate appropriate feedback and support the patient.

[0424] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0425] Step 1:

[0426] The server collects traffic information in real time and analyzes it using a generative AI model. This analysis derives the optimal transportation route. It receives current traffic data as input and sends information about the optimal route to the terminal as output. In this process, the server utilizes the generative AI model to perform pattern recognition on the data.

[0427] Step 2:

[0428] The device acquires the patient's biometric information and emotional state, and analyzes it using an emotion engine. It uses heart rate, voice tone, and facial expression data as input, and outputs an evaluation of the emotional state. Throughout this process, the device collects the necessary data through sensor devices and processes it in real time.

[0429] Step 3:

[0430] The terminal sends a warning signal to the server based on the anomaly detection results obtained from the emotion engine. The input is the evaluation result of the emotional state, and the output is a warning signal. In this step, the terminal automatically detects an anomaly and immediately issues a signal.

[0431] Step 4:

[0432] The terminal generates and provides voice feedback to the user based on the patient's condition. It uses the results of an emotional state assessment as input and generates voice messages as output. In this process, it selects an appropriate message based on instructions from the emotion engine and plays it back via a voice control device.

[0433] Step 5:

[0434] Based on autonomous driving technology, the system uses optimal route information to operate the transport vehicle. The input is optimal route information transmitted from the server, and the output is the efficient movement to the destination. In this step, a route-based motion plan is generated, and actuator control is performed for autonomous driving.

[0435] Step 6:

[0436] Before the user arrives at their destination, the terminal transmits patient information to the medical institution. Inputs include basic patient information and current status data, while output is the transmission of data to the medical institution. Here, the terminal ensures that the information is organized beforehand and transmitted via a secure communication method.

[0437] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0438] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0439] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0440] [Third Embodiment]

[0441] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0442] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0443] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0444] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0445] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0446] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0447] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0448] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0449] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0450] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0451] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0452] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0453] In this embodiment of the invention, a system that analyzes traffic information and the availability of medical facilities in real time is used to achieve rapid and appropriate patient transport in emergencies. The main components of the system and their operation are described in detail below.

[0454] First, when the terminal receives an emergency call, paramedics enter detailed information about the patient and use sensors to obtain biometric data. This includes the patient's heart rate, blood pressure, and body temperature. The terminal then sends this data to a server.

[0455] The server uses a generative model to analyze real-time traffic data and determine the optimal transport route. The server also checks the availability of medical facilities, evaluating information such as whether necessary specialists are available and whether there are vacant beds. Based on this information, it sends recommended medical facilities and route information back to the terminal.

[0456] The terminal uses autonomous driving technology to control the ambulance and guide it to its destination based on the optimal route. During this time, the terminal continuously collects the patient's biometric information and transmits the data to a server in real time. The server analyzes this information and immediately alerts paramedics if any abnormalities are detected.

[0457] As a concrete example, consider a case where a patient is found in a state close to cardiac arrest. In this case, the terminal detects a rapid change in heart rate and reports this abnormality to the server. Based on this information, the server quickly selects the nearest medical facility equipped with specialized cardiac equipment and transmits that information to the terminal. The terminal then carries out necessary stabilization measures and rapid transport to the medical facility.

[0458] Furthermore, the server transmits patient information to the systems of pre-selected medical institutions, prompting them to prepare for immediate treatment upon arrival. In this way, it is possible to shorten transport times, optimize medical preparations, and improve survival rates.

[0459] The following describes the processing flow.

[0460] Step 1:

[0461] The user receives an emergency call and enters the patient's basic information and location into the terminal. They also attach vital signs sensors to the patient and begin acquiring biometric data.

[0462] Step 2:

[0463] The terminal transmits the patient's basic information, location information, and biometric information to the server.

[0464] Step 3:

[0465] The server uses a generative model to analyze real-time traffic data and calculate the optimal transport route, taking into account information on traffic congestion and road closures.

[0466] Step 4:

[0467] The server checks the availability of medical facilities, confirming the presence of necessary specialists and bed vacancies. Based on this information, it selects the most suitable medical facility.

[0468] Step 5:

[0469] The server sends information about the optimal route and medical facilities back to the terminal.

[0470] Step 6:

[0471] The terminal uses autonomous driving technology to automatically set a route and dispatch the ambulance.

[0472] Step 7:

[0473] The device continuously transmits biometric information to the server in real time.

[0474] Step 8:

[0475] The server continuously analyzes biometric information and issues a warning to the user if an anomaly is detected.

[0476] Step 9:

[0477] Based on newly acquired traffic information, the server recalculates the route as needed and sends the updated information to the terminal.

[0478] Step 10:

[0479] The device transmits patient information to the selected medical institution before arrival, allowing the doctor to prepare.

[0480] Step 11:

[0481] The device arrives at its destination, and the patient is smoothly handed over to the medical facility.

[0482] (Example 1)

[0483] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0484] Modern emergency medical transport demands improved time efficiency and rapid patient transfer to appropriate medical facilities. However, many obstacles exist, including traffic congestion, fluctuations in the availability of medical facilities, and the difficulty of real-time monitoring of vital signs. Overcoming these obstacles is essential to further reduce transport time and waiting times before treatment begins.

[0485] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0486] In this invention, the server includes means for a generative model to analyze traffic information acquired through the vehicle's control device and calculate the optimal transport route; means for evaluating the acceptance capacity of medical facilities and selecting appropriate facilities; and means for monitoring biometric data in real time and issuing warnings when abnormalities occur. This enables efficient and rapid emergency transport.

[0487] A "generative model" is an algorithm used to analyze data and make predictions or decisions based on specific conditions.

[0488] "Traffic information" refers to data on congestion levels and travel times on roads and public transportation.

[0489] A "transportation route" is the optimal path for a vehicle to travel from its starting point to its destination.

[0490] "Acceptance capacity" refers to the immediate ability of a healthcare facility to admit patients and begin treatment.

[0491] "Biometric data" refers to information that indicates a patient's physical condition, such as heart rate, blood pressure, and body temperature.

[0492] "Autonomous driving technology" refers to technology that enables vehicles to operate automatically without human intervention.

[0493] A "warning" is a signal or notification used to alert relevant parties to danger when an abnormal situation occurs.

[0494] This invention provides a system for optimizing emergency medical transport by analyzing traffic information and monitoring the availability of medical facilities in real time. The following describes a specific implementation of this system.

[0495] The server uses a generative AI model to analyze real-time traffic information and calculate the optimal transport route. This analysis utilizes a traffic API to obtain the latest traffic conditions and road information. Based on the analysis results, the server determines the optimal route and transmits it to the terminal.

[0496] The terminal transmits patient information entered by paramedics and biometric data acquired using sensors to a server. The terminal also features autonomous driving technology and controls the ambulance according to the server's instructions, following the optimal route. Inside the ambulance, the patient's heart rate, blood pressure, and body temperature are monitored in real time, and any abnormalities are immediately reported to the server.

[0497] The user (in this case, an emergency medical technician) receives information from the server via a terminal and verifies whether the medical facility to which the patient is being transported is appropriate. In addition, the user uses the terminal to monitor the patient's condition and perform any necessary medical procedures during transport.

[0498] As a specific example, in the emergency transport of a patient suspected of having a heart condition, if the terminal detects a sudden change in heart rate from the acquired biometric data, the server calculates the shortest route to a cardiac specialist facility and transmits that information to the terminal. As a result, the ambulance can quickly reach its destination using autonomous driving technology.

[0499] An example of a prompt message might be, "Request the optimal route and recommended medical facilities for emergency transport of a patient with cardiac disease." This allows for prior contact with the receiving medical institution, ensuring that treatment can begin immediately upon arrival.

[0500] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0501] Step 1:

[0502] When the terminal receives an emergency call, the user enters patient information. Detailed information such as the patient's name, age, gender, and symptoms are manually registered on the terminal's interface. The terminal also acquires biometric data such as heart rate, blood pressure, and body temperature through sensors. The entered information, along with this biometric data, is transmitted to the server in packet format.

[0503] Step 2:

[0504] The server uses patient information and biometric data received from the terminal as input to analyze real-time traffic information using a generated AI model. It collects data on current congestion and the shortest route from a traffic API. By analyzing this data, it calculates the optimal route and outputs the result to the terminal. The analysis results include predicted arrival time and intermediate stop information.

[0505] Step 3:

[0506] The server evaluates the capacity of healthcare facilities. Based on the patient's symptoms, it generates a list of potential healthcare facilities and checks the availability of beds and specialists at each facility. It retrieves data through an interface with the healthcare institution's information system and selects the most suitable facility. The facility selection information is transmitted to the terminal.

[0507] Step 4:

[0508] The terminal controls the ambulance using autonomous driving technology based on the optimal route and medical facility information received from the server. It activates the automatic navigation system and heads towards the destination according to the recommended route. If new traffic information arises along the way, the terminal automatically recalculates and adjusts the route based on that information.

[0509] Step 5:

[0510] The terminal continuously collects biometric data during transport and transmits it to the server in real time. This information is monitored by the server to enable a rapid response if an anomaly is detected. The server sends a warning signal back to the terminal if an anomaly is detected, and also warns the user.

[0511] Step 6:

[0512] The server pre-transmits patient information to the designated healthcare facility. This data transmission allows medical staff to prepare for treatment, enabling efficient treatment upon the patient's arrival. The server integrates with the facility's system and notifies the terminal of the progress once the necessary preparations are complete.

[0513] (Application Example 1)

[0514] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0515] Rapid and accurate patient transport in emergencies is a critical challenge in healthcare settings. However, delays can occur due to traffic congestion and uncertainty regarding the availability of medical facilities. In addition, paramedics must perform numerous tasks during patient transport, which can impact patient care. To address these issues, a system is needed that optimizes routes and medical conditions in real time, enabling smooth transport.

[0516] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0517] In this invention, the server includes means for analyzing traffic information using a generative model and deriving the optimal transport route; means for evaluating the patient acceptance status at medical institutions and selecting an appropriate medical facility; means for monitoring the patient's vital signs in real time and issuing a warning signal if an abnormality is detected; means for providing information to emergency service operators and personnel and optimizing the state during transport; and means for checking the readiness status of the destination facility and enabling immediate treatment upon arrival. This enables rapid and appropriate patient transport even in emergency situations.

[0518] A "generative model" is an artificial intelligence technology used to derive optimal solutions based on data collected in real time.

[0519] "Traffic information" refers to data related to driving conditions such as road congestion and traffic jams.

[0520] A "transportation route" is information that indicates the optimal route from a certain point to a destination.

[0521] A "medical institution" refers to a facility that provides medical care and treatment to patients.

[0522] "Patient acceptance status" refers to the state of whether a medical institution is ready to examine or treat patients.

[0523] "Biometric information" refers to data about a person's physical condition, such as heart rate, blood pressure, and body temperature.

[0524] A "warning signal" is a notification issued to alert relevant parties when an anomaly is detected.

[0525] "Autonomous driving technology" refers to the ability of a vehicle to drive automatically without human intervention.

[0526] An "emergency service operator" is a professional responsible for communication and directing during emergencies.

[0527] "Team members" refers to personnel who have been specially trained to carry out emergency medical services.

[0528] "Information provision" refers to the act of conveying necessary data and knowledge to users.

[0529] "The receiving facility" refers to the medical institution that will accept and treat the patient.

[0530] "Preparation" refers to a state in which everything necessary for a particular action or activity is in place.

[0531] In implementing this invention, the server processes real-time data using a generative model to analyze traffic information and the availability status of medical facilities. The server uses Python and utilizes the TensorFlow library to build the AI ​​model. Real-time driving data collected through publicly available APIs is used for traffic information, and the availability status of medical facilities is obtained through OpenAPI.

[0532] The smartphone application allows emergency medical personnel to monitor patients' biometric information and transmit data to a server as needed. Android devices are used, and the program runs in Java or Kotlin. Patient information is collected from sensors via Bluetooth or Wi-Fi. This allows emergency personnel to constantly monitor the patient's condition, obtain route information, and transport the patient to the appropriate medical facility.

[0533] The autonomous vehicles utilize ROS (Robot Operating System) as their control system, automatically navigating the optimal route based on traffic conditions. This allows personnel to focus on patient care and information gathering.

[0534] As a concrete example, consider an operation to accept injured persons in transit during an emergency response. The server analyzes the injured person's biological condition and suggests the optimal medical facility and transport route. The input prompt to the generated AI model is, "Based on the injured person's location information, identify the optimal medical facility and route in real time."

[0535] In this way, rapid and accurate patient transport is achieved through the cooperation of servers, terminals, and users.

[0536] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0537] Step 1:

[0538] The terminal receives the emergency call. The user, an emergency medical technician, begins entering the patient's biometric information, while simultaneously collecting data through biosensors. The entered data includes heart rate, blood pressure, and body temperature, which are transmitted to the terminal via Bluetooth or Wi-Fi. The terminal receives this information and prepares it for transmission to the server.

[0539] Step 2:

[0540] The device sends collected biometric information to the server. The server receives this data in real time and compares it to normal values ​​using a generative AI model. The server performs data calculations, and if an abnormal value is detected, it immediately sends a warning signal to the device and generates further warning information regarding transportation options and healthcare selection.

[0541] Step 3:

[0542] The server obtains the latest traffic information via a public API and calculates the optimal transportation route using a generated AI model. This input includes current location and destination information. The model outputs the optimal route considering traffic conditions, which is then sent to the terminal. Furthermore, it uses OpenAPI to check the readiness status of each medical institution and selects the most suitable facility.

[0543] Step 4:

[0544] The server calculates the optimal route and transmits information about selected medical facilities to the terminal. The user, an emergency medical technician, transports the patient to the designated medical facility according to the instructions from the terminal. During transport, the terminal automatically provides operational instructions to the autonomous vehicle using ROS. The terminal dynamically changes the route according to the traffic conditions at the time, optimizing the transport to the destination.

[0545] Step 5:

[0546] The terminal continuously monitors the patient's vital signs during transport and sends the analysis results to the server. The server continues to monitor for any new abnormalities and sends additional instructions or warnings to the user as needed. This information is also shared before arrival at the medical facility to facilitate preparation for admission.

[0547] Step 6:

[0548] The user arrives at the designated medical facility and begins transporting the patient. Based on the patient information received from the server in advance, the medical facility can immediately begin treatment. The information sent from the server to the medical facility includes the patient's latest biological status and suggested necessary procedures.

[0549] Through the above series of processing steps, rapid and accurate patient transport in emergencies is achieved. The generating AI model performs data processing that takes into account traffic conditions and the status of medical facilities, based on the prompt message "Identify the optimal medical facility and route in real time based on the location information of the injured person."

[0550] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0551] In this embodiment of the invention, a system is provided that combines emotion recognition technology with rapid and safe patient transport in emergencies. This system is effective by integrating an emotion engine that analyzes the user's emotions, in addition to traffic information analysis using a generative model, real-time biometric information monitoring, and optimization of transport efficiency through autonomous driving technology.

[0552] First, the terminal receives an emergency call, and paramedics input the patient's basic information and location. Based on this information, the terminal activates an emotion engine to sense the emotions of the user during the ride. This emotion engine evaluates the user's emotional state in real time using voice tone, facial expressions, sweating, and heart rate data.

[0553] The server uses a generative model to analyze traffic data and determine the optimal transport route. It also queries the availability of medical facilities and selects the necessary medical facilities. The results of this processing are then sent to the terminal.

[0554] Based on the route information and medical facility information it receives, the terminal uses autonomous driving technology to accurately guide the ambulance to its destination. During the journey, the emotion engine continuously monitors the user's stress level and emotional changes, and if an abnormality is detected, the terminal sends an alert to the emergency medical staff via the server.

[0555] For example, if a patient is experiencing severe pain, the emotion engine recognizes this as a high stress level, and the server automatically adjusts transportation methods to ensure a safe and rapid response. It can also provide the user with voice feedback and relaxation instructions that correspond to specific emotional states.

[0556] Finally, the terminal transmits patient information to a pre-selected medical institution, allowing medical staff to prepare for arrival. In this way, time is reduced, the quality of medical care is improved, and the user's emotional well-being is simultaneously addressed, contributing to an increase in survival rates.

[0557] The following describes the processing flow.

[0558] Step 1:

[0559] The user receives an emergency call and enters the patient's basic information and location into the terminal. The terminal then begins acquiring biometric information from sensors attached to the patient.

[0560] Step 2:

[0561] The terminal transmits basic patient information, location information, biometric information, and user emotion data determined using an emotion engine to the server.

[0562] Step 3:

[0563] The server uses a generative model to analyze real-time traffic data and calculate the optimal transport route. This process takes into account traffic congestion and road closures.

[0564] Step 4:

[0565] The server investigates the acceptance status of each medical institution and selects facilities that can provide the necessary medical services. It then transmits the selected route and medical facility information to the terminal.

[0566] Step 5:

[0567] The terminal activates the autonomous driving system and dispatches the ambulance according to the received route information.

[0568] Step 6:

[0569] The device continuously transmits the patient's biometric information to the server, and the emotion engine also continuously monitors the user's emotions.

[0570] Step 7:

[0571] The server analyzes biometric and emotional data, provides feedback to the user if an anomaly is detected, and adjusts the autonomous driving route and speed as needed. It also issues warnings to emergency medical staff if deemed necessary.

[0572] Step 8:

[0573] The device will pre-send patient information to the medical institution where it is scheduled to arrive, facilitating treatment preparation.

[0574] Step 9:

[0575] The terminal arrives at its destination, the patient is safely handed over to medical facility staff, and medical treatment is initiated quickly.

[0576] (Example 2)

[0577] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0578] To ensure the rapid and safe transport of patients in emergencies, it is necessary to comprehensively evaluate multiple factors, such as traffic conditions, the capacity of medical facilities, and real-time monitoring of patients' emotions and biometric information, in order to provide the optimal transport approach. However, conventional systems can only address these factors individually, making integrated and efficient transport difficult.

[0579] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0580] In this invention, the server includes means for analyzing traffic information using a generative model and deriving the optimal transport route, means for evaluating the patient acceptance status at medical institutions and selecting an appropriate medical facility, and means for analyzing the patient's emotional state in real time and detecting changes in stress levels. This enables safe and rapid transport while considering the multifaceted condition of the patient.

[0581] A "generative model" is a mathematical model used to generate new information based on input data, and is particularly used in the analysis of traffic information.

[0582] "Traffic information" refers to real-time data on road and transportation systems, such as congestion levels and traffic restrictions.

[0583] A "transportation route" refers to the route selected to optimize travel from a certain point to a destination.

[0584] A "medical institution" is a facility that provides treatment and diagnosis for patients, and includes hospitals and clinics.

[0585] "Acceptance status" refers to the number of patients and the status of cases that a medical institution can currently handle.

[0586] "Emotional state" refers to the state of an individual's psychological and physiological emotional responses as observed in real time.

[0587] "Stress level" is a numerical representation of the degree of psychological and physiological pressure or tension an individual is experiencing.

[0588] "Autonomous driving technology" refers to technology that enables vehicles to operate automatically without human intervention.

[0589] "Biometric information" refers to data related to human vital signs such as heart rate, body temperature, and sweating.

[0590] A "warning signal" refers to a signal issued by a system or device to alert the user when it detects an abnormality.

[0591] This invention is a system that combines generative AI models, emotion recognition technology, and autonomous driving technology to optimize patient transport in emergencies. The system mainly consists of servers and terminals, each performing processing according to its respective role.

[0592] The server analyzes traffic information using a generative AI model. This AI model is based on algorithms learned from a wide range of datasets and analyzes data collected through Google Maps and similar real-time traffic information services. The server then uses this to derive the optimal transportation route to the destination. Additionally, the server references a database of healthcare facilities, evaluates the patient acceptance status of each facility, and selects the most suitable medical facility.

[0593] The terminal receives emergency calls and activates emotion recognition technology based on the patient's basic information entered by paramedics. This emotion recognition is performed using the terminal's built-in camera and microphone, as well as a wearable device capable of heart rate monitoring, for example, by using Microsoft's emotion recognition API. This technology allows for real-time analysis of the patient's emotional state from their voice tone, facial expressions, heart rate, etc. Based on the analysis results, the system suggests the most appropriate response if the stress level is high.

[0594] Autonomous driving technology, such as that used by Waymo, automatically operates ambulances based on safe transport routes transmitted from terminals. During operation, emotion recognition technology continuously monitors the patient's stress and emotional state, immediately reporting any abnormalities to the server and issuing warnings to medical staff if necessary.

[0595] For example, if a patient is experiencing severe pain, emotion recognition technology can detect this pain as a high stress level, and the server will prioritize recalculating the shortest and most optimal route to ensure safe and rapid transport. Furthermore, if necessary, the terminal can provide voice guidance to the patient to help them relax.

[0596] An example of a prompt message would be: "An emergency call has been received. Please enter patient information. The server will calculate the optimal transport route based on the received data, monitor the patient's emotional state in real time, and issue alerts to medical staff as needed. Please notify us when the patient has been transported to the appropriate medical facility."

[0597] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0598] Step 1:

[0599] The terminal receives an emergency call. Using a dedicated application installed on the terminal, paramedics input the patient's basic information and location. Based on this information, the terminal sends data to the server. The input includes the patient's name, age, symptoms, and location, and the output is a secure transmission of data to the server.

[0600] Step 2:

[0601] The device activates emotion recognition technology. Its camera and microphone capture the patient's voice tone and facial expressions, while a wearable device monitors their heart rate. This data is input into an emotion recognition API, which analyzes the user's state in real time. Inputs include voice, video, and biometric information, while output is an evaluation of the patient's emotional state.

[0602] Step 3:

[0603] The server analyzes received data using a generating AI model and processes traffic information. The server obtains real-time traffic information from traffic data providers and calculates the optimal transport route. The input is location information and real-time traffic conditions, and the output is the determination of the optimal transport route.

[0604] Step 4:

[0605] The server checks the acceptance status of medical institutions. It refers to its database of medical institutions, evaluates each institution's capacity and facilities, and selects the most appropriate medical facility. Inputs include the number of patients, symptoms, and acceptance status, while the output is the selection of the appropriate medical institution.

[0606] Step 5:

[0607] The terminal transmits route and medical facility information received from the server to the autonomous driving technology. The autonomous driving system operates the ambulance based on this information, adjusting the route in real time. The input is the optimal route and destination, and the output is the control of the autonomous driving state.

[0608] Step 6:

[0609] The terminal continuously monitors the patient's emotional state during operation. If the patient's stress level increases or an abnormality is detected, the terminal notifies medical staff via the server. The input is the result of emotion recognition, and the output is a warning signal in case of an abnormality.

[0610] Step 7:

[0611] The terminal transmits patient information to the medical institution before arrival. The information is sent to the medical institution via a server in advance, allowing medical staff to prepare. Inputs include patient symptoms and estimated arrival time, while output is the notification of information to the medical institution.

[0612] (Application Example 2)

[0613] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0614] In emergency patient transport, not only is rapid and safe transportation important, but the psychological care of patients and their caregivers during the journey is also crucial. However, conventional systems do not adequately monitor the user's mental state based on emotion recognition or provide appropriate feedback based on that, posing challenges in reducing anxiety and stress during transport. Furthermore, optimizing the transport route in response to changes in traffic conditions and the patient's condition is also difficult.

[0615] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0616] In this invention, the server includes means for analyzing traffic information using a generative model and deriving the optimal transport route, means for monitoring the patient's biometric information and emotional state in real time and issuing a warning signal when an abnormality is detected, and means for providing emotion-based voice feedback to stabilize the user's mental state. This makes it possible to transport the user quickly and efficiently while reducing stress and providing a sense of security.

[0617] A "generative model" is a form of machine learning that learns patterns from data and generates new information.

[0618] "Traffic information" refers to information about the condition of roads and transportation systems, including traffic congestion and road closures.

[0619] A "transportation route" refers to the optimal path to a destination, enabling efficient travel.

[0620] A "medical institution" is a facility that provides medical services, and includes hospitals and clinics.

[0621] "Patient acceptance status" refers to the current number of patients at a medical institution and the number of patients who can be treated.

[0622] "Biological information" refers to biological information obtained from the human body, such as heart rate, sweating, and body temperature.

[0623] "Emotional state" refers to an individual's emotional state, including stress, anxiety, and feelings of security.

[0624] A "warning signal" is a notification issued when a system detects an anomaly, indicating a situation that requires immediate attention.

[0625] "Autonomous driving technology" refers to technology that allows vehicles and equipment to move or perform tasks automatically without human intervention.

[0626] "Transportation means" refers to the means of moving goods or people from one place to another, and includes vehicles and machinery.

[0627] "Stabilizing one's mental state" refers to the process of calming an individual's emotions and reducing anxiety and stress.

[0628] "Voice feedback" is a technology that uses voice to provide information and instructions to users.

[0629] This invention provides a system for efficiently and safely transporting patients in emergencies. The server analyzes traffic information using a generative AI model to determine the optimal transport route. In doing so, the server receives real-time updated traffic data and constantly calculates the best route. Furthermore, the server has the function of evaluating the patient acceptance status of medical facilities and selecting an appropriate medical facility.

[0630] The device monitors the user's biometric information and emotional state 24 hours a day. It uses an emotion recognition engine to analyze voice tone, facial expressions, and vital data. If an abnormality is detected, the device immediately sends a warning signal to the server and medical staff. Furthermore, the user receives immediate voice feedback based on their emotional state, providing emotional support. This helps alleviate user stress and provide a sense of security.

[0631] Autonomous driving technology-based transportation systems travel along the most efficient route to their destination, following route information received from a server. This technology minimizes interference during operation and maximizes the efficiency and safety of transportation.

[0632] As a concrete example, a patient involved in an accident in the mountains could wear smart glasses and utilize this system to be safely transported to a medical facility in the shortest possible time. Furthermore, an example of a prompt message would be, "Use the emotion engine to detect if the patient is experiencing high levels of anxiety and suggest ways to provide reassurance." This input would allow the system to quickly generate appropriate feedback and support the patient.

[0633] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0634] Step 1:

[0635] The server collects traffic information in real time and analyzes it using a generative AI model. This analysis derives the optimal transportation route. It receives current traffic data as input and sends information about the optimal route to the terminal as output. In this process, the server utilizes the generative AI model to perform pattern recognition on the data.

[0636] Step 2:

[0637] The device acquires the patient's biometric information and emotional state, and analyzes it using an emotion engine. It uses heart rate, voice tone, and facial expression data as input, and outputs an evaluation of the emotional state. Throughout this process, the device collects the necessary data through sensor devices and processes it in real time.

[0638] Step 3:

[0639] The terminal sends a warning signal to the server based on the anomaly detection results obtained from the emotion engine. The input is the evaluation result of the emotional state, and the output is a warning signal. In this step, the terminal automatically detects an anomaly and immediately issues a signal.

[0640] Step 4:

[0641] The terminal generates and provides voice feedback to the user based on the patient's condition. It uses the results of an emotional state assessment as input and generates voice messages as output. In this process, it selects an appropriate message based on instructions from the emotion engine and plays it back via a voice control device.

[0642] Step 5:

[0643] Based on autonomous driving technology, the system uses optimal route information to operate the transport vehicle. The input is optimal route information transmitted from the server, and the output is the efficient movement to the destination. In this step, a route-based motion plan is generated, and actuator control is performed for autonomous driving.

[0644] Step 6:

[0645] Before the user arrives at their destination, the terminal transmits patient information to the medical institution. Inputs include basic patient information and current status data, while output is the transmission of data to the medical institution. Here, the terminal ensures that the information is organized beforehand and transmitted via a secure communication method.

[0646] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0647] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0648] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0649] [Fourth Embodiment]

[0650] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0651] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0652] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0653] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0654] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0655] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0656] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0657] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0658] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0659] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0660] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0661] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0662] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0663] In this embodiment of the invention, a system that analyzes traffic information and the availability of medical facilities in real time is used to achieve rapid and appropriate patient transport in emergencies. The main components of the system and their operation are described in detail below.

[0664] First, when the terminal receives an emergency call, paramedics enter detailed information about the patient and use sensors to obtain biometric data. This includes the patient's heart rate, blood pressure, and body temperature. The terminal then sends this data to a server.

[0665] The server uses a generative model to analyze real-time traffic data and determine the optimal transport route. The server also checks the availability of medical facilities, evaluating information such as whether necessary specialists are available and whether there are vacant beds. Based on this information, it sends recommended medical facilities and route information back to the terminal.

[0666] The terminal uses autonomous driving technology to control the ambulance and guide it to its destination based on the optimal route. During this time, the terminal continuously collects the patient's biometric information and transmits the data to a server in real time. The server analyzes this information and immediately alerts paramedics if any abnormalities are detected.

[0667] As a concrete example, consider a case where a patient is found in a state close to cardiac arrest. In this case, the terminal detects a rapid change in heart rate and reports this abnormality to the server. Based on this information, the server quickly selects the nearest medical facility equipped with specialized cardiac equipment and transmits that information to the terminal. The terminal then carries out necessary stabilization measures and rapid transport to the medical facility.

[0668] Furthermore, the server transmits patient information to the systems of pre-selected medical institutions, prompting them to prepare for immediate treatment upon arrival. In this way, it is possible to shorten transport times, optimize medical preparations, and improve survival rates.

[0669] The following describes the processing flow.

[0670] Step 1:

[0671] The user receives an emergency call and enters the patient's basic information and location into the terminal. They also attach vital signs sensors to the patient and begin acquiring biometric data.

[0672] Step 2:

[0673] The terminal transmits the patient's basic information, location information, and biometric information to the server.

[0674] Step 3:

[0675] The server uses a generative model to analyze real-time traffic data and calculate the optimal transport route, taking into account information on traffic congestion and road closures.

[0676] Step 4:

[0677] The server checks the availability of medical facilities, confirming the presence of necessary specialists and bed vacancies. Based on this information, it selects the most suitable medical facility.

[0678] Step 5:

[0679] The server sends information about the optimal route and medical facilities back to the terminal.

[0680] Step 6:

[0681] The terminal uses autonomous driving technology to automatically set a route and dispatch the ambulance.

[0682] Step 7:

[0683] The device continuously transmits biometric information to the server in real time.

[0684] Step 8:

[0685] The server continuously analyzes biometric information and issues a warning to the user if an anomaly is detected.

[0686] Step 9:

[0687] Based on newly acquired traffic information, the server recalculates the route as needed and sends the updated information to the terminal.

[0688] Step 10:

[0689] The device transmits patient information to the selected medical institution before arrival, allowing the doctor to prepare.

[0690] Step 11:

[0691] The device arrives at its destination, and the patient is smoothly handed over to the medical facility.

[0692] (Example 1)

[0693] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0694] Modern emergency medical transport demands improved time efficiency and rapid patient transfer to appropriate medical facilities. However, many obstacles exist, including traffic congestion, fluctuations in the availability of medical facilities, and the difficulty of real-time monitoring of vital signs. Overcoming these obstacles is essential to further reduce transport time and waiting times before treatment begins.

[0695] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0696] In this invention, the server includes means for a generative model to analyze traffic information acquired through the vehicle's control device and calculate the optimal transport route; means for evaluating the acceptance capacity of medical facilities and selecting appropriate facilities; and means for monitoring biometric data in real time and issuing warnings when abnormalities occur. This enables efficient and rapid emergency transport.

[0697] A "generative model" is an algorithm used to analyze data and make predictions or decisions based on specific conditions.

[0698] "Traffic information" refers to data on congestion levels and travel times on roads and public transportation.

[0699] A "transportation route" is the optimal path for a vehicle to travel from its starting point to its destination.

[0700] "Acceptance capacity" refers to the immediate ability of a healthcare facility to admit patients and begin treatment.

[0701] "Biometric data" refers to information that indicates a patient's physical condition, such as heart rate, blood pressure, and body temperature.

[0702] "Autonomous driving technology" refers to technology that enables vehicles to operate automatically without human intervention.

[0703] A "warning" is a signal or notification used to alert relevant parties to danger when an abnormal situation occurs.

[0704] This invention provides a system for optimizing emergency medical transport by analyzing traffic information and monitoring the availability of medical facilities in real time. The following describes a specific implementation of this system.

[0705] The server uses a generative AI model to analyze real-time traffic information and calculate the optimal transport route. This analysis utilizes a traffic API to obtain the latest traffic conditions and road information. Based on the analysis results, the server determines the optimal route and transmits it to the terminal.

[0706] The terminal transmits patient information entered by paramedics and biometric data acquired using sensors to a server. The terminal also features autonomous driving technology and controls the ambulance according to the server's instructions, following the optimal route. Inside the ambulance, the patient's heart rate, blood pressure, and body temperature are monitored in real time, and any abnormalities are immediately reported to the server.

[0707] The user (in this case, an emergency medical technician) receives information from the server via a terminal and verifies whether the medical facility to which the patient is being transported is appropriate. In addition, the user uses the terminal to monitor the patient's condition and perform any necessary medical procedures during transport.

[0708] As a specific example, in the emergency transport of a patient suspected of having a heart condition, if the terminal detects a sudden change in heart rate from the acquired biometric data, the server calculates the shortest route to a cardiac specialist facility and transmits that information to the terminal. As a result, the ambulance can quickly reach its destination using autonomous driving technology.

[0709] An example of a prompt message might be, "Request the optimal route and recommended medical facilities for emergency transport of a patient with cardiac disease." This allows for prior contact with the receiving medical institution, ensuring that treatment can begin immediately upon arrival.

[0710] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0711] Step 1:

[0712] When the terminal receives an emergency call, the user enters patient information. Detailed information such as the patient's name, age, gender, and symptoms are manually registered on the terminal's interface. The terminal also acquires biometric data such as heart rate, blood pressure, and body temperature through sensors. The entered information, along with this biometric data, is transmitted to the server in packet format.

[0713] Step 2:

[0714] The server uses patient information and biometric data received from the terminal as input to analyze real-time traffic information using a generated AI model. It collects data on current congestion and the shortest route from a traffic API. By analyzing this data, it calculates the optimal route and outputs the result to the terminal. The analysis results include predicted arrival time and intermediate stop information.

[0715] Step 3:

[0716] The server evaluates the capacity of healthcare facilities. Based on the patient's symptoms, it generates a list of potential healthcare facilities and checks the availability of beds and specialists at each facility. It retrieves data through an interface with the healthcare institution's information system and selects the most suitable facility. The facility selection information is transmitted to the terminal.

[0717] Step 4:

[0718] The terminal controls the ambulance using autonomous driving technology based on the optimal route and medical facility information received from the server. It activates the automatic navigation system and heads towards the destination according to the recommended route. If new traffic information arises along the way, the terminal automatically recalculates and adjusts the route based on that information.

[0719] Step 5:

[0720] The terminal continuously collects biometric data during transport and transmits it to the server in real time. This information is monitored by the server to enable a rapid response if an anomaly is detected. The server sends a warning signal back to the terminal if an anomaly is detected, and also warns the user.

[0721] Step 6:

[0722] The server pre-transmits patient information to the designated healthcare facility. This data transmission allows medical staff to prepare for treatment, enabling efficient treatment upon the patient's arrival. The server integrates with the facility's system and notifies the terminal of the progress once the necessary preparations are complete.

[0723] (Application Example 1)

[0724] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0725] Rapid and accurate patient transport in emergencies is a critical challenge in healthcare settings. However, delays can occur due to traffic congestion and uncertainty regarding the availability of medical facilities. In addition, paramedics must perform numerous tasks during patient transport, which can impact patient care. To address these issues, a system is needed that optimizes routes and medical conditions in real time, enabling smooth transport.

[0726] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0727] In this invention, the server includes means for analyzing traffic information using a generative model and deriving the optimal transport route; means for evaluating the patient acceptance status at medical institutions and selecting an appropriate medical facility; means for monitoring the patient's vital signs in real time and issuing a warning signal if an abnormality is detected; means for providing information to emergency service operators and personnel and optimizing the state during transport; and means for checking the readiness status of the destination facility and enabling immediate treatment upon arrival. This enables rapid and appropriate patient transport even in emergency situations.

[0728] A "generative model" is an artificial intelligence technology used to derive optimal solutions based on data collected in real time.

[0729] "Traffic information" refers to data related to driving conditions such as road congestion and traffic jams.

[0730] A "transportation route" is information that indicates the optimal route from a certain point to a destination.

[0731] A "medical institution" refers to a facility that provides medical care and treatment to patients.

[0732] "Patient acceptance status" refers to the state of whether a medical institution is ready to examine or treat patients.

[0733] "Biometric information" refers to data about a person's physical condition, such as heart rate, blood pressure, and body temperature.

[0734] A "warning signal" is a notification issued to alert relevant parties when an anomaly is detected.

[0735] "Autonomous driving technology" refers to the ability of a vehicle to drive automatically without human intervention.

[0736] An "emergency service operator" is a professional responsible for communication and directing during emergencies.

[0737] "Team members" refers to personnel who have been specially trained to carry out emergency medical services.

[0738] "Information provision" refers to the act of conveying necessary data and knowledge to users.

[0739] "The receiving facility" refers to the medical institution that will accept and treat the patient.

[0740] "Preparation" refers to a state in which everything necessary for a particular action or activity is in place.

[0741] In implementing this invention, the server processes real-time data using a generative model to analyze traffic information and the availability status of medical facilities. The server uses Python and utilizes the TensorFlow library to build the AI ​​model. Real-time driving data collected through publicly available APIs is used for traffic information, and the availability status of medical facilities is obtained through OpenAPI.

[0742] The smartphone application allows emergency medical personnel to monitor patients' biometric information and transmit data to a server as needed. Android devices are used, and the program runs in Java or Kotlin. Patient information is collected from sensors via Bluetooth or Wi-Fi. This allows emergency personnel to constantly monitor the patient's condition, obtain route information, and transport the patient to the appropriate medical facility.

[0743] The autonomous vehicles utilize ROS (Robot Operating System) as their control system, automatically navigating the optimal route based on traffic conditions. This allows personnel to focus on patient care and information gathering.

[0744] As a concrete example, consider an operation to accept injured persons in transit during an emergency response. The server analyzes the injured person's biological condition and suggests the optimal medical facility and transport route. The input prompt to the generated AI model is, "Based on the injured person's location information, identify the optimal medical facility and route in real time."

[0745] In this way, rapid and accurate patient transport is achieved through the cooperation of servers, terminals, and users.

[0746] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0747] Step 1:

[0748] The terminal receives the emergency call. The user, an emergency medical technician, begins entering the patient's biometric information, while simultaneously collecting data through biosensors. The entered data includes heart rate, blood pressure, and body temperature, which are transmitted to the terminal via Bluetooth or Wi-Fi. The terminal receives this information and prepares it for transmission to the server.

[0749] Step 2:

[0750] The device sends collected biometric information to the server. The server receives this data in real time and compares it to normal values ​​using a generative AI model. The server performs data calculations, and if an abnormal value is detected, it immediately sends a warning signal to the device and generates further warning information regarding transportation options and healthcare selection.

[0751] Step 3:

[0752] The server obtains the latest traffic information via a public API and calculates the optimal transportation route using a generated AI model. This input includes current location and destination information. The model outputs the optimal route considering traffic conditions, which is then sent to the terminal. Furthermore, it uses OpenAPI to check the readiness status of each medical institution and selects the most suitable facility.

[0753] Step 4:

[0754] The server calculates the optimal route and transmits information about selected medical facilities to the terminal. The user, an emergency medical technician, transports the patient to the designated medical facility according to the instructions from the terminal. During transport, the terminal automatically provides operational instructions to the autonomous vehicle using ROS. The terminal dynamically changes the route according to the traffic conditions at the time, optimizing the transport to the destination.

[0755] Step 5:

[0756] The terminal continuously monitors the patient's vital signs during transport and sends the analysis results to the server. The server continues to monitor for any new abnormalities and sends additional instructions or warnings to the user as needed. This information is also shared before arrival at the medical facility to facilitate preparation for admission.

[0757] Step 6:

[0758] The user arrives at the designated medical facility and begins transporting the patient. Based on the patient information received from the server in advance, the medical facility can immediately begin treatment. The information sent from the server to the medical facility includes the patient's latest biological status and suggested necessary procedures.

[0759] Through the above series of processing steps, rapid and accurate patient transport in emergencies is achieved. The generating AI model performs data processing that takes into account traffic conditions and the status of medical facilities, based on the prompt message "Identify the optimal medical facility and route in real time based on the location information of the injured person."

[0760] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0761] In this embodiment of the invention, a system is provided that combines emotion recognition technology with rapid and safe patient transport in emergencies. This system is effective by integrating an emotion engine that analyzes the user's emotions, in addition to traffic information analysis using a generative model, real-time biometric information monitoring, and optimization of transport efficiency through autonomous driving technology.

[0762] First, the terminal receives an emergency call, and paramedics input the patient's basic information and location. Based on this information, the terminal activates an emotion engine to sense the emotions of the user during the ride. This emotion engine evaluates the user's emotional state in real time using voice tone, facial expressions, sweating, and heart rate data.

[0763] The server uses a generative model to analyze traffic data and determine the optimal transport route. It also queries the availability of medical facilities and selects the necessary medical facilities. The results of this processing are then sent to the terminal.

[0764] Based on the route information and medical facility information it receives, the terminal uses autonomous driving technology to accurately guide the ambulance to its destination. During the journey, the emotion engine continuously monitors the user's stress level and emotional changes, and if an abnormality is detected, the terminal sends an alert to the emergency medical staff via the server.

[0765] For example, if a patient is experiencing severe pain, the emotion engine recognizes this as a high stress level, and the server automatically adjusts transportation methods to ensure a safe and rapid response. It can also provide the user with voice feedback and relaxation instructions that correspond to specific emotional states.

[0766] Finally, the terminal transmits patient information to a pre-selected medical institution, allowing medical staff to prepare for arrival. In this way, time is reduced, the quality of medical care is improved, and the user's emotional well-being is simultaneously addressed, contributing to an increase in survival rates.

[0767] The following describes the processing flow.

[0768] Step 1:

[0769] The user receives an emergency call and enters the patient's basic information and location into the terminal. The terminal then begins acquiring biometric information from sensors attached to the patient.

[0770] Step 2:

[0771] The terminal transmits basic patient information, location information, biometric information, and user emotion data determined using an emotion engine to the server.

[0772] Step 3:

[0773] The server uses a generative model to analyze real-time traffic data and calculate the optimal transport route. This process takes into account traffic congestion and road closures.

[0774] Step 4:

[0775] The server investigates the acceptance status of each medical institution and selects facilities that can provide the necessary medical services. It then transmits the selected route and medical facility information to the terminal.

[0776] Step 5:

[0777] The terminal activates the autonomous driving system and dispatches the ambulance according to the received route information.

[0778] Step 6:

[0779] The device continuously transmits the patient's biometric information to the server, and the emotion engine also continuously monitors the user's emotions.

[0780] Step 7:

[0781] The server analyzes biometric and emotional data, provides feedback to the user if an anomaly is detected, and adjusts the autonomous driving route and speed as needed. It also issues warnings to emergency medical staff if deemed necessary.

[0782] Step 8:

[0783] The device will pre-send patient information to the medical institution where it is scheduled to arrive, facilitating treatment preparation.

[0784] Step 9:

[0785] The terminal arrives at its destination, the patient is safely handed over to medical facility staff, and medical treatment is initiated quickly.

[0786] (Example 2)

[0787] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0788] To ensure the rapid and safe transport of patients in emergencies, it is necessary to comprehensively evaluate multiple factors, such as traffic conditions, the capacity of medical facilities, and real-time monitoring of patients' emotions and biometric information, in order to provide the optimal transport approach. However, conventional systems can only address these factors individually, making integrated and efficient transport difficult.

[0789] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0790] In this invention, the server includes means for analyzing traffic information using a generative model and deriving the optimal transport route, means for evaluating the patient acceptance status at medical institutions and selecting an appropriate medical facility, and means for analyzing the patient's emotional state in real time and detecting changes in stress levels. This enables safe and rapid transport while considering the multifaceted condition of the patient.

[0791] A "generative model" is a mathematical model used to generate new information based on input data, and is particularly used in the analysis of traffic information.

[0792] "Traffic information" refers to real-time data on road and transportation systems, such as congestion levels and traffic restrictions.

[0793] A "transportation route" refers to the route selected to optimize travel from a certain point to a destination.

[0794] A "medical institution" is a facility that provides treatment and diagnosis for patients, and includes hospitals and clinics.

[0795] "Acceptance status" refers to the number of patients and the status of cases that a medical institution can currently handle.

[0796] "Emotional state" refers to the state of an individual's psychological and physiological emotional responses as observed in real time.

[0797] "Stress level" is a numerical representation of the degree of psychological and physiological pressure or tension an individual is experiencing.

[0798] "Autonomous driving technology" refers to technology that enables vehicles to operate automatically without human intervention.

[0799] "Biometric information" refers to data related to human vital signs such as heart rate, body temperature, and sweating.

[0800] A "warning signal" refers to a signal issued by a system or device to alert the user when it detects an abnormality.

[0801] This invention is a system that combines generative AI models, emotion recognition technology, and autonomous driving technology to optimize patient transport in emergencies. The system mainly consists of servers and terminals, each performing processing according to its respective role.

[0802] The server analyzes traffic information using a generative AI model. This AI model is based on algorithms learned from a wide range of datasets and analyzes data collected through Google Maps and similar real-time traffic information services. The server then uses this to derive the optimal transportation route to the destination. Additionally, the server references a database of healthcare facilities, evaluates the patient acceptance status of each facility, and selects the most suitable medical facility.

[0803] The terminal receives emergency calls and activates emotion recognition technology based on the patient's basic information entered by paramedics. This emotion recognition is performed using the terminal's built-in camera and microphone, as well as a wearable device capable of heart rate monitoring, for example, by using Microsoft's emotion recognition API. This technology allows for real-time analysis of the patient's emotional state from their voice tone, facial expressions, heart rate, etc. Based on the analysis results, the system suggests the most appropriate response if the stress level is high.

[0804] Autonomous driving technology, such as that used by Waymo, automatically operates ambulances based on safe transport routes transmitted from terminals. During operation, emotion recognition technology continuously monitors the patient's stress and emotional state, immediately reporting any abnormalities to the server and issuing warnings to medical staff if necessary.

[0805] For example, if a patient is experiencing severe pain, emotion recognition technology can detect this pain as a high stress level, and the server will prioritize recalculating the shortest and most optimal route to ensure safe and rapid transport. Furthermore, if necessary, the terminal can provide voice guidance to the patient to help them relax.

[0806] An example of a prompt message would be: "An emergency call has been received. Please enter patient information. The server will calculate the optimal transport route based on the received data, monitor the patient's emotional state in real time, and issue alerts to medical staff as needed. Please notify us when the patient has been transported to the appropriate medical facility."

[0807] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0808] Step 1:

[0809] The terminal receives an emergency call. Using a dedicated application installed on the terminal, paramedics input the patient's basic information and location. Based on this information, the terminal sends data to the server. The input includes the patient's name, age, symptoms, and location, and the output is a secure transmission of data to the server.

[0810] Step 2:

[0811] The device activates emotion recognition technology. Its camera and microphone capture the patient's voice tone and facial expressions, while a wearable device monitors their heart rate. This data is input into an emotion recognition API, which analyzes the user's state in real time. Inputs include voice, video, and biometric information, while output is an evaluation of the patient's emotional state.

[0812] Step 3:

[0813] The server analyzes received data using a generating AI model and processes traffic information. The server obtains real-time traffic information from traffic data providers and calculates the optimal transport route. The input is location information and real-time traffic conditions, and the output is the determination of the optimal transport route.

[0814] Step 4:

[0815] The server checks the acceptance status of medical institutions. It refers to its database of medical institutions, evaluates each institution's capacity and facilities, and selects the most appropriate medical facility. Inputs include the number of patients, symptoms, and acceptance status, while the output is the selection of the appropriate medical institution.

[0816] Step 5:

[0817] The terminal transmits route and medical facility information received from the server to the autonomous driving technology. The autonomous driving system operates the ambulance based on this information, adjusting the route in real time. The input is the optimal route and destination, and the output is the control of the autonomous driving state.

[0818] Step 6:

[0819] The terminal continuously monitors the patient's emotional state during operation. If the patient's stress level increases or an abnormality is detected, the terminal notifies medical staff via the server. The input is the result of emotion recognition, and the output is a warning signal in case of an abnormality.

[0820] Step 7:

[0821] The terminal transmits patient information to the medical institution before arrival. The information is sent to the medical institution via a server in advance, allowing medical staff to prepare. Inputs include patient symptoms and estimated arrival time, while output is the notification of information to the medical institution.

[0822] (Application Example 2)

[0823] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0824] In emergency patient transport, not only is rapid and safe transportation important, but the psychological care of patients and their caregivers during the journey is also crucial. However, conventional systems do not adequately monitor the user's mental state based on emotion recognition or provide appropriate feedback based on that, posing challenges in reducing anxiety and stress during transport. Furthermore, optimizing the transport route in response to changes in traffic conditions and the patient's condition is also difficult.

[0825] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0826] In this invention, the server includes means for analyzing traffic information using a generative model and deriving the optimal transport route, means for monitoring the patient's biometric information and emotional state in real time and issuing a warning signal when an abnormality is detected, and means for providing emotion-based voice feedback to stabilize the user's mental state. This makes it possible to transport the user quickly and efficiently while reducing stress and providing a sense of security.

[0827] A "generative model" is a form of machine learning that learns patterns from data and generates new information.

[0828] "Traffic information" refers to information about the condition of roads and transportation systems, including traffic congestion and road closures.

[0829] A "transportation route" refers to the optimal path to a destination, enabling efficient travel.

[0830] A "medical institution" is a facility that provides medical services, and includes hospitals and clinics.

[0831] "Patient acceptance status" refers to the current number of patients at a medical institution and the number of patients who can be treated.

[0832] "Biological information" refers to biological information obtained from the human body, such as heart rate, sweating, and body temperature.

[0833] "Emotional state" refers to an individual's emotional state, including stress, anxiety, and feelings of security.

[0834] A "warning signal" is a notification issued when a system detects an anomaly, indicating a situation that requires immediate attention.

[0835] "Autonomous driving technology" refers to technology that allows vehicles and equipment to move or perform tasks automatically without human intervention.

[0836] "Transportation means" refers to the means of moving goods or people from one place to another, and includes vehicles and machinery.

[0837] "Stabilizing one's mental state" refers to the process of calming an individual's emotions and reducing anxiety and stress.

[0838] "Voice feedback" is a technology that uses voice to provide information and instructions to users.

[0839] This invention provides a system for efficiently and safely transporting patients in emergencies. The server analyzes traffic information using a generative AI model to determine the optimal transport route. In doing so, the server receives real-time updated traffic data and constantly calculates the best route. Furthermore, the server has the function of evaluating the patient acceptance status of medical facilities and selecting an appropriate medical facility.

[0840] The device monitors the user's biometric information and emotional state 24 hours a day. It uses an emotion recognition engine to analyze voice tone, facial expressions, and vital data. If an abnormality is detected, the device immediately sends a warning signal to the server and medical staff. Furthermore, the user receives immediate voice feedback based on their emotional state, providing emotional support. This helps alleviate user stress and provide a sense of security.

[0841] Autonomous driving technology-based transportation systems travel along the most efficient route to their destination, following route information received from a server. This technology minimizes interference during operation and maximizes the efficiency and safety of transportation.

[0842] As a concrete example, a patient involved in an accident in the mountains could wear smart glasses and utilize this system to be safely transported to a medical facility in the shortest possible time. Furthermore, an example of a prompt message would be, "Use the emotion engine to detect if the patient is experiencing high levels of anxiety and suggest ways to provide reassurance." This input would allow the system to quickly generate appropriate feedback and support the patient.

[0843] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0844] Step 1:

[0845] The server collects traffic information in real time and analyzes it using a generative AI model. This analysis derives the optimal transportation route. It receives current traffic data as input and sends information about the optimal route to the terminal as output. In this process, the server utilizes the generative AI model to perform pattern recognition on the data.

[0846] Step 2:

[0847] The device acquires the patient's biometric information and emotional state, and analyzes it using an emotion engine. It uses heart rate, voice tone, and facial expression data as input, and outputs an evaluation of the emotional state. Throughout this process, the device collects the necessary data through sensor devices and processes it in real time.

[0848] Step 3:

[0849] The terminal sends a warning signal to the server based on the anomaly detection results obtained from the emotion engine. The input is the evaluation result of the emotional state, and the output is a warning signal. In this step, the terminal automatically detects an anomaly and immediately issues a signal.

[0850] Step 4:

[0851] The terminal generates and provides voice feedback to the user based on the patient's condition. It uses the results of an emotional state assessment as input and generates voice messages as output. In this process, it selects an appropriate message based on instructions from the emotion engine and plays it back via a voice control device.

[0852] Step 5:

[0853] Based on autonomous driving technology, the system uses optimal route information to operate the transport vehicle. The input is optimal route information transmitted from the server, and the output is the efficient movement to the destination. In this step, a route-based motion plan is generated, and actuator control is performed for autonomous driving.

[0854] Step 6:

[0855] Before the user arrives at their destination, the terminal transmits patient information to the medical institution. Inputs include basic patient information and current status data, while output is the transmission of data to the medical institution. Here, the terminal ensures that the information is organized beforehand and transmitted via a secure communication method.

[0856] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0857] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0858] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0859] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0860] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0861] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0862] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0863] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0864] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0865] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0866] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0867] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0868] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0869] 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.

[0870] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0871] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0872] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0873] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0874] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0875] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0876] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0877] The following is further disclosed regarding the embodiments described above.

[0878] (Claim 1)

[0879] A means of analyzing traffic information using a generative model and deriving the optimal transport route,

[0880] A means of evaluating the patient acceptance situation at medical institutions and selecting appropriate medical facilities,

[0881] A means of monitoring a patient's vital signs in real time and issuing a warning signal if an abnormality is detected,

[0882] A means of controlling transportation methods utilizing autonomous driving technology and optimizing transportation efficiency,

[0883] A system that includes this.

[0884] (Claim 2)

[0885] The system according to claim 1, wherein the transportation means dynamically changes its route based on new traffic information.

[0886] (Claim 3)

[0887] The system according to claim 1, which transmits patient information to a medical institution in advance before arrival at the medical facility to facilitate preparation for treatment.

[0888] "Example 1"

[0889] (Claim 1)

[0890] A means for calculating the optimal transport route by having a generative model analyze traffic information acquired through the vehicle's control system,

[0891] A means of evaluating the capacity of healthcare facilities to accept patients and selecting appropriate facilities,

[0892] A means of monitoring biometric data in real time and issuing warnings when an anomaly occurs,

[0893] A means to improve the operability of transportation methods using autonomous driving technology and to achieve efficient travel,

[0894] A system that includes this.

[0895] (Claim 2)

[0896] The system according to claim 1, wherein the means of transport dynamically modifies its route based on newly acquired traffic information.

[0897] (Claim 3)

[0898] The system according to claim 1, which transmits individual patient data to a medical facility in advance before the patient reaches the medical facility, thereby facilitating preparation for treatment.

[0899] "Application Example 1"

[0900] (Claim 1)

[0901] A means of analyzing traffic information using a generative model and deriving the optimal transport route,

[0902] A means of evaluating the patient acceptance situation at medical institutions and selecting appropriate medical facilities,

[0903] A means of monitoring a patient's vital signs in real time and issuing a warning signal if an abnormality is detected,

[0904] A means of controlling transportation methods utilizing autonomous driving technology and optimizing transportation efficiency,

[0905] Providing information for emergency service operators and personnel, and means to optimize conditions during transit,

[0906] A means to check the readiness of the receiving facility and enable immediate treatment upon arrival,

[0907] A system that includes this.

[0908] (Claim 2)

[0909] The system according to claim 1, wherein the transport means dynamically changes its route based on new traffic information, thereby reducing the amount of work required from the crew.

[0910] (Claim 3)

[0911] The system according to claim 1, which transmits patient information before arrival at the medical facility and optimizes the arrival status while facilitating treatment and preparation.

[0912] "Example 2 of combining an emotion engine"

[0913] (Claim 1)

[0914] A means of analyzing traffic information using a generative model and deriving the optimal transport route,

[0915] A means of evaluating the patient acceptance situation at medical institutions and selecting appropriate medical facilities,

[0916] A means to analyze the patient's emotional state in real time and detect changes in stress levels,

[0917] A means of monitoring a patient's vital signs in real time and issuing a warning signal if an abnormality is detected,

[0918] A means of controlling transportation methods utilizing autonomous driving technology and optimizing transportation efficiency,

[0919] A system that includes this.

[0920] (Claim 2)

[0921] The system according to claim 1, wherein the transportation means dynamically changes its route based on new traffic information.

[0922] (Claim 3)

[0923] The system according to claim 1, which transmits patient information to a medical institution in advance before arrival at the medical facility to facilitate preparation for treatment.

[0924] "Application example 2 when combining with an emotional engine"

[0925] (Claim 1)

[0926] A means of analyzing traffic information using a generative model and deriving the optimal transport route,

[0927] A means of evaluating the patient acceptance situation at medical institutions and selecting appropriate medical facilities,

[0928] A means for monitoring a patient's biological information and emotional state in real time and issuing a warning signal if an abnormality is detected,

[0929] A means of controlling transportation methods utilizing autonomous driving technology and optimizing transportation efficiency,

[0930] It provides emotion-based voice feedback as a means to stabilize the user's mental state,

[0931] A system that includes this.

[0932] (Claim 2)

[0933] The system according to claim 1, wherein the transportation means dynamically changes its route based on new traffic information and the user's emotional state.

[0934] (Claim 3)

[0935] The system according to claim 1, which transmits patient information to a medical institution in advance before the patient arrives at the medical facility to facilitate preparation for treatment, and provides the medical institution with countermeasures based on the patient's mental state. [Explanation of Symbols]

[0936] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of analyzing traffic information using a generative model and deriving the optimal transport route, A means of evaluating the patient acceptance situation at medical institutions and selecting appropriate medical facilities, A means of monitoring a patient's vital signs in real time and issuing a warning signal if an abnormality is detected, A means of controlling transportation methods utilizing autonomous driving technology and optimizing transportation efficiency, A system that includes this.

2. The system according to claim 1, wherein the transportation means dynamically changes its route based on new traffic information.

3. The system according to claim 1, which transmits patient information to a medical institution in advance before the patient arrives at the medical facility, thereby facilitating preparation for treatment.

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