System

Abstract

A system is provided.SOLUTION: A system, comprising: a terminal collecting a video, an audio, and position information; a server receiving the video, the audio, and the position information from the terminal; the server analyzing the video, the audio, and the position information by using generative artificial intelligence; the server setting a priority of a rescue operation according to an analysis result and generating a specific instruction; the server sending the specific instruction to smart glasses of an on-site rescuer; and the on-site rescuer confirming the instruction through the smart glasses and performing the rescue operation.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] When carrying out disaster relief activities, rescue organizations have difficulty grasping the damage situation in real time, and delays and inaccuracies in information hinder prompt and effective relief efforts. In particular, there are many challenges in setting appropriate rescue priorities and ensuring safe evacuation routes. This invention aims to solve these challenges by providing a system that efficiently acquires real-time information from disaster areas, quickly analyzes it, and provides appropriate instructions to rescuers. [Means for solving the problem]

[0005] The present invention provides a system in which a terminal collects video, audio, and location information, which a server receives via satellite communication and analyzes using generative artificial intelligence. The server determines the priority of rescue operations based on the analysis results, generates specific instructions, and immediately sends them to the smart glasses of local rescuers. Local rescuers can check the instructions on the smart glasses and carry out rescue operations quickly. Furthermore, local rescuers can report the progress of rescue operations to the server through the smart glasses. This enables real-time situation assessment and appropriate response, resulting in faster and more effective rescue operations.

[0006] "Terminal" refers to equipment or devices for collecting video, audio, and location information.

[0007] "Server" refers to a central computer system that receives data sent from terminals, analyzes the data, and processes it to generate instructions.

[0008] "Generative AI" refers to an AI algorithm that analyzes collected information and determines necessary rescue instructions and priorities based on the analysis results.

[0009] "Video" refers to visual information collected by a camera or other imaging device.

[0010] "Sound" refers to auditory information collected by a sound-collecting device such as a microphone.

[0011] "Location Information" refers to geographic coordinates or location information obtained using location tracking technologies such as GPS.

[0012] "Satellite communications" refers to a means of communication for transmitting and receiving data via satellite.

[0013] "Smart glasses" refer to eyeglass-type devices that can be worn by rescuers and display real-time information and instructions.

[0014] "Relief workers" refers to people engaged in relief activities on the ground.

[0015] "Instructions" refer to the information and guidelines for action necessary for rescue operations that are generated based on the analysis results.

[0016] "Real-time" refers to information being collected and processed immediately, with little or no delay.

[0017] "Analysis" refers to the process of analyzing collected data and assessing the situation based on that data.

[0018] "Priority" refers to the degree to which relief efforts are determined to be most urgent. [Brief explanation of the drawings]

[0019] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0020] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0021] First, the terms used in the following description will be explained.

[0022] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0023] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0024] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0025] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0026] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0027] [First embodiment]

[0028] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0029] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0030] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0031] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0032] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0033] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0034] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0035] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0037] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0038] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0039] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0040] As a specific embodiment of this invention, we propose a system using a terminal, a server, generative artificial intelligence, and smart glasses. This system will enable rapid and effective rescue operations in the event of a disaster.

[0041] System Configuration

[0042] Terminal

[0043] The terminals are devices used to collect video, audio, and location information at disaster sites. Specifically, they include drones, handheld cameras, microphones, and GPS devices. These terminals collect information on the situation in the disaster area in real time and transmit the data to a server via satellite communication.

[0044] Server and Generative Artificial Intelligence (AI)

[0045] The server receives the data sent from the device and analyzes it using generative artificial intelligence (AI). Specifically, the following process is performed:

[0046] 1. The server stores the video, audio, and location information received from the device.

[0047] 2. AI analyzes the footage and identifies the locations of collapsed buildings and victims.

[0048] 3. Audio analysis detects cries for help and dangerous sounds (e.g., the sound of a collapse).

[0049] 4. Combine location information to determine which areas need urgent assistance.

[0050] Smart Glasses

[0051] The smart glasses used by local rescuers display instructions sent from the server. Specific operating procedures include the following:

[0052] 1. The rescuer puts on the smart glasses and connects them to the system.

[0053] 2. The instructions generated by the server based on the analysis results are displayed on the smart glasses.

[0054] 3. Rescuers should check the displayed instructions, move quickly to the designated location and begin rescue operations.

[0055] 4. Report progress and new information on rescue operations to the server via smart glasses.

[0056] Examples

[0057] Example 1: When a major earthquake occurs

[0058] 1. The drone terminal flies over the disaster area, captures footage, and sends it to the server.

[0059] 2. AI analyzes the video on the server and identifies the collapsed building.

[0060] 3. The server determines priorities and gives top priority to areas with particularly high risk of rescue.

[0061] 4. The server generates specific instructions and sends them to the smart glasses.

[0062] 5. Rescuers follow the instructions on the smart glasses to reach the scene via a safe route and begin rescue operations.

[0063] Example 2: Post-tsunami relief efforts

[0064] 1. Collect information on the flooded area using a mobile camera and GPS device as terminals.

[0065] 2. The server receives the data live and the AI ​​analyzes the extent of the damage.

[0066] 3. Identify high-priority areas, and the server generates rescue instructions based on that information.

[0067] 4. The smart glasses display instructions saying, "The area ahead is heavily flooded. Carry out rescue operations with caution."

[0068] 5. Rescuers confirm instructions and begin rescuing victims from a safe location.

[0069] This system makes it possible to grasp the situation in disaster areas in real time and support rapid and accurate relief efforts. Analysis by generative AI and display of instructions by smart glasses can help local rescuers act quickly and effectively.

[0070] The processing flow will be explained below.

[0071] Step 1:

[0072] The device collects video, audio, and location information. Specifically, the drone flies over the affected area, taking high-resolution video with a camera, recording audio from the scene with a microphone, and obtaining location information with a GPS device.

[0073] Step 2:

[0074] The device transmits the collected video, audio, and location information to a server in real time. This is done using satellite communication, and the communication signal reaches the server via a communications satellite.

[0075] Step 3:

[0076] The server receives the data sent from the device. The server first stores the received video, audio, and location information data and prepares it for subsequent analysis.

[0077] Step 4:

[0078] The server passes the received data to the artificial intelligence (AI) that generates it, which then begins analyzing it. The AI ​​first analyzes the video data and identifies collapsed buildings and abnormalities. For example, it marks collapsed buildings with a red frame.

[0079] Step 5:

[0080] The server uses AI to perform audio analysis, detecting cries for help and other important sounds (such as the sound of a collapse) from the audio data, thereby narrowing down the areas where victims may be present.

[0081] Step 6:

[0082] The server analyzes the location data and combines it with the results of video and audio analysis to determine the level of danger and urgency in a specific area and set rescue priorities.

[0083] Step 7:

[0084] The server generates specific instructions based on the analysis results, such as "Area A has many collapsed buildings. Urgency is high. Area B is flooded. Caution."

[0085] Step 8:

[0086] The server sends the generated instructions to the local rescuer's smart glasses. The data packet reaches the rescuer's smart glasses via the communication path.

[0087] Step 9:

[0088] The user checks the instructions on the smart glasses, and the rescuer reads the instructions displayed on the smart glasses and understands the necessary actions.

[0089] Step 10:

[0090] The user follows the instructions of the smart glasses to carry out rescue operations, specifically by searching for and rescuing victims while following a safe route and beginning rescue operations.

[0091] Step 11:

[0092] The user reports the progress of the rescue operation to the server. For example, a report such as "5 victims have been rescued in Area A" is sent to the server through the smart glasses.

[0093] This series of processing steps creates a system that can smoothly collect information, analyze it, generate instructions, and carry out rescue operations in real time.

[0094] Example 1

[0095] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0096] When a disaster occurs, there are often delays in quickly and accurately grasping the situation on the ground and issuing instructions for effective relief activities. As a result, the efficiency of relief activities declines and the rescue of victims is delayed. In addition, the number of information devices carried by local rescuers is limited, so there is a lack of means to quickly receive appropriate instructions.

[0097] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0098] In this invention, the server includes: a terminal for collecting video, audio, and location information; a server for receiving the video, audio, and location information from the terminal; a server for analyzing the video, audio, and location information using a generating artificial intelligence; a server for prioritizing rescue operations based on the analysis results and generating specific instructions; a generating artificial intelligence for analyzing the video to identify the locations of collapsed structures and victims; a generating artificial intelligence for analyzing the audio to detect calls for help and dangerous sounds; a server for combining the video, audio, and location information to determine areas requiring emergency support; a server for transmitting the specific instructions to smart glasses worn by local rescuers; and a server for the local rescuers to check the instructions on the smart glasses and carry out rescue operations. This enables real-time situation assessment of the disaster site and rapid and accurate rescue operation instructions.

[0099] "Terminals" are devices that collect video, audio, and location information at disaster sites, including drones, handheld cameras, microphones, and GPS devices.

[0100] A "server" is a computer system that receives data sent from a terminal, analyzes it, and generates instructions.

[0101] "Video" refers to data that represents visual information from the disaster site. This includes videos and images taken using drones or handheld cameras.

[0102] "Audio" refers to data representing sound information from disaster sites, including calls for help and dangerous sounds collected using microphones.

[0103] "Location information" is data that indicates the latitude and longitude of specific points in a disaster site. It is collected using GPS devices.

[0104] "Generative artificial intelligence" is a machine learning model or algorithm that analyzes collected data and extracts the necessary information.

[0105] "Priority of rescue operations" is a criterion for determining the urgency based on the analysis results and the order in which rescue operations should be carried out.

[0106] "Specific instructions" are detailed instructions such as specific actions rescuers should take on site and routes to take.

[0107] "Smart glasses" are wearable devices worn by local rescuers that display instructions from a server.

[0108] "Analyzing the video" means analyzing the collected video data and identifying the locations of collapsed structures and victims.

[0109] "Analyzing voice" means analyzing collected voice data to detect cries for help or dangerous sounds.

[0110] "Determining areas needing emergency assistance" means combining video, audio, and location information to identify which areas are most in need of assistance.

[0111] "Satellite communications" is a communication method for sending and receiving data via artificial satellites.

[0112] As a specific embodiment of this invention, we propose a system using a terminal, a server, generative artificial intelligence (AI), and smart glasses, which will enable rapid and effective rescue operations in the event of a disaster.

[0113] Terminal

[0114] The terminals are devices used to collect video, audio, and location information at disaster sites. Specifically, they include drones, handheld cameras, microphones, and GPS devices. These terminals collect information on the situation in the disaster area in real time and transmit the data to a server via satellite communication.

[0115] Server and Generative Artificial Intelligence (AI)

[0116] The server receives data sent from the device and analyzes it using artificial intelligence (AI). Specifically, the following process is performed:

[0117] 1. Data Receipt and Storage

[0118] The server receives and stores the video, audio, and location information sent from the device.

[0119] 2. Video Analysis

[0120] AI analyzes the video and identifies the locations of collapsed structures and victims. For example, AI uses pattern recognition technology in the video to detect collapsed buildings.

[0121] 3. Audio Analysis

[0122] For voice data, AI detects dangerous sounds such as cries for help or the sound of a collapse. Speech recognition technology extracts specific voice patterns and detects information with a high level of urgency.

[0123] 4. Location information analysis

[0124] By analyzing location information and plotting the damage situation on a map, it is possible to identify which areas require emergency assistance.

[0125] Smart Glasses

[0126] The smart glasses used by local rescuers display instructions sent from the server, including the following specific operating procedures:

[0127] 1. System Connection

[0128] The user (local rescuer) wears the smart glasses and connects them to the system.

[0129] 2. Instruction display

[0130] The server generates instructions based on the analysis results and displays them on the smart glasses, allowing rescuers to instantly understand areas with high emergency levels and safe routes to take.

[0131] 3. Relief operations

[0132] Rescuers follow the displayed instructions to quickly move to the designated location and begin rescue operations. The smart glasses also have the function of reporting the progress of rescue operations and new information to the server.

[0133] Examples

[0134] Example 1: When a major earthquake occurs

[0135] The drone terminal flies over the disaster area, taking pictures and sending them to a server. AI on the server analyzes the pictures and identifies collapsed buildings. The server then determines priorities and prioritizes areas with particularly high risk for rescue. The server then sends instructions generated by the server to the smart glasses. Rescuers follow the instructions on the smart glasses to reach the scene via a safe route and begin rescue operations.

[0136] Example 2: Post-tsunami relief efforts

[0137] The device uses a portable camera and GPS device to collect information on the status of the flooded area. The server receives the data, and AI analyzes the extent of the damage. The server identifies high-priority areas and generates rescue instructions based on that information. The smart glasses display the following instruction: "The area ahead is heavily flooded. Carry out rescue operations with caution." Rescuers confirm the instructions and rescue victims from a safe location.

[0138] Prompt Sentence Examples

[0139] Possible prompts for the generative AI model include:

[0140] Prompt statement example 1:

[0141] Analyze the disaster situation and determine the priority of relief efforts.

[0142] Prompt statement example 2:

[0143] Use video data to identify the locations of collapsed buildings and victims, and quickly generate rescue instructions.

[0144] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0145] Program processing steps

[0146] Step 1:

[0147] The terminal collects video, audio, and location information and sends it to a server. Specifically, drones, portable cameras, microphones, and GPS devices are used to collect information on the situation in the disaster area in real time. The collected data is sent to the server via satellite communication. The input is video, audio, and location information obtained from the disaster site, and the output is the data sent to the server. As an example of how it works, a drone flies over a wide area of ​​the disaster area and collects video and location information in real time.

[0148] Step 2:

[0149] The server stores the data received from the device. The input is the video, audio, and location information sent from the device, and the output is data stored in a database on the server. For example, the server uses a secure communication protocol to safely store the received data and prepare it for subsequent analysis.

[0150] Step 3:

[0151] The server analyzes the video using generative artificial intelligence (AI). The input is the stored video data, and the output is the location information of collapsed structures and victims. Specifically, the AI ​​analyzes characteristic patterns in the video and identifies the location of collapsed buildings. For example, it identifies collapsed buildings by detecting abnormal shapes or color changes.

[0152] Step 4:

[0153] The server uses AI to analyze the voice. The input is the stored voice data, and the output is the detection results of calls for help and dangerous sounds. Specifically, it uses voice recognition technology to extract specific phrases and sound patterns and identify information with a high level of urgency. For example, it identifies the sound of a collapse or a call for help.

[0154] Step 5:

[0155] The server analyzes the location information and identifies areas requiring emergency assistance. The input is the stored location data, and the output is a list of identified high-priority areas for assistance. Specifically, the server plots the location information on a map and determines priorities according to the level of damage. For example, it compares GPS data with map information to mark high-risk areas.

[0156] Step 6:

[0157] The server generates instructions for rescue operations based on the analysis results. The inputs are the video analysis results, audio analysis results, and location information analysis results, and the output is specific rescue instructions. In concrete terms, the server inputs prompts into the generative AI model to generate rescue instructions. For example, it gives the AI ​​a prompt such as "Plan a route for the rescue team based on the disaster situation."

[0158] Step 7:

[0159] The server sends the generated instructions to the smart glasses. The input is the generated rescue instructions, and the output is the instructions displayed on the smart glasses. Specifically, the server sends instruction data to the smart glasses via wireless communication. For example, it sends an instruction such as "There is a victim 200 meters northeast. Carry out rescue using a safe route."

[0160] Step 8:

[0161] The user uses smart glasses to carry out rescue operations. The input is instructions displayed on the smart glasses, and the output is a progress report of the rescue operations. Specifically, the user follows the instructions to move to the disaster area and carry out the rescue operations. For example, the user follows the instructions on the smart glasses to go to the designated location and rescue the victims.

[0162] This step-by-step process will enable rapid and accurate relief efforts in the event of a disaster.

[0163] (Application example 1)

[0164] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0165] Conventional rescue operation systems have had difficulty quickly and accurately grasping the situation at a disaster site and providing appropriate instructions to local rescuers. Furthermore, systems for detecting abnormalities in real time and responding immediately have been inadequate for security services. Therefore, there is a demand for effective support in rescue operations and security monitoring. The purpose of this invention is to solve these problems and realize an efficient and effective support system.

[0166] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0167] In this invention, the server includes: a means for a terminal to collect video, audio, and location information; a means for the server to receive the video, audio, and location information from the terminal; a means for the server to analyze the video, audio, and location information using artificial intelligence; a means for the server to prioritize rescue operations based on the analysis results and generate specific instructions; a means for the server to transmit the specific instructions to the smart glasses of local rescuers; a means for the local rescuers to check the instructions on the smart glasses and perform rescue operations; and a means for detecting abnormalities and checking the situation in real time through the smart glasses worn by security personnel. This enables not only fast and accurate rescue operations in the event of a disaster, but also real-time detection of abnormalities in security services and immediate response.

[0168] A "terminal" is a device for collecting video, audio, and location information.

[0169] A "server" is a computer system that receives video, audio, and location information from a terminal and analyzes and stores that data.

[0170] "Footage" refers to visual information collected through equipment such as drones and fixed cameras.

[0171] "Audio" is information about sounds collected through a device such as a microphone.

[0172] "Location information" is data that indicates a geographic location, such as that collected through a GPS device.

[0173] "Generative artificial intelligence (AI)" is a general term for algorithms and technologies that analyze collected video, audio, and location information and generate judgments and instructions based on the results.

[0174] "Relief operations" are a series of actions and measures taken to support victims of a disaster.

[0175] "Smart glasses" are wearable devices that have the ability to display digital information, and are worn by local rescuers and security personnel to check instructions from the server.

[0176] A "security officer" is a person whose job is to detect anomalies and check the situation.

[0177] "Real-time" refers to immediate processing or information transmission with little or no delay.

[0178] "Anomaly detection" is the process of identifying events that deviate from normal conditions or behavior.

[0179] "Situation check" refers to understanding the current state and progress of activities based on collected information.

[0180] System Program

[0181] This system includes a program consisting of the following elements:

[0182] 1. Device (collection of video, audio, and location information):

[0183] It uses hardware such as drones, fixed cameras, handheld cameras, microphones, and GPS devices.

[0184] These devices are responsible for collecting video, audio, and location information in real time.

[0185] 2. Data transmission:

[0186] The data collected by the terminal is sent to a server via satellite communication or the Internet.

[0187] A communication module is used for this process.

[0188] 3. Server (receiving and analyzing data):

[0189] The server receives and stores the video, audio, and location information transmitted from the terminal.

[0190] Generative artificial intelligence (AI) within the server analyzes the data using OpenCV and other AI modules.

[0191] Specifically, AI detects suspicious people, suspicious sounds, and unnatural movements of people and objects.

[0192] 4. Instruction generation and transmission:

[0193] The server generates specific instructions based on the results of the AI ​​analysis and sends them to the smart glasses.

[0194] For example, an instruction such as "There is a suspicious person on the north side of the building. Approach carefully and check." may be generated.

[0195] 5. Smart Glasses (Local Support and Instructions):

[0196] Local rescuers and security personnel wear smart glasses and check instructions from the server.

[0197] Real-time feedback is possible by reporting the progress of on-site response and new information to the server via smart glasses.

[0198] Natural language explanations

[0199] 1. Hardware:

[0200] Drones, fixed cameras and handheld cameras will be used to capture footage.

[0201] A microphone is used to capture audio, and a GPS device is used to capture location information.

[0202] Smart glasses will be used to display real-time instructions and assist with on-site operations.

[0203] 2. Software:

[0204] OpenCV: Used for video data acquisition and initial analysis.

[0205] AI analyzer: Performs detailed analysis of video, audio, and location information and detects anomalies.

[0206] Communication module: Sends data from the device to the server and sends instructions from the server to the smart glasses.

[0207] 3. Data processing and calculation:

[0208] After the data is sent to the server, a generative artificial intelligence (AI) analyzes it.

[0209] Video analysis: Detect building collapses and suspicious individuals.

[0210] Audio analysis: Detects cries for help and dangerous sounds.

[0211] Identify affected areas and abnormal areas based on location information.

[0212] Set priorities and generate instructions based on the results of AI analysis.

[0213] Examples of concrete examples and prompts

[0214] Example 1: Suspicious person detection

[0215] Drones and fixed cameras collect live footage.

[0216] The AI ​​detects suspicious activity from the video and displays a message on the smart glasses saying, "There is a suspicious person on the north side of the building. Please check."

[0217] Prompt Sentence Examples

[0218] "Please analyze the following data and detect suspicious individuals. Also, please generate appropriate instructions based on the detection results."

[0219] Live video data, audio data, and location information:

[0220] Video Frame: ...

[0221] Audio fragment: ...

[0222] Location Data: ...

[0223] This enables the system to respond quickly and accurately in the event of a disaster or at the scene of a security service.In order to put the present invention into practice, it is important to use the above-mentioned hardware and software in combination.

[0224] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0225] Step 1:

[0226] The device collects video, audio, and location information. Specifically, drones, fixed cameras, and portable cameras capture video of the scene, microphones record audio, and GPS devices acquire location data. The input is the actual video, audio, and location information of the scene, and the output is this digital data.

[0227] Step 2:

[0228] The data collected by the device is sent to a server via satellite or the Internet. At this stage, the communication module on the device is used. The input is the collected digital data of video, audio, and location information, and the output is the data sent to the server.

[0229] Step 3:

[0230] The server receives and stores the video, audio, and location information sent from the device. Specifically, the server's internal storage stores the data. The input is the digital data sent from the device, and the output is the stored data.

[0231] Step 4:

[0232] The server's artificial intelligence (AI) analyzes the received data. At this stage, the AI ​​module analyzes the video using libraries such as OpenCV, and the audio processing algorithm analyzes the audio, which is then combined with location data to detect anomalies. The input is the stored digital data of video, audio, and location information, and the output is the analysis results (for example, detection of a suspicious person or suspicious sound).

[0233] Step 5:

[0234] Based on the analysis results, the server generates specific instructions. As a specific action, the AI ​​generates appropriate instructions based on the analysis results. For example, "There is a suspicious person on the north side of the building. Approach carefully and check." The input is the AI's analysis results, and the output is the generated instructions.

[0235] Step 6:

[0236] The server sends the generated instructions to the smart glasses. The instructions are sent through the communication module. The input is the generated instruction text, and the output is the instruction displayed on the smart glasses.

[0237] Step 7:

[0238] The user (local rescuer or security officer) uses the smart glasses to check instructions and respond at the scene. Specifically, the user checks the scene and takes necessary action based on the instructions displayed on the smart glasses. The input is the instructions displayed on the smart glasses, and the output is specific response activities at the scene and their progress information.

[0239] Step 8:

[0240] The user reports the progress of the response to the server through the smart glasses. At this stage, the report is made using the communication function of the smart glasses. The input is the new on-site information and progress information acquired by the user, and the output is the progress data sent to the server.

[0241] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0242] As a specific embodiment of this invention, we propose a system using a terminal, a server, generative artificial intelligence, smart glasses, and an emotion engine. This system enables rapid and effective rescue operations in the event of a disaster, while optimizing the psychological and emotional state of rescuers.

[0243] System Configuration

[0244] Terminal

[0245] The terminals are devices used to collect video, audio, and location information at disaster sites. Specifically, they include drones, handheld cameras, microphones, and GPS devices. These terminals collect information on the situation in the disaster area in real time and transmit the data to a server via satellite communication.

[0246] Server and Generative Artificial Intelligence (AI)

[0247] The server receives the data sent from the device and analyzes it using generative artificial intelligence (AI). Specifically, the following process is performed:

[0248] 1. The server stores the video, audio, and location information data received from the device.

[0249] 2. AI analyzes the video data and identifies collapsed buildings and abnormal areas.

[0250] 3. Audio analysis detects cries for help and dangerous sounds (e.g., the sound of a collapse).

[0251] 4. Combine location information to determine which areas need urgent assistance.

[0252] Smart Glasses

[0253] The smart glasses used by local rescuers display instructions sent from the server. Specific operating procedures include the following:

[0254] 1. The rescuer puts on the smart glasses and connects them to the system.

[0255] 2. The instructions generated by the server based on the analysis results are displayed on the smart glasses.

[0256] 3. Rescuers should check the displayed instructions, move quickly to the designated location and begin rescue operations.

[0257] 4. Report progress and new information on rescue operations to the server via smart glasses.

[0258] Emotion Engine

[0259] The emotion engine recognizes and analyzes the rescuer's emotions in real time and provides appropriate feedback based on the results. This function allows the rescuer's psychological and emotional state to be monitored, and stress reduction measures and relaxation instructions can be implemented as needed.

[0260] 1. The emotion engine analyzes the facial expressions and voice of rescuers through smart glasses.

[0261] 2. The server receives the data from the emotion engine and evaluates the rescuer's emotional state.

[0262] 3. The server generates additional instructions or feedback (e.g., encouraging messages or suggestions for taking a break) based on the emotional state and sends them to the smart glasses.

[0263] Examples

[0264] Example 1: When a major earthquake occurs

[0265] 1. The drone terminal flies over the disaster area, captures footage, and sends it to the server.

[0266] 2. AI analyzes the video on the server and identifies the collapsed building.

[0267] 3. The server determines priorities and gives top priority to areas with particularly high risk of rescue.

[0268] 4. The server generates specific instructions and sends them to the smart glasses.

[0269] 5. Rescuers follow the instructions on the smart glasses to reach the scene via a safe route and begin rescue operations.

[0270] 6. The emotion engine monitors the rescuer's emotional state and sends encouragement or instructions to relax as needed.

[0271] Example 2: Post-tsunami relief efforts

[0272] 1. Collect information on the flooded area using a mobile camera and GPS device as terminals.

[0273] 2. The server receives the data live and the AI ​​analyzes the extent of the damage.

[0274] 3. Identify high-priority areas, and the server generates rescue instructions based on that information.

[0275] 4. The smart glasses display instructions saying, "The area ahead is heavily flooded. Carry out rescue operations with caution."

[0276] 5. Rescuers confirm instructions and begin rescuing victims from a safe location.

[0277] 6. The emotion engine monitors the rescuer's emotional state and provides psychological support as needed.

[0278] This system allows for a real-time understanding of the situation in disaster areas, enabling rapid and accurate support for relief efforts. Furthermore, the introduction of an emotion engine helps maintain the psychological and emotional health of relief workers, enabling sustainable relief efforts.

[0279] The processing flow will be explained below.

[0280] Step 1:

[0281] The device collects video, audio, and location information. Specifically, the drone flies over the affected area, taking high-resolution video with a camera, recording audio from the scene with a microphone, and obtaining location information with a GPS device.

[0282] Step 2:

[0283] The device transmits the collected video, audio, and location information to a server in real time. This is done using satellite communication, and the communication signal reaches the server via a communications satellite.

[0284] Step 3:

[0285] The server receives the data sent from the device. The server first stores the received video, audio, and location information data and prepares it for subsequent analysis.

[0286] Step 4:

[0287] The server passes the received data to the artificial intelligence (AI) that generates it, which then begins analyzing it. The AI ​​analyzes the video data and identifies collapsed buildings and abnormalities. For example, it marks collapsed buildings with a red frame.

[0288] Step 5:

[0289] The server uses AI to perform audio analysis, detecting cries for help and other important sounds (such as the sound of a collapse) from the audio data, thereby narrowing down the areas where victims may be present.

[0290] Step 6:

[0291] The server analyzes the location data and combines it with the results of video and audio analysis to determine the level of danger and urgency in a specific area and set rescue priorities.

[0292] Step 7:

[0293] The server generates specific instructions based on the analysis results, such as "Area A has many collapsed buildings. Urgency is high. Area B is flooded. Caution."

[0294] Step 8:

[0295] The server sends the generated instructions to the local rescuer's smart glasses. The data packet reaches the rescuer's smart glasses via the communication path.

[0296] Step 9:

[0297] The user checks the instructions on the smart glasses, and the rescuer reads the instructions displayed on the smart glasses and understands the necessary actions.

[0298] Step 10:

[0299] The user follows the instructions of the smart glasses to carry out rescue operations, specifically by searching for and rescuing victims while following a safe route and beginning rescue operations.

[0300] Step 11:

[0301] The user reports the progress of the rescue operation to the server. For example, a report such as "5 victims have been rescued in Area A" is sent to the server through the smart glasses.

[0302] Step 12:

[0303] The emotion engine analyzes the facial expressions and voice of the rescuer through the smart glasses. Facial expression analysis uses facial recognition technology, and voice analysis uses emotion recognition algorithms.

[0304] Step 13:

[0305] The server receives the data from the emotion engine and evaluates the rescuer's emotional state, generating an evaluation result such as "high fatigue" or "stressed."

[0306] Step 14:

[0307] The server generates additional instructions and feedback based on the user's emotional state and sends them to the smart glasses, such as messages like "We recommend you take a break" or "Please relax."

[0308] Step 15:

[0309] The user sees additional feedback through the smart glasses, and the rescuer receives feedback based on their emotional state, allowing them to take a break or relax if necessary.

[0310] This series of steps enables real-time information collection, analysis, instruction generation, and emotion monitoring, supporting rapid and accurate rescue operations while maintaining the psychological and emotional well-being of rescuers.

[0311] Example 2

[0312] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0313] To carry out rapid and effective relief efforts during disasters, it is necessary to grasp the situation in the affected area in real time and make quick decisions. However, rescuers on the ground work under high-stress conditions, so their mental and emotional health must also be maintained. Currently, there is a lack of systems that can simultaneously meet these complex demands.

[0314] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0315] In this invention, the server includes: a terminal collecting video, audio, and location information; a server receiving the video, audio, and location information from the terminal; a server analyzing the video, audio, and location information using artificial intelligence; a server prioritizing rescue operations and generating specific instructions based on the analysis results; a server transmitting the specific instructions to a visual display device of a local rescuer; a server allowing the local rescuer to check the instructions on the visual display device and perform rescue operations; and a server recognizing and analyzing the emotional state of the rescuer in real time and providing feedback. This enables rapid and effective rescue operations in the event of a disaster and also maintains the mental and emotional health of the rescuer.

[0316] "Terminal" means equipment for collecting video, audio, and location information, including drones, handheld cameras, microphones, and GPS devices.

[0317] A "server" is a central control device that receives and analyzes data sent from the terminals, and generates and sends instructions and feedback.

[0318] "Footage" refers to visual information obtained from devices such as cameras and drones, and records the situation and environment of the disaster-stricken area.

[0319] "Audio" refers to auditory information acquired from a terminal such as a microphone, and records the voices of rescuers and environmental sounds at the scene.

[0320] "Location information" refers to geographic coordinate information obtained from a terminal such as a GPS device, and identifies the location and movement of a target.

[0321] "Generative artificial intelligence (AI)" refers to algorithms or models that analyze data received by a server and identify specific patterns or anomalies.

[0322] "Analysis" is the process of extracting specific information and patterns from received data to produce meaningful results.

[0323] "Priority" is a criterion for evaluating the urgency and importance of a response in relief operations, and determines which areas and items will be given priority.

[0324] "Instructions" are specific actions or responses generated by the server, providing guidelines or procedures for on-site rescuers to follow.

[0325] A "visual display device" is a device worn by a rescuer to visually confirm instructions and feedback from the server, and includes smart glasses and the like.

[0326] "Emotional state" refers to the mental and emotional well-being of field rescuers, including psychological changes such as stress and fatigue.

[0327] "Feedback" refers to advice and instructions provided by the server based on the rescuer's emotional state, with the aim of providing emotional support and correcting behavior.

[0328] MODE FOR CARRYING OUT THE INVENTION

[0329] As a specific embodiment of this invention, we propose a system using a terminal, a server, a generative AI model, a visual display device, and an emotion engine. This system enables rapid and effective rescue operations in the event of a disaster, while optimizing the psychological and emotional state of rescuers.

[0330] System Configuration

[0331] Terminal

[0332] The terminals are devices used to collect video, audio, and location information at disaster sites. Specifically, they include drones, handheld cameras, microphones, and GPS devices. These terminals collect information on the situation in the disaster area in real time and transmit the data to a server via satellite communication.

[0333] Server and Generative Artificial Intelligence (AI)

[0334] The server receives the data sent from the device and analyzes it using a generative AI model. The specific processing is as follows: The server stores the video, audio, and location information data received from the device, and the AI ​​analyzes the video data to identify collapsed buildings and abnormal areas. It also uses audio analysis to detect cries for help and dangerous sounds (such as the sound of a collapse). It also combines this with location information to determine which areas require emergency assistance.

[0335] Visual Display Devices

[0336] The visual display device used by the local rescuer displays the instructions sent from the server. Specifically, when the rescuer wears the visual display device and connects it to the system, instructions generated by the server based on the analysis results are displayed on the display. The rescuer checks the displayed instructions, quickly moves to the designated location, and begins rescue operations. The rescuer also reports the progress of rescue operations and new information to the server via the visual display device.

[0337] Emotion Engine

[0338] The emotion engine recognizes and analyzes the rescuer's emotions in real time and provides appropriate feedback based on the results. This function allows the rescuer's psychological and emotional state to be monitored and stress reduction measures or relaxation instructions to be implemented as needed. Specifically, the emotion engine analyzes the rescuer's facial expressions and voice via a visual display device. The server receives data from the emotion engine and evaluates the rescuer's emotional state. Furthermore, the server generates additional instructions or feedback (e.g., encouraging messages or suggestions for taking a break) based on the emotional state and sends them to the visual display device.

[0339] Examples

[0340] Example 1: When a major earthquake occurs

[0341] The drone terminal flies over the disaster area, taking footage and sending it to a server. AI on the server analyzes the footage and identifies collapsed buildings. The server then determines priorities and prioritizes areas with particularly high risk for rescue. The server then generates specific instructions and sends them to a visual display device. Rescuers follow the instructions to reach the scene via a safe route and begin rescue operations. The emotion engine monitors the emotional state of the rescuers and sends encouragement or instructions to relax as needed.

[0342] Example prompt sentence:

[0343] A major earthquake occurs. Check the situation in the affected areas. Analyze the footage taken by the drone and identify the collapsed buildings.

[0344] Example 2: Post-tsunami relief efforts

[0345] The terminal uses a portable camera and GPS device to collect information on the status of the flooded area. The server receives the data, and AI analyzes the extent of the damage. High-priority areas are identified, and the server generates rescue instructions based on that information. A visual display device displays the instruction, "The area ahead is heavily flooded. Carry out rescue operations with caution." Rescuers confirm the instructions and rescue victims from a safe location. An emotion engine monitors the emotional state of rescuers and provides psychological support as needed.

[0346] Example prompt sentence:

[0347] After a tsunami occurs, collect information about the flooded area. Use handheld cameras and GPS devices to analyze the extent of the flooding.

[0348] This invention allows us to grasp the situation in disaster areas in real time and support rapid and accurate relief efforts. Furthermore, the introduction of an emotion engine will help maintain the psychological and emotional health of rescuers, enabling sustainable relief efforts.

[0349] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0350] System program processing flow and specific procedures

[0351] Step 1: The device collects data and sends it to the server

[0352] The device arrives at the disaster site and collects video, audio, and location information. The input is video, audio, and location data obtained from cameras, microphones, and GPS devices. The device collects this data and transmits it to a server via satellite communication. For example, a drone could fly over a disaster area, capture high-resolution video, and upload the data to a server in real time.

[0353] Step 2: The server receives and stores the data

[0354] The server receives video, audio, and location information sent from the device. The input is the data sent from the device, and the server stores it in a database in a specified format. For example, specific operations include storing video data in video storage, audio data in audio storage, and location information in a geospatial database. The output is the stored data.

[0355] Step 3: The server passes the data to the AI ​​and requests it for analysis.

[0356] The server passes the stored data to a generative artificial intelligence (AI) model and requests it to analyze it. The input is the video, audio, and location information stored in the database, and the server provides this data to the AI. The AI ​​identifies collapsed buildings and abnormalities from the video, and detects calls for help and dangerous sounds from the audio. For example, a specific operation would be for the AI ​​to analyze a frame of video and label the location of a collapsed building. The output is the analysis results.

[0357] Step 4: The server generates instructions based on the analysis results.

[0358] Based on the results of the AI ​​analysis, the server determines which areas require emergency response. The input is the AI ​​analysis results, and the server generates specific instructions based on the results of the judgment. By inputting instructions into the generative AI model, specific instructions in natural language are generated. For example, generating an instruction such as "A collapsed building has been found in this area, so carry out emergency rescue operations" is a specific action. The output is the generated instructions.

[0359] Step 5: The server sends instructions to the smart glasses

[0360] The server sends the generated instructions to the visual display device (smart glasses) of the local rescuer. The input is the generated instructions, which the server sends to the visual display device. For example, the specific action is to display "Head to the intersection of 5th Street" on the smart glasses display and hear the same instruction aloud. The output is the instructions received by the rescuer.

[0361] Step 6: The user acts according to the instructions of the smart glasses.

[0362] The user acts according to the instructions displayed on the smart glasses. The input is the instructions displayed on the smart glasses, and the user performs activities in the disaster area based on the instructions. For example, specific actions include passing through the disaster area following the displayed route and starting rescue operations around collapsed buildings. The output is the progress of the rescue operations.

[0363] Step 7: The server monitors the user's emotional state through the emotion engine and sends feedback.

[0364] The server uses an emotion engine to analyze the user's emotional state in real time. The input is facial expression and voice data obtained from the smart glasses' camera and microphone, which the server analyzes to evaluate the user's emotional state. If necessary, feedback is generated and sent to the smart glasses. For example, if the server detects that the rescuer is very tired, it could send a message to the smart glasses saying, "Please take a 10-minute break." The output is feedback provided to the rescuer.

[0365] (Application example 2)

[0366] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0367] A system that can provide quick and accurate instructions is needed for on-site relief efforts. However, current systems make it difficult for relief workers to understand the optimal route and actions in real time, which places a heavy psychological burden on them. Similarly, in delivery services, there is a lack of systems that allow delivery workers to understand the optimal route in real time and improve delivery efficiency, as well as the psychological burden on delivery workers.

[0368] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: a means for the terminal to collect video, audio, and location information; a means for the server to receive video, audio, and location information from the terminal; a means for the server to analyze the video, audio, and location information using artificial intelligence; a means for the server to prioritize rescue operations based on the analysis results and generate specific instructions; a means for the server to send the specific instructions to the smart glasses of the local rescuer; a means for the local rescuer to check the instructions on the smart glasses and perform the rescue operation; a means for the server to generate an optimized delivery route and send the instructions to the smart glasses; and a means for using an emotion engine to analyze the emotional state of the delivery person and generate appropriate feedback. This enables efficient real-time support in rescue operations and delivery services with reduced psychological burden.

[0369] A "terminal" is a device for collecting video, audio, and location information.

[0370] A "server" is a computer system that analyzes video, audio, and location information received from a terminal and generates instructions and feedback.

[0371] "Generative AI" is an AI that has the ability to analyze collected data and generate instructions regarding disasters and deliveries.

[0372] "Smart glasses" are wearable devices worn by users that provide visual instructions and feedback in real time.

[0373] "Analysis results" are the results obtained by analyzing the data collected by the server and generating AI.

[0374] "Instructions" are specific guidelines for action or instructions generated based on the analysis results.

[0375] "Delivery route optimization" is the process of calculating the optimal route for a delivery person to reach their destination efficiently.

[0376] The "emotion engine" is a system that analyzes the emotional state of delivery personnel and rescuers and provides feedback based on the results.

[0377] "Feedback" is a message of advice or support to the user that is generated based on information analyzed by the emotion engine.

[0378] As an embodiment of this invention, we propose a system that combines a terminal, a server, smart glasses, an emotion engine, and generative artificial intelligence (AI). This system improves delivery efficiency and optimizes the emotional state of delivery personnel.

[0379] System Configuration

[0380] 1. Terminal

[0381] The terminal is a device for collecting video, audio, and location information. Specifically, it includes smartphones and tablets. These terminals are carried by delivery personnel, collect information on the situation during delivery, and send the data to a server.

[0382] 2. Server and Generative Artificial Intelligence (AI)

[0383] The server receives the data sent from the terminal and analyzes it using generative artificial intelligence (AI). The specific processing steps are as follows:

[0384] The server stores the video, audio, and location information data received from the terminal.

[0385] AI analyzes video data to identify delivery progress and obstacles along the way (e.g., using OpenCV for video analysis).

[0386] For voice analysis, Google (registered trademark) Cloud Speech-to-Text API is used, converting voice data into text and analyzing it with an emotion engine.

[0387] Analyzes GPS location information and optimizes delivery routes.

[0388] 3. Smart Glasses

[0389] The smart glasses used by the delivery person display instructions sent from the server, and perform the following operations depending on the situation during the delivery:

[0390] The delivery person wears smart glasses and connects them to the system.

[0391] Instructions generated by the server based on the analysis results are displayed on the smart glasses.

[0392] The delivery person checks the displayed instructions and proceeds along the optimal route.

[0393] 4. Emotion Engine

[0394] The emotion engine recognizes and analyzes the delivery person's emotions in real time and provides appropriate feedback based on the results. This function monitors the delivery person's psychological and emotional state and provides stress reduction measures and relaxation instructions as needed.

[0395] The emotion engine analyzes the delivery person's facial expressions and voice through smart glasses (e.g., using Amazon Rekognition to perform facial expression analysis).

[0396] A server receives the data from the emotion engine and evaluates the emotional state of the delivery person.

[0397] The server generates additional instructions or feedback (e.g., encouraging messages or suggestions for taking a break) based on the emotional state and sends them to the smart glasses.

[0398] Examples

[0399] Example 1: Standard delivery

[0400] 1. The delivery person wears smart glasses and the device collects location information.

[0401] 2. AI analyzes the video on the server and determines the progress of the delivery.

[0402] 3. The server determines the priority and generates the optimal delivery route.

[0403] 4. The server generates specific instructions and sends them to the smart glasses.

[0404] 5. The delivery person follows the instructions on the smart glasses to complete the delivery via a safe route.

[0405] 6. The emotion engine monitors the delivery person's emotional state and sends encouragement or instructions to relax as needed.

[0406] Example 2: Managing stress during delivery

[0407] 1. In congested roads, a delivery person puts on smart glasses and begins their delivery.

[0408] 2. The server receives the location information and video data and analyzes the delivery route.

[0409] 3. The emotion engine analyzes the delivery person's facial expression data to detect signs of stress.

[0410] 4. The server generates an encouraging message such as "Take a deep breath and relax" and sends it to the smart glasses.

[0411] 5. The delivery person follows instructions, relaxes to reduce stress, and continues with the delivery.

[0412] Prompt Sentence Examples

[0413] "If a user is driving with a grumpy expression, generate a message suggesting they relax."

[0414] "When a delivery person is nearing their destination, display a message informing them of their progress."

[0415] These procedures can provide a system that improves delivery efficiency and provides psychological support to delivery personnel.

[0416] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0417] Step 1:

[0418] The device (smartphone or tablet) collects video, audio, and location information. The user (delivery person) turns on the device, takes video of the delivery with the camera, records audio with the microphone, and obtains location information via GPS. This data is sent to the server. The input is video, audio, and GPS data, and the output is a data package of these.

[0419] Step 2:

[0420] The server receives video, audio, and location information from the device. The server temporarily stores the received data and checks whether the data was received correctly. The input is the data package sent from the device, and the output is the data stored in the server.

[0421] Step 3:

[0422] The server uses generative artificial intelligence (AI) to analyze video, audio, and location information. The server uses OpenCV to analyze the video data and identify the delivery progress and obstacles along the way. Next, it converts the audio data into text using Google Cloud Speech-to-Text, which is then analyzed using an emotion engine. It also analyzes GPS data and calculates the delivery route. The inputs are video, audio, and GPS data, and the output is the analysis results, including progress and optimal route information.

[0423] Step 4:

[0424] The server generates an optimized delivery route based on the analysis results and generates specific instructions.The server generates an optimized delivery route based on the analysis results and generates specific instructions along that route (e.g., "turn right at the next intersection").The input is the analysis results, and the output is the optimized delivery route and specific instructions.

[0425] Step 5:

[0426] The server sends the specific instructions to the local rescuer's smart glasses. The server then sends the generated instructions to the delivery person's smart glasses in real time. The input is the specific instructions, and the output is the instructions displayed on the smart glasses.

[0427] Step 6:

[0428] The local rescuer checks the instructions on the smart glasses and carries out the delivery. The user (delivery person) checks the instructions displayed on the smart glasses and follows them to make the delivery. The input is the instructions displayed on the smart glasses, and the output is the progress of the delivery work.

[0429] Step 7:

[0430] The server monitors the emotional state of the delivery person while they are making a delivery and generates appropriate feedback using an emotion engine. The server analyzes the delivery person's facial expression data acquired by the smart glasses using Amazon Rekognition and their voice data using the emotion engine. The input is facial expression data and voice data, and the output is an appropriate feedback message (e.g., "Take a deep breath and relax").

[0431] Step 8:

[0432] The server sends the generated feedback to the smart glasses, which the delivery person receives. The server also sends the generated feedback message to the smart glasses, which the delivery person receives and acts upon. The input is the feedback message, and the output is the action taken by the delivery person (relax, take a break, etc.).

[0433] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0434] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0435] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0436] [Second embodiment]

[0437] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0438] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0439] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0440] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0441] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0442] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0443] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0444] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0445] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0446] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0447] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0448] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0449] As a specific embodiment of this invention, we propose a system using a terminal, a server, generative artificial intelligence, and smart glasses. This system will enable rapid and effective rescue operations in the event of a disaster.

[0450] System Configuration

[0451] Terminal

[0452] The terminals are devices used to collect video, audio, and location information at disaster sites. Specifically, they include drones, handheld cameras, microphones, and GPS devices. These terminals collect information on the situation in the disaster area in real time and transmit the data to a server via satellite communication.

[0453] Server and Generative Artificial Intelligence (AI)

[0454] The server receives the data sent from the device and analyzes it using generative artificial intelligence (AI). Specifically, the following process is performed:

[0455] 1. The server stores the video, audio, and location information received from the device.

[0456] 2. AI analyzes the footage and identifies the locations of collapsed buildings and victims.

[0457] 3. Audio analysis detects cries for help and dangerous sounds (e.g., the sound of a collapse).

[0458] 4. Combine location information to determine which areas need urgent assistance.

[0459] Smart Glasses

[0460] The smart glasses used by local rescuers display instructions sent from the server. Specific operating procedures include the following:

[0461] 1. The rescuer puts on the smart glasses and connects them to the system.

[0462] 2. The instructions generated by the server based on the analysis results are displayed on the smart glasses.

[0463] 3. Rescuers should check the displayed instructions, move quickly to the designated location and begin rescue operations.

[0464] 4. Report progress and new information on rescue operations to the server via smart glasses.

[0465] Examples

[0466] Example 1: When a major earthquake occurs

[0467] 1. The drone terminal flies over the disaster area, captures footage, and sends it to the server.

[0468] 2. AI analyzes the video on the server and identifies the collapsed building.

[0469] 3. The server determines priorities and gives top priority to areas with particularly high risk of rescue.

[0470] 4. The server generates specific instructions and sends them to the smart glasses.

[0471] 5. Rescuers follow the instructions on the smart glasses to reach the scene via a safe route and begin rescue operations.

[0472] Example 2: Post-tsunami relief efforts

[0473] 1. Collect information on the flooded area using a mobile camera and GPS device as terminals.

[0474] 2. The server receives the data live and the AI ​​analyzes the extent of the damage.

[0475] 3. Identify high-priority areas, and the server generates rescue instructions based on that information.

[0476] 4. The smart glasses display instructions saying, "The area ahead is heavily flooded. Carry out rescue operations with caution."

[0477] 5. Rescuers confirm instructions and begin rescuing victims from a safe location.

[0478] This system makes it possible to grasp the situation in disaster areas in real time and support rapid and accurate relief efforts. Analysis by generative AI and display of instructions by smart glasses can help local rescuers act quickly and effectively.

[0479] The processing flow will be explained below.

[0480] Step 1:

[0481] The device collects video, audio, and location information. Specifically, the drone flies over the affected area, taking high-resolution video with a camera, recording audio from the scene with a microphone, and obtaining location information with a GPS device.

[0482] Step 2:

[0483] The device transmits the collected video, audio, and location information to a server in real time. This is done using satellite communication, and the communication signal reaches the server via a communications satellite.

[0484] Step 3:

[0485] The server receives the data sent from the device. The server first stores the received video, audio, and location information data and prepares it for subsequent analysis.

[0486] Step 4:

[0487] The server passes the received data to the artificial intelligence (AI) that generates it, which then begins analyzing it. The AI ​​first analyzes the video data and identifies collapsed buildings and abnormalities. For example, it marks collapsed buildings with a red frame.

[0488] Step 5:

[0489] The server uses AI to perform audio analysis, detecting cries for help and other important sounds (such as the sound of a collapse) from the audio data, thereby narrowing down the areas where victims may be present.

[0490] Step 6:

[0491] The server analyzes the location data and combines it with the results of video and audio analysis to determine the level of danger and urgency in a specific area and set rescue priorities.

[0492] Step 7:

[0493] The server generates specific instructions based on the analysis results, such as "Area A has many collapsed buildings. Urgency is high. Area B is flooded. Caution."

[0494] Step 8:

[0495] The server sends the generated instructions to the local rescuer's smart glasses. The data packet reaches the rescuer's smart glasses via the communication path.

[0496] Step 9:

[0497] The user checks the instructions on the smart glasses, and the rescuer reads the instructions displayed on the smart glasses and understands the necessary actions.

[0498] Step 10:

[0499] The user follows the instructions of the smart glasses to carry out rescue operations, specifically by searching for and rescuing victims while following a safe route and beginning rescue operations.

[0500] Step 11:

[0501] The user reports the progress of the rescue operation to the server. For example, a report such as "5 victims have been rescued in Area A" is sent to the server through the smart glasses.

[0502] This series of processing steps creates a system that can smoothly collect information, analyze it, generate instructions, and carry out rescue operations in real time.

[0503] Example 1

[0504] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0505] When a disaster occurs, there are often delays in quickly and accurately grasping the situation on the ground and issuing instructions for effective relief activities. As a result, the efficiency of relief activities declines and the rescue of victims is delayed. In addition, the number of information devices carried by local rescuers is limited, so there is a lack of means to quickly receive appropriate instructions.

[0506] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0507] In this invention, the server includes: a terminal for collecting video, audio, and location information; a server for receiving the video, audio, and location information from the terminal; a server for analyzing the video, audio, and location information using a generating artificial intelligence; a server for prioritizing rescue operations based on the analysis results and generating specific instructions; a generating artificial intelligence for analyzing the video to identify the locations of collapsed structures and victims; a generating artificial intelligence for analyzing the audio to detect calls for help and dangerous sounds; a server for combining the video, audio, and location information to determine areas requiring emergency support; a server for transmitting the specific instructions to smart glasses worn by local rescuers; and a server for the local rescuers to check the instructions on the smart glasses and carry out rescue operations. This enables real-time situation assessment of the disaster site and rapid and accurate rescue operation instructions.

[0508] "Terminals" are devices that collect video, audio, and location information at disaster sites, including drones, handheld cameras, microphones, and GPS devices.

[0509] A "server" is a computer system that receives data sent from a terminal, analyzes it, and generates instructions.

[0510] "Video" refers to data that represents visual information from the disaster site. This includes videos and images taken using drones or handheld cameras.

[0511] "Audio" refers to data representing sound information from disaster sites, including calls for help and dangerous sounds collected using microphones.

[0512] "Location information" is data that indicates the latitude and longitude of specific points in a disaster site. It is collected using GPS devices.

[0513] "Generative artificial intelligence" is a machine learning model or algorithm that analyzes collected data and extracts the necessary information.

[0514] "Priority of rescue operations" is a criterion for determining the urgency based on the analysis results and the order in which rescue operations should be carried out.

[0515] "Specific instructions" are detailed instructions such as specific actions rescuers should take on site and routes to take.

[0516] "Smart glasses" are wearable devices worn by local rescuers that display instructions from a server.

[0517] "Analyzing the video" means analyzing the collected video data and identifying the locations of collapsed structures and victims.

[0518] "Analyzing voice" means analyzing collected voice data to detect cries for help or dangerous sounds.

[0519] "Determining areas needing emergency assistance" means combining video, audio, and location information to identify which areas are most in need of assistance.

[0520] "Satellite communications" is a communication method for sending and receiving data via artificial satellites.

[0521] As a specific embodiment of this invention, we propose a system using a terminal, a server, generative artificial intelligence (AI), and smart glasses, which will enable rapid and effective rescue operations in the event of a disaster.

[0522] Terminal

[0523] The terminals are devices used to collect video, audio, and location information at disaster sites. Specifically, they include drones, handheld cameras, microphones, and GPS devices. These terminals collect information on the situation in the disaster area in real time and transmit the data to a server via satellite communication.

[0524] Server and Generative Artificial Intelligence (AI)

[0525] The server receives data sent from the device and analyzes it using artificial intelligence (AI). Specifically, the following process is performed:

[0526] 1. Data Receipt and Storage

[0527] The server receives and stores the video, audio, and location information sent from the device.

[0528] 2. Video Analysis

[0529] AI analyzes the video and identifies the locations of collapsed structures and victims. For example, AI uses pattern recognition technology in the video to detect collapsed buildings.

[0530] 3. Audio Analysis

[0531] For voice data, AI detects dangerous sounds such as cries for help or the sound of a collapse. Speech recognition technology extracts specific voice patterns and detects information with a high level of urgency.

[0532] 4. Location information analysis

[0533] By analyzing location information and plotting the damage situation on a map, it is possible to identify which areas require emergency assistance.

[0534] Smart Glasses

[0535] The smart glasses used by local rescuers display instructions sent from the server, including the following specific operating procedures:

[0536] 1. System Connection

[0537] The user (local rescuer) wears the smart glasses and connects them to the system.

[0538] 2. Instruction display

[0539] The server generates instructions based on the analysis results and displays them on the smart glasses, allowing rescuers to instantly understand areas with high emergency levels and safe routes to take.

[0540] 3. Relief operations

[0541] Rescuers follow the displayed instructions to quickly move to the designated location and begin rescue operations. The smart glasses also have the function of reporting the progress of rescue operations and new information to the server.

[0542] Examples

[0543] Example 1: When a major earthquake occurs

[0544] The drone terminal flies over the disaster area, taking pictures and sending them to a server. AI on the server analyzes the pictures and identifies collapsed buildings. The server then determines priorities and prioritizes areas with particularly high risk for rescue. The server then sends instructions generated by the server to the smart glasses. Rescuers follow the instructions on the smart glasses to reach the scene via a safe route and begin rescue operations.

[0545] Example 2: Post-tsunami relief efforts

[0546] The device uses a portable camera and GPS device to collect information on the status of the flooded area. The server receives the data, and AI analyzes the extent of the damage. The server identifies high-priority areas and generates rescue instructions based on that information. The smart glasses display the following instruction: "The area ahead is heavily flooded. Carry out rescue operations with caution." Rescuers confirm the instructions and rescue victims from a safe location.

[0547] Prompt Sentence Examples

[0548] Possible prompts for the generative AI model include:

[0549] Prompt statement example 1:

[0550] Analyze the disaster situation and determine the priority of relief efforts.

[0551] Prompt statement example 2:

[0552] Use video data to identify the locations of collapsed buildings and victims, and quickly generate rescue instructions.

[0553] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0554] Program processing steps

[0555] Step 1:

[0556] The terminal collects video, audio, and location information and sends it to a server. Specifically, drones, portable cameras, microphones, and GPS devices are used to collect information on the situation in the disaster area in real time. The collected data is sent to the server via satellite communication. The input is video, audio, and location information obtained from the disaster site, and the output is the data sent to the server. As an example of how it works, a drone flies over a wide area of ​​the disaster area and collects video and location information in real time.

[0557] Step 2:

[0558] The server stores the data received from the device. The input is the video, audio, and location information sent from the device, and the output is data stored in a database on the server. For example, the server uses a secure communication protocol to safely store the received data and prepare it for subsequent analysis.

[0559] Step 3:

[0560] The server analyzes the video using generative artificial intelligence (AI). The input is the stored video data, and the output is the location information of collapsed structures and victims. Specifically, the AI ​​analyzes characteristic patterns in the video and identifies the location of collapsed buildings. For example, it identifies collapsed buildings by detecting abnormal shapes or color changes.

[0561] Step 4:

[0562] The server uses AI to analyze the voice. The input is the stored voice data, and the output is the detection results of calls for help and dangerous sounds. Specifically, it uses voice recognition technology to extract specific phrases and sound patterns and identify information with a high level of urgency. For example, it identifies the sound of a collapse or a call for help.

[0563] Step 5:

[0564] The server analyzes the location information and identifies areas requiring emergency assistance. The input is the stored location data, and the output is a list of identified high-priority areas for assistance. Specifically, the server plots the location information on a map and determines priorities according to the level of damage. For example, it compares GPS data with map information to mark high-risk areas.

[0565] Step 6:

[0566] The server generates instructions for rescue operations based on the analysis results. The inputs are the video analysis results, audio analysis results, and location information analysis results, and the output is specific rescue instructions. In concrete terms, the server inputs prompts into the generative AI model to generate rescue instructions. For example, it gives the AI ​​a prompt such as "Plan a route for the rescue team based on the disaster situation."

[0567] Step 7:

[0568] The server sends the generated instructions to the smart glasses. The input is the generated rescue instructions, and the output is the instructions displayed on the smart glasses. Specifically, the server sends instruction data to the smart glasses via wireless communication. For example, it sends an instruction such as "There is a victim 200 meters northeast. Carry out rescue using a safe route."

[0569] Step 8:

[0570] The user uses smart glasses to carry out rescue operations. The input is instructions displayed on the smart glasses, and the output is a progress report of the rescue operations. Specifically, the user follows the instructions to move to the disaster area and carry out the rescue operations. For example, the user follows the instructions on the smart glasses to go to the designated location and rescue the victims.

[0571] This step-by-step process will enable rapid and accurate relief efforts in the event of a disaster.

[0572] (Application example 1)

[0573] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0574] Conventional rescue operation systems have had difficulty quickly and accurately grasping the situation at a disaster site and providing appropriate instructions to local rescuers. Furthermore, systems for detecting abnormalities in real time and responding immediately have been inadequate for security services. Therefore, there is a demand for effective support in rescue operations and security monitoring. The purpose of this invention is to solve these problems and realize an efficient and effective support system.

[0575] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0576] In this invention, the server includes: a means for a terminal to collect video, audio, and location information; a means for the server to receive the video, audio, and location information from the terminal; a means for the server to analyze the video, audio, and location information using artificial intelligence; a means for the server to prioritize rescue operations based on the analysis results and generate specific instructions; a means for the server to transmit the specific instructions to the smart glasses of local rescuers; a means for the local rescuers to check the instructions on the smart glasses and perform rescue operations; and a means for detecting abnormalities and checking the situation in real time through the smart glasses worn by security personnel. This enables not only fast and accurate rescue operations in the event of a disaster, but also real-time detection of abnormalities in security services and immediate response.

[0577] A "terminal" is a device for collecting video, audio, and location information.

[0578] A "server" is a computer system that receives video, audio, and location information from a terminal and analyzes and stores that data.

[0579] "Footage" refers to visual information collected through equipment such as drones and fixed cameras.

[0580] "Audio" is information about sounds collected through a device such as a microphone.

[0581] "Location information" is data that indicates a geographic location, such as that collected through a GPS device.

[0582] "Generative artificial intelligence (AI)" is a general term for algorithms and technologies that analyze collected video, audio, and location information and generate judgments and instructions based on the results.

[0583] "Relief operations" are a series of actions and measures taken to support victims of a disaster.

[0584] "Smart glasses" are wearable devices that have the ability to display digital information, and are worn by local rescuers and security personnel to check instructions from the server.

[0585] A "security officer" is a person whose job is to detect anomalies and check the situation.

[0586] "Real-time" refers to immediate processing or information transmission with little or no delay.

[0587] "Anomaly detection" is the process of identifying events that deviate from normal conditions or behavior.

[0588] "Situation check" refers to understanding the current state and progress of activities based on collected information.

[0589] System Program

[0590] This system includes a program consisting of the following elements:

[0591] 1. Device (collection of video, audio, and location information):

[0592] It uses hardware such as drones, fixed cameras, handheld cameras, microphones, and GPS devices.

[0593] These devices are responsible for collecting video, audio, and location information in real time.

[0594] 2. Data transmission:

[0595] The data collected by the terminal is sent to a server via satellite communication or the Internet.

[0596] A communication module is used for this process.

[0597] 3. Server (receiving and analyzing data):

[0598] The server receives and stores the video, audio, and location information transmitted from the terminal.

[0599] Generative artificial intelligence (AI) within the server analyzes the data using OpenCV and other AI modules.

[0600] Specifically, AI detects suspicious people, suspicious sounds, and unnatural movements of people and objects.

[0601] 4. Instruction generation and transmission:

[0602] The server generates specific instructions based on the results of the AI ​​analysis and sends them to the smart glasses.

[0603] For example, an instruction such as "There is a suspicious person on the north side of the building. Approach carefully and check." may be generated.

[0604] 5. Smart Glasses (Local Support and Instructions):

[0605] Local rescuers and security personnel wear smart glasses and check instructions from the server.

[0606] Real-time feedback is possible by reporting the progress of on-site response and new information to the server via smart glasses.

[0607] Natural language explanations

[0608] 1. Hardware:

[0609] Drones, fixed cameras and handheld cameras will be used to capture footage.

[0610] A microphone is used to capture audio, and a GPS device is used to capture location information.

[0611] Smart glasses will be used to display real-time instructions and assist with on-site operations.

[0612] 2. Software:

[0613] OpenCV: Used for video data acquisition and initial analysis.

[0614] AI analyzer: Performs detailed analysis of video, audio, and location information and detects anomalies.

[0615] Communication module: Sends data from the device to the server and sends instructions from the server to the smart glasses.

[0616] 3. Data processing and calculation:

[0617] After the data is sent to the server, a generative artificial intelligence (AI) analyzes it.

[0618] Video analysis: Detect building collapses and suspicious individuals.

[0619] Audio analysis: Detects cries for help and dangerous sounds.

[0620] Identify affected areas and abnormal areas based on location information.

[0621] Set priorities and generate instructions based on the results of AI analysis.

[0622] Examples of concrete examples and prompts

[0623] Example 1: Suspicious person detection

[0624] Drones and fixed cameras collect live footage.

[0625] The AI ​​detects suspicious activity from the video and displays a message on the smart glasses saying, "There is a suspicious person on the north side of the building. Please check."

[0626] Prompt Sentence Examples

[0627] "Please analyze the following data and detect suspicious individuals. Also, please generate appropriate instructions based on the detection results."

[0628] Live video data, audio data, and location information:

[0629] Video Frame: ...

[0630] Audio fragment: ...

[0631] Location Data: ...

[0632] This enables the system to respond quickly and accurately in the event of a disaster or at the scene of a security service.In order to put the present invention into practice, it is important to use the above-mentioned hardware and software in combination.

[0633] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0634] Step 1:

[0635] The device collects video, audio, and location information. Specifically, drones, fixed cameras, and portable cameras capture video of the scene, microphones record audio, and GPS devices acquire location data. The input is the actual video, audio, and location information of the scene, and the output is this digital data.

[0636] Step 2:

[0637] The data collected by the device is sent to a server via satellite or the Internet. At this stage, the communication module on the device is used. The input is the collected digital data of video, audio, and location information, and the output is the data sent to the server.

[0638] Step 3:

[0639] The server receives and stores the video, audio, and location information sent from the device. Specifically, the server's internal storage stores the data. The input is the digital data sent from the device, and the output is the stored data.

[0640] Step 4:

[0641] The server's artificial intelligence (AI) analyzes the received data. At this stage, the AI ​​module analyzes the video using libraries such as OpenCV, and the audio processing algorithm analyzes the audio, which is then combined with location data to detect anomalies. The input is the stored digital data of video, audio, and location information, and the output is the analysis results (for example, detection of a suspicious person or suspicious sound).

[0642] Step 5:

[0643] Based on the analysis results, the server generates specific instructions. As a specific action, the AI ​​generates appropriate instructions based on the analysis results. For example, "There is a suspicious person on the north side of the building. Approach carefully and check." The input is the AI's analysis results, and the output is the generated instructions.

[0644] Step 6:

[0645] The server sends the generated instructions to the smart glasses. The instructions are sent through the communication module. The input is the generated instruction text, and the output is the instruction displayed on the smart glasses.

[0646] Step 7:

[0647] The user (local rescuer or security officer) uses the smart glasses to check instructions and respond at the scene. Specifically, the user checks the scene and takes necessary action based on the instructions displayed on the smart glasses. The input is the instructions displayed on the smart glasses, and the output is specific response activities at the scene and their progress information.

[0648] Step 8:

[0649] The user reports the progress of the response to the server through the smart glasses. At this stage, the report is made using the communication function of the smart glasses. The input is the new on-site information and progress information acquired by the user, and the output is the progress data sent to the server.

[0650] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0651] As a specific embodiment of this invention, we propose a system using a terminal, a server, generative artificial intelligence, smart glasses, and an emotion engine. This system enables rapid and effective rescue operations in the event of a disaster, while optimizing the psychological and emotional state of rescuers.

[0652] System Configuration

[0653] Terminal

[0654] The terminals are devices used to collect video, audio, and location information at disaster sites. Specifically, they include drones, handheld cameras, microphones, and GPS devices. These terminals collect information on the situation in the disaster area in real time and transmit the data to a server via satellite communication.

[0655] Server and Generative Artificial Intelligence (AI)

[0656] The server receives the data sent from the device and analyzes it using generative artificial intelligence (AI). Specifically, the following process is performed:

[0657] 1. The server stores the video, audio, and location information data received from the device.

[0658] 2. AI analyzes the video data and identifies collapsed buildings and abnormal areas.

[0659] 3. Audio analysis detects cries for help and dangerous sounds (e.g., the sound of a collapse).

[0660] 4. Combine location information to determine which areas need urgent assistance.

[0661] Smart Glasses

[0662] The smart glasses used by local rescuers display instructions sent from the server. Specific operating procedures include the following:

[0663] 1. The rescuer puts on the smart glasses and connects them to the system.

[0664] 2. The instructions generated by the server based on the analysis results are displayed on the smart glasses.

[0665] 3. Rescuers should check the displayed instructions, move quickly to the designated location and begin rescue operations.

[0666] 4. Report progress and new information on rescue operations to the server via smart glasses.

[0667] Emotion Engine

[0668] The emotion engine recognizes and analyzes the rescuer's emotions in real time and provides appropriate feedback based on the results. This function allows the rescuer's psychological and emotional state to be monitored, and stress reduction measures and relaxation instructions can be implemented as needed.

[0669] 1. The emotion engine analyzes the facial expressions and voice of rescuers through smart glasses.

[0670] 2. The server receives the data from the emotion engine and evaluates the rescuer's emotional state.

[0671] 3. The server generates additional instructions or feedback (e.g., encouraging messages or suggestions for taking a break) based on the emotional state and sends them to the smart glasses.

[0672] Examples

[0673] Example 1: When a major earthquake occurs

[0674] 1. The drone terminal flies over the disaster area, captures footage, and sends it to the server.

[0675] 2. AI analyzes the video on the server and identifies the collapsed building.

[0676] 3. The server determines priorities and gives top priority to areas with particularly high risk of rescue.

[0677] 4. The server generates specific instructions and sends them to the smart glasses.

[0678] 5. Rescuers follow the instructions on the smart glasses to reach the scene via a safe route and begin rescue operations.

[0679] 6. The emotion engine monitors the rescuer's emotional state and sends encouragement or instructions to relax as needed.

[0680] Example 2: Post-tsunami relief efforts

[0681] 1. Collect information on the flooded area using a mobile camera and GPS device as terminals.

[0682] 2. The server receives the data live and the AI ​​analyzes the extent of the damage.

[0683] 3. Identify high-priority areas, and the server generates rescue instructions based on that information.

[0684] 4. The smart glasses display instructions saying, "The area ahead is heavily flooded. Carry out rescue operations with caution."

[0685] 5. Rescuers confirm instructions and begin rescuing victims from a safe location.

[0686] 6. The emotion engine monitors the rescuer's emotional state and provides psychological support as needed.

[0687] This system allows for a real-time understanding of the situation in disaster areas, enabling rapid and accurate support for relief efforts. Furthermore, the introduction of an emotion engine helps maintain the psychological and emotional health of relief workers, enabling sustainable relief efforts.

[0688] The processing flow will be explained below.

[0689] Step 1:

[0690] The device collects video, audio, and location information. Specifically, the drone flies over the affected area, taking high-resolution video with a camera, recording audio from the scene with a microphone, and obtaining location information with a GPS device.

[0691] Step 2:

[0692] The device transmits the collected video, audio, and location information to a server in real time. This is done using satellite communication, and the communication signal reaches the server via a communications satellite.

[0693] Step 3:

[0694] The server receives the data sent from the device. The server first stores the received video, audio, and location information data and prepares it for subsequent analysis.

[0695] Step 4:

[0696] The server passes the received data to the artificial intelligence (AI) that generates it, which then begins analyzing it. The AI ​​analyzes the video data and identifies collapsed buildings and abnormalities. For example, it marks collapsed buildings with a red frame.

[0697] Step 5:

[0698] The server uses AI to perform audio analysis, detecting cries for help and other important sounds (such as the sound of a collapse) from the audio data, thereby narrowing down the areas where victims may be present.

[0699] Step 6:

[0700] The server analyzes the location data and combines it with the results of video and audio analysis to determine the level of danger and urgency in a specific area and set rescue priorities.

[0701] Step 7:

[0702] The server generates specific instructions based on the analysis results, such as "Area A has many collapsed buildings. Urgency is high. Area B is flooded. Caution."

[0703] Step 8:

[0704] The server sends the generated instructions to the local rescuer's smart glasses. The data packet reaches the rescuer's smart glasses via the communication path.

[0705] Step 9:

[0706] The user checks the instructions on the smart glasses, and the rescuer reads the instructions displayed on the smart glasses and understands the necessary actions.

[0707] Step 10:

[0708] The user follows the instructions of the smart glasses to carry out rescue operations, specifically by searching for and rescuing victims while following a safe route and beginning rescue operations.

[0709] Step 11:

[0710] The user reports the progress of the rescue operation to the server. For example, a report such as "5 victims have been rescued in Area A" is sent to the server through the smart glasses.

[0711] Step 12:

[0712] The emotion engine analyzes the facial expressions and voice of the rescuer through the smart glasses. Facial expression analysis uses facial recognition technology, and voice analysis uses emotion recognition algorithms.

[0713] Step 13:

[0714] The server receives the data from the emotion engine and evaluates the rescuer's emotional state, generating an evaluation result such as "high fatigue" or "stressed."

[0715] Step 14:

[0716] The server generates additional instructions and feedback based on the user's emotional state and sends them to the smart glasses, such as messages like "We recommend you take a break" or "Please relax."

[0717] Step 15:

[0718] The user sees additional feedback through the smart glasses, and the rescuer receives feedback based on their emotional state, allowing them to take a break or relax if necessary.

[0719] This series of steps enables real-time information collection, analysis, instruction generation, and emotion monitoring, supporting rapid and accurate rescue operations while maintaining the psychological and emotional well-being of rescuers.

[0720] Example 2

[0721] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0722] To carry out rapid and effective relief efforts during disasters, it is necessary to grasp the situation in the affected area in real time and make quick decisions. However, rescuers on the ground work under high-stress conditions, so their mental and emotional health must also be maintained. Currently, there is a lack of systems that can simultaneously meet these complex demands.

[0723] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0724] In this invention, the server includes: a terminal collecting video, audio, and location information; a server receiving the video, audio, and location information from the terminal; a server analyzing the video, audio, and location information using artificial intelligence; a server prioritizing rescue operations and generating specific instructions based on the analysis results; a server transmitting the specific instructions to a visual display device of a local rescuer; a server allowing the local rescuer to check the instructions on the visual display device and perform rescue operations; and a server recognizing and analyzing the emotional state of the rescuer in real time and providing feedback. This enables rapid and effective rescue operations in the event of a disaster and also maintains the mental and emotional health of the rescuer.

[0725] "Terminal" means equipment for collecting video, audio, and location information, including drones, handheld cameras, microphones, and GPS devices.

[0726] A "server" is a central control device that receives and analyzes data sent from the terminals, and generates and sends instructions and feedback.

[0727] "Footage" refers to visual information obtained from devices such as cameras and drones, and records the situation and environment of the disaster-stricken area.

[0728] "Audio" refers to auditory information acquired from a terminal such as a microphone, and records the voices of rescuers and environmental sounds at the scene.

[0729] "Location information" refers to geographic coordinate information obtained from a terminal such as a GPS device, and identifies the location and movement of a target.

[0730] "Generative artificial intelligence (AI)" refers to algorithms or models that analyze data received by a server and identify specific patterns or anomalies.

[0731] "Analysis" is the process of extracting specific information and patterns from received data to produce meaningful results.

[0732] "Priority" is a criterion for evaluating the urgency and importance of a response in relief operations, and determines which areas and items will be given priority.

[0733] "Instructions" are specific actions or responses generated by the server, providing guidelines or procedures for on-site rescuers to follow.

[0734] A "visual display device" is a device worn by a rescuer to visually confirm instructions and feedback from the server, and includes smart glasses and the like.

[0735] "Emotional state" refers to the mental and emotional well-being of field rescuers, including psychological changes such as stress and fatigue.

[0736] "Feedback" refers to advice and instructions provided by the server based on the rescuer's emotional state, with the aim of providing emotional support and correcting behavior.

[0737] MODE FOR CARRYING OUT THE INVENTION

[0738] As a specific embodiment of this invention, we propose a system using a terminal, a server, a generative AI model, a visual display device, and an emotion engine. This system enables rapid and effective rescue operations in the event of a disaster, while optimizing the psychological and emotional state of rescuers.

[0739] System Configuration

[0740] Terminal

[0741] The terminals are devices used to collect video, audio, and location information at disaster sites. Specifically, they include drones, handheld cameras, microphones, and GPS devices. These terminals collect information on the situation in the disaster area in real time and transmit the data to a server via satellite communication.

[0742] Server and Generative Artificial Intelligence (AI)

[0743] The server receives the data sent from the device and analyzes it using a generative AI model. The specific processing is as follows: The server stores the video, audio, and location information data received from the device, and the AI ​​analyzes the video data to identify collapsed buildings and abnormal areas. It also uses audio analysis to detect cries for help and dangerous sounds (such as the sound of a collapse). It also combines this with location information to determine which areas require emergency assistance.

[0744] Visual Display Devices

[0745] The visual display device used by the local rescuer displays the instructions sent from the server. Specifically, when the rescuer wears the visual display device and connects it to the system, instructions generated by the server based on the analysis results are displayed on the display. The rescuer checks the displayed instructions, quickly moves to the designated location, and begins rescue operations. The rescuer also reports the progress of rescue operations and new information to the server via the visual display device.

[0746] Emotion Engine

[0747] The emotion engine recognizes and analyzes the rescuer's emotions in real time and provides appropriate feedback based on the results. This function allows the rescuer's psychological and emotional state to be monitored and stress reduction measures or relaxation instructions to be implemented as needed. Specifically, the emotion engine analyzes the rescuer's facial expressions and voice via a visual display device. The server receives data from the emotion engine and evaluates the rescuer's emotional state. Furthermore, the server generates additional instructions or feedback (e.g., encouraging messages or suggestions for taking a break) based on the emotional state and sends them to the visual display device.

[0748] Examples

[0749] Example 1: When a major earthquake occurs

[0750] The drone terminal flies over the disaster area, taking footage and sending it to a server. AI on the server analyzes the footage and identifies collapsed buildings. The server then determines priorities and prioritizes areas with particularly high risk for rescue. The server then generates specific instructions and sends them to a visual display device. Rescuers follow the instructions to reach the scene via a safe route and begin rescue operations. The emotion engine monitors the emotional state of the rescuers and sends encouragement or instructions to relax as needed.

[0751] Example prompt sentence:

[0752] A major earthquake occurs. Check the situation in the affected areas. Analyze the footage taken by the drone and identify the collapsed buildings.

[0753] Example 2: Post-tsunami relief efforts

[0754] The terminal uses a portable camera and GPS device to collect information on the status of the flooded area. The server receives the data, and AI analyzes the extent of the damage. High-priority areas are identified, and the server generates rescue instructions based on that information. A visual display device displays the instruction, "The area ahead is heavily flooded. Carry out rescue operations with caution." Rescuers confirm the instructions and rescue victims from a safe location. An emotion engine monitors the emotional state of rescuers and provides psychological support as needed.

[0755] Example prompt sentence:

[0756] After a tsunami occurs, collect information about the flooded area. Use handheld cameras and GPS devices to analyze the extent of the flooding.

[0757] This invention allows us to grasp the situation in disaster areas in real time and support rapid and accurate relief efforts. Furthermore, the introduction of an emotion engine will help maintain the psychological and emotional health of rescuers, enabling sustainable relief efforts.

[0758] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0759] System program processing flow and specific procedures

[0760] Step 1: The device collects data and sends it to the server

[0761] The device arrives at the disaster site and collects video, audio, and location information. The input is video, audio, and location data obtained from cameras, microphones, and GPS devices. The device collects this data and transmits it to a server via satellite communication. For example, a drone could fly over a disaster area, capture high-resolution video, and upload the data to a server in real time.

[0762] Step 2: The server receives and stores the data

[0763] The server receives video, audio, and location information sent from the device. The input is the data sent from the device, and the server stores it in a database in a specified format. For example, specific operations include storing video data in video storage, audio data in audio storage, and location information in a geospatial database. The output is the stored data.

[0764] Step 3: The server passes the data to the AI ​​and requests it for analysis.

[0765] The server passes the stored data to a generative artificial intelligence (AI) model and requests it to analyze it. The input is the video, audio, and location information stored in the database, and the server provides this data to the AI. The AI ​​identifies collapsed buildings and abnormalities from the video, and detects calls for help and dangerous sounds from the audio. For example, a specific operation would be for the AI ​​to analyze a frame of video and label the location of a collapsed building. The output is the analysis results.

[0766] Step 4: The server generates instructions based on the analysis results.

[0767] Based on the results of the AI ​​analysis, the server determines which areas require emergency response. The input is the AI ​​analysis results, and the server generates specific instructions based on the results of the judgment. By inputting instructions into the generative AI model, specific instructions in natural language are generated. For example, generating an instruction such as "A collapsed building has been found in this area, so carry out emergency rescue operations" is a specific action. The output is the generated instructions.

[0768] Step 5: The server sends instructions to the smart glasses

[0769] The server sends the generated instructions to the visual display device (smart glasses) of the local rescuer. The input is the generated instructions, which the server sends to the visual display device. For example, the specific action is to display "Head to the intersection of 5th Street" on the smart glasses display and hear the same instruction aloud. The output is the instructions received by the rescuer.

[0770] Step 6: The user acts according to the instructions of the smart glasses.

[0771] The user acts according to the instructions displayed on the smart glasses. The input is the instructions displayed on the smart glasses, and the user performs activities in the disaster area based on the instructions. For example, specific actions include passing through the disaster area following the displayed route and starting rescue operations around collapsed buildings. The output is the progress of the rescue operations.

[0772] Step 7: The server monitors the user's emotional state through the emotion engine and sends feedback.

[0773] The server uses an emotion engine to analyze the user's emotional state in real time. The input is facial expression and voice data obtained from the smart glasses' camera and microphone, which the server analyzes to evaluate the user's emotional state. If necessary, feedback is generated and sent to the smart glasses. For example, if the server detects that the rescuer is very tired, it could send a message to the smart glasses saying, "Please take a 10-minute break." The output is feedback provided to the rescuer.

[0774] (Application example 2)

[0775] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0776] A system that can provide quick and accurate instructions is needed for on-site relief efforts. However, current systems make it difficult for relief workers to understand the optimal route and actions in real time, which places a heavy psychological burden on them. Similarly, in delivery services, there is a lack of systems that allow delivery workers to understand the optimal route in real time and improve delivery efficiency, as well as the psychological burden on delivery workers.

[0777] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: a means for the terminal to collect video, audio, and location information; a means for the server to receive video, audio, and location information from the terminal; a means for the server to analyze the video, audio, and location information using artificial intelligence; a means for the server to prioritize rescue operations based on the analysis results and generate specific instructions; a means for the server to send the specific instructions to the smart glasses of the local rescuer; a means for the local rescuer to check the instructions on the smart glasses and perform the rescue operation; a means for the server to generate an optimized delivery route and send the instructions to the smart glasses; and a means for using an emotion engine to analyze the emotional state of the delivery person and generate appropriate feedback. This enables efficient real-time support in rescue operations and delivery services with reduced psychological burden.

[0778] A "terminal" is a device for collecting video, audio, and location information.

[0779] A "server" is a computer system that analyzes video, audio, and location information received from a terminal and generates instructions and feedback.

[0780] "Generative AI" is an AI that has the ability to analyze collected data and generate instructions regarding disasters and deliveries.

[0781] "Smart glasses" are wearable devices worn by users that provide visual instructions and feedback in real time.

[0782] "Analysis results" are the results obtained by analyzing the data collected by the server and generating AI.

[0783] "Instructions" are specific guidelines for action or instructions generated based on the analysis results.

[0784] "Delivery route optimization" is the process of calculating the optimal route for a delivery person to reach their destination efficiently.

[0785] The "emotion engine" is a system that analyzes the emotional state of delivery personnel and rescuers and provides feedback based on the results.

[0786] "Feedback" is a message of advice or support to the user that is generated based on information analyzed by the emotion engine.

[0787] As an embodiment of this invention, we propose a system that combines a terminal, a server, smart glasses, an emotion engine, and generative artificial intelligence (AI). This system improves delivery efficiency and optimizes the emotional state of delivery personnel.

[0788] System Configuration

[0789] 1. Terminal

[0790] The terminal is a device for collecting video, audio, and location information. Specifically, it includes smartphones and tablets. These terminals are carried by delivery personnel, collect information on the situation during delivery, and send the data to a server.

[0791] 2. Server and Generative Artificial Intelligence (AI)

[0792] The server receives the data sent from the terminal and analyzes it using generative artificial intelligence (AI). The specific processing steps are as follows:

[0793] The server stores the video, audio, and location information data received from the terminal.

[0794] AI analyzes video data to identify delivery progress and obstacles along the way (e.g., using OpenCV for video analysis).

[0795] For voice analysis, the Google Cloud Speech-to-Text API is used to convert voice data into text and analyze it using an emotion engine.

[0796] Analyzes GPS location information and optimizes delivery routes.

[0797] 3. Smart Glasses

[0798] The smart glasses used by the delivery person display instructions sent from the server, and perform the following operations depending on the situation during the delivery:

[0799] The delivery person wears smart glasses and connects them to the system.

[0800] Instructions generated by the server based on the analysis results are displayed on the smart glasses.

[0801] The delivery person checks the displayed instructions and proceeds along the optimal route.

[0802] 4. Emotion Engine

[0803] The emotion engine recognizes and analyzes the delivery person's emotions in real time and provides appropriate feedback based on the results. This function monitors the delivery person's psychological and emotional state and provides stress reduction measures and relaxation instructions as needed.

[0804] The emotion engine analyzes the delivery person's facial expressions and voice through smart glasses (e.g., using Amazon Rekognition to perform facial expression analysis).

[0805] A server receives the data from the emotion engine and evaluates the emotional state of the delivery person.

[0806] The server generates additional instructions or feedback (e.g., encouraging messages or suggestions for taking a break) based on the emotional state and sends them to the smart glasses.

[0807] Examples

[0808] Example 1: Standard delivery

[0809] 1. The delivery person wears smart glasses and the device collects location information.

[0810] 2. AI analyzes the video on the server and determines the progress of the delivery.

[0811] 3. The server determines the priority and generates the optimal delivery route.

[0812] 4. The server generates specific instructions and sends them to the smart glasses.

[0813] 5. The delivery person follows the instructions on the smart glasses to complete the delivery via a safe route.

[0814] 6. The emotion engine monitors the delivery person's emotional state and sends encouragement or instructions to relax as needed.

[0815] Example 2: Managing stress during delivery

[0816] 1. In congested roads, a delivery person puts on smart glasses and begins their delivery.

[0817] 2. The server receives the location information and video data and analyzes the delivery route.

[0818] 3. The emotion engine analyzes the delivery person's facial expression data to detect signs of stress.

[0819] 4. The server generates an encouraging message such as "Take a deep breath and relax" and sends it to the smart glasses.

[0820] 5. The delivery person follows instructions, relaxes to reduce stress, and continues with the delivery.

[0821] Prompt Sentence Examples

[0822] "If a user is driving with a grumpy expression, generate a message suggesting they relax."

[0823] "When a delivery person is nearing their destination, display a message informing them of their progress."

[0824] These procedures can provide a system that improves delivery efficiency and provides psychological support to delivery personnel.

[0825] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0826] Step 1:

[0827] The device (smartphone or tablet) collects video, audio, and location information. The user (delivery person) turns on the device, takes video of the delivery with the camera, records audio with the microphone, and obtains location information via GPS. This data is sent to the server. The input is video, audio, and GPS data, and the output is a data package of these.

[0828] Step 2:

[0829] The server receives video, audio, and location information from the device. The server temporarily stores the received data and checks whether the data was received correctly. The input is the data package sent from the device, and the output is the data stored in the server.

[0830] Step 3:

[0831] The server uses generative artificial intelligence (AI) to analyze video, audio, and location information. The server uses OpenCV to analyze the video data and identify the delivery progress and obstacles along the way. Next, it converts the audio data into text using Google Cloud Speech-to-Text, which is then analyzed using an emotion engine. It also analyzes GPS data and calculates the delivery route. The inputs are video, audio, and GPS data, and the output is the analysis results, including progress and optimal route information.

[0832] Step 4:

[0833] The server generates an optimized delivery route based on the analysis results and generates specific instructions.The server generates an optimized delivery route based on the analysis results and generates specific instructions along that route (e.g., "turn right at the next intersection").The input is the analysis results, and the output is the optimized delivery route and specific instructions.

[0834] Step 5:

[0835] The server sends the specific instructions to the local rescuer's smart glasses. The server then sends the generated instructions to the delivery person's smart glasses in real time. The input is the specific instructions, and the output is the instructions displayed on the smart glasses.

[0836] Step 6:

[0837] The local rescuer checks the instructions on the smart glasses and carries out the delivery. The user (delivery person) checks the instructions displayed on the smart glasses and follows them to make the delivery. The input is the instructions displayed on the smart glasses, and the output is the progress of the delivery work.

[0838] Step 7:

[0839] The server monitors the emotional state of the delivery person while they are making a delivery and generates appropriate feedback using an emotion engine. The server analyzes the delivery person's facial expression data acquired by the smart glasses using Amazon Rekognition and their voice data using the emotion engine. The input is facial expression data and voice data, and the output is an appropriate feedback message (e.g., "Take a deep breath and relax").

[0840] Step 8:

[0841] The server sends the generated feedback to the smart glasses, which the delivery person receives. The server also sends the generated feedback message to the smart glasses, which the delivery person receives and acts upon. The input is the feedback message, and the output is the action taken by the delivery person (relax, take a break, etc.).

[0842] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0843] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0844] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0845] [Third embodiment]

[0846] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0847] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0848] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0849] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0850] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0851] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0852] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0853] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0854] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0855] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0856] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0857] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0858] As a specific embodiment of this invention, we propose a system using a terminal, a server, generative artificial intelligence, and smart glasses. This system will enable rapid and effective rescue operations in the event of a disaster.

[0859] System Configuration

[0860] Terminal

[0861] The terminals are devices used to collect video, audio, and location information at disaster sites. Specifically, they include drones, handheld cameras, microphones, and GPS devices. These terminals collect information on the situation in the disaster area in real time and transmit the data to a server via satellite communication.

[0862] Server and Generative Artificial Intelligence (AI)

[0863] The server receives the data sent from the device and analyzes it using generative artificial intelligence (AI). Specifically, the following process is performed:

[0864] 1. The server stores the video, audio, and location information received from the device.

[0865] 2. AI analyzes the footage and identifies the locations of collapsed buildings and victims.

[0866] 3. Audio analysis detects cries for help and dangerous sounds (e.g., the sound of a collapse).

[0867] 4. Combine location information to determine which areas need urgent assistance.

[0868] Smart Glasses

[0869] The smart glasses used by local rescuers display instructions sent from the server. Specific operating procedures include the following:

[0870] 1. The rescuer puts on the smart glasses and connects them to the system.

[0871] 2. The instructions generated by the server based on the analysis results are displayed on the smart glasses.

[0872] 3. Rescuers should check the displayed instructions, move quickly to the designated location and begin rescue operations.

[0873] 4. Report progress and new information on rescue operations to the server via smart glasses.

[0874] Examples

[0875] Example 1: When a major earthquake occurs

[0876] 1. The drone terminal flies over the disaster area, captures footage, and sends it to the server.

[0877] 2. AI analyzes the video on the server and identifies the collapsed building.

[0878] 3. The server determines priorities and gives top priority to areas with particularly high risk of rescue.

[0879] 4. The server generates specific instructions and sends them to the smart glasses.

[0880] 5. Rescuers follow the instructions on the smart glasses to reach the scene via a safe route and begin rescue operations.

[0881] Example 2: Post-tsunami relief efforts

[0882] 1. Collect information on the flooded area using a mobile camera and GPS device as terminals.

[0883] 2. The server receives the data live and the AI ​​analyzes the extent of the damage.

[0884] 3. Identify high-priority areas, and the server generates rescue instructions based on that information.

[0885] 4. The smart glasses display instructions saying, "The area ahead is heavily flooded. Carry out rescue operations with caution."

[0886] 5. Rescuers confirm instructions and begin rescuing victims from a safe location.

[0887] This system makes it possible to grasp the situation in disaster areas in real time and support rapid and accurate relief efforts. Analysis by generative AI and display of instructions by smart glasses can help local rescuers act quickly and effectively.

[0888] The processing flow will be explained below.

[0889] Step 1:

[0890] The device collects video, audio, and location information. Specifically, the drone flies over the affected area, taking high-resolution video with a camera, recording audio from the scene with a microphone, and obtaining location information with a GPS device.

[0891] Step 2:

[0892] The device transmits the collected video, audio, and location information to a server in real time. This is done using satellite communication, and the communication signal reaches the server via a communications satellite.

[0893] Step 3:

[0894] The server receives the data sent from the device. The server first stores the received video, audio, and location information data and prepares it for subsequent analysis.

[0895] Step 4:

[0896] The server passes the received data to the artificial intelligence (AI) that generates it, which then begins analyzing it. The AI ​​first analyzes the video data and identifies collapsed buildings and abnormalities. For example, it marks collapsed buildings with a red frame.

[0897] Step 5:

[0898] The server uses AI to perform audio analysis, detecting cries for help and other important sounds (such as the sound of a collapse) from the audio data, thereby narrowing down the areas where victims may be present.

[0899] Step 6:

[0900] The server analyzes the location data and combines it with the results of video and audio analysis to determine the level of danger and urgency in a specific area and set rescue priorities.

[0901] Step 7:

[0902] The server generates specific instructions based on the analysis results, such as "Area A has many collapsed buildings. Urgency is high. Area B is flooded. Caution."

[0903] Step 8:

[0904] The server sends the generated instructions to the local rescuer's smart glasses. The data packet reaches the rescuer's smart glasses via the communication path.

[0905] Step 9:

[0906] The user checks the instructions on the smart glasses, and the rescuer reads the instructions displayed on the smart glasses and understands the necessary actions.

[0907] Step 10:

[0908] The user follows the instructions of the smart glasses to carry out rescue operations, specifically by searching for and rescuing victims while following a safe route and beginning rescue operations.

[0909] Step 11:

[0910] The user reports the progress of the rescue operation to the server. For example, a report such as "5 victims have been rescued in Area A" is sent to the server through the smart glasses.

[0911] This series of processing steps creates a system that can smoothly collect information, analyze it, generate instructions, and carry out rescue operations in real time.

[0912] Example 1

[0913] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0914] When a disaster occurs, there are often delays in quickly and accurately grasping the situation on the ground and issuing instructions for effective relief activities. As a result, the efficiency of relief activities declines and the rescue of victims is delayed. In addition, the number of information devices carried by local rescuers is limited, so there is a lack of means to quickly receive appropriate instructions.

[0915] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0916] In this invention, the server includes: a terminal for collecting video, audio, and location information; a server for receiving the video, audio, and location information from the terminal; a server for analyzing the video, audio, and location information using a generating artificial intelligence; a server for prioritizing rescue operations based on the analysis results and generating specific instructions; a generating artificial intelligence for analyzing the video to identify the locations of collapsed structures and victims; a generating artificial intelligence for analyzing the audio to detect calls for help and dangerous sounds; a server for combining the video, audio, and location information to determine areas requiring emergency support; a server for transmitting the specific instructions to smart glasses worn by local rescuers; and a server for the local rescuers to check the instructions on the smart glasses and carry out rescue operations. This enables real-time situation assessment of the disaster site and rapid and accurate rescue operation instructions.

[0917] "Terminals" are devices that collect video, audio, and location information at disaster sites, including drones, handheld cameras, microphones, and GPS devices.

[0918] A "server" is a computer system that receives data sent from a terminal, analyzes it, and generates instructions.

[0919] "Video" refers to data that represents visual information from the disaster site. This includes videos and images taken using drones or handheld cameras.

[0920] "Audio" refers to data representing sound information from disaster sites, including calls for help and dangerous sounds collected using microphones.

[0921] "Location information" is data that indicates the latitude and longitude of specific points in a disaster site. It is collected using GPS devices.

[0922] "Generative artificial intelligence" is a machine learning model or algorithm that analyzes collected data and extracts the necessary information.

[0923] "Priority of rescue operations" is a criterion for determining the urgency based on the analysis results and the order in which rescue operations should be carried out.

[0924] "Specific instructions" are detailed instructions such as specific actions rescuers should take on site and routes to take.

[0925] "Smart glasses" are wearable devices worn by local rescuers that display instructions from a server.

[0926] "Analyzing the video" means analyzing the collected video data and identifying the locations of collapsed structures and victims.

[0927] "Analyzing voice" means analyzing collected voice data to detect cries for help or dangerous sounds.

[0928] "Determining areas needing emergency assistance" means combining video, audio, and location information to identify which areas are most in need of assistance.

[0929] "Satellite communications" is a communication method for sending and receiving data via artificial satellites.

[0930] As a specific embodiment of this invention, we propose a system using a terminal, a server, generative artificial intelligence (AI), and smart glasses, which will enable rapid and effective rescue operations in the event of a disaster.

[0931] Terminal

[0932] The terminals are devices used to collect video, audio, and location information at disaster sites. Specifically, they include drones, handheld cameras, microphones, and GPS devices. These terminals collect information on the situation in the disaster area in real time and transmit the data to a server via satellite communication.

[0933] Server and Generative Artificial Intelligence (AI)

[0934] The server receives data sent from the device and analyzes it using artificial intelligence (AI). Specifically, the following process is performed:

[0935] 1. Data Receipt and Storage

[0936] The server receives and stores the video, audio, and location information sent from the device.

[0937] 2. Video Analysis

[0938] AI analyzes the video and identifies the locations of collapsed structures and victims. For example, AI uses pattern recognition technology in the video to detect collapsed buildings.

[0939] 3. Audio Analysis

[0940] For voice data, AI detects dangerous sounds such as cries for help or the sound of a collapse. Speech recognition technology extracts specific voice patterns and detects information with a high level of urgency.

[0941] 4. Location information analysis

[0942] By analyzing location information and plotting the damage situation on a map, it is possible to identify which areas require emergency assistance.

[0943] Smart Glasses

[0944] The smart glasses used by local rescuers display instructions sent from the server, including the following specific operating procedures:

[0945] 1. System Connection

[0946] The user (local rescuer) wears the smart glasses and connects them to the system.

[0947] 2. Instruction display

[0948] The server generates instructions based on the analysis results and displays them on the smart glasses, allowing rescuers to instantly understand areas with high emergency levels and safe routes to take.

[0949] 3. Relief operations

[0950] Rescuers follow the displayed instructions to quickly move to the designated location and begin rescue operations. The smart glasses also have the function of reporting the progress of rescue operations and new information to the server.

[0951] Examples

[0952] Example 1: When a major earthquake occurs

[0953] The drone terminal flies over the disaster area, taking pictures and sending them to a server. AI on the server analyzes the pictures and identifies collapsed buildings. The server then determines priorities and prioritizes areas with particularly high risk for rescue. The server then sends instructions generated by the server to the smart glasses. Rescuers follow the instructions on the smart glasses to reach the scene via a safe route and begin rescue operations.

[0954] Example 2: Post-tsunami relief efforts

[0955] The device uses a portable camera and GPS device to collect information on the status of the flooded area. The server receives the data, and AI analyzes the extent of the damage. The server identifies high-priority areas and generates rescue instructions based on that information. The smart glasses display the following instruction: "The area ahead is heavily flooded. Carry out rescue operations with caution." Rescuers confirm the instructions and rescue victims from a safe location.

[0956] Prompt Sentence Examples

[0957] Possible prompts for the generative AI model include:

[0958] Prompt statement example 1:

[0959] Analyze the disaster situation and determine the priority of relief efforts.

[0960] Prompt statement example 2:

[0961] Use video data to identify the locations of collapsed buildings and victims, and quickly generate rescue instructions.

[0962] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0963] Program processing steps

[0964] Step 1:

[0965] The terminal collects video, audio, and location information and sends it to a server. Specifically, drones, portable cameras, microphones, and GPS devices are used to collect information on the situation in the disaster area in real time. The collected data is sent to the server via satellite communication. The input is video, audio, and location information obtained from the disaster site, and the output is the data sent to the server. As an example of how it works, a drone flies over a wide area of ​​the disaster area and collects video and location information in real time.

[0966] Step 2:

[0967] The server stores the data received from the device. The input is the video, audio, and location information sent from the device, and the output is data stored in a database on the server. For example, the server uses a secure communication protocol to safely store the received data and prepare it for subsequent analysis.

[0968] Step 3:

[0969] The server analyzes the video using generative artificial intelligence (AI). The input is the stored video data, and the output is the location information of collapsed structures and victims. Specifically, the AI ​​analyzes characteristic patterns in the video and identifies the location of collapsed buildings. For example, it identifies collapsed buildings by detecting abnormal shapes or color changes.

[0970] Step 4:

[0971] The server uses AI to analyze the voice. The input is the stored voice data, and the output is the detection results of calls for help and dangerous sounds. Specifically, it uses voice recognition technology to extract specific phrases and sound patterns and identify information with a high level of urgency. For example, it identifies the sound of a collapse or a call for help.

[0972] Step 5:

[0973] The server analyzes the location information and identifies areas requiring emergency assistance. The input is the stored location data, and the output is a list of identified high-priority areas for assistance. Specifically, the server plots the location information on a map and determines priorities according to the level of damage. For example, it compares GPS data with map information to mark high-risk areas.

[0974] Step 6:

[0975] The server generates instructions for rescue operations based on the analysis results. The inputs are the video analysis results, audio analysis results, and location information analysis results, and the output is specific rescue instructions. In concrete terms, the server inputs prompts into the generative AI model to generate rescue instructions. For example, it gives the AI ​​a prompt such as "Plan a route for the rescue team based on the disaster situation."

[0976] Step 7:

[0977] The server sends the generated instructions to the smart glasses. The input is the generated rescue instructions, and the output is the instructions displayed on the smart glasses. Specifically, the server sends instruction data to the smart glasses via wireless communication. For example, it sends an instruction such as "There is a victim 200 meters northeast. Carry out rescue using a safe route."

[0978] Step 8:

[0979] The user uses smart glasses to carry out rescue operations. The input is instructions displayed on the smart glasses, and the output is a progress report of the rescue operations. Specifically, the user follows the instructions to move to the disaster area and carry out the rescue operations. For example, the user follows the instructions on the smart glasses to go to the designated location and rescue the victims.

[0980] This step-by-step process will enable rapid and accurate relief efforts in the event of a disaster.

[0981] (Application example 1)

[0982] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0983] Conventional rescue operation systems have had difficulty quickly and accurately grasping the situation at a disaster site and providing appropriate instructions to local rescuers. Furthermore, systems for detecting abnormalities in real time and responding immediately have been inadequate for security services. Therefore, there is a demand for effective support in rescue operations and security monitoring. The purpose of this invention is to solve these problems and realize an efficient and effective support system.

[0984] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0985] In this invention, the server includes: a means for a terminal to collect video, audio, and location information; a means for the server to receive the video, audio, and location information from the terminal; a means for the server to analyze the video, audio, and location information using artificial intelligence; a means for the server to prioritize rescue operations based on the analysis results and generate specific instructions; a means for the server to transmit the specific instructions to the smart glasses of local rescuers; a means for the local rescuers to check the instructions on the smart glasses and perform rescue operations; and a means for detecting abnormalities and checking the situation in real time through the smart glasses worn by security personnel. This enables not only fast and accurate rescue operations in the event of a disaster, but also real-time detection of abnormalities in security services and immediate response.

[0986] A "terminal" is a device for collecting video, audio, and location information.

[0987] A "server" is a computer system that receives video, audio, and location information from a terminal and analyzes and stores that data.

[0988] "Footage" refers to visual information collected through equipment such as drones and fixed cameras.

[0989] "Audio" is information about sounds collected through a device such as a microphone.

[0990] "Location information" is data that indicates a geographic location, such as that collected through a GPS device.

[0991] "Generative artificial intelligence (AI)" is a general term for algorithms and technologies that analyze collected video, audio, and location information and generate judgments and instructions based on the results.

[0992] "Relief operations" are a series of actions and measures taken to support victims of a disaster.

[0993] "Smart glasses" are wearable devices that have the ability to display digital information, and are worn by local rescuers and security personnel to check instructions from the server.

[0994] A "security officer" is a person whose job is to detect anomalies and check the situation.

[0995] "Real-time" refers to immediate processing or information transmission with little or no delay.

[0996] "Anomaly detection" is the process of identifying events that deviate from normal conditions or behavior.

[0997] "Situation check" refers to understanding the current state and progress of activities based on collected information.

[0998] System Program

[0999] This system includes a program consisting of the following elements:

[1000] 1. Device (collection of video, audio, and location information):

[1001] It uses hardware such as drones, fixed cameras, handheld cameras, microphones, and GPS devices.

[1002] These devices are responsible for collecting video, audio, and location information in real time.

[1003] 2. Data transmission:

[1004] The data collected by the terminal is sent to a server via satellite communication or the Internet.

[1005] A communication module is used for this process.

[1006] 3. Server (receiving and analyzing data):

[1007] The server receives and stores the video, audio, and location information transmitted from the terminal.

[1008] Generative artificial intelligence (AI) within the server analyzes the data using OpenCV and other AI modules.

[1009] Specifically, AI detects suspicious people, suspicious sounds, and unnatural movements of people and objects.

[1010] 4. Instruction generation and transmission:

[1011] The server generates specific instructions based on the results of the AI ​​analysis and sends them to the smart glasses.

[1012] For example, an instruction such as "There is a suspicious person on the north side of the building. Approach carefully and check." may be generated.

[1013] 5. Smart Glasses (Local Support and Instructions):

[1014] Local rescuers and security personnel wear smart glasses and check instructions from the server.

[1015] Real-time feedback is possible by reporting the progress of on-site response and new information to the server via smart glasses.

[1016] Natural language explanations

[1017] 1. Hardware:

[1018] Drones, fixed cameras and handheld cameras will be used to capture footage.

[1019] A microphone is used to capture audio, and a GPS device is used to capture location information.

[1020] Smart glasses will be used to display real-time instructions and assist with on-site operations.

[1021] 2. Software:

[1022] OpenCV: Used for video data acquisition and initial analysis.

[1023] AI analyzer: Performs detailed analysis of video, audio, and location information and detects anomalies.

[1024] Communication module: Sends data from the device to the server and sends instructions from the server to the smart glasses.

[1025] 3. Data processing and calculation:

[1026] After the data is sent to the server, a generative artificial intelligence (AI) analyzes it.

[1027] Video analysis: Detect building collapses and suspicious individuals.

[1028] Audio analysis: Detects cries for help and dangerous sounds.

[1029] Identify affected areas and abnormal areas based on location information.

[1030] Set priorities and generate instructions based on the results of AI analysis.

[1031] Examples of concrete examples and prompts

[1032] Example 1: Suspicious person detection

[1033] Drones and fixed cameras collect live footage.

[1034] The AI ​​detects suspicious activity from the video and displays a message on the smart glasses saying, "There is a suspicious person on the north side of the building. Please check."

[1035] Prompt Sentence Examples

[1036] "Please analyze the following data and detect suspicious individuals. Also, please generate appropriate instructions based on the detection results."

[1037] Live video data, audio data, and location information:

[1038] Video Frame: ...

[1039] Audio fragment: ...

[1040] Location Data: ...

[1041] This enables the system to respond quickly and accurately in the event of a disaster or at the scene of a security service.In order to put the present invention into practice, it is important to use the above-mentioned hardware and software in combination.

[1042] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1043] Step 1:

[1044] The device collects video, audio, and location information. Specifically, drones, fixed cameras, and portable cameras capture video of the scene, microphones record audio, and GPS devices acquire location data. The input is the actual video, audio, and location information of the scene, and the output is this digital data.

[1045] Step 2:

[1046] The data collected by the device is sent to a server via satellite or the Internet. At this stage, the communication module on the device is used. The input is the collected digital data of video, audio, and location information, and the output is the data sent to the server.

[1047] Step 3:

[1048] The server receives and stores the video, audio, and location information sent from the device. Specifically, the server's internal storage stores the data. The input is the digital data sent from the device, and the output is the stored data.

[1049] Step 4:

[1050] The server's artificial intelligence (AI) analyzes the received data. At this stage, the AI ​​module analyzes the video using libraries such as OpenCV, and the audio processing algorithm analyzes the audio, which is then combined with location data to detect anomalies. The input is the stored digital data of video, audio, and location information, and the output is the analysis results (for example, detection of a suspicious person or suspicious sound).

[1051] Step 5:

[1052] Based on the analysis results, the server generates specific instructions. As a specific action, the AI ​​generates appropriate instructions based on the analysis results. For example, "There is a suspicious person on the north side of the building. Approach carefully and check." The input is the AI's analysis results, and the output is the generated instructions.

[1053] Step 6:

[1054] The server sends the generated instructions to the smart glasses. The instructions are sent through the communication module. The input is the generated instruction text, and the output is the instruction displayed on the smart glasses.

[1055] Step 7:

[1056] The user (local rescuer or security officer) uses the smart glasses to check instructions and respond at the scene. Specifically, the user checks the scene and takes necessary action based on the instructions displayed on the smart glasses. The input is the instructions displayed on the smart glasses, and the output is specific response activities at the scene and their progress information.

[1057] Step 8:

[1058] The user reports the progress of the response to the server through the smart glasses. At this stage, the report is made using the communication function of the smart glasses. The input is the new on-site information and progress information acquired by the user, and the output is the progress data sent to the server.

[1059] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1060] As a specific embodiment of this invention, we propose a system using a terminal, a server, generative artificial intelligence, smart glasses, and an emotion engine. This system enables rapid and effective rescue operations in the event of a disaster, while optimizing the psychological and emotional state of rescuers.

[1061] System Configuration

[1062] Terminal

[1063] The terminals are devices used to collect video, audio, and location information at disaster sites. Specifically, they include drones, handheld cameras, microphones, and GPS devices. These terminals collect information on the situation in the disaster area in real time and transmit the data to a server via satellite communication.

[1064] Server and Generative Artificial Intelligence (AI)

[1065] The server receives the data sent from the device and analyzes it using generative artificial intelligence (AI). Specifically, the following process is performed:

[1066] 1. The server stores the video, audio, and location information data received from the device.

[1067] 2. AI analyzes the video data and identifies collapsed buildings and abnormal areas.

[1068] 3. Audio analysis detects cries for help and dangerous sounds (e.g., the sound of a collapse).

[1069] 4. Combine location information to determine which areas need urgent assistance.

[1070] Smart Glasses

[1071] The smart glasses used by local rescuers display instructions sent from the server. Specific operating procedures include the following:

[1072] 1. The rescuer puts on the smart glasses and connects them to the system.

[1073] 2. The instructions generated by the server based on the analysis results are displayed on the smart glasses.

[1074] 3. Rescuers should check the displayed instructions, move quickly to the designated location and begin rescue operations.

[1075] 4. Report progress and new information on rescue operations to the server via smart glasses.

[1076] Emotion Engine

[1077] The emotion engine recognizes and analyzes the rescuer's emotions in real time and provides appropriate feedback based on the results. This function allows the rescuer's psychological and emotional state to be monitored, and stress reduction measures and relaxation instructions can be implemented as needed.

[1078] 1. The emotion engine analyzes the facial expressions and voice of rescuers through smart glasses.

[1079] 2. The server receives the data from the emotion engine and evaluates the rescuer's emotional state.

[1080] 3. The server generates additional instructions or feedback (e.g., encouraging messages or suggestions for taking a break) based on the emotional state and sends them to the smart glasses.

[1081] Examples

[1082] Example 1: When a major earthquake occurs

[1083] 1. The drone terminal flies over the disaster area, captures footage, and sends it to the server.

[1084] 2. AI analyzes the video on the server and identifies the collapsed building.

[1085] 3. The server determines priorities and gives top priority to areas with particularly high risk of rescue.

[1086] 4. The server generates specific instructions and sends them to the smart glasses.

[1087] 5. Rescuers follow the instructions on the smart glasses to reach the scene via a safe route and begin rescue operations.

[1088] 6. The emotion engine monitors the rescuer's emotional state and sends encouragement or instructions to relax as needed.

[1089] Example 2: Post-tsunami relief efforts

[1090] 1. Collect information on the flooded area using a mobile camera and GPS device as terminals.

[1091] 2. The server receives the data live and the AI ​​analyzes the extent of the damage.

[1092] 3. Identify high-priority areas, and the server generates rescue instructions based on that information.

[1093] 4. The smart glasses display instructions saying, "The area ahead is heavily flooded. Carry out rescue operations with caution."

[1094] 5. Rescuers confirm instructions and begin rescuing victims from a safe location.

[1095] 6. The emotion engine monitors the rescuer's emotional state and provides psychological support as needed.

[1096] This system allows for a real-time understanding of the situation in disaster areas, enabling rapid and accurate support for relief efforts. Furthermore, the introduction of an emotion engine helps maintain the psychological and emotional health of relief workers, enabling sustainable relief efforts.

[1097] The processing flow will be explained below.

[1098] Step 1:

[1099] The device collects video, audio, and location information. Specifically, the drone flies over the affected area, taking high-resolution video with a camera, recording audio from the scene with a microphone, and obtaining location information with a GPS device.

[1100] Step 2:

[1101] The device transmits the collected video, audio, and location information to a server in real time. This is done using satellite communication, and the communication signal reaches the server via a communications satellite.

[1102] Step 3:

[1103] The server receives the data sent from the device. The server first stores the received video, audio, and location information data and prepares it for subsequent analysis.

[1104] Step 4:

[1105] The server passes the received data to the artificial intelligence (AI) that generates it, which then begins analyzing it. The AI ​​analyzes the video data and identifies collapsed buildings and abnormalities. For example, it marks collapsed buildings with a red frame.

[1106] Step 5:

[1107] The server uses AI to perform audio analysis, detecting cries for help and other important sounds (such as the sound of a collapse) from the audio data, thereby narrowing down the areas where victims may be present.

[1108] Step 6:

[1109] The server analyzes the location data and combines it with the results of video and audio analysis to determine the level of danger and urgency in a specific area and set rescue priorities.

[1110] Step 7:

[1111] The server generates specific instructions based on the analysis results, such as "Area A has many collapsed buildings. Urgency is high. Area B is flooded. Caution."

[1112] Step 8:

[1113] The server sends the generated instructions to the local rescuer's smart glasses. The data packet reaches the rescuer's smart glasses via the communication path.

[1114] Step 9:

[1115] The user checks the instructions on the smart glasses, and the rescuer reads the instructions displayed on the smart glasses and understands the necessary actions.

[1116] Step 10:

[1117] The user follows the instructions of the smart glasses to carry out rescue operations, specifically by searching for and rescuing victims while following a safe route and beginning rescue operations.

[1118] Step 11:

[1119] The user reports the progress of the rescue operation to the server. For example, a report such as "5 victims have been rescued in Area A" is sent to the server through the smart glasses.

[1120] Step 12:

[1121] The emotion engine analyzes the facial expressions and voice of the rescuer through the smart glasses. Facial expression analysis uses facial recognition technology, and voice analysis uses emotion recognition algorithms.

[1122] Step 13:

[1123] The server receives the data from the emotion engine and evaluates the rescuer's emotional state, generating an evaluation result such as "high fatigue" or "stressed."

[1124] Step 14:

[1125] The server generates additional instructions and feedback based on the user's emotional state and sends them to the smart glasses, such as messages like "We recommend you take a break" or "Please relax."

[1126] Step 15:

[1127] The user sees additional feedback through the smart glasses, and the rescuer receives feedback based on their emotional state, allowing them to take a break or relax if necessary.

[1128] This series of steps enables real-time information collection, analysis, instruction generation, and emotion monitoring, supporting rapid and accurate rescue operations while maintaining the psychological and emotional well-being of rescuers.

[1129] Example 2

[1130] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1131] To carry out rapid and effective relief efforts during disasters, it is necessary to grasp the situation in the affected area in real time and make quick decisions. However, rescuers on the ground work under high-stress conditions, so their mental and emotional health must also be maintained. Currently, there is a lack of systems that can simultaneously meet these complex demands.

[1132] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1133] In this invention, the server includes: a terminal collecting video, audio, and location information; a server receiving the video, audio, and location information from the terminal; a server analyzing the video, audio, and location information using artificial intelligence; a server prioritizing rescue operations and generating specific instructions based on the analysis results; a server transmitting the specific instructions to a visual display device of a local rescuer; a server allowing the local rescuer to check the instructions on the visual display device and perform rescue operations; and a server recognizing and analyzing the emotional state of the rescuer in real time and providing feedback. This enables rapid and effective rescue operations in the event of a disaster and also maintains the mental and emotional health of the rescuer.

[1134] "Terminal" means equipment for collecting video, audio, and location information, including drones, handheld cameras, microphones, and GPS devices.

[1135] A "server" is a central control device that receives and analyzes data sent from the terminals, and generates and sends instructions and feedback.

[1136] "Footage" refers to visual information obtained from devices such as cameras and drones, and records the situation and environment of the disaster-stricken area.

[1137] "Audio" refers to auditory information acquired from a terminal such as a microphone, and records the voices of rescuers and environmental sounds at the scene.

[1138] "Location information" refers to geographic coordinate information obtained from a terminal such as a GPS device, and identifies the location and movement of a target.

[1139] "Generative artificial intelligence (AI)" refers to algorithms or models that analyze data received by a server and identify specific patterns or anomalies.

[1140] "Analysis" is the process of extracting specific information and patterns from received data to produce meaningful results.

[1141] "Priority" is a criterion for evaluating the urgency and importance of a response in relief operations, and determines which areas and items will be given priority.

[1142] "Instructions" are specific actions or responses generated by the server, providing guidelines or procedures for on-site rescuers to follow.

[1143] A "visual display device" is a device worn by a rescuer to visually confirm instructions and feedback from the server, and includes smart glasses and the like.

[1144] "Emotional state" refers to the mental and emotional well-being of field rescuers, including psychological changes such as stress and fatigue.

[1145] "Feedback" refers to advice and instructions provided by the server based on the rescuer's emotional state, with the aim of providing emotional support and correcting behavior.

[1146] MODE FOR CARRYING OUT THE INVENTION

[1147] As a specific embodiment of this invention, we propose a system using a terminal, a server, a generative AI model, a visual display device, and an emotion engine. This system enables rapid and effective rescue operations in the event of a disaster, while optimizing the psychological and emotional state of rescuers.

[1148] System Configuration

[1149] Terminal

[1150] The terminals are devices used to collect video, audio, and location information at disaster sites. Specifically, they include drones, handheld cameras, microphones, and GPS devices. These terminals collect information on the situation in the disaster area in real time and transmit the data to a server via satellite communication.

[1151] Server and Generative Artificial Intelligence (AI)

[1152] The server receives the data sent from the device and analyzes it using a generative AI model. The specific processing is as follows: The server stores the video, audio, and location information data received from the device, and the AI ​​analyzes the video data to identify collapsed buildings and abnormal areas. It also uses audio analysis to detect cries for help and dangerous sounds (such as the sound of a collapse). It also combines this with location information to determine which areas require emergency assistance.

[1153] Visual Display Devices

[1154] The visual display device used by the local rescuer displays the instructions sent from the server. Specifically, when the rescuer wears the visual display device and connects it to the system, instructions generated by the server based on the analysis results are displayed on the display. The rescuer checks the displayed instructions, quickly moves to the designated location, and begins rescue operations. The rescuer also reports the progress of rescue operations and new information to the server via the visual display device.

[1155] Emotion Engine

[1156] The emotion engine recognizes and analyzes the rescuer's emotions in real time and provides appropriate feedback based on the results. This function allows the rescuer's psychological and emotional state to be monitored and stress reduction measures or relaxation instructions to be implemented as needed. Specifically, the emotion engine analyzes the rescuer's facial expressions and voice via a visual display device. The server receives data from the emotion engine and evaluates the rescuer's emotional state. Furthermore, the server generates additional instructions or feedback (e.g., encouraging messages or suggestions for taking a break) based on the emotional state and sends them to the visual display device.

[1157] Examples

[1158] Example 1: When a major earthquake occurs

[1159] The drone terminal flies over the disaster area, taking footage and sending it to a server. AI on the server analyzes the footage and identifies collapsed buildings. The server then determines priorities and prioritizes areas with particularly high risk for rescue. The server then generates specific instructions and sends them to a visual display device. Rescuers follow the instructions to reach the scene via a safe route and begin rescue operations. The emotion engine monitors the emotional state of the rescuers and sends encouragement or instructions to relax as needed.

[1160] Example prompt sentence:

[1161] A major earthquake occurs. Check the situation in the affected areas. Analyze the footage taken by the drone and identify the collapsed buildings.

[1162] Example 2: Post-tsunami relief efforts

[1163] The terminal uses a portable camera and GPS device to collect information on the status of the flooded area. The server receives the data, and AI analyzes the extent of the damage. High-priority areas are identified, and the server generates rescue instructions based on that information. A visual display device displays the instruction, "The area ahead is heavily flooded. Carry out rescue operations with caution." Rescuers confirm the instructions and rescue victims from a safe location. An emotion engine monitors the emotional state of rescuers and provides psychological support as needed.

[1164] Example prompt sentence:

[1165] After a tsunami occurs, collect information about the flooded area. Use handheld cameras and GPS devices to analyze the extent of the flooding.

[1166] This invention allows us to grasp the situation in disaster areas in real time and support rapid and accurate relief efforts. Furthermore, the introduction of an emotion engine will help maintain the psychological and emotional health of rescuers, enabling sustainable relief efforts.

[1167] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1168] System program processing flow and specific procedures

[1169] Step 1: The device collects data and sends it to the server

[1170] The device arrives at the disaster site and collects video, audio, and location information. The input is video, audio, and location data obtained from cameras, microphones, and GPS devices. The device collects this data and transmits it to a server via satellite communication. For example, a drone could fly over a disaster area, capture high-resolution video, and upload the data to a server in real time.

[1171] Step 2: The server receives and stores the data

[1172] The server receives video, audio, and location information sent from the device. The input is the data sent from the device, and the server stores it in a database in a specified format. For example, specific operations include storing video data in video storage, audio data in audio storage, and location information in a geospatial database. The output is the stored data.

[1173] Step 3: The server passes the data to the AI ​​and requests it for analysis.

[1174] The server passes the stored data to a generative artificial intelligence (AI) model and requests it to analyze it. The input is the video, audio, and location information stored in the database, and the server provides this data to the AI. The AI ​​identifies collapsed buildings and abnormalities from the video, and detects calls for help and dangerous sounds from the audio. For example, a specific operation would be for the AI ​​to analyze a frame of video and label the location of a collapsed building. The output is the analysis results.

[1175] Step 4: The server generates instructions based on the analysis results.

[1176] Based on the results of the AI ​​analysis, the server determines which areas require emergency response. The input is the AI ​​analysis results, and the server generates specific instructions based on the results of the judgment. By inputting instructions into the generative AI model, specific instructions in natural language are generated. For example, generating an instruction such as "A collapsed building has been found in this area, so carry out emergency rescue operations" is a specific action. The output is the generated instructions.

[1177] Step 5: The server sends instructions to the smart glasses

[1178] The server sends the generated instructions to the visual display device (smart glasses) of the local rescuer. The input is the generated instructions, which the server sends to the visual display device. For example, the specific action is to display "Head to the intersection of 5th Street" on the smart glasses display and hear the same instruction aloud. The output is the instructions received by the rescuer.

[1179] Step 6: The user acts according to the instructions of the smart glasses.

[1180] The user acts according to the instructions displayed on the smart glasses. The input is the instructions displayed on the smart glasses, and the user performs activities in the disaster area based on the instructions. For example, specific actions include passing through the disaster area following the displayed route and starting rescue operations around collapsed buildings. The output is the progress of the rescue operations.

[1181] Step 7: The server monitors the user's emotional state through the emotion engine and sends feedback.

[1182] The server uses an emotion engine to analyze the user's emotional state in real time. The input is facial expression and voice data obtained from the smart glasses' camera and microphone, which the server analyzes to evaluate the user's emotional state. If necessary, feedback is generated and sent to the smart glasses. For example, if the server detects that the rescuer is very tired, it could send a message to the smart glasses saying, "Please take a 10-minute break." The output is feedback provided to the rescuer.

[1183] (Application example 2)

[1184] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1185] A system that can provide quick and accurate instructions is needed for on-site relief efforts. However, current systems make it difficult for relief workers to understand the optimal route and actions in real time, which places a heavy psychological burden on them. Similarly, in delivery services, there is a lack of systems that allow delivery workers to understand the optimal route in real time and improve delivery efficiency, as well as the psychological burden on delivery workers.

[1186] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: a means for the terminal to collect video, audio, and location information; a means for the server to receive video, audio, and location information from the terminal; a means for the server to analyze the video, audio, and location information using artificial intelligence; a means for the server to prioritize rescue operations based on the analysis results and generate specific instructions; a means for the server to send the specific instructions to the smart glasses of the local rescuer; a means for the local rescuer to check the instructions on the smart glasses and perform the rescue operation; a means for the server to generate an optimized delivery route and send the instructions to the smart glasses; and a means for using an emotion engine to analyze the emotional state of the delivery person and generate appropriate feedback. This enables efficient real-time support in rescue operations and delivery services with reduced psychological burden.

[1187] A "terminal" is a device for collecting video, audio, and location information.

[1188] A "server" is a computer system that analyzes video, audio, and location information received from a terminal and generates instructions and feedback.

[1189] "Generative AI" is an AI that has the ability to analyze collected data and generate instructions regarding disasters and deliveries.

[1190] "Smart glasses" are wearable devices worn by users that provide visual instructions and feedback in real time.

[1191] "Analysis results" are the results obtained by analyzing the data collected by the server and generating AI.

[1192] "Instructions" are specific guidelines for action or instructions generated based on the analysis results.

[1193] "Delivery route optimization" is the process of calculating the optimal route for a delivery person to reach their destination efficiently.

[1194] The "emotion engine" is a system that analyzes the emotional state of delivery personnel and rescuers and provides feedback based on the results.

[1195] "Feedback" is a message of advice or support to the user that is generated based on information analyzed by the emotion engine.

[1196] As an embodiment of this invention, we propose a system that combines a terminal, a server, smart glasses, an emotion engine, and generative artificial intelligence (AI). This system improves delivery efficiency and optimizes the emotional state of delivery personnel.

[1197] System Configuration

[1198] 1. Terminal

[1199] The terminal is a device for collecting video, audio, and location information. Specifically, it includes smartphones and tablets. These terminals are carried by delivery personnel, collect information on the situation during delivery, and send the data to a server.

[1200] 2. Server and Generative Artificial Intelligence (AI)

[1201] The server receives the data sent from the terminal and analyzes it using generative artificial intelligence (AI). The specific processing steps are as follows:

[1202] The server stores the video, audio, and location information data received from the terminal.

[1203] AI analyzes video data to identify delivery progress and obstacles along the way (e.g., using OpenCV for video analysis).

[1204] For voice analysis, the Google Cloud Speech-to-Text API is used to convert voice data into text and analyze it using an emotion engine.

[1205] Analyzes GPS location information and optimizes delivery routes.

[1206] 3. Smart Glasses

[1207] The smart glasses used by the delivery person display instructions sent from the server, and perform the following operations depending on the situation during the delivery:

[1208] The delivery person wears smart glasses and connects them to the system.

[1209] Instructions generated by the server based on the analysis results are displayed on the smart glasses.

[1210] The delivery person checks the displayed instructions and proceeds along the optimal route.

[1211] 4. Emotion Engine

[1212] The emotion engine recognizes and analyzes the delivery person's emotions in real time and provides appropriate feedback based on the results. This function monitors the delivery person's psychological and emotional state and provides stress reduction measures and relaxation instructions as needed.

[1213] The emotion engine analyzes the delivery person's facial expressions and voice through smart glasses (e.g., using Amazon Rekognition to perform facial expression analysis).

[1214] A server receives the data from the emotion engine and evaluates the emotional state of the delivery person.

[1215] The server generates additional instructions or feedback (e.g., encouraging messages or suggestions for taking a break) based on the emotional state and sends them to the smart glasses.

[1216] Examples

[1217] Example 1: Standard delivery

[1218] 1. The delivery person wears smart glasses and the device collects location information.

[1219] 2. AI analyzes the video on the server and determines the progress of the delivery.

[1220] 3. The server determines the priority and generates the optimal delivery route.

[1221] 4. The server generates specific instructions and sends them to the smart glasses.

[1222] 5. The delivery person follows the instructions on the smart glasses to complete the delivery via a safe route.

[1223] 6. The emotion engine monitors the delivery person's emotional state and sends encouragement or instructions to relax as needed.

[1224] Example 2: Managing stress during delivery

[1225] 1. In congested roads, a delivery person puts on smart glasses and begins their delivery.

[1226] 2. The server receives the location information and video data and analyzes the delivery route.

[1227] 3. The emotion engine analyzes the delivery person's facial expression data to detect signs of stress.

[1228] 4. The server generates an encouraging message such as "Take a deep breath and relax" and sends it to the smart glasses.

[1229] 5. The delivery person follows instructions, relaxes to reduce stress, and continues with the delivery.

[1230] Prompt Sentence Examples

[1231] "If a user is driving with a grumpy expression, generate a message suggesting they relax."

[1232] "When a delivery person is nearing their destination, display a message informing them of their progress."

[1233] These procedures can provide a system that improves delivery efficiency and provides psychological support to delivery personnel.

[1234] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1235] Step 1:

[1236] The device (smartphone or tablet) collects video, audio, and location information. The user (delivery person) turns on the device, takes video of the delivery with the camera, records audio with the microphone, and obtains location information via GPS. This data is sent to the server. The input is video, audio, and GPS data, and the output is a data package of these.

[1237] Step 2:

[1238] The server receives video, audio, and location information from the device. The server temporarily stores the received data and checks whether the data was received correctly. The input is the data package sent from the device, and the output is the data stored in the server.

[1239] Step 3:

[1240] The server uses generative artificial intelligence (AI) to analyze video, audio, and location information. The server uses OpenCV to analyze the video data and identify the delivery progress and obstacles along the way. Next, it converts the audio data into text using Google Cloud Speech-to-Text, which is then analyzed using an emotion engine. It also analyzes GPS data and calculates the delivery route. The inputs are video, audio, and GPS data, and the output is the analysis results, including progress and optimal route information.

[1241] Step 4:

[1242] The server generates an optimized delivery route based on the analysis results and generates specific instructions.The server generates an optimized delivery route based on the analysis results and generates specific instructions along that route (e.g., "turn right at the next intersection").The input is the analysis results, and the output is the optimized delivery route and specific instructions.

[1243] Step 5:

[1244] The server sends the specific instructions to the local rescuer's smart glasses. The server then sends the generated instructions to the delivery person's smart glasses in real time. The input is the specific instructions, and the output is the instructions displayed on the smart glasses.

[1245] Step 6:

[1246] The local rescuer checks the instructions on the smart glasses and carries out the delivery. The user (delivery person) checks the instructions displayed on the smart glasses and follows them to make the delivery. The input is the instructions displayed on the smart glasses, and the output is the progress of the delivery work.

[1247] Step 7:

[1248] The server monitors the emotional state of the delivery person while they are making a delivery and generates appropriate feedback using an emotion engine. The server analyzes the delivery person's facial expression data acquired by the smart glasses using Amazon Rekognition and their voice data using the emotion engine. The input is facial expression data and voice data, and the output is an appropriate feedback message (e.g., "Take a deep breath and relax").

[1249] Step 8:

[1250] The server sends the generated feedback to the smart glasses, which the delivery person receives. The server also sends the generated feedback message to the smart glasses, which the delivery person receives and acts upon. The input is the feedback message, and the output is the action taken by the delivery person (relax, take a break, etc.).

[1251] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1252] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1253] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1254] [Fourth embodiment]

[1255] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1256] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1257] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1258] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1259] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1260] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1261] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1262] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1263] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1264] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1265] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1266] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1267] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1268] As a specific embodiment of this invention, we propose a system using a terminal, a server, generative artificial intelligence, and smart glasses. This system will enable rapid and effective rescue operations in the event of a disaster.

[1269] System Configuration

[1270] Terminal

[1271] The terminals are devices used to collect video, audio, and location information at disaster sites. Specifically, they include drones, handheld cameras, microphones, and GPS devices. These terminals collect information on the situation in the disaster area in real time and transmit the data to a server via satellite communication.

[1272] Server and Generative Artificial Intelligence (AI)

[1273] The server receives the data sent from the device and analyzes it using generative artificial intelligence (AI). Specifically, the following process is performed:

[1274] 1. The server stores the video, audio, and location information received from the device.

[1275] 2. AI analyzes the footage and identifies the locations of collapsed buildings and victims.

[1276] 3. Audio analysis detects cries for help and dangerous sounds (e.g., the sound of a collapse).

[1277] 4. Combine location information to determine which areas need urgent assistance.

[1278] Smart Glasses

[1279] The smart glasses used by local rescuers display instructions sent from the server. Specific operating procedures include the following:

[1280] 1. The rescuer puts on the smart glasses and connects them to the system.

[1281] 2. The instructions generated by the server based on the analysis results are displayed on the smart glasses.

[1282] 3. Rescuers should check the displayed instructions, move quickly to the designated location and begin rescue operations.

[1283] 4. Report progress and new information on rescue operations to the server via smart glasses.

[1284] Examples

[1285] Example 1: When a major earthquake occurs

[1286] 1. The drone terminal flies over the disaster area, captures footage, and sends it to the server.

[1287] 2. AI analyzes the video on the server and identifies the collapsed building.

[1288] 3. The server determines priorities and gives top priority to areas with particularly high risk of rescue.

[1289] 4. The server generates specific instructions and sends them to the smart glasses.

[1290] 5. Rescuers follow the instructions on the smart glasses to reach the scene via a safe route and begin rescue operations.

[1291] Example 2: Post-tsunami relief efforts

[1292] 1. Collect information on the flooded area using a mobile camera and GPS device as terminals.

[1293] 2. The server receives the data live and the AI ​​analyzes the extent of the damage.

[1294] 3. Identify high-priority areas, and the server generates rescue instructions based on that information.

[1295] 4. The smart glasses display instructions saying, "The area ahead is heavily flooded. Carry out rescue operations with caution."

[1296] 5. Rescuers confirm instructions and begin rescuing victims from a safe location.

[1297] This system makes it possible to grasp the situation in disaster areas in real time and support rapid and accurate relief efforts. Analysis by generative AI and display of instructions by smart glasses can help local rescuers act quickly and effectively.

[1298] The processing flow will be explained below.

[1299] Step 1:

[1300] The device collects video, audio, and location information. Specifically, the drone flies over the affected area, taking high-resolution video with a camera, recording audio from the scene with a microphone, and obtaining location information with a GPS device.

[1301] Step 2:

[1302] The device transmits the collected video, audio, and location information to a server in real time. This is done using satellite communication, and the communication signal reaches the server via a communications satellite.

[1303] Step 3:

[1304] The server receives the data sent from the device. The server first stores the received video, audio, and location information data and prepares it for subsequent analysis.

[1305] Step 4:

[1306] The server passes the received data to the artificial intelligence (AI) that generates it, which then begins analyzing it. The AI ​​first analyzes the video data and identifies collapsed buildings and abnormalities. For example, it marks collapsed buildings with a red frame.

[1307] Step 5:

[1308] The server uses AI to perform audio analysis, detecting cries for help and other important sounds (such as the sound of a collapse) from the audio data, thereby narrowing down the areas where victims may be present.

[1309] Step 6:

[1310] The server analyzes the location data and combines it with the results of video and audio analysis to determine the level of danger and urgency in a specific area and set rescue priorities.

[1311] Step 7:

[1312] The server generates specific instructions based on the analysis results, such as "Area A has many collapsed buildings. Urgency is high. Area B is flooded. Caution."

[1313] Step 8:

[1314] The server sends the generated instructions to the local rescuer's smart glasses. The data packet reaches the rescuer's smart glasses via the communication path.

[1315] Step 9:

[1316] The user checks the instructions on the smart glasses, and the rescuer reads the instructions displayed on the smart glasses and understands the necessary actions.

[1317] Step 10:

[1318] The user follows the instructions of the smart glasses to carry out rescue operations, specifically by searching for and rescuing victims while following a safe route and beginning rescue operations.

[1319] Step 11:

[1320] The user reports the progress of the rescue operation to the server. For example, a report such as "5 victims have been rescued in Area A" is sent to the server through the smart glasses.

[1321] This series of processing steps creates a system that can smoothly collect information, analyze it, generate instructions, and carry out rescue operations in real time.

[1322] Example 1

[1323] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1324] When a disaster occurs, there are often delays in quickly and accurately grasping the situation on the ground and issuing instructions for effective relief activities. As a result, the efficiency of relief activities declines and the rescue of victims is delayed. In addition, the number of information devices carried by local rescuers is limited, so there is a lack of means to quickly receive appropriate instructions.

[1325] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1326] In this invention, the server includes: a terminal for collecting video, audio, and location information; a server for receiving the video, audio, and location information from the terminal; a server for analyzing the video, audio, and location information using a generating artificial intelligence; a server for prioritizing rescue operations based on the analysis results and generating specific instructions; a generating artificial intelligence for analyzing the video to identify the locations of collapsed structures and victims; a generating artificial intelligence for analyzing the audio to detect calls for help and dangerous sounds; a server for combining the video, audio, and location information to determine areas requiring emergency support; a server for transmitting the specific instructions to smart glasses worn by local rescuers; and a server for the local rescuers to check the instructions on the smart glasses and carry out rescue operations. This enables real-time situation assessment of the disaster site and rapid and accurate rescue operation instructions.

[1327] "Terminals" are devices that collect video, audio, and location information at disaster sites, including drones, handheld cameras, microphones, and GPS devices.

[1328] A "server" is a computer system that receives data sent from a terminal, analyzes it, and generates instructions.

[1329] "Video" refers to data that represents visual information from the disaster site. This includes videos and images taken using drones or handheld cameras.

[1330] "Audio" refers to data representing sound information from disaster sites, including calls for help and dangerous sounds collected using microphones.

[1331] "Location information" is data that indicates the latitude and longitude of specific points in a disaster site. It is collected using GPS devices.

[1332] "Generative artificial intelligence" is a machine learning model or algorithm that analyzes collected data and extracts the necessary information.

[1333] "Priority of rescue operations" is a criterion for determining the urgency based on the analysis results and the order in which rescue operations should be carried out.

[1334] "Specific instructions" are detailed instructions such as specific actions rescuers should take on site and routes to take.

[1335] "Smart glasses" are wearable devices worn by local rescuers that display instructions from a server.

[1336] "Analyzing the video" means analyzing the collected video data and identifying the locations of collapsed structures and victims.

[1337] "Analyzing voice" means analyzing collected voice data to detect cries for help or dangerous sounds.

[1338] "Determining areas needing emergency assistance" means combining video, audio, and location information to identify which areas are most in need of assistance.

[1339] "Satellite communications" is a communication method for sending and receiving data via artificial satellites.

[1340] As a specific embodiment of this invention, we propose a system using a terminal, a server, generative artificial intelligence (AI), and smart glasses, which will enable rapid and effective rescue operations in the event of a disaster.

[1341] Terminal

[1342] The terminals are devices used to collect video, audio, and location information at disaster sites. Specifically, they include drones, handheld cameras, microphones, and GPS devices. These terminals collect information on the situation in the disaster area in real time and transmit the data to a server via satellite communication.

[1343] Server and Generative Artificial Intelligence (AI)

[1344] The server receives data sent from the device and analyzes it using artificial intelligence (AI). Specifically, the following process is performed:

[1345] 1. Data Receipt and Storage

[1346] The server receives and stores the video, audio, and location information sent from the device.

[1347] 2. Video Analysis

[1348] AI analyzes the video and identifies the locations of collapsed structures and victims. For example, AI uses pattern recognition technology in the video to detect collapsed buildings.

[1349] 3. Audio Analysis

[1350] For voice data, AI detects dangerous sounds such as cries for help or the sound of a collapse. Speech recognition technology extracts specific voice patterns and detects information with a high level of urgency.

[1351] 4. Location information analysis

[1352] By analyzing location information and plotting the damage situation on a map, it is possible to identify which areas require emergency assistance.

[1353] Smart Glasses

[1354] The smart glasses used by local rescuers display instructions sent from the server, including the following specific operating procedures:

[1355] 1. System Connection

[1356] The user (local rescuer) wears the smart glasses and connects them to the system.

[1357] 2. Instruction display

[1358] The server generates instructions based on the analysis results and displays them on the smart glasses, allowing rescuers to instantly understand areas with high emergency levels and safe routes to take.

[1359] 3. Relief operations

[1360] Rescuers follow the displayed instructions to quickly move to the designated location and begin rescue operations. The smart glasses also have the function of reporting the progress of rescue operations and new information to the server.

[1361] Examples

[1362] Example 1: When a major earthquake occurs

[1363] The drone terminal flies over the disaster area, taking pictures and sending them to a server. AI on the server analyzes the pictures and identifies collapsed buildings. The server then determines priorities and prioritizes areas with particularly high risk for rescue. The server then sends instructions generated by the server to the smart glasses. Rescuers follow the instructions on the smart glasses to reach the scene via a safe route and begin rescue operations.

[1364] Example 2: Post-tsunami relief efforts

[1365] The device uses a portable camera and GPS device to collect information on the status of the flooded area. The server receives the data, and AI analyzes the extent of the damage. The server identifies high-priority areas and generates rescue instructions based on that information. The smart glasses display the following instruction: "The area ahead is heavily flooded. Carry out rescue operations with caution." Rescuers confirm the instructions and rescue victims from a safe location.

[1366] Prompt Sentence Examples

[1367] Possible prompts for the generative AI model include:

[1368] Prompt statement example 1:

[1369] Analyze the disaster situation and determine the priority of relief efforts.

[1370] Prompt statement example 2:

[1371] Use video data to identify the locations of collapsed buildings and victims, and quickly generate rescue instructions.

[1372] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1373] Program processing steps

[1374] Step 1:

[1375] The terminal collects video, audio, and location information and sends it to a server. Specifically, drones, portable cameras, microphones, and GPS devices are used to collect information on the situation in the disaster area in real time. The collected data is sent to the server via satellite communication. The input is video, audio, and location information obtained from the disaster site, and the output is the data sent to the server. As an example of how it works, a drone flies over a wide area of ​​the disaster area and collects video and location information in real time.

[1376] Step 2:

[1377] The server stores the data received from the device. The input is the video, audio, and location information sent from the device, and the output is data stored in a database on the server. For example, the server uses a secure communication protocol to safely store the received data and prepare it for subsequent analysis.

[1378] Step 3:

[1379] The server analyzes the video using generative artificial intelligence (AI). The input is the stored video data, and the output is the location information of collapsed structures and victims. Specifically, the AI ​​analyzes characteristic patterns in the video and identifies the location of collapsed buildings. For example, it identifies collapsed buildings by detecting abnormal shapes or color changes.

[1380] Step 4:

[1381] The server uses AI to analyze the voice. The input is the stored voice data, and the output is the detection results of calls for help and dangerous sounds. Specifically, it uses voice recognition technology to extract specific phrases and sound patterns and identify information with a high level of urgency. For example, it identifies the sound of a collapse or a call for help.

[1382] Step 5:

[1383] The server analyzes the location information and identifies areas requiring emergency assistance. The input is the stored location data, and the output is a list of identified high-priority areas for assistance. Specifically, the server plots the location information on a map and determines priorities according to the level of damage. For example, it compares GPS data with map information to mark high-risk areas.

[1384] Step 6:

[1385] The server generates instructions for rescue operations based on the analysis results. The inputs are the video analysis results, audio analysis results, and location information analysis results, and the output is specific rescue instructions. In concrete terms, the server inputs prompts into the generative AI model to generate rescue instructions. For example, it gives the AI ​​a prompt such as "Plan a route for the rescue team based on the disaster situation."

[1386] Step 7:

[1387] The server sends the generated instructions to the smart glasses. The input is the generated rescue instructions, and the output is the instructions displayed on the smart glasses. Specifically, the server sends instruction data to the smart glasses via wireless communication. For example, it sends an instruction such as "There is a victim 200 meters northeast. Carry out rescue using a safe route."

[1388] Step 8:

[1389] The user uses smart glasses to carry out rescue operations. The input is instructions displayed on the smart glasses, and the output is a progress report of the rescue operations. Specifically, the user follows the instructions to move to the disaster area and carry out the rescue operations. For example, the user follows the instructions on the smart glasses to go to the designated location and rescue the victims.

[1390] This step-by-step process will enable rapid and accurate relief efforts in the event of a disaster.

[1391] (Application example 1)

[1392] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1393] Conventional rescue operation systems have had difficulty quickly and accurately grasping the situation at a disaster site and providing appropriate instructions to local rescuers. Furthermore, systems for detecting abnormalities in real time and responding immediately have been inadequate for security services. Therefore, there is a demand for effective support in rescue operations and security monitoring. The purpose of this invention is to solve these problems and realize an efficient and effective support system.

[1394] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1395] In this invention, the server includes: a means for a terminal to collect video, audio, and location information; a means for the server to receive the video, audio, and location information from the terminal; a means for the server to analyze the video, audio, and location information using artificial intelligence; a means for the server to prioritize rescue operations based on the analysis results and generate specific instructions; a means for the server to transmit the specific instructions to the smart glasses of local rescuers; a means for the local rescuers to check the instructions on the smart glasses and perform rescue operations; and a means for detecting abnormalities and checking the situation in real time through the smart glasses worn by security personnel. This enables not only fast and accurate rescue operations in the event of a disaster, but also real-time detection of abnormalities in security services and immediate response.

[1396] A "terminal" is a device for collecting video, audio, and location information.

[1397] A "server" is a computer system that receives video, audio, and location information from a terminal and analyzes and stores that data.

[1398] "Footage" refers to visual information collected through equipment such as drones and fixed cameras.

[1399] "Audio" is information about sounds collected through a device such as a microphone.

[1400] "Location information" is data that indicates a geographic location, such as that collected through a GPS device.

[1401] "Generative artificial intelligence (AI)" is a general term for algorithms and technologies that analyze collected video, audio, and location information and generate judgments and instructions based on the results.

[1402] "Relief operations" are a series of actions and measures taken to support victims of a disaster.

[1403] "Smart glasses" are wearable devices that have the ability to display digital information, and are worn by local rescuers and security personnel to check instructions from the server.

[1404] A "security officer" is a person whose job is to detect anomalies and check the situation.

[1405] "Real-time" refers to immediate processing or information transmission with little or no delay.

[1406] "Anomaly detection" is the process of identifying events that deviate from normal conditions or behavior.

[1407] "Situation check" refers to understanding the current state and progress of activities based on collected information.

[1408] System Program

[1409] This system includes a program consisting of the following elements:

[1410] 1. Device (collection of video, audio, and location information):

[1411] It uses hardware such as drones, fixed cameras, handheld cameras, microphones, and GPS devices.

[1412] These devices are responsible for collecting video, audio, and location information in real time.

[1413] 2. Data transmission:

[1414] The data collected by the terminal is sent to a server via satellite communication or the Internet.

[1415] A communication module is used for this process.

[1416] 3. Server (receiving and analyzing data):

[1417] The server receives and stores the video, audio, and location information transmitted from the terminal.

[1418] Generative artificial intelligence (AI) within the server analyzes the data using OpenCV and other AI modules.

[1419] Specifically, AI detects suspicious people, suspicious sounds, and unnatural movements of people and objects.

[1420] 4. Instruction generation and transmission:

[1421] The server generates specific instructions based on the results of the AI ​​analysis and sends them to the smart glasses.

[1422] For example, an instruction such as "There is a suspicious person on the north side of the building. Approach carefully and check." may be generated.

[1423] 5. Smart Glasses (Local Support and Instructions):

[1424] Local rescuers and security personnel wear smart glasses and check instructions from the server.

[1425] Real-time feedback is possible by reporting the progress of on-site response and new information to the server via smart glasses.

[1426] Natural language explanations

[1427] 1. Hardware:

[1428] Drones, fixed cameras and handheld cameras will be used to capture footage.

[1429] A microphone is used to capture audio, and a GPS device is used to capture location information.

[1430] Smart glasses will be used to display real-time instructions and assist with on-site operations.

[1431] 2. Software:

[1432] OpenCV: Used for video data acquisition and initial analysis.

[1433] AI analyzer: Performs detailed analysis of video, audio, and location information and detects anomalies.

[1434] Communication module: Sends data from the device to the server and sends instructions from the server to the smart glasses.

[1435] 3. Data processing and calculation:

[1436] After the data is sent to the server, a generative artificial intelligence (AI) analyzes it.

[1437] Video analysis: Detect building collapses and suspicious individuals.

[1438] Audio analysis: Detects cries for help and dangerous sounds.

[1439] Identify affected areas and abnormal areas based on location information.

[1440] Set priorities and generate instructions based on the results of AI analysis.

[1441] Examples of concrete examples and prompts

[1442] Example 1: Suspicious person detection

[1443] Drones and fixed cameras collect live footage.

[1444] The AI ​​detects suspicious activity from the video and displays a message on the smart glasses saying, "There is a suspicious person on the north side of the building. Please check."

[1445] Prompt Sentence Examples

[1446] "Please analyze the following data and detect suspicious individuals. Also, please generate appropriate instructions based on the detection results."

[1447] Live video data, audio data, and location information:

[1448] Video Frame: ...

[1449] Audio fragment: ...

[1450] Location Data: ...

[1451] This enables the system to respond quickly and accurately in the event of a disaster or at the scene of a security service.In order to put the present invention into practice, it is important to use the above-mentioned hardware and software in combination.

[1452] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1453] Step 1:

[1454] The device collects video, audio, and location information. Specifically, drones, fixed cameras, and portable cameras capture video of the scene, microphones record audio, and GPS devices acquire location data. The input is the actual video, audio, and location information of the scene, and the output is this digital data.

[1455] Step 2:

[1456] The data collected by the device is sent to a server via satellite or the Internet. At this stage, the communication module on the device is used. The input is the collected digital data of video, audio, and location information, and the output is the data sent to the server.

[1457] Step 3:

[1458] The server receives and stores the video, audio, and location information sent from the device. Specifically, the server's internal storage stores the data. The input is the digital data sent from the device, and the output is the stored data.

[1459] Step 4:

[1460] The server's artificial intelligence (AI) analyzes the received data. At this stage, the AI ​​module analyzes the video using libraries such as OpenCV, and the audio processing algorithm analyzes the audio, which is then combined with location data to detect anomalies. The input is the stored digital data of video, audio, and location information, and the output is the analysis results (for example, detection of a suspicious person or suspicious sound).

[1461] Step 5:

[1462] Based on the analysis results, the server generates specific instructions. As a specific action, the AI ​​generates appropriate instructions based on the analysis results. For example, "There is a suspicious person on the north side of the building. Approach carefully and check." The input is the AI's analysis results, and the output is the generated instructions.

[1463] Step 6:

[1464] The server sends the generated instructions to the smart glasses. The instructions are sent through the communication module. The input is the generated instruction text, and the output is the instruction displayed on the smart glasses.

[1465] Step 7:

[1466] The user (local rescuer or security officer) uses the smart glasses to check instructions and respond at the scene. Specifically, the user checks the scene and takes necessary action based on the instructions displayed on the smart glasses. The input is the instructions displayed on the smart glasses, and the output is specific response activities at the scene and their progress information.

[1467] Step 8:

[1468] The user reports the progress of the response to the server through the smart glasses. At this stage, the report is made using the communication function of the smart glasses. The input is the new on-site information and progress information acquired by the user, and the output is the progress data sent to the server.

[1469] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1470] As a specific embodiment of this invention, we propose a system using a terminal, a server, generative artificial intelligence, smart glasses, and an emotion engine. This system enables rapid and effective rescue operations in the event of a disaster, while optimizing the psychological and emotional state of rescuers.

[1471] System Configuration

[1472] Terminal

[1473] The terminals are devices used to collect video, audio, and location information at disaster sites. Specifically, they include drones, handheld cameras, microphones, and GPS devices. These terminals collect information on the situation in the disaster area in real time and transmit the data to a server via satellite communication.

[1474] Server and Generative Artificial Intelligence (AI)

[1475] The server receives the data sent from the device and analyzes it using generative artificial intelligence (AI). Specifically, the following process is performed:

[1476] 1. The server stores the video, audio, and location information data received from the device.

[1477] 2. AI analyzes the video data and identifies collapsed buildings and abnormal areas.

[1478] 3. Audio analysis detects cries for help and dangerous sounds (e.g., the sound of a collapse).

[1479] 4. Combine location information to determine which areas need urgent assistance.

[1480] Smart Glasses

[1481] The smart glasses used by local rescuers display instructions sent from the server. Specific operating procedures include the following:

[1482] 1. The rescuer puts on the smart glasses and connects them to the system.

[1483] 2. The instructions generated by the server based on the analysis results are displayed on the smart glasses.

[1484] 3. Rescuers should check the displayed instructions, move quickly to the designated location and begin rescue operations.

[1485] 4. Report progress and new information on rescue operations to the server via smart glasses.

[1486] Emotion Engine

[1487] The emotion engine recognizes and analyzes the rescuer's emotions in real time and provides appropriate feedback based on the results. This function allows the rescuer's psychological and emotional state to be monitored, and stress reduction measures and relaxation instructions can be implemented as needed.

[1488] 1. The emotion engine analyzes the facial expressions and voice of rescuers through smart glasses.

[1489] 2. The server receives the data from the emotion engine and evaluates the rescuer's emotional state.

[1490] 3. The server generates additional instructions or feedback (e.g., encouraging messages or suggestions for taking a break) based on the emotional state and sends them to the smart glasses.

[1491] Examples

[1492] Example 1: When a major earthquake occurs

[1493] 1. The drone terminal flies over the disaster area, captures footage, and sends it to the server.

[1494] 2. AI analyzes the video on the server and identifies the collapsed building.

[1495] 3. The server determines priorities and gives top priority to areas with particularly high risk of rescue.

[1496] 4. The server generates specific instructions and sends them to the smart glasses.

[1497] 5. Rescuers follow the instructions on the smart glasses to reach the scene via a safe route and begin rescue operations.

[1498] 6. The emotion engine monitors the rescuer's emotional state and sends encouragement or instructions to relax as needed.

[1499] Example 2: Post-tsunami relief efforts

[1500] 1. Collect information on the flooded area using a mobile camera and GPS device as terminals.

[1501] 2. The server receives the data live and the AI ​​analyzes the extent of the damage.

[1502] 3. Identify high-priority areas, and the server generates rescue instructions based on that information.

[1503] 4. The smart glasses display instructions saying, "The area ahead is heavily flooded. Carry out rescue operations with caution."

[1504] 5. Rescuers confirm instructions and begin rescuing victims from a safe location.

[1505] 6. The emotion engine monitors the rescuer's emotional state and provides psychological support as needed.

[1506] This system allows for a real-time understanding of the situation in disaster areas, enabling rapid and accurate support for relief efforts. Furthermore, the introduction of an emotion engine helps maintain the psychological and emotional health of relief workers, enabling sustainable relief efforts.

[1507] The processing flow will be explained below.

[1508] Step 1:

[1509] The device collects video, audio, and location information. Specifically, the drone flies over the affected area, taking high-resolution video with a camera, recording audio from the scene with a microphone, and obtaining location information with a GPS device.

[1510] Step 2:

[1511] The device transmits the collected video, audio, and location information to a server in real time. This is done using satellite communication, and the communication signal reaches the server via a communications satellite.

[1512] Step 3:

[1513] The server receives the data sent from the device. The server first stores the received video, audio, and location information data and prepares it for subsequent analysis.

[1514] Step 4:

[1515] The server passes the received data to the artificial intelligence (AI) that generates it, which then begins analyzing it. The AI ​​analyzes the video data and identifies collapsed buildings and abnormalities. For example, it marks collapsed buildings with a red frame.

[1516] Step 5:

[1517] The server uses AI to perform audio analysis, detecting cries for help and other important sounds (such as the sound of a collapse) from the audio data, thereby narrowing down the areas where victims may be present.

[1518] Step 6:

[1519] The server analyzes the location data and combines it with the results of video and audio analysis to determine the level of danger and urgency in a specific area and set rescue priorities.

[1520] Step 7:

[1521] The server generates specific instructions based on the analysis results, such as "Area A has many collapsed buildings. Urgency is high. Area B is flooded. Caution."

[1522] Step 8:

[1523] The server sends the generated instructions to the local rescuer's smart glasses. The data packet reaches the rescuer's smart glasses via the communication path.

[1524] Step 9:

[1525] The user checks the instructions on the smart glasses, and the rescuer reads the instructions displayed on the smart glasses and understands the necessary actions.

[1526] Step 10:

[1527] The user follows the instructions of the smart glasses to carry out rescue operations, specifically by searching for and rescuing victims while following a safe route and beginning rescue operations.

[1528] Step 11:

[1529] The user reports the progress of the rescue operation to the server. For example, a report such as "5 victims have been rescued in Area A" is sent to the server through the smart glasses.

[1530] Step 12:

[1531] The emotion engine analyzes the facial expressions and voice of the rescuer through the smart glasses. Facial expression analysis uses facial recognition technology, and voice analysis uses emotion recognition algorithms.

[1532] Step 13:

[1533] The server receives the data from the emotion engine and evaluates the rescuer's emotional state, generating an evaluation result such as "high fatigue" or "stressed."

[1534] Step 14:

[1535] The server generates additional instructions and feedback based on the user's emotional state and sends them to the smart glasses, such as messages like "We recommend you take a break" or "Please relax."

[1536] Step 15:

[1537] The user sees additional feedback through the smart glasses, and the rescuer receives feedback based on their emotional state, allowing them to take a break or relax if necessary.

[1538] This series of steps enables real-time information collection, analysis, instruction generation, and emotion monitoring, supporting rapid and accurate rescue operations while maintaining the psychological and emotional well-being of rescuers.

[1539] Example 2

[1540] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1541] To carry out rapid and effective relief efforts during disasters, it is necessary to grasp the situation in the affected area in real time and make quick decisions. However, rescuers on the ground work under high-stress conditions, so their mental and emotional health must also be maintained. Currently, there is a lack of systems that can simultaneously meet these complex demands.

[1542] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1543] In this invention, the server includes: a terminal collecting video, audio, and location information; a server receiving the video, audio, and location information from the terminal; a server analyzing the video, audio, and location information using artificial intelligence; a server prioritizing rescue operations and generating specific instructions based on the analysis results; a server transmitting the specific instructions to a visual display device of a local rescuer; a server allowing the local rescuer to check the instructions on the visual display device and perform rescue operations; and a server recognizing and analyzing the emotional state of the rescuer in real time and providing feedback. This enables rapid and effective rescue operations in the event of a disaster and also maintains the mental and emotional health of the rescuer.

[1544] "Terminal" means equipment for collecting video, audio, and location information, including drones, handheld cameras, microphones, and GPS devices.

[1545] A "server" is a central control device that receives and analyzes data sent from the terminals, and generates and sends instructions and feedback.

[1546] "Footage" refers to visual information obtained from devices such as cameras and drones, and records the situation and environment of the disaster-stricken area.

[1547] "Audio" refers to auditory information acquired from a terminal such as a microphone, and records the voices of rescuers and environmental sounds at the scene.

[1548] "Location information" refers to geographic coordinate information obtained from a terminal such as a GPS device, and identifies the location and movement of a target.

[1549] "Generative artificial intelligence (AI)" refers to algorithms or models that analyze data received by a server and identify specific patterns or anomalies.

[1550] "Analysis" is the process of extracting specific information and patterns from received data to produce meaningful results.

[1551] "Priority" is a criterion for evaluating the urgency and importance of a response in relief operations, and determines which areas and items will be given priority.

[1552] "Instructions" are specific actions or responses generated by the server, providing guidelines or procedures for on-site rescuers to follow.

[1553] A "visual display device" is a device worn by a rescuer to visually confirm instructions and feedback from the server, and includes smart glasses and the like.

[1554] "Emotional state" refers to the mental and emotional well-being of field rescuers, including psychological changes such as stress and fatigue.

[1555] "Feedback" refers to advice and instructions provided by the server based on the rescuer's emotional state, with the aim of providing emotional support and correcting behavior.

[1556] MODE FOR CARRYING OUT THE INVENTION

[1557] As a specific embodiment of this invention, we propose a system using a terminal, a server, a generative AI model, a visual display device, and an emotion engine. This system enables rapid and effective rescue operations in the event of a disaster, while optimizing the psychological and emotional state of rescuers.

[1558] System Configuration

[1559] Terminal

[1560] The terminals are devices used to collect video, audio, and location information at disaster sites. Specifically, they include drones, handheld cameras, microphones, and GPS devices. These terminals collect information on the situation in the disaster area in real time and transmit the data to a server via satellite communication.

[1561] Server and Generative Artificial Intelligence (AI)

[1562] The server receives the data sent from the device and analyzes it using a generative AI model. The specific processing is as follows: The server stores the video, audio, and location information data received from the device, and the AI ​​analyzes the video data to identify collapsed buildings and abnormal areas. It also uses audio analysis to detect cries for help and dangerous sounds (such as the sound of a collapse). It also combines this with location information to determine which areas require emergency assistance.

[1563] Visual Display Devices

[1564] The visual display device used by the local rescuer displays the instructions sent from the server. Specifically, when the rescuer wears the visual display device and connects it to the system, instructions generated by the server based on the analysis results are displayed on the display. The rescuer checks the displayed instructions, quickly moves to the designated location, and begins rescue operations. The rescuer also reports the progress of rescue operations and new information to the server via the visual display device.

[1565] Emotion Engine

[1566] The emotion engine recognizes and analyzes the rescuer's emotions in real time and provides appropriate feedback based on the results. This function allows the rescuer's psychological and emotional state to be monitored and stress reduction measures or relaxation instructions to be implemented as needed. Specifically, the emotion engine analyzes the rescuer's facial expressions and voice via a visual display device. The server receives data from the emotion engine and evaluates the rescuer's emotional state. Furthermore, the server generates additional instructions or feedback (e.g., encouraging messages or suggestions for taking a break) based on the emotional state and sends them to the visual display device.

[1567] Examples

[1568] Example 1: When a major earthquake occurs

[1569] The drone terminal flies over the disaster area, taking footage and sending it to a server. AI on the server analyzes the footage and identifies collapsed buildings. The server then determines priorities and prioritizes areas with particularly high risk for rescue. The server then generates specific instructions and sends them to a visual display device. Rescuers follow the instructions to reach the scene via a safe route and begin rescue operations. The emotion engine monitors the emotional state of the rescuers and sends encouragement or instructions to relax as needed.

[1570] Example prompt sentence:

[1571] A major earthquake occurs. Check the situation in the affected areas. Analyze the footage taken by the drone and identify the collapsed buildings.

[1572] Example 2: Post-tsunami relief efforts

[1573] The terminal uses a portable camera and GPS device to collect information on the status of the flooded area. The server receives the data, and AI analyzes the extent of the damage. High-priority areas are identified, and the server generates rescue instructions based on that information. A visual display device displays the instruction, "The area ahead is heavily flooded. Carry out rescue operations with caution." Rescuers confirm the instructions and rescue victims from a safe location. An emotion engine monitors the emotional state of rescuers and provides psychological support as needed.

[1574] Example prompt sentence:

[1575] After a tsunami occurs, collect information about the flooded area. Use handheld cameras and GPS devices to analyze the extent of the flooding.

[1576] This invention allows us to grasp the situation in disaster areas in real time and support rapid and accurate relief efforts. Furthermore, the introduction of an emotion engine will help maintain the psychological and emotional health of rescuers, enabling sustainable relief efforts.

[1577] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1578] System program processing flow and specific procedures

[1579] Step 1: The device collects data and sends it to the server

[1580] The device arrives at the disaster site and collects video, audio, and location information. The input is video, audio, and location data obtained from cameras, microphones, and GPS devices. The device collects this data and transmits it to a server via satellite communication. For example, a drone could fly over a disaster area, capture high-resolution video, and upload the data to a server in real time.

[1581] Step 2: The server receives and stores the data

[1582] The server receives video, audio, and location information sent from the device. The input is the data sent from the device, and the server stores it in a database in a specified format. For example, specific operations include storing video data in video storage, audio data in audio storage, and location information in a geospatial database. The output is the stored data.

[1583] Step 3: The server passes the data to the AI ​​and requests it for analysis.

[1584] The server passes the stored data to a generative artificial intelligence (AI) model and requests it to analyze it. The input is the video, audio, and location information stored in the database, and the server provides this data to the AI. The AI ​​identifies collapsed buildings and abnormalities from the video, and detects calls for help and dangerous sounds from the audio. For example, a specific operation would be for the AI ​​to analyze a frame of video and label the location of a collapsed building. The output is the analysis results.

[1585] Step 4: The server generates instructions based on the analysis results.

[1586] Based on the results of the AI ​​analysis, the server determines which areas require emergency response. The input is the AI ​​analysis results, and the server generates specific instructions based on the results of the judgment. By inputting instructions into the generative AI model, specific instructions in natural language are generated. For example, generating an instruction such as "A collapsed building has been found in this area, so carry out emergency rescue operations" is a specific action. The output is the generated instructions.

[1587] Step 5: The server sends instructions to the smart glasses

[1588] The server sends the generated instructions to the visual display device (smart glasses) of the local rescuer. The input is the generated instructions, which the server sends to the visual display device. For example, the specific action is to display "Head to the intersection of 5th Street" on the smart glasses display and hear the same instruction aloud. The output is the instructions received by the rescuer.

[1589] Step 6: The user acts according to the instructions of the smart glasses.

[1590] The user acts according to the instructions displayed on the smart glasses. The input is the instructions displayed on the smart glasses, and the user performs activities in the disaster area based on the instructions. For example, specific actions include passing through the disaster area following the displayed route and starting rescue operations around collapsed buildings. The output is the progress of the rescue operations.

[1591] Step 7: The server monitors the user's emotional state through the emotion engine and sends feedback.

[1592] The server uses an emotion engine to analyze the user's emotional state in real time. The input is facial expression and voice data obtained from the smart glasses' camera and microphone, which the server analyzes to evaluate the user's emotional state. If necessary, feedback is generated and sent to the smart glasses. For example, if the server detects that the rescuer is very tired, it could send a message to the smart glasses saying, "Please take a 10-minute break." The output is feedback provided to the rescuer.

[1593] (Application example 2)

[1594] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1595] A system that can provide quick and accurate instructions is needed for on-site relief efforts. However, current systems make it difficult for relief workers to understand the optimal route and actions in real time, which places a heavy psychological burden on them. Similarly, in delivery services, there is a lack of systems that allow delivery workers to understand the optimal route in real time and improve delivery efficiency, as well as the psychological burden on delivery workers.

[1596] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: a means for the terminal to collect video, audio, and location information; a means for the server to receive video, audio, and location information from the terminal; a means for the server to analyze the video, audio, and location information using artificial intelligence; a means for the server to prioritize rescue operations based on the analysis results and generate specific instructions; a means for the server to send the specific instructions to the smart glasses of the local rescuer; a means for the local rescuer to check the instructions on the smart glasses and perform the rescue operation; a means for the server to generate an optimized delivery route and send the instructions to the smart glasses; and a means for using an emotion engine to analyze the emotional state of the delivery person and generate appropriate feedback. This enables efficient real-time support in rescue operations and delivery services with reduced psychological burden.

[1597] A "terminal" is a device for collecting video, audio, and location information.

[1598] A "server" is a computer system that analyzes video, audio, and location information received from a terminal and generates instructions and feedback.

[1599] "Generative AI" is an AI that has the ability to analyze collected data and generate instructions regarding disasters and deliveries.

[1600] "Smart glasses" are wearable devices worn by users that provide visual instructions and feedback in real time.

[1601] "Analysis results" are the results obtained by analyzing the data collected by the server and generating AI.

[1602] "Instructions" are specific guidelines for action or instructions generated based on the analysis results.

[1603] "Delivery route optimization" is the process of calculating the optimal route for a delivery person to reach their destination efficiently.

[1604] The "emotion engine" is a system that analyzes the emotional state of delivery personnel and rescuers and provides feedback based on the results.

[1605] "Feedback" is a message of advice or support to the user that is generated based on information analyzed by the emotion engine.

[1606] As an embodiment of this invention, we propose a system that combines a terminal, a server, smart glasses, an emotion engine, and generative artificial intelligence (AI). This system improves delivery efficiency and optimizes the emotional state of delivery personnel.

[1607] System Configuration

[1608] 1. Terminal

[1609] The terminal is a device for collecting video, audio, and location information. Specifically, it includes smartphones and tablets. These terminals are carried by delivery personnel, collect information on the situation during delivery, and send the data to a server.

[1610] 2. Server and Generative Artificial Intelligence (AI)

[1611] The server receives the data sent from the terminal and analyzes it using generative artificial intelligence (AI). The specific processing steps are as follows:

[1612] The server stores the video, audio, and location information data received from the terminal.

[1613] AI analyzes video data to identify delivery progress and obstacles along the way (e.g., using OpenCV for video analysis).

[1614] For voice analysis, the Google Cloud Speech-to-Text API is used to convert voice data into text and analyze it using an emotion engine.

[1615] Analyzes GPS location information and optimizes delivery routes.

[1616] 3. Smart Glasses

[1617] The smart glasses used by the delivery person display instructions sent from the server, and perform the following operations depending on the situation during the delivery:

[1618] The delivery person wears smart glasses and connects them to the system.

[1619] Instructions generated by the server based on the analysis results are displayed on the smart glasses.

[1620] The delivery person checks the displayed instructions and proceeds along the optimal route.

[1621] 4. Emotion Engine

[1622] The emotion engine recognizes and analyzes the delivery person's emotions in real time and provides appropriate feedback based on the results. This function monitors the delivery person's psychological and emotional state and provides stress reduction measures and relaxation instructions as needed.

[1623] The emotion engine analyzes the delivery person's facial expressions and voice through smart glasses (e.g., using Amazon Rekognition to perform facial expression analysis).

[1624] A server receives the data from the emotion engine and evaluates the emotional state of the delivery person.

[1625] The server generates additional instructions or feedback (e.g., encouraging messages or suggestions for taking a break) based on the emotional state and sends them to the smart glasses.

[1626] Examples

[1627] Example 1: Standard delivery

[1628] 1. The delivery person wears smart glasses and the device collects location information.

[1629] 2. AI analyzes the video on the server and determines the progress of the delivery.

[1630] 3. The server determines the priority and generates the optimal delivery route.

[1631] 4. The server generates specific instructions and sends them to the smart glasses.

[1632] 5. The delivery person follows the instructions on the smart glasses to complete the delivery via a safe route.

[1633] 6. The emotion engine monitors the delivery person's emotional state and sends encouragement or instructions to relax as needed.

[1634] Example 2: Managing stress during delivery

[1635] 1. In congested roads, a delivery person puts on smart glasses and begins their delivery.

[1636] 2. The server receives the location information and video data and analyzes the delivery route.

[1637] 3. The emotion engine analyzes the delivery person's facial expression data to detect signs of stress.

[1638] 4. The server generates an encouraging message such as "Take a deep breath and relax" and sends it to the smart glasses.

[1639] 5. The delivery person follows instructions, relaxes to reduce stress, and continues with the delivery.

[1640] Prompt Sentence Examples

[1641] "If a user is driving with a grumpy expression, generate a message suggesting they relax."

[1642] "When a delivery person is nearing their destination, display a message informing them of their progress."

[1643] These procedures can provide a system that improves delivery efficiency and provides psychological support to delivery personnel.

[1644] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1645] Step 1:

[1646] The device (smartphone or tablet) collects video, audio, and location information. The user (delivery person) turns on the device, takes video of the delivery with the camera, records audio with the microphone, and obtains location information via GPS. This data is sent to the server. The input is video, audio, and GPS data, and the output is a data package of these.

[1647] Step 2:

[1648] The server receives video, audio, and location information from the device. The server temporarily stores the received data and checks whether the data was received correctly. The input is the data package sent from the device, and the output is the data stored in the server.

[1649] Step 3:

[1650] The server uses generative artificial intelligence (AI) to analyze video, audio, and location information. The server uses OpenCV to analyze the video data and identify the delivery progress and obstacles along the way. Next, it converts the audio data into text using Google Cloud Speech-to-Text, which is then analyzed using an emotion engine. It also analyzes GPS data and calculates the delivery route. The inputs are video, audio, and GPS data, and the output is the analysis results, including progress and optimal route information.

[1651] Step 4:

[1652] The server generates an optimized delivery route based on the analysis results and generates specific instructions.The server generates an optimized delivery route based on the analysis results and generates specific instructions along that route (e.g., "turn right at the next intersection").The input is the analysis results, and the output is the optimized delivery route and specific instructions.

[1653] Step 5:

[1654] The server sends the specific instructions to the local rescuer's smart glasses. The server then sends the generated instructions to the delivery person's smart glasses in real time. The input is the specific instructions, and the output is the instructions displayed on the smart glasses.

[1655] Step 6:

[1656] The local rescuer checks the instructions on the smart glasses and carries out the delivery. The user (delivery person) checks the instructions displayed on the smart glasses and follows them to make the delivery. The input is the instructions displayed on the smart glasses, and the output is the progress of the delivery work.

[1657] Step 7:

[1658] The server monitors the emotional state of the delivery person while they are making a delivery and generates appropriate feedback using an emotion engine. The server analyzes the delivery person's facial expression data acquired by the smart glasses using Amazon Rekognition and their voice data using the emotion engine. The input is facial expression data and voice data, and the output is an appropriate feedback message (e.g., "Take a deep breath and relax").

[1659] Step 8:

[1660] The server sends the generated feedback to the smart glasses, which the delivery person receives. The server also sends the generated feedback message to the smart glasses, which the delivery person receives and acts upon. The input is the feedback message, and the output is the action taken by the delivery person (relax, take a break, etc.).

[1661] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1662] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1663] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1664] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1665] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1666] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1667] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1668] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1669] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1670] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1671] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1672] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1673] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1674] 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.

[1675] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1676] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1677] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1678] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1679] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1680] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1681] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1682] The following is further disclosed regarding the above embodiment.

[1683] (Claim 1)

[1684] A means for the device to collect video, audio, and location information;

[1685] A server receives video, audio, and location information from the terminal;

[1686] A means for the server to analyze the video, audio, and location information using artificial intelligence;

[1687] a means for the server to prioritize rescue operations based on the analysis results and generate specific instructions;

[1688] A means for the server to send the specific instructions to the smart glasses of the local rescuers;

[1689] A means for local rescuers to check instructions on the smart glasses and carry out rescue operations;

[1690] A system including:

[1691] (Claim 2)

[1692] 10. The system of claim 1, further comprising means for the terminal to transmit data to the server via satellite communication.

[1693] (Claim 3)

[1694] 10. The system of claim 1, further comprising means for local rescuers to report progress of rescue operations to a server using the smart glasses.

[1695] "Example 1"

[1696] (Claim 1)

[1697] A means for the device to collect video, audio, and location information;

[1698] A server receives video, audio, and location information from the terminal;

[1699] A means for the server to analyze the video, audio, and location information using artificial intelligence;

[1700] a means for the server to prioritize rescue operations based on the analysis results and generate specific instructions;

[1701] A means for the generative AI to analyze the footage to identify the location of collapsed structures and victims;

[1702] A means for the generative artificial intelligence to analyze audio to detect cries for help or dangerous sounds;

[1703] a means for combining the video, audio, and location information to determine areas requiring emergency assistance;

[1704] A means for the server to send the specific instructions to the smart glasses of the local rescuers;

[1705] A means for local rescuers to check instructions on the smart glasses and carry out rescue operations;

[1706] A system including:

[1707] (Claim 2)

[1708] 10. The system of claim 1, further comprising means for the terminal to transmit data to the server via satellite communication.

[1709] (Claim 3)

[1710] 10. The system of claim 1, further comprising means for local rescuers to report progress of rescue operations to a server using the smart glasses.

[1711] "Application Example 1"

[1712] (Claim 1)

[1713] A means for the device to collect video, audio, and location information;

[1714] A server receives video, audio, and location information from the terminal;

[1715] A means for the server to analyze the video, audio, and location information using artificial intelligence;

[1716] a means for the server to prioritize rescue operations based on the analysis results and generate specific instructions;

[1717] A means for the server to send the specific instructions to the smart glasses of the local rescuers;

[1718] A means for local rescuers to check instructions on the smart glasses and carry out rescue operations;

[1719] A means to detect abnormalities in real time and check the situation through smart glasses worn by security personnel,

[1720] A system including:

[1721] (Claim 2)

[1722] 10. The system of claim 1, further comprising means for the terminal to transmit data to the server via satellite communication.

[1723] (Claim 3)

[1724] 10. The system of claim 1, further comprising means for local rescuers to report progress of rescue operations to a server using the smart glasses.

[1725] (Claim 4)

[1726] 10. The system of claim 1, further comprising means for receiving instructions from the server and displaying them on the smart glasses.

[1727] "Example 2: Combining Emotion Engines"

[1728] (Claim 1)

[1729] A means for the device to collect video, audio, and location information;

[1730] A server receives video, audio, and location information from the terminal;

[1731] A means for the server to analyze the video, audio, and location information using artificial intelligence;

[1732] a means for the server to prioritize rescue operations based on the analysis results and generate specific instructions;

[1733] means for the server to transmit said specific instructions to a visual display device of a field rescuer;

[1734] a means for a local rescuer to check instructions on the visual display device and carry out rescue operations;

[1735] a means for the server to recognize and analyze the emotional state of the rescuer in real time and provide feedback;

[1736] A system including:

[1737] (Claim 2)

[1738] 10. The system of claim 1, further comprising means for the terminal to transmit data to the server via satellite communication.

[1739] (Claim 3)

[1740] 10. The system of claim 1, further comprising means for local rescuers to report progress of rescue operations to a server using the visual display device.

[1741] "Application example 2 when combining emotion engines"

[1742] (Claim 1)

[1743] A means for the device to collect video, audio, and location information;

[1744] A server receives video, audio, and location information from the terminal;

[1745] A means for the server to analyze the video, audio, and location information using artificial intelligence;

[1746] a means for the server to prioritize rescue operations based on the analysis results and generate specific instructions;

[1747] A means for the server to send the specific instructions to the smart glasses of the local rescuers;

[1748] A means for local rescuers to check instructions on the smart glasses and carry out rescue operations;

[1749] a means for the server to generate delivery route optimization instructions and transmit the instructions to the smart glasses;

[1750] A means for analyzing the emotional state of the delivery person using an emotion engine and generating appropriate feedback;

[1751] A system including:

[1752] (Claim 2)

[1753] 10. The system of claim 1, further comprising means for the terminal to transmit data to the server via satellite communication.

[1754] (Claim 3)

[1755] 10. The system of claim 1, further comprising means for local rescuers to report progress of rescue operations to a server using the smart glasses. [Explanation of symbols]

[1756] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for the device to collect video, audio, and location information; A server receives video, audio, and location information from the terminal; A means for the server to analyze the video, audio, and location information using artificial intelligence; a means for the server to prioritize rescue operations based on the analysis results and generate specific instructions; A means for the server to send the specific instructions to the smart glasses of the local rescuers; A means for local rescuers to check instructions on the smart glasses and carry out rescue operations; A system including:

2. 10. The system of claim 1, further comprising means for the terminal to transmit data to the server via satellite communication.

3. The system of claim 1 , further comprising means for local rescuers to report progress of rescue operations to a server using the smart glasses.

Images