system

Abstract

We provide the system. [Solution] An input method for disaster victims to enter information on the disaster situation, rescue requests, and necessary supplies, An analysis method using a generative AI model that analyzes input information to identify the extent of the damage and the type and quantity of necessary supplies and rescue efforts, A notification generation means that generates and sends notifications to the appropriate departments based on the analysis results, A route guidance system that uses GPS functionality to obtain the current location of disaster victims and guides them to the nearest evacuation shelter, A means of receiving information on the safety status of disaster victims and posting it on a disaster message board, A system that includes this.

Description

Technical Field

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

Background Art

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

Summary of the Invention

Problems to be Solved by the Invention

[0004] At the time of a disaster, there are serious problems that the telephone lines are often congested and communication failures occur frequently, so that the rescue requests of the disaster victims and the information on necessary supplies cannot be properly transmitted. In addition, since the relief supplies do not reach the appropriate places, the supplies that are really needed are often insufficient. As a result, it becomes difficult to ensure the safety of the disaster victims and to evacuate them quickly. Furthermore, the delay in transmitting the safety information increases the anxiety. It is necessary to solve these problems.

Means for Solving the Problems

[0005] This invention provides an input means for disaster victims to input information on the disaster situation, rescue requests, and necessary supplies. It also includes an analysis means using a generative AI model that analyzes the input information to identify the extent of the damage and the type and quantity of necessary supplies and rescue. Furthermore, it includes a notification generation means that generates and sends notifications to the appropriate departments based on the analysis results, thereby enabling rapid rescue and supply provision. In addition, it includes a route guidance means that uses GPS functionality to obtain the current location of disaster victims and guides them to the nearest evacuation center, ensuring their safe evacuation. Moreover, it includes a safety information means that receives information on the safety of disaster victims and posts it on a disaster message board, enabling rapid confirmation of their safety. This makes it possible to comprehensively cover information transmission, supply of goods, evacuation route guidance, and safety confirmation during a disaster.

[0006] Definition of Terms

[0007] "Disaster victims" refers to individuals who have been directly affected by a disaster.

[0008] "Disaster situation" refers to the overall situation, including damage to buildings, injuries, and changes in the surrounding environment, all resulting from the disaster.

[0009] A "rescue request" refers to a disaster victim asking for assistance such as an ambulance or rescue team.

[0010] "Information on necessary supplies" refers to the specific types and quantities of supplies that disaster victims need during a disaster.

[0011] "Input means" refers to a device or method for disaster victims to input data on disaster conditions, rescue requests, and necessary supplies.

[0012] "Analysis means" refers to a generative AI model that analyzes information obtained from input means to determine the extent of the damage and the type and quantity of necessary supplies and rescue efforts.

[0013] A "generative AI model" refers to artificial intelligence technology that analyzes input data and determines appropriate countermeasures.

[0014] "Notification generation means" refers to a device or method that generates and sends necessary notifications to the appropriate departments based on the analysis results from the analysis means.

[0015] "Notification" refers to information regarding disaster situations, rescue requests, and supply needs that is sent to the appropriate departments.

[0016] "Route guidance means" refers to a device or method that guides disaster victims to the nearest evacuation shelter safely based on their current location.

[0017] "GPS functionality" refers to the technology that uses the Global Positioning System to determine the current location of disaster victims.

[0018] "Safety information" refers to information used to inform people about the survival status and safety of disaster victims.

[0019] "Safety information means" refers to a device or method for receiving safety information from disaster victims and posting it on disaster message boards or similar platforms.

[0020] A "disaster message board" refers to a system and service that allows information about the safety of disaster victims to be made public and checked by relevant parties during a disaster. [Brief explanation of the drawing]

[0021] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0022] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0023] First, the terms used in the following description will be explained.

[0024] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), and APU (Accelerated Processing Unit).

[0025] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0026] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0027] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0028] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0029] [First Embodiment]

[0030] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0031] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0032] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0033] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0034] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0035] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0036] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0037] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0038] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0039] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0040] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0041] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0042] Modes for carrying out the invention

[0043] This invention utilizes data communication to centrally manage disaster relief requests, requests for necessary supplies, safety checks, and route guidance to evacuation centers, and implements a system that promptly notifies relevant parties using a generation AI model. A specific embodiment of this system is described in detail below.

[0044] (System Overview)

[0045] This system consists of terminals, servers, and generative AI models. Disaster victims (users) input data from terminals such as smartphones and tablets, and the server analyzes and notifies them of this information. Furthermore, the use of generative AI models ensures that the input data is processed efficiently and accurately.

[0046] (Detailed system processing flow)

[0047] 1. Information input processing

[0048] Users: Disaster victims use smartphones or tablets to input information about the disaster situation, rescue requests, necessary supplies, and their safety status. For example, they might input specific information such as, "My house is half-destroyed. I injured my leg. I need drinking water and a first-aid kit."

[0049] Terminal: The terminal organizes the input information and prepares it to be sent to the server via the data communication network.

[0050] 2. Information transmission process

[0051] Terminal: The organized information is sent to the server via the data communication network. Here, the information is converted to, for example, JSON format and sent using the HTTPS protocol.

[0052] 3. Information reception and analysis processing

[0053] Server: The server receives information sent from the user and passes it to the generating AI model. The server analyzes the information to identify the extent of the damage and the type and quantity of supplies and rescue needed.

[0054] Generative AI Model: This model analyzes the input data and identifies information such as "house partially destroyed," "foot injury," and "drinking water and first-aid kit."

[0055] 4. Notification generation process

[0056] Server: Based on the analysis results, it generates and sends necessary notifications to the relevant departments. For example, it creates and sends a "request for rescue of a foot injury" to emergency services and a "request for delivery of drinking water and first-aid kits" to support organizations.

[0057] 5. Route guidance processing

[0058] Device: Obtains the current location of disaster victims via GPS.

[0059] Server: Based on the acquired location information, it calculates a safe route to the nearest evacuation shelter. This is done by using a map service API to calculate the specific evacuation route.

[0060] Terminal: Displays routing information received from the server and guides disaster victims to evacuate safely.

[0061] 6. Safety confirmation process

[0062] User: Disaster victims enter their own safety information, for example, "I'm safe. I'm heading to the evacuation center."

[0063] Terminal: Send this safety information to the server.

[0064] Server: Posts received safety information to the disaster message board so that relevant parties can check it.

[0065] (Specific example)

[0066] Let's consider a scenario where victim A is at home during a disaster. A uses their smartphone to type, "My house is half-destroyed. I've injured my leg. I need drinking water and a first-aid kit." The device sends this information to a server, which analyzes it using a generative AI model. The server notifies emergency services of a "request for assistance regarding the leg injury" and aid organizations of a "request for delivery of drinking water and a first-aid kit." Furthermore, it obtains A's current location and guides them to the nearest evacuation center. When A enters a safety update such as, "I'm safe. I'm heading to the evacuation center," this information is posted on a disaster message board, allowing family and friends to see it.

[0067] In this way, this system comprehensively supports disaster relief efforts, including information dissemination, supply of relief goods, guidance on evacuation routes, and confirmation of the safety of disaster victims, thereby supporting their safety and rapid evacuation.

[0068] The following describes the processing flow.

[0069] (Specific processing flow of the program)

[0070] Step 1: Enter Information

[0071] Users: Disaster victims use smartphones or tablets to input information about the disaster situation, rescue requests, necessary supplies, and their safety status. They input specific information such as, "My house is half destroyed. I injured my leg. I need drinking water and a first-aid kit."

[0072] Terminal: The terminal organizes the input information and generates data packets.

[0073] Step 2: Send Information

[0074] Terminal: Sends organized information packets to the server via the data communication network. The information is converted to, for example, JSON format, and a POST request is sent to the server using the HTTPS protocol.

[0075] Step 3: Information Reception

[0076] Server: Receives information packets sent by the user on the receiving port. Decodes the received information for analysis.

[0077] Step 4: Information Analysis

[0078] Server: Passes the received information to the generating AI model for analysis. The generating AI model identifies the disaster situation, the extent of injuries, and the types and quantities of supplies needed.

[0079] Generative AI model: Based on input data, it identifies information such as "house partially destroyed," "foot injury," and "drinking water and first-aid kit."

[0080] Step 5: Generate Notification

[0081] Server: Based on the analysis results, it generates notifications for the relevant departments. For example, it creates a "request for assistance for a foot injury" for paramedics and a "request for delivery of drinking water and first-aid kits" for aid organizations.

[0082] Server: Sends generated notifications to the appropriate departments. This includes sending emails to emergency services via the mail server and sending POST requests to support organization APIs.

[0083] Step 6: Get current location

[0084] Terminal: Uses GPS functionality to obtain the current location of disaster victims. This utilizes the terminal's built-in GPS sensor.

[0085] Terminal: Transmits acquired location information to the server in real time.

[0086] Step 7: Route generation

[0087] Server: Based on acquired location information, it calculates a safe route to the nearest evacuation shelter. It uses a map service API to calculate the specific evacuation route.

[0088] Server: Converts secure routing information into data packets and sends them to the terminal.

[0089] Step 8: Route guidance display

[0090] Terminal: Displays route information received from the server on a map. This guides disaster victims so they can evacuate safely.

[0091] Step 9: Enter safety information

[0092] User: The disaster victim enters their own safety information. They report, "I'm safe. I'm heading to the evacuation center."

[0093] Terminal: Organizes safety information and sends it to the server.

[0094] Step 10: Posting safety information

[0095] Server: Analyzes received safety information and posts it on the disaster message board. It sends a POST request to the disaster message board API and posts the entered information.

[0096] Server: Notifies relevant parties of the posted safety information.

[0097] The above outlines the specific processing flow of this invention. This system enables rapid and accurate information transmission, supply of relief goods, guidance of evacuation routes, and confirmation of safety during disasters.

[0098] (Example 1)

[0099] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0100] During disasters, there is a need for a system that allows victims to quickly and efficiently request rescue, request necessary supplies, confirm their safety, and receive directions to evacuation centers, while appropriately notifying relevant organizations. However, existing systems have fragmented processes, from information input to notification, safety confirmation, and evacuation route guidance, making a rapid and efficient response difficult. Furthermore, information about victims has not always been accurately transmitted, sometimes leading to delays in providing appropriate assistance.

[0101] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0102] In this invention, the server includes an input device for disaster victims to input disaster conditions, rescue requests, and necessary supplies information; a transmission device that organizes the input information and transmits it to the server via a data communication network; an analysis device that receives the information transmitted by the user and performs analysis using a generation AI model; a notification generation device that generates and transmits notifications to the appropriate departments based on the analysis results; a route guidance device that acquires the current location of disaster victims using GPS functionality and guides them to the nearest evacuation center; and a safety information device that receives information on the safety of disaster victims and posts it on a disaster message board. This makes it possible to quickly and efficiently collect, analyze, and notify disaster victims' information during a disaster, thereby realizing appropriate support and safe evacuation.

[0103] An "input device" is a device used by disaster victims to input information about the disaster situation, rescue requests, and necessary supplies.

[0104] A "transmission device" is a device that organizes the input information and transmits it to a server via a data communication network.

[0105] An "analysis device" is a device that receives information sent from a user via a server and performs analysis using a generated AI model.

[0106] A "notification generation device" is a device that generates and sends notifications to the appropriate department based on the analysis results.

[0107] A "route guidance device" is a device that uses GPS functionality to obtain the current location of disaster victims and guides them to the nearest evacuation shelter.

[0108] A "safety information device" is a device that receives information about the safety of disaster victims and posts it on a disaster message board.

[0109] A "generative AI model" is an artificial intelligence model that analyzes input data and extracts necessary information.

[0110] A "data communication network" is a network used to transmit information from a terminal to a server.

[0111] A "server" is a device that analyzes received information and performs processing such as generating notifications and providing route guidance.

[0112] This invention realizes a system that uses a generative AI model to centrally manage information such as rescue requests, requests for necessary supplies, safety confirmations, and route guidance to evacuation shelters during disasters, and to quickly notify relevant organizations. This system mainly consists of terminals, servers, and generative AI models.

[0113] Hardware and software to be used

[0114] Device: Smartphone, tablet, or other mobile device

[0115] Servers: Cloud services and dedicated servers

[0116] Generative AI models: Generative AI models that perform natural language processing.

[0117] Data communication networks: Internet, mobile networks

[0118] GPS function: GPS module built into the device

[0119] Map Service API: A map service API for calculating evacuation routes.

[0120] Overview of Functions and Data Processing

[0121] Information entry

[0122] Users: Disaster victims use the terminal to input information about the current state of the disaster, requests for rescue, necessary supplies, and their safety status. Specifically, they might enter "My house is partially destroyed. I injured my leg. I need drinking water and a first-aid kit" into a form on the application.

[0123] Terminal: The terminal checks the format of the input information and converts it to an appropriate data format (e.g., JSON format).

[0124] Information transmission

[0125] Terminal: The formatted data is sent to the server using the HTTPS protocol. Encryption is also performed as needed.

[0126] Information reception and analysis

[0127] Server: The server passes the received data to the generating AI model. The AI ​​model analyzes the input language data and extracts important information (e.g., "house is partially destroyed," "leg injury," "drinking water and first-aid kit needed").

[0128] Notification generation

[0129] Server: Based on the analysis results, it generates and sends notifications to the appropriate relevant organizations. For example, it notifies emergency services of a "request for assistance for a leg injury" and aid organizations of a "request for delivery of drinking water and first-aid kits."

[0130] Route guidance

[0131] Terminal: The device uses GPS to obtain the current location of the disaster victim. The obtained location information is then sent to the server.

[0132] Server: Uses a map service API to calculate a safe route to the nearest evacuation shelter. The calculated route information is returned to the terminal.

[0133] Terminal: The terminal displays route information to the user and guides them along evacuation routes using a voice assistant and map display.

[0134] Confirmation of safety

[0135] User: Disaster victims enter their own safety information. For example, they might enter "I'm safe. I'm heading to the evacuation center."

[0136] Terminal: Safety information is sent to the server.

[0137] Server: Posts received safety information to the disaster message board so that relevant parties can check it.

[0138] Specific example

[0139] If person A is at home during a disaster, they would use their smartphone to input, "My house is half-destroyed. I've injured my leg. I need drinking water and a first-aid kit." The device would then organize this information and send it to a server, which would analyze it using a generative AI model. Based on the analysis, the server would notify emergency services of a "request for rescue due to leg injury" and support organizations of a "request for delivery of drinking water and a first-aid kit." Furthermore, the system would obtain A's current location and guide them to the nearest evacuation center via a safe route. If A then inputs a message confirming their safety, such as "I'm safe. I'm heading to the evacuation center," that information would be posted on the disaster message board.

[0140] Example of a prompt

[0141] "I entered specific information about the disaster, such as 'My house is half-destroyed,' 'I injured my leg,' and 'I need drinking water and a first-aid kit.' How will this information be organized and sent to the server?"

[0142] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0143] Step 1:

[0144] Users input information about the disaster situation, rescue requests, and necessary supplies. Using a smartphone or tablet application, users fill in specific information in designated input forms. For example, they might enter, "My house is partially destroyed. I injured my leg. I need drinking water and a first-aid kit." This input registers the specific needs and circumstances of the disaster victim.

[0145] Step 2:

[0146] The terminal retrieves information entered by the user. The terminal checks the format of the entered information and verifies the data's integrity. Information entered in the correct format is stored in a temporary database on the terminal.

[0147] Step 3:

[0148] The terminal organizes the verified information and converts it into JSON format. Data encryption is performed as needed during this process. The converted information is then sent to the server using the HTTPS protocol. This transmission allows the server to receive information about the victims' situations.

[0149] Step 4:

[0150] The server receives data in JSON format from the terminal. The server checks the integrity and completeness of the received data to ensure there is no invalid data. After verification, the server passes the data to the generating AI model.

[0151] Step 5:

[0152] The AI ​​model on the server analyzes the input data. The AI ​​model uses natural language processing techniques to categorize the information entered by disaster victims. For example, it extracts information such as "house partially destroyed," "leg injury," and "drinking water and first-aid kit." These analysis results are then used for the following processes on the server.

[0153] Step 6:

[0154] The server generates notifications based on the analysis results. The server generates notifications to be sent to relevant organizations according to the disaster situation and rescue requests. For example, based on the analysis results, it generates a notification for emergency services of "request for rescue of a leg injury" and a notification for aid organizations of "request for delivery of drinking water and first-aid kits."

[0155] Step 7:

[0156] The server sends the generated notification to the appropriate relevant organizations. The notification is sent using the appropriate protocol (e.g., email, SMS, API call). This allows the relevant organizations to quickly initiate appropriate action.

[0157] Step 8:

[0158] The device uses GPS to obtain the victim's current location. The obtained location information is sent to the server. This location information allows the victim's precise location to be determined.

[0159] Step 9:

[0160] Based on the acquired location information, the server uses a map service API to calculate a safe route to the nearest evacuation shelter. The calculated route information is then sent from the server to the terminal.

[0161] Step 10:

[0162] The terminal displays route information received from the server to the user. Based on this route information, the user can evacuate to the nearest shelter using voice and visual guides.

[0163] Step 11:

[0164] The user enters safety information. For example, they might enter, "I'm safe. I'm heading to the evacuation center." The device then sends this safety information to the server.

[0165] Step 12:

[0166] The server posts the received safety information to the disaster message board. This allows family members and other concerned parties to check the safety status of the victims. At the same time, it also sends notifications to relevant organizations.

[0167] (Application Example 1)

[0168] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0169] During disasters, it is crucial to efficiently and quickly transmit rescue requests and supply requests needed by victims to the relevant parties and ensure they receive appropriate support. Furthermore, while knowing the current location of victims, guiding them to evacuation routes, and confirming their safety are important, the lack of integrated management can lead to delays and confusion in information. Similarly, in food and supply deliveries during disasters, prompt guidance of delivery routes and reporting of delivery completion are essential. To address these challenges, a centralized management system is necessary.

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

[0171] In this invention, the server includes an input means for disaster victims to input disaster conditions, rescue requests, and necessary supplies information; an analysis means using a generative AI model to analyze the input information and identify the extent of the damage and the type and quantity of necessary supplies and rescue; a notification generation means to generate and send notifications to the appropriate departments based on the analysis results; a route guidance means to acquire the current location of disaster victims using GPS functionality and guide them to the nearest evacuation center; a safety information means to receive information on the safety of disaster victims and post it on a disaster message board; a route guidance means to acquire the current location of delivery personnel via GPS functionality and calculate a safe delivery route; and a delivery reporting means to report the completion of delivery. This enables centralized management of everything from disaster victim rescue requests to supply delivery, evacuation route guidance, and safety confirmation, allowing for a quick and efficient response.

[0172] "Disaster victims" refers to people who are facing emergencies such as natural disasters.

[0173] "Disaster situation" refers to the specific conditions and damage at a site during an emergency such as a natural disaster.

[0174] A "rescue request" refers to a request for rescue when a disaster victim needs assistance.

[0175] "Information on necessary supplies" refers to information about specific supplies such as food, water, and medical supplies that disaster victims need during a disaster.

[0176] "Input means" refers to devices or methods for disaster victims to input information about the disaster situation, rescue requests, and necessary supplies.

[0177] "Analysis means" refers to devices and methods for processing input data to identify the extent of damage and the types and quantities of necessary supplies.

[0178] A "generative AI model" refers to an artificial intelligence model designed to efficiently and accurately analyze input data.

[0179] "Notification generation means" refers to a device or method that generates and sends notifications to the appropriate relevant departments based on the analysis results.

[0180] "GPS function" refers to the function of a global positioning system used to acquire location information.

[0181] "Route guidance means" refers to devices or methods that guide users to the nearest evacuation center or delivery location based on acquired location information.

[0182] "Safety information means" refers to devices and methods for receiving information on the safety status of disaster victims and posting it on disaster message boards or similar platforms.

[0183] "Transmission means" refers to devices or methods for transmitting input information to a server via a data communication network.

[0184] "Delivery reporting means" refers to a device or method used to report the completion of a delivery after it has been completed.

[0185] This invention is a system for efficiently managing and rapidly transmitting information and supplies needed by disaster victims and related parties during a disaster. Specific embodiments of this system are described below.

[0186] System Overview

[0187] The system consists of terminals where disaster victims input information, a server that analyzes the information and sends notifications, and a generation AI model. Disaster victims input data from smartphones or tablets, and the server analyzes that information and takes appropriate action.

[0188] Hardware and software to be used

[0189] Smartphones and tablets: Devices used by disaster victims and delivery personnel.

[0190] Server (cloud service): Performs information analysis and notification processing.

[0191] GPS module: Obtains the current location of disaster victims and delivery personnel.

[0192] Generative AI model: Analyzes the input data.

[0193] Map service APIs (Google® Maps API, Apple Maps API, etc.): Used to calculate evacuation routes and delivery routes.

[0194] Information input and transmission process

[0195] Disaster victims use smartphones or tablets to input information about the disaster situation, rescue requests, and necessary supplies. The entered information is organized by the device, converted to JSON format via the data communication network, and sent to the server using the HTTPS protocol.

[0196] Information reception and analysis processing

[0197] The server receives information sent from disaster victims and passes it to a generative AI model. The generative AI model analyzes the input data and identifies specific information such as "house partially destroyed," "leg injury," and "drinking water and first-aid kit." Based on the identified information, the server generates and sends appropriate notifications to the necessary departments.

[0198] Guide to evacuation routes and delivery routes

[0199] The server uses GPS functionality to obtain the current locations of disaster victims and delivery personnel. Based on the acquired location information, it calculates a safe route to the nearest evacuation center or delivery location. This involves using a map service API to calculate the specific route. The calculated route information is sent to the terminal to guide users so that they can evacuate or make deliveries safely.

[0200] Delivery completion and safety confirmation.

[0201] After delivery personnel successfully deliver supplies, they send information via an application to report the completion of the delivery. The server receives this information and notifies the victims and relevant departments. Similarly, victims enter their own safety information, and the server posts it on a disaster message board.

[0202] Specific example

[0203] Example input from disaster victims

[0204] Person A, a victim of the disaster, is at home and uses their smartphone to input, "My house is half-destroyed. I've injured my leg. I need drinking water and a first-aid kit." The device organizes this information and sends it to a server. The server analyzes it using a generative AI model, sends appropriate notifications to the relevant departments, and guides Person A to the nearest evacuation center.

[0205] Examples of actions taken by delivery personnel

[0206] Delivery person B uses the app to receive a delivery request for "water, bread, rice, and a first-aid kit," and uses GPS to confirm their current location. The server calculates the optimal delivery route and sends that information to delivery person B's app. After completing the delivery, B reports completion via the app, and the server sends a notification.

[0207] Example of a prompt

[0208] (Prompt example 1):

[0209] User A has requested "rice, bread, water, and a first-aid kit" during a disaster. The server should analyze the received information and send a notification to the appropriate delivery department. Additionally, create a program to notify User A and the administrator once the delivery personnel have successfully delivered the supplies.

[0210] (Prompt example 2):

[0211] Please create a program that obtains the delivery person's current location using GPS, calculates a safe delivery route on the server, and displays it on the delivery person's device. Use a map service API for the calculation.

[0212] Thus, this invention enables a swift and efficient response by centrally managing everything from disaster victims' requests for rescue to the delivery of supplies, guidance on evacuation routes, and confirmation of their safety.

[0213] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0214] Step 1:

[0215] Information input processing

[0216] Users use smartphones or tablets to input information about the disaster situation, rescue requests, and necessary supplies. This input includes specific text information such as, "My house is partially destroyed. I injured my leg. I need drinking water and a first-aid kit." The entered information is organized by category depending on the device.

[0217] Input: Disaster situation, rescue request, and necessary supplies information entered by the user.

[0218] Output: Organized text data (JSON format)

[0219] Step 2:

[0220] Information transmission processing

[0221] The terminal sends the organized information to the server via the data communication network. In this process, the information is converted to JSON format and transmitted securely using the HTTPS protocol.

[0222] Input: Organized text data (JSON format)

[0223] Output: Information sent to the server

[0224] Specific operation: The device prepares to send data, and tapping the send button sends the information to the server.

[0225] Step 3:

[0226] Information reception and analysis processing

[0227] The server receives information sent by the user. The received data is passed to a generating AI model for analysis. Through the analysis, information is categorized, such as "house partially destroyed," "foot injury," and "drinking water and first-aid kit."

[0228] Input: Information sent to the server

[0229] Output: Analyzed information (categorized data)

[0230] Specific operation: The server receives data, inputs it into the AI ​​model, and receives the analysis results.

[0231] Step 4:

[0232] Notification generation process

[0233] Based on the analysis results, the server generates and sends notifications to the appropriate departments. For example, it notifies emergency services of a "request for assistance for a foot injury" and aid organizations of a "request for delivery of drinking water and first-aid kits."

[0234] Input: Analyzed information

[0235] Output: Notification to relevant departments

[0236] Specific operation: The server generates a notification message based on the analyzed data and sends it to the designated relevant department.

[0237] Step 5:

[0238] Evacuation route guidance processing

[0239] The server uses GPS to obtain the current location of disaster victims. Based on the obtained location information, it calculates a safe route to the nearest evacuation center. A map service API is used for this route calculation. The calculated route information is sent to the terminal and displayed to the user.

[0240] Input: Current location information of disaster victims

[0241] Output: Route information to the nearest evacuation shelter

[0242] Specific operation: The server acquires GPS data, calls a map service API to calculate the route, and then sends the route information to the terminal.

[0243] Step 6:

[0244] Delivery route guidance processing

[0245] The server obtains the delivery person's current location via GPS. Based on the obtained location information, it calculates a safe route to the delivery destination. This also utilizes a map service API. The calculated route information is sent to the delivery person's terminal and displayed to them.

[0246] Input: Delivery driver's current location information

[0247] Output: Route information to the delivery address

[0248] Specific operation: The server acquires GPS data, calls a map service API to calculate the route, and then sends the route information to the terminal.

[0249] Step 7:

[0250] Delivery completion report processing

[0251] After the delivery person safely delivers the supplies, they report the completion of the delivery through the application. The terminal sends this information to the server. The server notifies the disaster victims and relevant departments of the received completion information.

[0252] Input: Delivery completion information

[0253] Output: Notification to disaster victims and relevant departments

[0254] Specific operation: The delivery person taps the completion report button, and the device sends that information to the server. The server generates and sends a notification based on the received information.

[0255] Step 8:

[0256] Safety information processing

[0257] Disaster victims enter their safety status information using their smartphones. The device then sends this safety information to a server. The server posts the received safety information on a disaster message board so that relevant parties can check it.

[0258] Input: Information on the safety status of disaster victims

[0259] Output: Posting to the disaster message board

[0260] Specific operation: Disaster victims enter information into a safety status input form, and the terminal sends that information to the server. The server then posts the received information on a message board.

[0261] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0262] Modes for carrying out the invention

[0263] This invention provides a system that utilizes data communication to request rescue during disasters, request necessary supplies, confirm the safety of victims, provide route guidance to evacuation centers, and recognize the emotions of disaster victims to provide appropriate responses. Specifically, the system aims to more accurately understand the situation of disaster victims and provide appropriate support and guidance by combining a generative AI model and an emotion engine. The specific embodiments of this system are described in detail below.

[0264] (System Overview)

[0265] This system consists of terminals, servers, a generative AI model, and an emotion engine. Disaster victims (users) input data from terminals such as smartphones and tablets, and the server analyzes and notifies them of this information. Furthermore, the use of a generative AI model and an emotion engine ensures that the input data is processed efficiently and accurately.

[0266] (Detailed system processing flow)

[0267] 1. Information input processing

[0268] Users: Disaster victims use smartphones or tablets to input information about the disaster situation, rescue requests, necessary supplies, and their safety status. They input specific information such as, "My house is half-destroyed. I injured my leg. I need drinking water and a first-aid kit." User emotional data (voice, text, facial expressions, etc.) is also input.

[0269] Terminal: The terminal organizes the input information and generates data packets.

[0270] 2. Information transmission process

[0271] Terminal: Sends organized information packets to the server via the data communication network. The information is converted to, for example, JSON format, and a POST request is sent to the server using the HTTPS protocol.

[0272] 3. Information reception and analysis processing

[0273] Server: Receives information packets sent by the user on the receiving port. Decodes the received information for analysis.

[0274] Server: Passes the received information to the generative AI model and emotion engine for analysis. The generative AI model identifies the disaster situation, the extent of injuries, and the types and quantities of necessary supplies, while the emotion engine analyzes the user's emotional state.

[0275] 4. Notification generation process

[0276] Server: Generates notifications to relevant departments based on the analysis results. For example, it creates a "request for assistance for a leg injury" for paramedics and a "request for delivery of drinking water and first-aid kits" for aid organizations. It also generates notifications that take into account the mental state of the disaster victims based on the analysis results of the emotion engine.

[0277] Server: Sends generated notifications to the appropriate departments. This includes sending emails to emergency services via the mail server and sending POST requests to support organization APIs.

[0278] 5. Route guidance processing

[0279] Terminal: Uses GPS functionality to obtain the current location of disaster victims. This utilizes the terminal's built-in GPS sensor.

[0280] Server: Based on acquired location information, it calculates a safe route to the nearest evacuation shelter. It uses a map service API to calculate the specific evacuation route. In addition, it generates more reassuring guidance text according to the user's emotional state analyzed by the emotion engine.

[0281] Terminal: Displays route information received from the server on a map. This guides disaster victims so they can evacuate safely.

[0282] 6. Safety confirmation process

[0283] User: The affected person inputs their own safety information. They convey, "I'm safe. I'm going to the evacuation shelter."

[0284] Terminal: Organizes the safety information and sends it to the server.

[0285] Server: Analyzes the received safety information and posts it on the disaster message board. Sends a POST request to the disaster message board API to post the input information.

[0286] Server: Notifies the relevant parties of the posted safety information.

[0287] (Specific example)

[0288] Consider the case where victim A encounters a disaster at home. Victim A uses their smartphone to input, "My house is half-destroyed. I've injured my leg. I need drinking water and a first aid kit." The terminal sends this information to the server, and the server performs analysis using a generative AI model and an emotion engine. The server notifies the rescue team of the "request for assistance for leg injury" and notifies the support group of the "request for delivery of drinking water and first aid kit." Additionally, if the emotion engine analyzes that Victim A's stress level is high, a notification with more considerate guidance and additional mental support content is sent.

[0289] Also, obtains Victim A's current location and guides the route to the nearest evacuation shelter. When Victim A inputs safety information such as "I'm safe. I'm going to the evacuation shelter," that information is posted on the disaster message board and can be confirmed by family and friends.

[0290] In this way, this system comprehensively supports information transmission, supply of relief materials, guidance of evacuation routes, confirmation of safety, and support based on emotional state during disasters, thereby supporting the safety assurance and rapid evacuation of affected persons.

[0291] The following explains the processing flow.

[0292] (Specific processing flow of the program)

[0293] Step 1: Enter Information

[0294] Users: Disaster victims use smartphones or tablets to input information about the disaster situation, rescue requests, necessary supplies, and their safety status. For example, they might input specific information such as, "My house is half destroyed. I injured my leg. I need drinking water and a first-aid kit." They can also input emotions through voice data, text data, and facial image data.

[0295] Terminal: The terminal organizes this input information and generates data packets.

[0296] Step 2: Send Information

[0297] Terminal: Sends organized information packets to the server via the data communication network. The information is converted to, for example, JSON format, and a POST request is sent to the server using the HTTPS protocol.

[0298] Step 3: Information Reception

[0299] Server: Receives information packets sent from the user on the receiving port. Decodes the received information for analysis and passes it to the generative AI model and emotion engine.

[0300] Step 4: Information Analysis

[0301] Generative AI Model: Based on received data, it identifies disaster conditions, the extent of injuries, and the types and quantities of necessary supplies. For example, it identifies information such as "house partially destroyed," "leg injury," and "drinking water and first-aid kit."

[0302] Emotion Engine: Analyzes emotional data (voice, text, facial expressions) to identify the user's emotional state. For example, it evaluates stress levels, feelings of security, and feelings of anxiety.

[0303] Step 5: Generate Notification

[0304] Server: Based on the analysis results of the generative AI model and the sentiment engine, generate appropriate notifications. For example, create a "Request for assistance for foot injury" for the emergency team and a "Request for delivery of drinking water and first aid kits" for the support group. Depending on the results of the sentiment engine, notifications may also be generated to provide mental support when the stress level of the disaster victims is high.

[0305] Server: Send the generated notifications to the appropriate relevant departments. This includes methods such as sending emails to the emergency team via the mail server and sending POST requests to the support group API.

[0306] Step 6: Obtain current location

[0307] Terminal: Use the GPS function to obtain the current location of the disaster victim. This utilizes the built-in GPS sensor of the terminal.

[0308] Terminal: Send the obtained location information to the server in real-time.

[0309] Step 7: Generate route guidance

[0310] Server: Based on the obtained location information, calculate a safe route to the nearest shelter. Use the map service API to calculate the specific evacuation route. Depending on the user's emotional state analyzed by the sentiment engine, more reassuring guidance text may also be generated.

[0311] Server: Convert the safe route information into data packets and send it to the terminal.

[0312] Step 8: Display route guidance

[0313] Terminal: Display the route information received from the server on the map. This guides the disaster victim to evacuate safely.

[0314] Step 9: Input safety information

[0315] User: The disaster victim enters their own safety information. They report, "I'm safe. I'm heading to the evacuation center."

[0316] Terminal: Organizes safety information and sends it to the server.

[0317] Step 10: Posting safety information

[0318] Server: Analyzes received safety information and posts it on the disaster message board. It sends a POST request to the disaster message board API and posts the entered information.

[0319] Server: Notifies relevant parties of the posted safety information.

[0320] As a concrete example, consider a case where victim B experiences an earthquake and their home is partially destroyed. B inputs, "My house is partially destroyed. I injured my leg. I need drinking water and a first-aid kit," and also voice-inputs the emotion, "I'm very scared." The device sends this to a server, where a generative AI model and emotion engine analyze it. Then, a notification is sent to emergency services requesting assistance for a leg injury, to aid organizations requesting delivery of drinking water and a first-aid kit, and to psychiatric support teams that B is a victim at a high stress level. In addition, based on B's current location, directions to the nearest evacuation center are provided.

[0321] This system allows for a comprehensive understanding of the circumstances and mental state of disaster victims, enabling the provision of more appropriate support.

[0322] (Example 2)

[0323] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0324] Conventional disaster relief systems have faced challenges, particularly in providing support that takes into account the emotions and psychological state of victims, due to the limited information that victims can input. Furthermore, the entire process from information input to analysis, notification, and support is not carried out efficiently, and a rapid and appropriate response is required. As a result, there are situations where both the physical safety and psychological support of victims are insufficient.

[0325] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0326] In this invention, the server includes an input means for disaster victims to input disaster conditions, rescue requests, and necessary supplies information; an emotion data input means for inputting emotion data from voice data and facial expressions; an analysis means using a generative AI model to analyze the input information and identify the damage situation and the type and quantity of necessary supplies and rescue; an analysis means using an emotion engine to analyze the input emotion data and evaluate the emotional state of the disaster victim; a notification generation means to generate and send notifications to the appropriate departments based on the analysis results; a route guidance means to obtain the current location of the disaster victim using GPS functionality and guide them to the nearest evacuation center; and a safety information means to receive information on the safety status of the disaster victim and post it on a disaster message board. This enables support that takes into account the emotional state of the disaster victim, and realizes a swift and appropriate response.

[0327] "Disaster victims" refers to individuals or groups who have experienced and been affected by a disaster.

[0328] "Input means" refers to devices or software that users use to input information. Specifically, this refers to applications on smartphones and tablets.

[0329] "Emotional data input means" refers to devices or software used to input information related to a user's emotions, such as voice data or facial expressions.

[0330] A "generative AI model" refers to an algorithm that uses machine learning and data analysis techniques to identify the extent of damage and the types and quantities of necessary supplies from the input information.

[0331] "Analysis means" refers to devices or software that analyze and interpret input data.

[0332] An "emotion engine" refers to an algorithm or software that analyzes emotional data and evaluates the user's emotional state.

[0333] "Notification generation means" refers to a device or software that generates and sends notifications to the appropriate department based on the analysis results.

[0334] "Transmission means" refers to devices or software used to transmit input information to a server via a data communication network.

[0335] "GPS function" refers to the function of obtaining the current physical location using a global positioning system.

[0336] "Route guidance means" refers to devices or software that calculate the route to the destination based on acquired location information and guide the user.

[0337] "Safety information means" refers to devices or software that receive information on the safety status of disaster victims and post it on disaster message boards or similar platforms.

[0338] This invention is a system for effectively and quickly providing support to disaster victims, and is comprised of a terminal, a server, a generative AI model, and an emotion engine. Specific embodiments of this system are described in detail below.

[0339] (Terminal and user operation)

[0340] Users: Disaster victims use smartphones or tablets to input information about the disaster situation, rescue requests, and necessary supplies. For example, they might input specific information such as, "My house is half-destroyed. I injured my leg. I need drinking water and a first-aid kit." They also input emotional data through voice and facial expressions. The devices are equipped with emotional data input mechanisms, which allows for the collection of emotional data as well.

[0341] Terminal: Receives user input and organizes it into data packets. Voice data is converted into text data using a speech recognition engine. The organized data packets are converted into JSON format and sent to the server using the HTTPS protocol.

[0342] (Server analysis and notification)

[0343] Server: Receives data packets from users via the receiving port and decodes them. The decoded data is passed to the generative AI model and the emotion engine. The generative AI model analyzes the input text data to identify the disaster situation and the types and quantities of supplies needed. The emotion engine analyzes the emotion data to assess the emotional state of the victims.

[0344] Server: Based on the analysis results, it generates notifications for the appropriate departments (e.g., paramedics, aid organizations). The generation AI model creates notifications such as "request for assistance for a leg injury," and the emotion engine creates notifications such as "request for delivery of drinking water and a first-aid kit," taking into account the emotional state. The notifications are sent to the mail server or the aid organization's API.

[0345] (GPS and route guidance)

[0346] Device: Uses the built-in GPS sensor to obtain the user's current location. GPS data is converted to JSON format and sent to the server.

[0347] Server: Based on acquired location information, it calculates a safe route to the nearest evacuation shelter. It uses map services such as the Google Maps API to calculate a specific evacuation route and generates reassuring guidance text based on the analysis results of the emotion engine.

[0348] Terminal: Displays evacuation routes received from the server on a map, guiding users safely.

[0349] (Confirmation of safety)

[0350] User: Disaster victims enter their safety status. For example, they might enter, "I'm safe. I'm heading to the evacuation center."

[0351] Terminal: Organizes the entered safety information and sends it to the server.

[0352] Server: Analyzes received safety information and sends a POST request to the API to post it on the disaster message board. This allows family and friends to check on the safety of disaster victims.

[0353] (Specific example)

[0354] Person A, a victim of a disaster, is at home and uses their smartphone to type, "My house is half-destroyed. I've injured my leg. I need drinking water and a first-aid kit." This information is compiled on the device and sent to a server. On the server, a generative AI model analyzes the disaster situation and necessary supplies, and an emotion engine evaluates Person A's emotional state. The server then notifies emergency services of a "request for assistance regarding the leg injury" and aid organizations of a "request for delivery of drinking water and a first-aid kit." It also guides Person A along an evacuation route based on their GPS information, and when Person A enters their safety status, it is posted on a disaster message board, allowing family and friends to check on their safety.

[0355] (Example of prompt text to input to the generated AI model)

[0356] Please provide a detailed report of the disaster situation. Include specific information about the damage to your home, the extent of your injuries, and the types and quantities of supplies you need. Additionally, please describe your current emotional state.

[0357] In this way, the system can effectively ensure the safety and provide psychological support to disaster victims by allowing them to input information and quickly providing appropriate support that takes their emotional state into consideration.

[0358] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0359] Step 1:

[0360] Users use smartphones or tablets to input information about the disaster situation, rescue requests, and necessary supplies. For example, they might input specific information such as, "My house is half-destroyed. I injured my leg. I need drinking water and a first-aid kit." They also input emotional data through voice and facial expressions. This allows for the acquisition of both text data and emotional data through the input method.

[0361] Step 2:

[0362] The terminal receives input information from the user, organizes it, and converts it into data packets. Voice data is converted into text data by a speech recognition engine. The organized data packets are converted into JSON format and sent to the server via the data communication network. The input is the user's text and voice, and the output is data packets in JSON format.

[0363] Step 3:

[0364] The server receives data packets from the user via the receiving port and decodes them. The decoded data is passed to the generating AI model and emotion engine. Data ready for analysis becomes input, and data in an analyzable state becomes output.

[0365] Step 4:

[0366] The server decodes the data, which is then analyzed by a generating AI model to identify the disaster situation and the types and quantities of supplies needed. The generating AI model takes text data as input and outputs the identified disaster situation and supply information. Specifically, it extracts information such as "partially destroyed," "leg injury," and "drinking water and first-aid kit needed."

[0367] Step 5:

[0368] The server passes the decoded emotional data to the emotion engine, which analyzes the emotional state of the disaster victims. The emotion engine takes voice and facial expression data as input and evaluates stress levels and emotional states. The analysis results output emotional evaluations such as "high stress" or "fearful state."

[0369] Step 6:

[0370] The server generates notifications to the appropriate departments based on the analysis results of the generated AI model and the emotion engine. Specifically, it notifies emergency services of a "request for assistance for a leg injury" and aid organizations of a "request for delivery of drinking water and a first-aid kit." Furthermore, based on the results evaluated by the emotion engine, it generates notification content in more polite language. The input is the analysis results, and the output is the notification content.

[0371] Step 7:

[0372] The server sends the generated notification to the appropriate department. It sends an email to the emergency services via the mail server and a POST request to the support organization's API. The input is the notification content, and the output is the status of successful transmission.

[0373] Step 8:

[0374] The device uses its built-in GPS sensor to obtain the user's current location. The obtained location information is converted to JSON format and sent to the server. The input is GPS data, and the output is a packet of location data.

[0375] Step 9:

[0376] Based on location information acquired by the server, the system calculates a safe route to the nearest evacuation shelter. It uses the Google Maps API to determine the specific evacuation route. It also generates reassuring guidance messages based on the analysis results of an emotion engine. Input is location information, and output is evacuation route information.

[0377] Step 10:

[0378] The terminal displays evacuation routes received from the server on a map application, guiding the user safely. The input is evacuation route information, and the output is the route display on the map application.

[0379] Step 11:

[0380] The user enters their safety status. For example, they might enter, "I'm safe. I'm heading to the evacuation center." This completes the entry of their safety information.

[0381] Step 12:

[0382] The terminal organizes the safety information entered and sends it to the server. The input is the safety information, and the output is the data sent to the server.

[0383] Step 13:

[0384] The server analyzes the safety information it receives and sends a POST request to the API to post it on the disaster message board. The input is the safety information, and the output is the information posted on the disaster message board.

[0385] This processing flow ensures that information gathering, analysis, notification, evacuation guidance, and safety confirmation from disaster victims are carried out consistently.

[0386] (Application Example 2)

[0387] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0388] During disasters, it is crucial to quickly and accurately assess the situation of victims and provide appropriate support. However, conventional systems lack sufficient psychological support that takes into account the emotional state of victims. Furthermore, systems for promptly notifying the appropriate departments of analysis results and conducting effective rescue and support are not adequately developed, and accurate guidance to ensure the safe evacuation of victims is insufficient. These challenges need to be addressed.

[0389] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0390] In this invention, the server includes an input means for disaster victims to input disaster conditions, rescue requests, and necessary supplies information; an analysis means using a generative AI model to analyze the input information and identify the extent of the damage and the type and quantity of necessary supplies and rescue; an emotion analysis means to analyze the emotional data of disaster victims and identify whether psychological support is needed; a notification generation means to generate and send notifications to the appropriate departments based on the analysis results; a support provision means to provide appropriate psychological support to disaster victims based on the emotion analysis results; a route guidance means to acquire the current location of disaster victims using GPS functionality and guide them to the nearest evacuation center; and a safety information means to receive information on the safety of disaster victims and post it on a disaster message board. This enables a comprehensive understanding of the situation of disaster victims and allows for prompt and accurate support and guidance.

[0391] - "Input means" refers to devices or functions that allow disaster victims to input information about the disaster situation, rescue requests, and necessary supplies.

[0392] "Analysis means" refers to devices or functions that use a generative AI model to analyze input information and identify the extent of the damage and the types and quantities of necessary supplies and rescue efforts.

[0393] A "notification generation means" refers to a device or function that generates and sends notifications to the appropriate department based on the analysis results.

[0394] "Emotional analysis tools" refer to devices or functions that analyze the emotional data of disaster victims to determine whether or not they require psychological support.

[0395] "Means of providing support" refers to devices and functions that provide appropriate psychological support to disaster victims based on the results of emotion analysis.

[0396] A "route guidance system" refers to a device or function that uses GPS functionality to obtain the current location of disaster victims and guides them to the nearest evacuation shelter.

[0397] A "safety information system" refers to a device or function that receives information about the safety of disaster victims and posts it on a disaster message board.

[0398] A "generative AI model" is an artificial intelligence model that analyzes disaster situations, rescue requests, and necessary supplies to identify appropriate responses.

[0399] A "data communication network" is a network of communications used to transmit input information to a server.

[0400] A "server" is a computer system that receives, processes, and analyzes information sent by disaster victims.

[0401] overview

[0402] This invention is a system for more accurately understanding the situation of disaster victims and providing prompt and appropriate support. Disaster victims input information about the disaster situation, rescue requests, and necessary supplies from a device such as a smartphone or tablet, and transmit this information to a server via a data communication network. This server analyzes the information using a generative AI model and sentiment analysis means, generates appropriate notifications, and sends them. It also obtains the current location of disaster victims, guides them to the nearest evacuation center, and receives information on the safety status of disaster victims and posts it on a disaster message board.

[0403] Hardware and software to be used

[0404] Devices: Smartphones, tablets, smart glasses, head-mounted displays, robots

[0405] Server: High-performance computer system, Django framework (Python)

[0406] Database: PostgreSQL

[0407] Generative AI model: GPT-4(registered trademark) (OpenAI(registered trademark))

[0408] Emotion analysis software: Affectiva

[0409] Location services: Google Maps API

[0410] Program processing

[0411] Data entry and transmission

[0412] Users input disaster information, rescue requests, and necessary supplies using their devices. Simultaneously, emotional data such as audio and video is also input. This data is transmitted to a server via a data communication network.

[0413] Data Analysis

[0414] The server sends the received data to the GPT-4 AI model for analysis of the disaster situation and necessary supplies. It also uses Affectiva, emotion analysis software, to analyze the user's emotional state.

[0415] Generating and sending notifications

[0416] The server generates and sends notifications to the appropriate departments based on the analysis results. For example, it notifies emergency services of emergency requests and aid organizations of requests for the delivery of necessary supplies. Based on the emotion analysis results, it also generates notifications that take into account the mental state of the victims.

[0417] Route guidance

[0418] The system uses the GPS function of the device to obtain the current location of the disaster victim. The server uses the Google Maps API to calculate a safe route to the nearest evacuation center and sends that information to the device. The device displays this route information on a map to guide the disaster victim.

[0419] Confirmation of safety

[0420] Users input safety information using their devices and send it to the server. The server analyzes this information and posts it on a disaster message board. It also notifies relevant parties of the safety status.

[0421] Specific example

[0422] If a disaster victim, Person A, is at home, they will use their smartphone to input information such as, "My house is half-destroyed. I injured my leg. I need drinking water and a first-aid kit." Voice and facial expression data of Person A will also be input. This data is sent to a server and analyzed by a generative AI model and emotion analysis software. The server will notify emergency services to request rescue and aid organizations to deliver necessary supplies. Furthermore, if the analysis indicates that Person A's stress level is high, a notification offering psychological support will be generated. Person A's current location will be obtained, and a safe route to the nearest evacuation center will be provided. If Person A inputs a safety status message such as, "I'm safe. I'm heading to the evacuation center," this will be posted on the disaster message board and notified to relevant parties.

[0423] Example of a prompt

[0424] Please analyze the following disaster situation and determine whether assistance is needed.

[0425] Disaster situation: My house is partially destroyed. I injured my leg. I need drinking water and a first-aid kit.

[0426] Emotion data: emotion_audio_file.mp3

[0427] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0428] Step 1:

[0429] Information input processing

[0430] Users who are victims of the disaster use their smartphones or tablets to input information about the disaster situation, rescue requests, and necessary supplies. Specifically, they input text information such as, "My house is half destroyed. I injured my leg. I need drinking water and a first-aid kit," as well as voice and emotional data such as facial expressions. This information is taken into the device as input. The device organizes this information and converts it into data packets in formats such as JSON.

[0431] Step 2:

[0432] Information transmission processing

[0433] The terminal sends organized information packets to the server via the data communication network. Specifically, the terminal sends a POST request using the HTTPS protocol. In this case, the data packet containing input data (disaster situation, emotional data, etc.) becomes the input amount. This allows the necessary information to be instantly transmitted from the terminal to the server.

[0434] Step 3:

[0435] Information reception and analysis processing

[0436] The server receives information packets sent from the terminal. It receives the information at the receiving port, decodes it, and prepares it for analysis. The analysis is based on the generative AI model GPT-4 and the sentiment analysis software Affectiva. The data received as input is processed by the server in the preliminary stages of analysis.

[0437] Step 4:

[0438] Analysis using generative AI models

[0439] The received data is sent to the GPT-4 generation AI model to analyze the disaster situation and necessary supplies. A prompt message is generated and data is passed for analysis. Based on text information such as "My house is half-destroyed. I injured my leg. I need drinking water and a first-aid kit," the generation AI model understands the situation and identifies the necessary assistance. The output is a disaster situation report and a list of necessary supplies.

[0440] Step 5:

[0441] Emotion analysis

[0442] The server sends emotional data to Affectiva, which analyzes the user's emotional state. It takes audio and facial expression data as input for the analysis. Affectiva identifies the user's emotional state from the audio and video, and extracts information such as stress levels and tension. The output is the emotional analysis result.

[0443] Step 6:

[0444] Notification generation process

[0445] The server generates and sends notifications to the appropriate departments based on the analysis results. Specifically, it creates notification content based on the disaster situation and required supplies identified by the generation AI model, and the emotional state identified by Affectiva. It notifies emergency services of emergency requests and aid organizations of the delivery of necessary supplies. In addition, if the emotional analysis results indicate that psychological support is needed, it generates a customized notification. The output is the notification sent to each relevant party.

[0446] Step 7:

[0447] Route guidance processing

[0448] The server uses GPS to obtain the current location of disaster victims and guides them to the nearest evacuation shelter. Using the Google Maps API, it calculates specific evacuation routes and uses the current location as input to calculate the safest evacuation route. The output is the calculated route information. The server sends the route information to the terminal, which displays it on a map to guide the disaster victims.

[0449] Step 8:

[0450] Safety confirmation process

[0451] The user enters safety status information using a terminal and sends it to the server. The input data is information such as "I'm safe. I'm heading to the evacuation center." The server analyzes the received safety status information and posts it on the disaster message board. Furthermore, it notifies relevant parties of the safety status. The output is the safety status information posted on the disaster message board and the notifications sent to relevant parties.

[0452] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0453] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0454] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0455] [Second Embodiment]

[0456] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0457] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0458] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0459] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0460] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0461] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0462] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0463] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0464] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0465] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0466] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0467] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0468] Modes for carrying out the invention

[0469] This invention utilizes data communication to centrally manage disaster relief requests, requests for necessary supplies, safety checks, and route guidance to evacuation centers, and implements a system that promptly notifies relevant parties using a generation AI model. A specific embodiment of this system is described in detail below.

[0470] (System Overview)

[0471] This system consists of terminals, servers, and generative AI models. Disaster victims (users) input data from terminals such as smartphones and tablets, and the server analyzes and notifies them of this information. Furthermore, the use of generative AI models ensures that the input data is processed efficiently and accurately.

[0472] (Detailed system processing flow)

[0473] 1. Information input processing

[0474] Users: Disaster victims use smartphones or tablets to input information about the disaster situation, rescue requests, necessary supplies, and their safety status. For example, they might input specific information such as, "My house is half-destroyed. I injured my leg. I need drinking water and a first-aid kit."

[0475] Terminal: The terminal organizes the input information and prepares it to be sent to the server via the data communication network.

[0476] 2. Information transmission process

[0477] Terminal: The organized information is sent to the server via the data communication network. Here, the information is converted to, for example, JSON format and sent using the HTTPS protocol.

[0478] 3. Information reception and analysis processing

[0479] Server: The server receives information sent from the user and passes it to the generating AI model. The server analyzes the information to identify the extent of the damage and the type and quantity of supplies and rescue needed.

[0480] Generative AI Model: This model analyzes the input data and identifies information such as "house partially destroyed," "foot injury," and "drinking water and first-aid kit."

[0481] 4. Notification generation process

[0482] Server: Based on the analysis results, it generates and sends necessary notifications to the relevant departments. For example, it creates and sends a "request for rescue of a foot injury" to emergency services and a "request for delivery of drinking water and first-aid kits" to support organizations.

[0483] 5. Route guidance processing

[0484] Device: Obtains the current location of disaster victims via GPS.

[0485] Server: Based on the acquired location information, it calculates a safe route to the nearest evacuation shelter. This is done by using a map service API to calculate the specific evacuation route.

[0486] Terminal: Displays routing information received from the server and guides disaster victims to evacuate safely.

[0487] 6. Safety confirmation process

[0488] User: Disaster victims enter their own safety information, for example, "I'm safe. I'm heading to the evacuation center."

[0489] Terminal: Send this safety information to the server.

[0490] Server: Posts received safety information to the disaster message board so that relevant parties can check it.

[0491] (Specific example)

[0492] Let's consider a scenario where victim A is at home during a disaster. A uses their smartphone to type, "My house is half-destroyed. I've injured my leg. I need drinking water and a first-aid kit." The device sends this information to a server, which analyzes it using a generative AI model. The server notifies emergency services of a "request for assistance regarding the leg injury" and aid organizations of a "request for delivery of drinking water and a first-aid kit." Furthermore, it obtains A's current location and guides them to the nearest evacuation center. When A enters a safety update such as, "I'm safe. I'm heading to the evacuation center," this information is posted on a disaster message board, allowing family and friends to see it.

[0493] In this way, this system comprehensively supports disaster relief efforts, including information dissemination, supply of relief goods, guidance on evacuation routes, and confirmation of the safety of disaster victims, thereby supporting their safety and rapid evacuation.

[0494] The following describes the processing flow.

[0495] (Specific processing flow of the program)

[0496] Step 1: Enter Information

[0497] Users: Disaster victims use smartphones or tablets to input information about the disaster situation, rescue requests, necessary supplies, and their safety status. They input specific information such as, "My house is half destroyed. I injured my leg. I need drinking water and a first-aid kit."

[0498] Terminal: The terminal organizes the input information and generates data packets.

[0499] Step 2: Send Information

[0500] Terminal: Sends organized information packets to the server via the data communication network. The information is converted to, for example, JSON format, and a POST request is sent to the server using the HTTPS protocol.

[0501] Step 3: Information Reception

[0502] Server: Receives information packets sent by the user on the receiving port. Decodes the received information for analysis.

[0503] Step 4: Information Analysis

[0504] Server: Passes the received information to the generating AI model for analysis. The generating AI model identifies the disaster situation, the extent of injuries, and the types and quantities of supplies needed.

[0505] Generative AI model: Based on input data, it identifies information such as "house partially destroyed," "foot injury," and "drinking water and first-aid kit."

[0506] Step 5: Generate Notification

[0507] Server: Based on the analysis results, it generates notifications for the relevant departments. For example, it creates a "request for assistance for a foot injury" for paramedics and a "request for delivery of drinking water and first-aid kits" for aid organizations.

[0508] Server: Sends generated notifications to the appropriate departments. This includes sending emails to emergency services via the mail server and sending POST requests to support organization APIs.

[0509] Step 6: Get current location

[0510] Terminal: Uses GPS functionality to obtain the current location of disaster victims. This utilizes the terminal's built-in GPS sensor.

[0511] Terminal: Transmits acquired location information to the server in real time.

[0512] Step 7: Route generation

[0513] Server: Based on acquired location information, it calculates a safe route to the nearest evacuation shelter. It uses a map service API to calculate the specific evacuation route.

[0514] Server: Converts secure routing information into data packets and sends them to the terminal.

[0515] Step 8: Route guidance display

[0516] Terminal: Displays route information received from the server on a map. This guides disaster victims so they can evacuate safely.

[0517] Step 9: Enter safety information

[0518] User: The disaster victim enters their own safety information. They report, "I'm safe. I'm heading to the evacuation center."

[0519] Terminal: Organizes safety information and sends it to the server.

[0520] Step 10: Posting safety information

[0521] Server: Analyzes received safety information and posts it on the disaster message board. It sends a POST request to the disaster message board API and posts the entered information.

[0522] Server: Notifies relevant parties of the posted safety information.

[0523] The above outlines the specific processing flow of this invention. This system enables rapid and accurate information transmission, supply of relief goods, guidance of evacuation routes, and confirmation of safety during disasters.

[0524] (Example 1)

[0525] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0526] During disasters, there is a need for a system that allows victims to quickly and efficiently request rescue, request necessary supplies, confirm their safety, and receive directions to evacuation centers, while appropriately notifying relevant organizations. However, existing systems have fragmented processes, from information input to notification, safety confirmation, and evacuation route guidance, making a rapid and efficient response difficult. Furthermore, information about victims has not always been accurately transmitted, sometimes leading to delays in providing appropriate assistance.

[0527] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0528] In this invention, the server includes an input device for disaster victims to input disaster conditions, rescue requests, and necessary supplies information; a transmission device that organizes the input information and transmits it to the server via a data communication network; an analysis device that receives the information transmitted by the user and performs analysis using a generation AI model; a notification generation device that generates and transmits notifications to the appropriate departments based on the analysis results; a route guidance device that acquires the current location of disaster victims using GPS functionality and guides them to the nearest evacuation center; and a safety information device that receives information on the safety of disaster victims and posts it on a disaster message board. This makes it possible to quickly and efficiently collect, analyze, and notify disaster victims' information during a disaster, thereby realizing appropriate support and safe evacuation.

[0529] An "input device" is a device used by disaster victims to input information about the disaster situation, rescue requests, and necessary supplies.

[0530] A "transmission device" is a device that organizes the input information and transmits it to a server via a data communication network.

[0531] An "analysis device" is a device that receives information sent from a user via a server and performs analysis using a generated AI model.

[0532] A "notification generation device" is a device that generates and sends notifications to the appropriate department based on the analysis results.

[0533] A "route guidance device" is a device that uses GPS functionality to obtain the current location of disaster victims and guides them to the nearest evacuation shelter.

[0534] A "safety information device" is a device that receives information about the safety of disaster victims and posts it on a disaster message board.

[0535] A "generative AI model" is an artificial intelligence model that analyzes input data and extracts necessary information.

[0536] A "data communication network" is a network used to transmit information from a terminal to a server.

[0537] A "server" is a device that analyzes received information and performs processing such as generating notifications and providing route guidance.

[0538] This invention realizes a system that uses a generative AI model to centrally manage information such as rescue requests, requests for necessary supplies, safety confirmations, and route guidance to evacuation shelters during disasters, and to quickly notify relevant organizations. This system mainly consists of terminals, servers, and generative AI models.

[0539] Hardware and software to be used

[0540] Device: Smartphone, tablet, or other mobile device

[0541] Servers: Cloud services and dedicated servers

[0542] Generative AI models: Generative AI models that perform natural language processing.

[0543] Data communication networks: Internet, mobile networks

[0544] GPS function: GPS module built into the device

[0545] Map Service API: A map service API for calculating evacuation routes.

[0546] Overview of Functions and Data Processing

[0547] Information entry

[0548] Users: Disaster victims use the terminal to input information about the current state of the disaster, requests for rescue, necessary supplies, and their safety status. Specifically, they might enter "My house is partially destroyed. I injured my leg. I need drinking water and a first-aid kit" into a form on the application.

[0549] Terminal: The terminal checks the format of the input information and converts it to an appropriate data format (e.g., JSON format).

[0550] Information transmission

[0551] Terminal: The formatted data is sent to the server using the HTTPS protocol. Encryption is also performed as needed.

[0552] Information reception and analysis

[0553] Server: The server passes the received data to the generating AI model. The AI ​​model analyzes the input language data and extracts important information (e.g., "house is partially destroyed," "leg injury," "drinking water and first-aid kit needed").

[0554] Notification generation

[0555] Server: Based on the analysis results, it generates and sends notifications to the appropriate relevant organizations. For example, it notifies emergency services of a "request for assistance for a leg injury" and aid organizations of a "request for delivery of drinking water and first-aid kits."

[0556] Route guidance

[0557] Terminal: The device uses GPS to obtain the current location of the disaster victim. The obtained location information is then sent to the server.

[0558] Server: Uses a map service API to calculate a safe route to the nearest evacuation shelter. The calculated route information is returned to the terminal.

[0559] Terminal: The terminal displays route information to the user and guides them along evacuation routes using a voice assistant and map display.

[0560] Confirmation of safety

[0561] User: Disaster victims enter their own safety information. For example, they might enter "I'm safe. I'm heading to the evacuation center."

[0562] Terminal: Safety information is sent to the server.

[0563] Server: Posts received safety information to the disaster message board so that relevant parties can check it.

[0564] Specific example

[0565] If person A is at home during a disaster, they would use their smartphone to input, "My house is half-destroyed. I've injured my leg. I need drinking water and a first-aid kit." The device would then organize this information and send it to a server, which would analyze it using a generative AI model. Based on the analysis, the server would notify emergency services of a "request for rescue due to leg injury" and support organizations of a "request for delivery of drinking water and a first-aid kit." Furthermore, the system would obtain A's current location and guide them to the nearest evacuation center via a safe route. If A then inputs a message confirming their safety, such as "I'm safe. I'm heading to the evacuation center," that information would be posted on the disaster message board.

[0566] Example of a prompt

[0567] "I entered specific information about the disaster, such as 'My house is half-destroyed,' 'I injured my leg,' and 'I need drinking water and a first-aid kit.' How will this information be organized and sent to the server?"

[0568] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0569] Step 1:

[0570] Users input information about the disaster situation, rescue requests, and necessary supplies. Using a smartphone or tablet application, users fill in specific information in designated input forms. For example, they might enter, "My house is partially destroyed. I injured my leg. I need drinking water and a first-aid kit." This input registers the specific needs and circumstances of the disaster victim.

[0571] Step 2:

[0572] The terminal retrieves information entered by the user. The terminal checks the format of the entered information and verifies the data's integrity. Information entered in the correct format is stored in a temporary database on the terminal.

[0573] Step 3:

[0574] The terminal organizes the verified information and converts it into JSON format. Data encryption is performed as needed during this process. The converted information is then sent to the server using the HTTPS protocol. This transmission allows the server to receive information about the victims' situations.

[0575] Step 4:

[0576] The server receives data in JSON format from the terminal. The server checks the integrity and completeness of the received data to ensure there is no invalid data. After verification, the server passes the data to the generating AI model.

[0577] Step 5:

[0578] The AI ​​model on the server analyzes the input data. The AI ​​model uses natural language processing techniques to categorize the information entered by disaster victims. For example, it extracts information such as "house partially destroyed," "leg injury," and "drinking water and first-aid kit." These analysis results are then used for the following processes on the server.

[0579] Step 6:

[0580] The server generates notifications based on the analysis results. The server generates notifications to be sent to relevant organizations according to the disaster situation and rescue requests. For example, based on the analysis results, it generates a notification for emergency services of "request for rescue of a leg injury" and a notification for aid organizations of "request for delivery of drinking water and first-aid kits."

[0581] Step 7:

[0582] The server sends the generated notification to the appropriate relevant organizations. The notification is sent using the appropriate protocol (e.g., email, SMS, API call). This allows the relevant organizations to quickly initiate appropriate action.

[0583] Step 8:

[0584] The device uses GPS to obtain the victim's current location. The obtained location information is sent to the server. This location information allows the victim's precise location to be determined.

[0585] Step 9:

[0586] Based on the acquired location information, the server uses a map service API to calculate a safe route to the nearest evacuation shelter. The calculated route information is then sent from the server to the terminal.

[0587] Step 10:

[0588] The terminal displays route information received from the server to the user. Based on this route information, the user can evacuate to the nearest shelter using voice and visual guides.

[0589] Step 11:

[0590] The user enters safety information. For example, they might enter, "I'm safe. I'm heading to the evacuation center." The device then sends this safety information to the server.

[0591] Step 12:

[0592] The server posts the received safety information to the disaster message board. This allows family members and other concerned parties to check the safety status of the victims. At the same time, it also sends notifications to relevant organizations.

[0593] (Application Example 1)

[0594] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0595] During disasters, it is crucial to efficiently and quickly transmit rescue requests and supply requests needed by victims to the relevant parties and ensure they receive appropriate support. Furthermore, while knowing the current location of victims, guiding them to evacuation routes, and confirming their safety are important, the lack of integrated management can lead to delays and confusion in information. Similarly, in food and supply deliveries during disasters, prompt guidance of delivery routes and reporting of delivery completion are essential. To address these challenges, a centralized management system is necessary.

[0596] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0597] In this invention, the server includes an input means for disaster victims to input disaster conditions, rescue requests, and necessary supplies information; an analysis means using a generative AI model to analyze the input information and identify the extent of the damage and the type and quantity of necessary supplies and rescue; a notification generation means to generate and send notifications to the appropriate departments based on the analysis results; a route guidance means to acquire the current location of disaster victims using GPS functionality and guide them to the nearest evacuation center; a safety information means to receive information on the safety of disaster victims and post it on a disaster message board; a route guidance means to acquire the current location of delivery personnel via GPS functionality and calculate a safe delivery route; and a delivery reporting means to report the completion of delivery. This enables centralized management of everything from disaster victim rescue requests to supply delivery, evacuation route guidance, and safety confirmation, allowing for a quick and efficient response.

[0598] "Disaster victims" refers to people who are facing emergencies such as natural disasters.

[0599] "Disaster situation" refers to the specific conditions and damage at a site during an emergency such as a natural disaster.

[0600] A "rescue request" refers to a request for rescue when a disaster victim needs assistance.

[0601] "Information on necessary supplies" refers to information about specific supplies such as food, water, and medical supplies that disaster victims need during a disaster.

[0602] "Input means" refers to devices or methods for disaster victims to input information about the disaster situation, rescue requests, and necessary supplies.

[0603] "Analysis means" refers to devices and methods for processing input data to identify the extent of damage and the types and quantities of necessary supplies.

[0604] A "generative AI model" refers to an artificial intelligence model designed to efficiently and accurately analyze input data.

[0605] "Notification generation means" refers to a device or method that generates and sends notifications to the appropriate relevant departments based on the analysis results.

[0606] "GPS function" refers to the function of a global positioning system used to acquire location information.

[0607] "Route guidance means" refers to devices or methods that guide users to the nearest evacuation center or delivery location based on acquired location information.

[0608] "Safety information means" refers to devices and methods for receiving information on the safety status of disaster victims and posting it on disaster message boards or similar platforms.

[0609] "Transmission means" refers to devices or methods for transmitting input information to a server via a data communication network.

[0610] "Delivery reporting means" refers to a device or method used to report the completion of a delivery after it has been completed.

[0611] This invention is a system for efficiently managing and rapidly transmitting information and supplies needed by disaster victims and related parties during a disaster. Specific embodiments of this system are described below.

[0612] System Overview

[0613] The system consists of terminals where disaster victims input information, a server that analyzes the information and sends notifications, and a generation AI model. Disaster victims input data from smartphones or tablets, and the server analyzes that information and takes appropriate action.

[0614] Hardware and software to be used

[0615] Smartphones and tablets: Devices used by disaster victims and delivery personnel.

[0616] Server (cloud service): Performs information analysis and notification processing.

[0617] GPS module: Obtains the current location of disaster victims and delivery personnel.

[0618] Generative AI model: Analyzes the input data.

[0619] Map service APIs (Google Maps API, Apple Maps API, etc.): Used to calculate evacuation routes and delivery routes.

[0620] Information input and transmission process

[0621] Disaster victims use smartphones or tablets to input information about the disaster situation, rescue requests, and necessary supplies. The entered information is organized by the device, converted to JSON format via the data communication network, and sent to the server using the HTTPS protocol.

[0622] Information reception and analysis processing

[0623] The server receives information sent from disaster victims and passes it to a generative AI model. The generative AI model analyzes the input data and identifies specific information such as "house partially destroyed," "leg injury," and "drinking water and first-aid kit." Based on the identified information, the server generates and sends appropriate notifications to the necessary departments.

[0624] Guide to evacuation routes and delivery routes

[0625] The server uses GPS functionality to obtain the current locations of disaster victims and delivery personnel. Based on the acquired location information, it calculates a safe route to the nearest evacuation center or delivery location. This involves using a map service API to calculate the specific route. The calculated route information is sent to the terminal to guide users so that they can evacuate or make deliveries safely.

[0626] Delivery completion and safety confirmation.

[0627] After delivery personnel successfully deliver supplies, they send information via an application to report the completion of the delivery. The server receives this information and notifies the victims and relevant departments. Similarly, victims enter their own safety information, and the server posts it on a disaster message board.

[0628] Specific example

[0629] Example input from disaster victims

[0630] Person A, a victim of the disaster, is at home and uses their smartphone to input, "My house is half-destroyed. I've injured my leg. I need drinking water and a first-aid kit." The device organizes this information and sends it to a server. The server analyzes it using a generative AI model, sends appropriate notifications to the relevant departments, and guides Person A to the nearest evacuation center.

[0631] Examples of actions taken by delivery personnel

[0632] Delivery person B uses the app to receive a delivery request for "water, bread, rice, and a first-aid kit," and uses GPS to confirm their current location. The server calculates the optimal delivery route and sends that information to delivery person B's app. After completing the delivery, B reports completion via the app, and the server sends a notification.

[0633] Example of a prompt

[0634] (Prompt example 1):

[0635] User A has requested "rice, bread, water, and a first-aid kit" during a disaster. The server should analyze the received information and send a notification to the appropriate delivery department. Additionally, create a program to notify User A and the administrator once the delivery personnel have successfully delivered the supplies.

[0636] (Prompt example 2):

[0637] Please create a program that obtains the delivery person's current location using GPS, calculates a safe delivery route on the server, and displays it on the delivery person's device. Use a map service API for the calculation.

[0638] Thus, this invention enables a swift and efficient response by centrally managing everything from disaster victims' requests for rescue to the delivery of supplies, guidance on evacuation routes, and confirmation of their safety.

[0639] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0640] Step 1:

[0641] Information input processing

[0642] Users use smartphones or tablets to input information about the disaster situation, rescue requests, and necessary supplies. This input includes specific text information such as, "My house is partially destroyed. I injured my leg. I need drinking water and a first-aid kit." The entered information is organized by category depending on the device.

[0643] Input: Disaster situation, rescue request, and necessary supplies information entered by the user.

[0644] Output: Organized text data (JSON format)

[0645] Step 2:

[0646] Information transmission processing

[0647] The terminal sends the organized information to the server via the data communication network. In this process, the information is converted to JSON format and transmitted securely using the HTTPS protocol.

[0648] Input: Organized text data (JSON format)

[0649] Output: Information sent to the server

[0650] Specific operation: The device prepares to send data, and tapping the send button sends the information to the server.

[0651] Step 3:

[0652] Information reception and analysis processing

[0653] The server receives information sent by the user. The received data is passed to a generating AI model for analysis. Through the analysis, information is categorized, such as "house partially destroyed," "foot injury," and "drinking water and first-aid kit."

[0654] Input: Information sent to the server

[0655] Output: Analyzed information (categorized data)

[0656] Specific operation: The server receives data, inputs it into the AI ​​model, and receives the analysis results.

[0657] Step 4:

[0658] Notification generation process

[0659] Based on the analysis results, the server generates and sends notifications to the appropriate departments. For example, it notifies emergency services of a "request for assistance for a foot injury" and aid organizations of a "request for delivery of drinking water and first-aid kits."

[0660] Input: Analyzed information

[0661] Output: Notification to relevant departments

[0662] Specific operation: The server generates a notification message based on the analyzed data and sends it to the designated relevant department.

[0663] Step 5:

[0664] Evacuation route guidance processing

[0665] The server uses GPS to obtain the current location of disaster victims. Based on the obtained location information, it calculates a safe route to the nearest evacuation center. A map service API is used for this route calculation. The calculated route information is sent to the terminal and displayed to the user.

[0666] Input: Current location information of disaster victims

[0667] Output: Route information to the nearest evacuation shelter

[0668] Specific operation: The server acquires GPS data, calls a map service API to calculate the route, and then sends the route information to the terminal.

[0669] Step 6:

[0670] Delivery route guidance processing

[0671] The server obtains the delivery person's current location via GPS. Based on the obtained location information, it calculates a safe route to the delivery destination. This also utilizes a map service API. The calculated route information is sent to the delivery person's terminal and displayed to them.

[0672] Input: Delivery driver's current location information

[0673] Output: Route information to the delivery address

[0674] Specific operation: The server acquires GPS data, calls a map service API to calculate the route, and then sends the route information to the terminal.

[0675] Step 7:

[0676] Delivery completion report processing

[0677] After the delivery person safely delivers the supplies, they report the completion of the delivery through the application. The terminal sends this information to the server. The server notifies the disaster victims and relevant departments of the received completion information.

[0678] Input: Delivery completion information

[0679] Output: Notification to disaster victims and relevant departments

[0680] Specific operation: The delivery person taps the completion report button, and the device sends that information to the server. The server generates and sends a notification based on the received information.

[0681] Step 8:

[0682] Safety information processing

[0683] Disaster victims enter their safety status information using their smartphones. The device then sends this safety information to a server. The server posts the received safety information on a disaster message board so that relevant parties can check it.

[0684] Input: Information on the safety status of disaster victims

[0685] Output: Posting to the disaster message board

[0686] Specific operation: Disaster victims enter information into a safety status input form, and the terminal sends that information to the server. The server then posts the received information on a message board.

[0687] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0688] Modes for carrying out the invention

[0689] This invention provides a system that utilizes data communication to request rescue during disasters, request necessary supplies, confirm the safety of victims, provide route guidance to evacuation centers, and recognize the emotions of disaster victims to provide appropriate responses. Specifically, the system aims to more accurately understand the situation of disaster victims and provide appropriate support and guidance by combining a generative AI model and an emotion engine. The specific embodiments of this system are described in detail below.

[0690] (System Overview)

[0691] This system consists of terminals, servers, a generative AI model, and an emotion engine. Disaster victims (users) input data from terminals such as smartphones and tablets, and the server analyzes and notifies them of this information. Furthermore, the use of a generative AI model and an emotion engine ensures that the input data is processed efficiently and accurately.

[0692] (Detailed system processing flow)

[0693] 1. Information input processing

[0694] Users: Disaster victims use smartphones or tablets to input information about the disaster situation, rescue requests, necessary supplies, and their safety status. They input specific information such as, "My house is half-destroyed. I injured my leg. I need drinking water and a first-aid kit." User emotional data (voice, text, facial expressions, etc.) is also input.

[0695] Terminal: The terminal organizes the input information and generates data packets.

[0696] 2. Information transmission process

[0697] Terminal: Sends organized information packets to the server via the data communication network. The information is converted to, for example, JSON format, and a POST request is sent to the server using the HTTPS protocol.

[0698] 3. Information reception and analysis processing

[0699] Server: Receives information packets sent by the user on the receiving port. Decodes the received information for analysis.

[0700] Server: Passes the received information to the generative AI model and emotion engine for analysis. The generative AI model identifies the disaster situation, the extent of injuries, and the types and quantities of necessary supplies, while the emotion engine analyzes the user's emotional state.

[0701] 4. Notification generation process

[0702] Server: Generates notifications to relevant departments based on the analysis results. For example, it creates a "request for assistance for a leg injury" for paramedics and a "request for delivery of drinking water and first-aid kits" for aid organizations. It also generates notifications that take into account the mental state of the disaster victims based on the analysis results of the emotion engine.

[0703] Server: Sends generated notifications to the appropriate departments. This includes sending emails to emergency services via the mail server and sending POST requests to support organization APIs.

[0704] 5. Route guidance processing

[0705] Terminal: Uses GPS functionality to obtain the current location of disaster victims. This utilizes the terminal's built-in GPS sensor.

[0706] Server: Based on acquired location information, it calculates a safe route to the nearest evacuation shelter. It uses a map service API to calculate the specific evacuation route. In addition, it generates more reassuring guidance text according to the user's emotional state analyzed by the emotion engine.

[0707] Terminal: Displays route information received from the server on a map. This guides disaster victims so they can evacuate safely.

[0708] 6. Safety confirmation process

[0709] User: The disaster victim enters their own safety information. They report, "I'm safe. I'm heading to the evacuation center."

[0710] Terminal: Organizes safety information and sends it to the server.

[0711] Server: Analyzes received safety information and posts it on the disaster message board. It sends a POST request to the disaster message board API and posts the entered information.

[0712] Server: Notifies relevant parties of the posted safety information.

[0713] (Specific example)

[0714] Let's consider a scenario where victim A is at home during a disaster. A uses their smartphone to type, "My house is half-destroyed. I've injured my leg. I need drinking water and a first-aid kit." The device sends this information to a server, which analyzes it using a generative AI model and an emotion engine. The server notifies emergency services of a "request for assistance regarding the leg injury" and aid organizations of a "request for delivery of drinking water and a first-aid kit." Furthermore, if the emotion engine analyzes A's stress level as high, it sends a notification offering more detailed guidance and additional emotional support.

[0715] The system also obtains A's current location and guides them to the nearest evacuation center. When A enters a message indicating their safety, such as "I'm safe. I'm heading to the evacuation center," that information is posted on a disaster message board, allowing family and friends to see it.

[0716] In this way, this system comprehensively supports the safety of disaster victims and their rapid evacuation by providing information dissemination, supply of relief goods, guidance on evacuation routes, confirmation of safety, and support based on emotional state during disasters.

[0717] The following describes the processing flow.

[0718] (Specific processing flow of the program)

[0719] Step 1: Enter Information

[0720] Users: Disaster victims use smartphones or tablets to input information about the disaster situation, rescue requests, necessary supplies, and their safety status. For example, they might input specific information such as, "My house is half destroyed. I injured my leg. I need drinking water and a first-aid kit." They can also input emotions through voice data, text data, and facial image data.

[0721] Terminal: The terminal organizes this input information and generates data packets.

[0722] Step 2: Send Information

[0723] Terminal: Sends organized information packets to the server via the data communication network. The information is converted to, for example, JSON format, and a POST request is sent to the server using the HTTPS protocol.

[0724] Step 3: Information Reception

[0725] Server: Receives information packets sent from the user on the receiving port. Decodes the received information for analysis and passes it to the generative AI model and emotion engine.

[0726] Step 4: Information Analysis

[0727] Generative AI Model: Based on received data, it identifies disaster conditions, the extent of injuries, and the types and quantities of necessary supplies. For example, it identifies information such as "house partially destroyed," "leg injury," and "drinking water and first-aid kit."

[0728] Emotion Engine: Analyzes emotional data (voice, text, facial expressions) to identify the user's emotional state. For example, it evaluates stress levels, feelings of security, and feelings of anxiety.

[0729] Step 5: Generate Notification

[0730] Server: Based on the analysis results of the generation AI model and the emotion engine, it generates appropriate notifications. For example, it creates a "request for assistance for a leg injury" for paramedics and a "request for delivery of drinking water and first-aid kits" for aid organizations. Depending on the results of the emotion engine, it may also generate notifications that provide emotional support if the stress level of the disaster victim is high.

[0731] Server: Sends generated notifications to the appropriate departments. This includes sending emails to emergency services via a mail server or sending POST requests to support organization APIs.

[0732] Step 6: Get current location

[0733] Terminal: Uses GPS functionality to obtain the current location of disaster victims. This utilizes the terminal's built-in GPS sensor.

[0734] Terminal: Transmits acquired location information to the server in real time.

[0735] Step 7: Route generation

[0736] Server: Based on acquired location information, it calculates a safe route to the nearest evacuation shelter. It uses a map service API to calculate the specific evacuation route. In addition, it may generate more reassuring guidance messages based on the user's emotional state analyzed by the emotion engine.

[0737] Server: Converts secure routing information into data packets and sends them to the terminal.

[0738] Step 8: Route guidance display

[0739] Terminal: Displays route information received from the server on a map. This guides disaster victims so they can evacuate safely.

[0740] Step 9: Enter safety information

[0741] User: The disaster victim enters their own safety information. They report, "I'm safe. I'm heading to the evacuation center."

[0742] Terminal: Organizes safety information and sends it to the server.

[0743] Step 10: Posting safety information

[0744] Server: Analyzes received safety information and posts it on the disaster message board. It sends a POST request to the disaster message board API and posts the entered information.

[0745] Server: Notifies relevant parties of the posted safety information.

[0746] As a concrete example, consider a case where victim B experiences an earthquake and their home is partially destroyed. B inputs, "My house is partially destroyed. I injured my leg. I need drinking water and a first-aid kit," and also voice-inputs the emotion, "I'm very scared." The device sends this to a server, where a generative AI model and emotion engine analyze it. Then, a notification is sent to emergency services requesting assistance for a leg injury, to aid organizations requesting delivery of drinking water and a first-aid kit, and to psychiatric support teams that B is a victim at a high stress level. In addition, based on B's current location, directions to the nearest evacuation center are provided.

[0747] This system allows for a comprehensive understanding of the circumstances and mental state of disaster victims, enabling the provision of more appropriate support.

[0748] (Example 2)

[0749] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0750] Conventional disaster relief systems have faced challenges, particularly in providing support that takes into account the emotions and psychological state of victims, due to the limited information that victims can input. Furthermore, the entire process from information input to analysis, notification, and support is not carried out efficiently, and a rapid and appropriate response is required. As a result, there are situations where both the physical safety and psychological support of victims are insufficient.

[0751] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0752] In this invention, the server includes an input means for disaster victims to input disaster conditions, rescue requests, and necessary supplies information; an emotion data input means for inputting emotion data from voice data and facial expressions; an analysis means using a generative AI model to analyze the input information and identify the damage situation and the type and quantity of necessary supplies and rescue; an analysis means using an emotion engine to analyze the input emotion data and evaluate the emotional state of the disaster victim; a notification generation means to generate and send notifications to the appropriate departments based on the analysis results; a route guidance means to obtain the current location of the disaster victim using GPS functionality and guide them to the nearest evacuation center; and a safety information means to receive information on the safety status of the disaster victim and post it on a disaster message board. This enables support that takes into account the emotional state of the disaster victim, and realizes a swift and appropriate response.

[0753] "Disaster victims" refers to individuals or groups who have experienced and been affected by a disaster.

[0754] "Input means" refers to devices or software that users use to input information. Specifically, this refers to applications on smartphones and tablets.

[0755] "Emotional data input means" refers to devices or software used to input information related to a user's emotions, such as voice data or facial expressions.

[0756] A "generative AI model" refers to an algorithm that uses machine learning and data analysis techniques to identify the extent of damage and the types and quantities of necessary supplies from the input information.

[0757] "Analysis means" refers to devices or software that analyze and interpret input data.

[0758] An "emotion engine" refers to an algorithm or software that analyzes emotional data and evaluates the user's emotional state.

[0759] "Notification generation means" refers to a device or software that generates and sends notifications to the appropriate department based on the analysis results.

[0760] "Transmission means" refers to devices or software used to transmit input information to a server via a data communication network.

[0761] "GPS function" refers to the function of obtaining the current physical location using a global positioning system.

[0762] "Route guidance means" refers to devices or software that calculate the route to the destination based on acquired location information and guide the user.

[0763] "Safety information means" refers to devices or software that receive information on the safety status of disaster victims and post it on disaster message boards or similar platforms.

[0764] This invention is a system for effectively and quickly providing support to disaster victims, and is comprised of a terminal, a server, a generative AI model, and an emotion engine. Specific embodiments of this system are described in detail below.

[0765] (Terminal and user operation)

[0766] Users: Disaster victims use smartphones or tablets to input information about the disaster situation, rescue requests, and necessary supplies. For example, they might input specific information such as, "My house is half-destroyed. I injured my leg. I need drinking water and a first-aid kit." They also input emotional data through voice and facial expressions. The devices are equipped with emotional data input mechanisms, which allows for the collection of emotional data as well.

[0767] Terminal: Receives user input and organizes it into data packets. Voice data is converted into text data using a speech recognition engine. The organized data packets are converted into JSON format and sent to the server using the HTTPS protocol.

[0768] (Server analysis and notification)

[0769] Server: Receives data packets from users via the receiving port and decodes them. The decoded data is passed to the generative AI model and the emotion engine. The generative AI model analyzes the input text data to identify the disaster situation and the types and quantities of supplies needed. The emotion engine analyzes the emotion data to assess the emotional state of the victims.

[0770] Server: Based on the analysis results, it generates notifications for the appropriate departments (e.g., paramedics, aid organizations). The generation AI model creates notifications such as "request for assistance for a leg injury," and the emotion engine creates notifications such as "request for delivery of drinking water and a first-aid kit," taking into account the emotional state. The notifications are sent to the mail server or the aid organization's API.

[0771] (GPS and route guidance)

[0772] Device: Uses the built-in GPS sensor to obtain the user's current location. GPS data is converted to JSON format and sent to the server.

[0773] Server: Based on acquired location information, it calculates a safe route to the nearest evacuation shelter. It uses map services such as the Google Maps API to calculate a specific evacuation route and generates reassuring guidance text based on the analysis results of the emotion engine.

[0774] Terminal: Displays evacuation routes received from the server on a map, guiding users safely.

[0775] (Confirmation of safety)

[0776] User: Disaster victims enter their safety status. For example, they might enter, "I'm safe. I'm heading to the evacuation center."

[0777] Terminal: Organizes the entered safety information and sends it to the server.

[0778] Server: Analyzes received safety information and sends a POST request to the API to post it on the disaster message board. This allows family and friends to check on the safety of disaster victims.

[0779] (Specific example)

[0780] Person A, a victim of a disaster, is at home and uses their smartphone to type, "My house is half-destroyed. I've injured my leg. I need drinking water and a first-aid kit." This information is compiled on the device and sent to a server. On the server, a generative AI model analyzes the disaster situation and necessary supplies, and an emotion engine evaluates Person A's emotional state. The server then notifies emergency services of a "request for assistance regarding the leg injury" and aid organizations of a "request for delivery of drinking water and a first-aid kit." It also guides Person A along an evacuation route based on their GPS information, and when Person A enters their safety status, it is posted on a disaster message board, allowing family and friends to check on their safety.

[0781] (Example of prompt text to input to the generated AI model)

[0782] Please provide a detailed report of the disaster situation. Include specific information about the damage to your home, the extent of your injuries, and the types and quantities of supplies you need. Additionally, please describe your current emotional state.

[0783] In this way, the system can effectively ensure the safety and provide psychological support to disaster victims by allowing them to input information and quickly providing appropriate support that takes their emotional state into consideration.

[0784] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0785] Step 1:

[0786] Users use smartphones or tablets to input information about the disaster situation, rescue requests, and necessary supplies. For example, they might input specific information such as, "My house is half-destroyed. I injured my leg. I need drinking water and a first-aid kit." They also input emotional data through voice and facial expressions. This allows for the acquisition of both text data and emotional data through the input method.

[0787] Step 2:

[0788] The terminal receives input information from the user, organizes it, and converts it into data packets. Voice data is converted into text data by a speech recognition engine. The organized data packets are converted into JSON format and sent to the server via the data communication network. The input is the user's text and voice, and the output is data packets in JSON format.

[0789] Step 3:

[0790] The server receives data packets from the user via the receiving port and decodes them. The decoded data is passed to the generating AI model and emotion engine. Data ready for analysis becomes input, and data in an analyzable state becomes output.

[0791] Step 4:

[0792] The server decodes the data, which is then analyzed by a generating AI model to identify the disaster situation and the types and quantities of supplies needed. The generating AI model takes text data as input and outputs the identified disaster situation and supply information. Specifically, it extracts information such as "partially destroyed," "leg injury," and "drinking water and first-aid kit needed."

[0793] Step 5:

[0794] The server passes the decoded emotional data to the emotion engine, which analyzes the emotional state of the disaster victims. The emotion engine takes voice and facial expression data as input and evaluates stress levels and emotional states. The analysis results output emotional evaluations such as "high stress" or "fearful state."

[0795] Step 6:

[0796] The server generates notifications to the appropriate departments based on the analysis results of the generated AI model and the emotion engine. Specifically, it notifies emergency services of a "request for assistance for a leg injury" and aid organizations of a "request for delivery of drinking water and a first-aid kit." Furthermore, based on the results evaluated by the emotion engine, it generates notification content in more polite language. The input is the analysis results, and the output is the notification content.

[0797] Step 7:

[0798] The server sends the generated notification to the appropriate department. It sends an email to the emergency services via the mail server and a POST request to the support organization's API. The input is the notification content, and the output is the status of successful transmission.

[0799] Step 8:

[0800] The device uses its built-in GPS sensor to obtain the user's current location. The obtained location information is converted to JSON format and sent to the server. The input is GPS data, and the output is a packet of location data.

[0801] Step 9:

[0802] Based on location information acquired by the server, the system calculates a safe route to the nearest evacuation shelter. It uses the Google Maps API to determine the specific evacuation route. It also generates reassuring guidance messages based on the analysis results of an emotion engine. Input is location information, and output is evacuation route information.

[0803] Step 10:

[0804] The terminal displays evacuation routes received from the server on a map application, guiding the user safely. The input is evacuation route information, and the output is the route display on the map application.

[0805] Step 11:

[0806] The user enters their safety status. For example, they might enter, "I'm safe. I'm heading to the evacuation center." This completes the entry of their safety information.

[0807] Step 12:

[0808] The terminal organizes the safety information entered and sends it to the server. The input is the safety information, and the output is the data sent to the server.

[0809] Step 13:

[0810] The server analyzes the safety information it receives and sends a POST request to the API to post it on the disaster message board. The input is the safety information, and the output is the information posted on the disaster message board.

[0811] This processing flow ensures that information gathering, analysis, notification, evacuation guidance, and safety confirmation from disaster victims are carried out consistently.

[0812] (Application Example 2)

[0813] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0814] During disasters, it is crucial to quickly and accurately assess the situation of victims and provide appropriate support. However, conventional systems lack sufficient psychological support that takes into account the emotional state of victims. Furthermore, systems for promptly notifying the appropriate departments of analysis results and conducting effective rescue and support are not adequately developed, and accurate guidance to ensure the safe evacuation of victims is insufficient. These challenges need to be addressed.

[0815] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0816] In this invention, the server includes an input means for disaster victims to input disaster conditions, rescue requests, and necessary supplies information; an analysis means using a generative AI model to analyze the input information and identify the extent of the damage and the type and quantity of necessary supplies and rescue; an emotion analysis means to analyze the emotional data of disaster victims and identify whether psychological support is needed; a notification generation means to generate and send notifications to the appropriate departments based on the analysis results; a support provision means to provide appropriate psychological support to disaster victims based on the emotion analysis results; a route guidance means to acquire the current location of disaster victims using GPS functionality and guide them to the nearest evacuation center; and a safety information means to receive information on the safety of disaster victims and post it on a disaster message board. This enables a comprehensive understanding of the situation of disaster victims and allows for prompt and accurate support and guidance.

[0817] - "Input means" refers to devices or functions that allow disaster victims to input information about the disaster situation, rescue requests, and necessary supplies.

[0818] "Analysis means" refers to devices or functions that use a generative AI model to analyze input information and identify the extent of the damage and the types and quantities of necessary supplies and rescue efforts.

[0819] A "notification generation means" refers to a device or function that generates and sends notifications to the appropriate department based on the analysis results.

[0820] "Emotional analysis tools" refer to devices or functions that analyze the emotional data of disaster victims to determine whether or not they require psychological support.

[0821] "Means of providing support" refers to devices and functions that provide appropriate psychological support to disaster victims based on the results of emotion analysis.

[0822] A "route guidance system" refers to a device or function that uses GPS functionality to obtain the current location of disaster victims and guides them to the nearest evacuation shelter.

[0823] A "safety information system" refers to a device or function that receives information about the safety of disaster victims and posts it on a disaster message board.

[0824] A "generative AI model" is an artificial intelligence model that analyzes disaster situations, rescue requests, and necessary supplies to identify appropriate responses.

[0825] A "data communication network" is a network of communications used to transmit input information to a server.

[0826] A "server" is a computer system that receives, processes, and analyzes information sent by disaster victims.

[0827] overview

[0828] This invention is a system for more accurately understanding the situation of disaster victims and providing prompt and appropriate support. Disaster victims input information about the disaster situation, rescue requests, and necessary supplies from a device such as a smartphone or tablet, and transmit this information to a server via a data communication network. This server analyzes the information using a generative AI model and sentiment analysis means, generates appropriate notifications, and sends them. It also obtains the current location of disaster victims, guides them to the nearest evacuation center, and receives information on the safety status of disaster victims and posts it on a disaster message board.

[0829] Hardware and software to be used

[0830] Devices: Smartphones, tablets, smart glasses, head-mounted displays, robots

[0831] Server: High-performance computer system, Django framework (Python)

[0832] Database: PostgreSQL

[0833] Generative AI model: GPT-4 (OpenAI)

[0834] Emotion analysis software: Affectiva

[0835] Location services: Google Maps API

[0836] Program processing

[0837] Data entry and transmission

[0838] Users input disaster information, rescue requests, and necessary supplies using their devices. Simultaneously, emotional data such as audio and video is also input. This data is transmitted to a server via a data communication network.

[0839] Data Analysis

[0840] The server sends the received data to the GPT-4 AI model for analysis of the disaster situation and necessary supplies. It also uses Affectiva, emotion analysis software, to analyze the user's emotional state.

[0841] Generating and sending notifications

[0842] The server generates and sends notifications to the appropriate departments based on the analysis results. For example, it notifies emergency services of emergency requests and aid organizations of requests for the delivery of necessary supplies. Based on the emotion analysis results, it also generates notifications that take into account the mental state of the victims.

[0843] Route guidance

[0844] The system uses the GPS function of the device to obtain the current location of the disaster victim. The server uses the Google Maps API to calculate a safe route to the nearest evacuation center and sends that information to the device. The device displays this route information on a map to guide the disaster victim.

[0845] Confirmation of safety

[0846] Users input safety information using their devices and send it to the server. The server analyzes this information and posts it on a disaster message board. It also notifies relevant parties of the safety status.

[0847] Specific example

[0848] If a disaster victim, Person A, is at home, they will use their smartphone to input information such as, "My house is half-destroyed. I injured my leg. I need drinking water and a first-aid kit." Voice and facial expression data of Person A will also be input. This data is sent to a server and analyzed by a generative AI model and emotion analysis software. The server will notify emergency services to request rescue and aid organizations to deliver necessary supplies. Furthermore, if the analysis indicates that Person A's stress level is high, a notification offering psychological support will be generated. Person A's current location will be obtained, and a safe route to the nearest evacuation center will be provided. If Person A inputs a safety status message such as, "I'm safe. I'm heading to the evacuation center," this will be posted on the disaster message board and notified to relevant parties.

[0849] Example of a prompt

[0850] Please analyze the following disaster situation and determine whether assistance is needed.

[0851] Disaster situation: My house is partially destroyed. I injured my leg. I need drinking water and a first-aid kit.

[0852] Emotion data: emotion_audio_file.mp3

[0853] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0854] Step 1:

[0855] Information input processing

[0856] Users who are victims of the disaster use their smartphones or tablets to input information about the disaster situation, rescue requests, and necessary supplies. Specifically, they input text information such as, "My house is half destroyed. I injured my leg. I need drinking water and a first-aid kit," as well as voice and emotional data such as facial expressions. This information is taken into the device as input. The device organizes this information and converts it into data packets in formats such as JSON.

[0857] Step 2:

[0858] Information transmission processing

[0859] The terminal sends organized information packets to the server via the data communication network. Specifically, the terminal sends a POST request using the HTTPS protocol. In this case, the data packet containing input data (disaster situation, emotional data, etc.) becomes the input amount. This allows the necessary information to be instantly transmitted from the terminal to the server.

[0860] Step 3:

[0861] Information reception and analysis processing

[0862] The server receives information packets sent from the terminal. It receives the information at the receiving port, decodes it, and prepares it for analysis. The analysis is based on the generative AI model GPT-4 and the sentiment analysis software Affectiva. The data received as input is processed by the server in the preliminary stages of analysis.

[0863] Step 4:

[0864] Analysis using generative AI models

[0865] The received data is sent to the GPT-4 generation AI model to analyze the disaster situation and necessary supplies. A prompt message is generated and data is passed for analysis. Based on text information such as "My house is half-destroyed. I injured my leg. I need drinking water and a first-aid kit," the generation AI model understands the situation and identifies the necessary assistance. The output is a disaster situation report and a list of necessary supplies.

[0866] Step 5:

[0867] Emotion analysis

[0868] The server sends emotional data to Affectiva, which analyzes the user's emotional state. It takes audio and facial expression data as input for the analysis. Affectiva identifies the user's emotional state from the audio and video, and extracts information such as stress levels and tension. The output is the emotional analysis result.

[0869] Step 6:

[0870] Notification generation process

[0871] The server generates and sends notifications to the appropriate departments based on the analysis results. Specifically, it creates notification content based on the disaster situation and required supplies identified by the generation AI model, and the emotional state identified by Affectiva. It notifies emergency services of emergency requests and aid organizations of the delivery of necessary supplies. In addition, if the emotional analysis results indicate that psychological support is needed, it generates a customized notification. The output is the notification sent to each relevant party.

[0872] Step 7:

[0873] Route guidance processing

[0874] The server uses GPS to obtain the current location of disaster victims and guides them to the nearest evacuation shelter. Using the Google Maps API, it calculates specific evacuation routes and uses the current location as input to calculate the safest evacuation route. The output is the calculated route information. The server sends the route information to the terminal, which displays it on a map to guide the disaster victims.

[0875] Step 8:

[0876] Safety confirmation process

[0877] The user enters safety status information using a terminal and sends it to the server. The input data is information such as "I'm safe. I'm heading to the evacuation center." The server analyzes the received safety status information and posts it on the disaster message board. Furthermore, it notifies relevant parties of the safety status. The output is the safety status information posted on the disaster message board and the notifications sent to relevant parties.

[0878] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0879] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0880] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0881] [Third Embodiment]

[0882] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0883] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0884] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0885] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0886] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0887] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0888] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0889] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0890] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0891] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0892] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0893] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0894] Modes for carrying out the invention

[0895] This invention utilizes data communication to centrally manage disaster relief requests, requests for necessary supplies, safety checks, and route guidance to evacuation centers, and implements a system that promptly notifies relevant parties using a generation AI model. A specific embodiment of this system is described in detail below.

[0896] (System Overview)

[0897] This system consists of terminals, servers, and generative AI models. Disaster victims (users) input data from terminals such as smartphones and tablets, and the server analyzes and notifies them of this information. Furthermore, the use of generative AI models ensures that the input data is processed efficiently and accurately.

[0898] (Detailed system processing flow)

[0899] 1. Information input processing

[0900] Users: Disaster victims use smartphones or tablets to input information about the disaster situation, rescue requests, necessary supplies, and their safety status. For example, they might input specific information such as, "My house is half-destroyed. I injured my leg. I need drinking water and a first-aid kit."

[0901] Terminal: The terminal organizes the input information and prepares it to be sent to the server via the data communication network.

[0902] 2. Information transmission process

[0903] Terminal: The organized information is sent to the server via the data communication network. Here, the information is converted to, for example, JSON format and sent using the HTTPS protocol.

[0904] 3. Information reception and analysis processing

[0905] Server: The server receives information sent from the user and passes it to the generating AI model. The server analyzes the information to identify the extent of the damage and the type and quantity of supplies and rescue needed.

[0906] Generative AI Model: This model analyzes the input data and identifies information such as "house partially destroyed," "foot injury," and "drinking water and first-aid kit."

[0907] 4. Notification generation process

[0908] Server: Based on the analysis results, it generates and sends necessary notifications to the relevant departments. For example, it creates and sends a "request for rescue of a foot injury" to emergency services and a "request for delivery of drinking water and first-aid kits" to support organizations.

[0909] 5. Route guidance processing

[0910] Device: Obtains the current location of disaster victims via GPS.

[0911] Server: Based on the acquired location information, it calculates a safe route to the nearest evacuation shelter. This is done by using a map service API to calculate the specific evacuation route.

[0912] Terminal: Displays routing information received from the server and guides disaster victims to evacuate safely.

[0913] 6. Safety confirmation process

[0914] User: Disaster victims enter their own safety information, for example, "I'm safe. I'm heading to the evacuation center."

[0915] Terminal: Send this safety information to the server.

[0916] Server: Posts received safety information to the disaster message board so that relevant parties can check it.

[0917] (Specific example)

[0918] Let's consider a scenario where victim A is at home during a disaster. A uses their smartphone to type, "My house is half-destroyed. I've injured my leg. I need drinking water and a first-aid kit." The device sends this information to a server, which analyzes it using a generative AI model. The server notifies emergency services of a "request for assistance regarding the leg injury" and aid organizations of a "request for delivery of drinking water and a first-aid kit." Furthermore, it obtains A's current location and guides them to the nearest evacuation center. When A enters a safety update such as, "I'm safe. I'm heading to the evacuation center," this information is posted on a disaster message board, allowing family and friends to see it.

[0919] In this way, this system comprehensively supports disaster relief efforts, including information dissemination, supply of relief goods, guidance on evacuation routes, and confirmation of the safety of disaster victims, thereby supporting their safety and rapid evacuation.

[0920] The following describes the processing flow.

[0921] (Specific processing flow of the program)

[0922] Step 1: Enter Information

[0923] Users: Disaster victims use smartphones or tablets to input information about the disaster situation, rescue requests, necessary supplies, and their safety status. They input specific information such as, "My house is half destroyed. I injured my leg. I need drinking water and a first-aid kit."

[0924] Terminal: The terminal organizes the input information and generates data packets.

[0925] Step 2: Send Information

[0926] Terminal: Sends organized information packets to the server via the data communication network. The information is converted to, for example, JSON format, and a POST request is sent to the server using the HTTPS protocol.

[0927] Step 3: Information Reception

[0928] Server: Receives information packets sent by the user on the receiving port. Decodes the received information for analysis.

[0929] Step 4: Information Analysis

[0930] Server: Passes the received information to the generating AI model for analysis. The generating AI model identifies the disaster situation, the extent of injuries, and the types and quantities of supplies needed.

[0931] Generative AI model: Based on input data, it identifies information such as "house partially destroyed," "foot injury," and "drinking water and first-aid kit."

[0932] Step 5: Generate Notification

[0933] Server: Based on the analysis results, it generates notifications for the relevant departments. For example, it creates a "request for assistance for a foot injury" for paramedics and a "request for delivery of drinking water and first-aid kits" for aid organizations.

[0934] Server: Sends generated notifications to the appropriate departments. This includes sending emails to emergency services via the mail server and sending POST requests to support organization APIs.

[0935] Step 6: Get current location

[0936] Terminal: Uses GPS functionality to obtain the current location of disaster victims. This utilizes the terminal's built-in GPS sensor.

[0937] Terminal: Transmits acquired location information to the server in real time.

[0938] Step 7: Route generation

[0939] Server: Based on acquired location information, it calculates a safe route to the nearest evacuation shelter. It uses a map service API to calculate the specific evacuation route.

[0940] Server: Converts secure routing information into data packets and sends them to the terminal.

[0941] Step 8: Route guidance display

[0942] Terminal: Displays route information received from the server on a map. This guides disaster victims so they can evacuate safely.

[0943] Step 9: Enter safety information

[0944] User: The disaster victim enters their own safety information. They report, "I'm safe. I'm heading to the evacuation center."

[0945] Terminal: Organizes safety information and sends it to the server.

[0946] Step 10: Posting safety information

[0947] Server: Analyzes received safety information and posts it on the disaster message board. It sends a POST request to the disaster message board API and posts the entered information.

[0948] Server: Notifies relevant parties of the posted safety information.

[0949] The above outlines the specific processing flow of this invention. This system enables rapid and accurate information transmission, supply of relief goods, guidance of evacuation routes, and confirmation of safety during disasters.

[0950] (Example 1)

[0951] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0952] During disasters, there is a need for a system that allows victims to quickly and efficiently request rescue, request necessary supplies, confirm their safety, and receive directions to evacuation centers, while appropriately notifying relevant organizations. However, existing systems have fragmented processes, from information input to notification, safety confirmation, and evacuation route guidance, making a rapid and efficient response difficult. Furthermore, information about victims has not always been accurately transmitted, sometimes leading to delays in providing appropriate assistance.

[0953] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0954] In this invention, the server includes an input device for disaster victims to input disaster conditions, rescue requests, and necessary supplies information; a transmission device that organizes the input information and transmits it to the server via a data communication network; an analysis device that receives the information transmitted by the user and performs analysis using a generation AI model; a notification generation device that generates and transmits notifications to the appropriate departments based on the analysis results; a route guidance device that acquires the current location of disaster victims using GPS functionality and guides them to the nearest evacuation center; and a safety information device that receives information on the safety of disaster victims and posts it on a disaster message board. This makes it possible to quickly and efficiently collect, analyze, and notify disaster victims' information during a disaster, thereby realizing appropriate support and safe evacuation.

[0955] An "input device" is a device used by disaster victims to input information about the disaster situation, rescue requests, and necessary supplies.

[0956] A "transmission device" is a device that organizes the input information and transmits it to a server via a data communication network.

[0957] An "analysis device" is a device that receives information sent from a user via a server and performs analysis using a generated AI model.

[0958] A "notification generation device" is a device that generates and sends notifications to the appropriate department based on the analysis results.

[0959] A "route guidance device" is a device that uses GPS functionality to obtain the current location of disaster victims and guides them to the nearest evacuation shelter.

[0960] A "safety information device" is a device that receives information about the safety of disaster victims and posts it on a disaster message board.

[0961] A "generative AI model" is an artificial intelligence model that analyzes input data and extracts necessary information.

[0962] A "data communication network" is a network used to transmit information from a terminal to a server.

[0963] A "server" is a device that analyzes received information and performs processing such as generating notifications and providing route guidance.

[0964] This invention realizes a system that uses a generative AI model to centrally manage information such as rescue requests, requests for necessary supplies, safety confirmations, and route guidance to evacuation shelters during disasters, and to quickly notify relevant organizations. This system mainly consists of terminals, servers, and generative AI models.

[0965] Hardware and software to be used

[0966] Device: Smartphone, tablet, or other mobile device

[0967] Servers: Cloud services and dedicated servers

[0968] Generative AI models: Generative AI models that perform natural language processing.

[0969] Data communication networks: Internet, mobile networks

[0970] GPS function: GPS module built into the device

[0971] Map Service API: A map service API for calculating evacuation routes.

[0972] Overview of Functions and Data Processing

[0973] Information entry

[0974] Users: Disaster victims use the terminal to input information about the current state of the disaster, requests for rescue, necessary supplies, and their safety status. Specifically, they might enter "My house is partially destroyed. I injured my leg. I need drinking water and a first-aid kit" into a form on the application.

[0975] Terminal: The terminal checks the format of the input information and converts it to an appropriate data format (e.g., JSON format).

[0976] Information transmission

[0977] Terminal: The formatted data is sent to the server using the HTTPS protocol. Encryption is also performed as needed.

[0978] Information reception and analysis

[0979] Server: The server passes the received data to the generating AI model. The AI ​​model analyzes the input language data and extracts important information (e.g., "house is partially destroyed," "leg injury," "drinking water and first-aid kit needed").

[0980] Notification generation

[0981] Server: Based on the analysis results, it generates and sends notifications to the appropriate relevant organizations. For example, it notifies emergency services of a "request for assistance for a leg injury" and aid organizations of a "request for delivery of drinking water and first-aid kits."

[0982] Route guidance

[0983] Terminal: The device uses GPS to obtain the current location of the disaster victim. The obtained location information is then sent to the server.

[0984] Server: Uses a map service API to calculate a safe route to the nearest evacuation shelter. The calculated route information is returned to the terminal.

[0985] Terminal: The terminal displays route information to the user and guides them along evacuation routes using a voice assistant and map display.

[0986] Confirmation of safety

[0987] User: Disaster victims enter their own safety information. For example, they might enter "I'm safe. I'm heading to the evacuation center."

[0988] Terminal: Safety information is sent to the server.

[0989] Server: Posts received safety information to the disaster message board so that relevant parties can check it.

[0990] Specific example

[0991] If person A is at home during a disaster, they would use their smartphone to input, "My house is half-destroyed. I've injured my leg. I need drinking water and a first-aid kit." The device would then organize this information and send it to a server, which would analyze it using a generative AI model. Based on the analysis, the server would notify emergency services of a "request for rescue due to leg injury" and support organizations of a "request for delivery of drinking water and a first-aid kit." Furthermore, the system would obtain A's current location and guide them to the nearest evacuation center via a safe route. If A then inputs a message confirming their safety, such as "I'm safe. I'm heading to the evacuation center," that information would be posted on the disaster message board.

[0992] Example of a prompt

[0993] "I entered specific information about the disaster, such as 'My house is half-destroyed,' 'I injured my leg,' and 'I need drinking water and a first-aid kit.' How will this information be organized and sent to the server?"

[0994] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0995] Step 1:

[0996] Users input information about the disaster situation, rescue requests, and necessary supplies. Using a smartphone or tablet application, users fill in specific information in designated input forms. For example, they might enter, "My house is partially destroyed. I injured my leg. I need drinking water and a first-aid kit." This input registers the specific needs and circumstances of the disaster victim.

[0997] Step 2:

[0998] The terminal retrieves information entered by the user. The terminal checks the format of the entered information and verifies the data's integrity. Information entered in the correct format is stored in a temporary database on the terminal.

[0999] Step 3:

[1000] The terminal organizes the verified information and converts it into JSON format. Data encryption is performed as needed during this process. The converted information is then sent to the server using the HTTPS protocol. This transmission allows the server to receive information about the victims' situations.

[1001] Step 4:

[1002] The server receives data in JSON format from the terminal. The server checks the integrity and completeness of the received data to ensure there is no invalid data. After verification, the server passes the data to the generating AI model.

[1003] Step 5:

[1004] The AI ​​model on the server analyzes the input data. The AI ​​model uses natural language processing techniques to categorize the information entered by disaster victims. For example, it extracts information such as "house partially destroyed," "leg injury," and "drinking water and first-aid kit." These analysis results are then used for the following processes on the server.

[1005] Step 6:

[1006] The server generates notifications based on the analysis results. The server generates notifications to be sent to relevant organizations according to the disaster situation and rescue requests. For example, based on the analysis results, it generates a notification for emergency services of "request for rescue of a leg injury" and a notification for aid organizations of "request for delivery of drinking water and first-aid kits."

[1007] Step 7:

[1008] The server sends the generated notification to the appropriate relevant organizations. The notification is sent using the appropriate protocol (e.g., email, SMS, API call). This allows the relevant organizations to quickly initiate appropriate action.

[1009] Step 8:

[1010] The device uses GPS to obtain the victim's current location. The obtained location information is sent to the server. This location information allows the victim's precise location to be determined.

[1011] Step 9:

[1012] Based on the acquired location information, the server uses a map service API to calculate a safe route to the nearest evacuation shelter. The calculated route information is then sent from the server to the terminal.

[1013] Step 10:

[1014] The terminal displays route information received from the server to the user. Based on this route information, the user can evacuate to the nearest shelter using voice and visual guides.

[1015] Step 11:

[1016] The user enters safety information. For example, they might enter, "I'm safe. I'm heading to the evacuation center." The device then sends this safety information to the server.

[1017] Step 12:

[1018] The server posts the received safety information to the disaster message board. This allows family members and other concerned parties to check the safety status of the victims. At the same time, it also sends notifications to relevant organizations.

[1019] (Application Example 1)

[1020] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1021] During disasters, it is crucial to efficiently and quickly transmit rescue requests and supply requests needed by victims to the relevant parties and ensure they receive appropriate support. Furthermore, while knowing the current location of victims, guiding them to evacuation routes, and confirming their safety are important, the lack of integrated management can lead to delays and confusion in information. Similarly, in food and supply deliveries during disasters, prompt guidance of delivery routes and reporting of delivery completion are essential. To address these challenges, a centralized management system is necessary.

[1022] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1023] In this invention, the server includes an input means for disaster victims to input disaster conditions, rescue requests, and necessary supplies information; an analysis means using a generative AI model to analyze the input information and identify the extent of the damage and the type and quantity of necessary supplies and rescue; a notification generation means to generate and send notifications to the appropriate departments based on the analysis results; a route guidance means to acquire the current location of disaster victims using GPS functionality and guide them to the nearest evacuation center; a safety information means to receive information on the safety of disaster victims and post it on a disaster message board; a route guidance means to acquire the current location of delivery personnel via GPS functionality and calculate a safe delivery route; and a delivery reporting means to report the completion of delivery. This enables centralized management of everything from disaster victim rescue requests to supply delivery, evacuation route guidance, and safety confirmation, allowing for a quick and efficient response.

[1024] "Disaster victims" refers to people who are facing emergencies such as natural disasters.

[1025] "Disaster situation" refers to the specific conditions and damage at a site during an emergency such as a natural disaster.

[1026] A "rescue request" refers to a request for rescue when a disaster victim needs assistance.

[1027] "Information on necessary supplies" refers to information about specific supplies such as food, water, and medical supplies that disaster victims need during a disaster.

[1028] "Input means" refers to devices or methods for disaster victims to input information about the disaster situation, rescue requests, and necessary supplies.

[1029] "Analysis means" refers to devices and methods for processing input data to identify the extent of damage and the types and quantities of necessary supplies.

[1030] A "generative AI model" refers to an artificial intelligence model designed to efficiently and accurately analyze input data.

[1031] "Notification generation means" refers to a device or method that generates and sends notifications to the appropriate relevant departments based on the analysis results.

[1032] "GPS function" refers to the function of a global positioning system used to acquire location information.

[1033] "Route guidance means" refers to devices or methods that guide users to the nearest evacuation center or delivery location based on acquired location information.

[1034] "Safety information means" refers to devices and methods for receiving information on the safety status of disaster victims and posting it on disaster message boards or similar platforms.

[1035] "Transmission means" refers to devices or methods for transmitting input information to a server via a data communication network.

[1036] "Delivery reporting means" refers to a device or method used to report the completion of a delivery after it has been completed.

[1037] This invention is a system for efficiently managing and rapidly transmitting information and supplies needed by disaster victims and related parties during a disaster. Specific embodiments of this system are described below.

[1038] System Overview

[1039] The system consists of terminals where disaster victims input information, a server that analyzes the information and sends notifications, and a generation AI model. Disaster victims input data from smartphones or tablets, and the server analyzes that information and takes appropriate action.

[1040] Hardware and software to be used

[1041] Smartphones and tablets: Devices used by disaster victims and delivery personnel.

[1042] Server (cloud service): Performs information analysis and notification processing.

[1043] GPS module: Obtains the current location of disaster victims and delivery personnel.

[1044] Generative AI model: Analyzes the input data.

[1045] Map service APIs (Google Maps API, Apple Maps API, etc.): Used to calculate evacuation routes and delivery routes.

[1046] Information input and transmission process

[1047] Disaster victims use smartphones or tablets to input information about the disaster situation, rescue requests, and necessary supplies. The entered information is organized by the device, converted to JSON format via the data communication network, and sent to the server using the HTTPS protocol.

[1048] Information reception and analysis processing

[1049] The server receives information sent from disaster victims and passes it to a generative AI model. The generative AI model analyzes the input data and identifies specific information such as "house partially destroyed," "leg injury," and "drinking water and first-aid kit." Based on the identified information, the server generates and sends appropriate notifications to the necessary departments.

[1050] Guide to evacuation routes and delivery routes

[1051] The server uses GPS functionality to obtain the current locations of disaster victims and delivery personnel. Based on the acquired location information, it calculates a safe route to the nearest evacuation center or delivery location. This involves using a map service API to calculate the specific route. The calculated route information is sent to the terminal to guide users so that they can evacuate or make deliveries safely.

[1052] Delivery completion and safety confirmation.

[1053] After delivery personnel successfully deliver supplies, they send information via an application to report the completion of the delivery. The server receives this information and notifies the victims and relevant departments. Similarly, victims enter their own safety information, and the server posts it on a disaster message board.

[1054] Specific example

[1055] Example input from disaster victims

[1056] Person A, a victim of the disaster, is at home and uses their smartphone to input, "My house is half-destroyed. I've injured my leg. I need drinking water and a first-aid kit." The device organizes this information and sends it to a server. The server analyzes it using a generative AI model, sends appropriate notifications to the relevant departments, and guides Person A to the nearest evacuation center.

[1057] Examples of actions taken by delivery personnel

[1058] Delivery person B uses the app to receive a delivery request for "water, bread, rice, and a first-aid kit," and uses GPS to confirm their current location. The server calculates the optimal delivery route and sends that information to delivery person B's app. After completing the delivery, B reports completion via the app, and the server sends a notification.

[1059] Example of a prompt

[1060] (Prompt example 1):

[1061] User A has requested "rice, bread, water, and a first-aid kit" during a disaster. The server should analyze the received information and send a notification to the appropriate delivery department. Additionally, create a program to notify User A and the administrator once the delivery personnel have successfully delivered the supplies.

[1062] (Prompt example 2):

[1063] Please create a program that obtains the delivery person's current location using GPS, calculates a safe delivery route on the server, and displays it on the delivery person's device. Use a map service API for the calculation.

[1064] Thus, this invention enables a swift and efficient response by centrally managing everything from disaster victims' requests for rescue to the delivery of supplies, guidance on evacuation routes, and confirmation of their safety.

[1065] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1066] Step 1:

[1067] Information input processing

[1068] Users use smartphones or tablets to input information about the disaster situation, rescue requests, and necessary supplies. This input includes specific text information such as, "My house is partially destroyed. I injured my leg. I need drinking water and a first-aid kit." The entered information is organized by category depending on the device.

[1069] Input: Disaster situation, rescue request, and necessary supplies information entered by the user.

[1070] Output: Organized text data (JSON format)

[1071] Step 2:

[1072] Information transmission processing

[1073] The terminal sends the organized information to the server via the data communication network. In this process, the information is converted to JSON format and transmitted securely using the HTTPS protocol.

[1074] Input: Organized text data (JSON format)

[1075] Output: Information sent to the server

[1076] Specific operation: The device prepares to send data, and tapping the send button sends the information to the server.

[1077] Step 3:

[1078] Information reception and analysis processing

[1079] The server receives information sent by the user. The received data is passed to a generating AI model for analysis. Through the analysis, information is categorized, such as "house partially destroyed," "foot injury," and "drinking water and first-aid kit."

[1080] Input: Information sent to the server

[1081] Output: Analyzed information (categorized data)

[1082] Specific operation: The server receives data, inputs it into the AI ​​model, and receives the analysis results.

[1083] Step 4:

[1084] Notification generation process

[1085] Based on the analysis results, the server generates and sends notifications to the appropriate departments. For example, it notifies emergency services of a "request for assistance for a foot injury" and aid organizations of a "request for delivery of drinking water and first-aid kits."

[1086] Input: Analyzed information

[1087] Output: Notification to relevant departments

[1088] Specific operation: The server generates a notification message based on the analyzed data and sends it to the designated relevant department.

[1089] Step 5:

[1090] Evacuation route guidance processing

[1091] The server uses GPS to obtain the current location of disaster victims. Based on the obtained location information, it calculates a safe route to the nearest evacuation center. A map service API is used for this route calculation. The calculated route information is sent to the terminal and displayed to the user.

[1092] Input: Current location information of disaster victims

[1093] Output: Route information to the nearest evacuation shelter

[1094] Specific operation: The server acquires GPS data, calls a map service API to calculate the route, and then sends the route information to the terminal.

[1095] Step 6:

[1096] Delivery route guidance processing

[1097] The server obtains the delivery person's current location via GPS. Based on the obtained location information, it calculates a safe route to the delivery destination. This also utilizes a map service API. The calculated route information is sent to the delivery person's terminal and displayed to them.

[1098] Input: Delivery driver's current location information

[1099] Output: Route information to the delivery address

[1100] Specific operation: The server acquires GPS data, calls a map service API to calculate the route, and then sends the route information to the terminal.

[1101] Step 7:

[1102] Delivery completion report processing

[1103] After the delivery person safely delivers the supplies, they report the completion of the delivery through the application. The terminal sends this information to the server. The server notifies the disaster victims and relevant departments of the received completion information.

[1104] Input: Delivery completion information

[1105] Output: Notification to disaster victims and relevant departments

[1106] Specific operation: The delivery person taps the completion report button, and the device sends that information to the server. The server generates and sends a notification based on the received information.

[1107] Step 8:

[1108] Safety information processing

[1109] Disaster victims enter their safety status information using their smartphones. The device then sends this safety information to a server. The server posts the received safety information on a disaster message board so that relevant parties can check it.

[1110] Input: Information on the safety status of disaster victims

[1111] Output: Posting to the disaster message board

[1112] Specific operation: Disaster victims enter information into a safety status input form, and the terminal sends that information to the server. The server then posts the received information on a message board.

[1113] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1114] Modes for carrying out the invention

[1115] This invention provides a system that utilizes data communication to request rescue during disasters, request necessary supplies, confirm the safety of victims, provide route guidance to evacuation centers, and recognize the emotions of disaster victims to provide appropriate responses. Specifically, the system aims to more accurately understand the situation of disaster victims and provide appropriate support and guidance by combining a generative AI model and an emotion engine. The specific embodiments of this system are described in detail below.

[1116] (System Overview)

[1117] This system consists of terminals, servers, a generative AI model, and an emotion engine. Disaster victims (users) input data from terminals such as smartphones and tablets, and the server analyzes and notifies them of this information. Furthermore, the use of a generative AI model and an emotion engine ensures that the input data is processed efficiently and accurately.

[1118] (Detailed system processing flow)

[1119] 1. Information input processing

[1120] Users: Disaster victims use smartphones or tablets to input information about the disaster situation, rescue requests, necessary supplies, and their safety status. They input specific information such as, "My house is half-destroyed. I injured my leg. I need drinking water and a first-aid kit." User emotional data (voice, text, facial expressions, etc.) is also input.

[1121] Terminal: The terminal organizes the input information and generates data packets.

[1122] 2. Information transmission process

[1123] Terminal: Sends organized information packets to the server via the data communication network. The information is converted to, for example, JSON format, and a POST request is sent to the server using the HTTPS protocol.

[1124] 3. Information reception and analysis processing

[1125] Server: Receives information packets sent by the user on the receiving port. Decodes the received information for analysis.

[1126] Server: Passes the received information to the generative AI model and emotion engine for analysis. The generative AI model identifies the disaster situation, the extent of injuries, and the types and quantities of necessary supplies, while the emotion engine analyzes the user's emotional state.

[1127] 4. Notification generation process

[1128] Server: Generates notifications to relevant departments based on the analysis results. For example, it creates a "request for assistance for a leg injury" for paramedics and a "request for delivery of drinking water and first-aid kits" for aid organizations. It also generates notifications that take into account the mental state of the disaster victims based on the analysis results of the emotion engine.

[1129] Server: Sends generated notifications to the appropriate departments. This includes sending emails to emergency services via the mail server and sending POST requests to support organization APIs.

[1130] 5. Route guidance processing

[1131] Terminal: Uses GPS functionality to obtain the current location of disaster victims. This utilizes the terminal's built-in GPS sensor.

[1132] Server: Based on acquired location information, it calculates a safe route to the nearest evacuation shelter. It uses a map service API to calculate the specific evacuation route. In addition, it generates more reassuring guidance text according to the user's emotional state analyzed by the emotion engine.

[1133] Terminal: Displays route information received from the server on a map. This guides disaster victims so they can evacuate safely.

[1134] 6. Safety confirmation process

[1135] User: The disaster victim enters their own safety information. They report, "I'm safe. I'm heading to the evacuation center."

[1136] Terminal: Organizes safety information and sends it to the server.

[1137] Server: Analyzes received safety information and posts it on the disaster message board. It sends a POST request to the disaster message board API and posts the entered information.

[1138] Server: Notifies relevant parties of the posted safety information.

[1139] (Specific example)

[1140] Let's consider a scenario where victim A is at home during a disaster. A uses their smartphone to type, "My house is half-destroyed. I've injured my leg. I need drinking water and a first-aid kit." The device sends this information to a server, which analyzes it using a generative AI model and an emotion engine. The server notifies emergency services of a "request for assistance regarding the leg injury" and aid organizations of a "request for delivery of drinking water and a first-aid kit." Furthermore, if the emotion engine analyzes A's stress level as high, it sends a notification offering more detailed guidance and additional emotional support.

[1141] The system also obtains A's current location and guides them to the nearest evacuation center. When A enters a message indicating their safety, such as "I'm safe. I'm heading to the evacuation center," that information is posted on a disaster message board, allowing family and friends to see it.

[1142] In this way, this system comprehensively supports the safety of disaster victims and their rapid evacuation by providing information dissemination, supply of relief goods, guidance on evacuation routes, confirmation of safety, and support based on emotional state during disasters.

[1143] The following describes the processing flow.

[1144] (Specific processing flow of the program)

[1145] Step 1: Enter Information

[1146] Users: Disaster victims use smartphones or tablets to input information about the disaster situation, rescue requests, necessary supplies, and their safety status. For example, they might input specific information such as, "My house is half destroyed. I injured my leg. I need drinking water and a first-aid kit." They can also input emotions through voice data, text data, and facial image data.

[1147] Terminal: The terminal organizes this input information and generates data packets.

[1148] Step 2: Send Information

[1149] Terminal: Sends organized information packets to the server via the data communication network. The information is converted to, for example, JSON format, and a POST request is sent to the server using the HTTPS protocol.

[1150] Step 3: Information Reception

[1151] Server: Receives information packets sent from the user on the receiving port. Decodes the received information for analysis and passes it to the generative AI model and emotion engine.

[1152] Step 4: Information Analysis

[1153] Generative AI Model: Based on received data, it identifies disaster conditions, the extent of injuries, and the types and quantities of necessary supplies. For example, it identifies information such as "house partially destroyed," "leg injury," and "drinking water and first-aid kit."

[1154] Emotion Engine: Analyzes emotional data (voice, text, facial expressions) to identify the user's emotional state. For example, it evaluates stress levels, feelings of security, and feelings of anxiety.

[1155] Step 5: Generate Notification

[1156] Server: Based on the analysis results of the generation AI model and the emotion engine, it generates appropriate notifications. For example, it creates a "request for assistance for a leg injury" for paramedics and a "request for delivery of drinking water and first-aid kits" for aid organizations. Depending on the results of the emotion engine, it may also generate notifications that provide emotional support if the stress level of the disaster victim is high.

[1157] Server: Sends generated notifications to the appropriate departments. This includes sending emails to emergency services via a mail server or sending POST requests to support organization APIs.

[1158] Step 6: Get current location

[1159] Terminal: Uses GPS functionality to obtain the current location of disaster victims. This utilizes the terminal's built-in GPS sensor.

[1160] Terminal: Transmits acquired location information to the server in real time.

[1161] Step 7: Route generation

[1162] Server: Based on acquired location information, it calculates a safe route to the nearest evacuation shelter. It uses a map service API to calculate the specific evacuation route. In addition, it may generate more reassuring guidance messages based on the user's emotional state analyzed by the emotion engine.

[1163] Server: Converts secure routing information into data packets and sends them to the terminal.

[1164] Step 8: Route guidance display

[1165] Terminal: Displays route information received from the server on a map. This guides disaster victims so they can evacuate safely.

[1166] Step 9: Enter safety information

[1167] User: The disaster victim enters their own safety information. They report, "I'm safe. I'm heading to the evacuation center."

[1168] Terminal: Organizes safety information and sends it to the server.

[1169] Step 10: Posting safety information

[1170] Server: Analyzes received safety information and posts it on the disaster message board. It sends a POST request to the disaster message board API and posts the entered information.

[1171] Server: Notifies relevant parties of the posted safety information.

[1172] As a concrete example, consider a case where victim B experiences an earthquake and their home is partially destroyed. B inputs, "My house is partially destroyed. I injured my leg. I need drinking water and a first-aid kit," and also voice-inputs the emotion, "I'm very scared." The device sends this to a server, where a generative AI model and emotion engine analyze it. Then, a notification is sent to emergency services requesting assistance for a leg injury, to aid organizations requesting delivery of drinking water and a first-aid kit, and to psychiatric support teams that B is a victim at a high stress level. In addition, based on B's current location, directions to the nearest evacuation center are provided.

[1173] This system allows for a comprehensive understanding of the circumstances and mental state of disaster victims, enabling the provision of more appropriate support.

[1174] (Example 2)

[1175] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1176] Conventional disaster relief systems have faced challenges, particularly in providing support that takes into account the emotions and psychological state of victims, due to the limited information that victims can input. Furthermore, the entire process from information input to analysis, notification, and support is not carried out efficiently, and a rapid and appropriate response is required. As a result, there are situations where both the physical safety and psychological support of victims are insufficient.

[1177] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1178] In this invention, the server includes an input means for disaster victims to input disaster conditions, rescue requests, and necessary supplies information; an emotion data input means for inputting emotion data from voice data and facial expressions; an analysis means using a generative AI model to analyze the input information and identify the damage situation and the type and quantity of necessary supplies and rescue; an analysis means using an emotion engine to analyze the input emotion data and evaluate the emotional state of the disaster victim; a notification generation means to generate and send notifications to the appropriate departments based on the analysis results; a route guidance means to obtain the current location of the disaster victim using GPS functionality and guide them to the nearest evacuation center; and a safety information means to receive information on the safety status of the disaster victim and post it on a disaster message board. This enables support that takes into account the emotional state of the disaster victim, and realizes a swift and appropriate response.

[1179] "Disaster victims" refers to individuals or groups who have experienced and been affected by a disaster.

[1180] "Input means" refers to devices or software that users use to input information. Specifically, this refers to applications on smartphones and tablets.

[1181] "Emotional data input means" refers to devices or software used to input information related to a user's emotions, such as voice data or facial expressions.

[1182] A "generative AI model" refers to an algorithm that uses machine learning and data analysis techniques to identify the extent of damage and the types and quantities of necessary supplies from the input information.

[1183] "Analysis means" refers to devices or software that analyze and interpret input data.

[1184] An "emotion engine" refers to an algorithm or software that analyzes emotional data and evaluates the user's emotional state.

[1185] "Notification generation means" refers to a device or software that generates and sends notifications to the appropriate department based on the analysis results.

[1186] "Transmission means" refers to devices or software used to transmit input information to a server via a data communication network.

[1187] "GPS function" refers to the function of obtaining the current physical location using a global positioning system.

[1188] "Route guidance means" refers to devices or software that calculate the route to the destination based on acquired location information and guide the user.

[1189] "Safety information means" refers to devices or software that receive information on the safety status of disaster victims and post it on disaster message boards or similar platforms.

[1190] This invention is a system for effectively and quickly providing support to disaster victims, and is comprised of a terminal, a server, a generative AI model, and an emotion engine. Specific embodiments of this system are described in detail below.

[1191] (Terminal and user operation)

[1192] Users: Disaster victims use smartphones or tablets to input information about the disaster situation, rescue requests, and necessary supplies. For example, they might input specific information such as, "My house is half-destroyed. I injured my leg. I need drinking water and a first-aid kit." They also input emotional data through voice and facial expressions. The devices are equipped with emotional data input mechanisms, which allows for the collection of emotional data as well.

[1193] Terminal: Receives user input and organizes it into data packets. Voice data is converted into text data using a speech recognition engine. The organized data packets are converted into JSON format and sent to the server using the HTTPS protocol.

[1194] (Server analysis and notification)

[1195] Server: Receives data packets from users via the receiving port and decodes them. The decoded data is passed to the generative AI model and the emotion engine. The generative AI model analyzes the input text data to identify the disaster situation and the types and quantities of supplies needed. The emotion engine analyzes the emotion data to assess the emotional state of the victims.

[1196] Server: Based on the analysis results, it generates notifications for the appropriate departments (e.g., paramedics, aid organizations). The generation AI model creates notifications such as "request for assistance for a leg injury," and the emotion engine creates notifications such as "request for delivery of drinking water and a first-aid kit," taking into account the emotional state. The notifications are sent to the mail server or the aid organization's API.

[1197] (GPS and route guidance)

[1198] Device: Uses the built-in GPS sensor to obtain the user's current location. GPS data is converted to JSON format and sent to the server.

[1199] Server: Based on acquired location information, it calculates a safe route to the nearest evacuation shelter. It uses map services such as the Google Maps API to calculate a specific evacuation route and generates reassuring guidance text based on the analysis results of the emotion engine.

[1200] Terminal: Displays evacuation routes received from the server on a map, guiding users safely.

[1201] (Confirmation of safety)

[1202] User: Disaster victims enter their safety status. For example, they might enter, "I'm safe. I'm heading to the evacuation center."

[1203] Terminal: Organizes the entered safety information and sends it to the server.

[1204] Server: Analyzes received safety information and sends a POST request to the API to post it on the disaster message board. This allows family and friends to check on the safety of disaster victims.

[1205] (Specific example)

[1206] Person A, a victim of a disaster, is at home and uses their smartphone to type, "My house is half-destroyed. I've injured my leg. I need drinking water and a first-aid kit." This information is compiled on the device and sent to a server. On the server, a generative AI model analyzes the disaster situation and necessary supplies, and an emotion engine evaluates Person A's emotional state. The server then notifies emergency services of a "request for assistance regarding the leg injury" and aid organizations of a "request for delivery of drinking water and a first-aid kit." It also guides Person A along an evacuation route based on their GPS information, and when Person A enters their safety status, it is posted on a disaster message board, allowing family and friends to check on their safety.

[1207] (Example of prompt text to input to the generated AI model)

[1208] Please provide a detailed report of the disaster situation. Include specific information about the damage to your home, the extent of your injuries, and the types and quantities of supplies you need. Additionally, please describe your current emotional state.

[1209] In this way, the system can effectively ensure the safety and provide psychological support to disaster victims by allowing them to input information and quickly providing appropriate support that takes their emotional state into consideration.

[1210] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1211] Step 1:

[1212] Users use smartphones or tablets to input information about the disaster situation, rescue requests, and necessary supplies. For example, they might input specific information such as, "My house is half-destroyed. I injured my leg. I need drinking water and a first-aid kit." They also input emotional data through voice and facial expressions. This allows for the acquisition of both text data and emotional data through the input method.

[1213] Step 2:

[1214] The terminal receives input information from the user, organizes it, and converts it into data packets. Voice data is converted into text data by a speech recognition engine. The organized data packets are converted into JSON format and sent to the server via the data communication network. The input is the user's text and voice, and the output is data packets in JSON format.

[1215] Step 3:

[1216] The server receives data packets from the user via the receiving port and decodes them. The decoded data is passed to the generating AI model and emotion engine. Data ready for analysis becomes input, and data in an analyzable state becomes output.

[1217] Step 4:

[1218] The server decodes the data, which is then analyzed by a generating AI model to identify the disaster situation and the types and quantities of supplies needed. The generating AI model takes text data as input and outputs the identified disaster situation and supply information. Specifically, it extracts information such as "partially destroyed," "leg injury," and "drinking water and first-aid kit needed."

[1219] Step 5:

[1220] The server passes the decoded emotional data to the emotion engine, which analyzes the emotional state of the disaster victims. The emotion engine takes voice and facial expression data as input and evaluates stress levels and emotional states. The analysis results output emotional evaluations such as "high stress" or "fearful state."

[1221] Step 6:

[1222] The server generates notifications to the appropriate departments based on the analysis results of the generated AI model and the emotion engine. Specifically, it notifies emergency services of a "request for assistance for a leg injury" and aid organizations of a "request for delivery of drinking water and a first-aid kit." Furthermore, based on the results evaluated by the emotion engine, it generates notification content in more polite language. The input is the analysis results, and the output is the notification content.

[1223] Step 7:

[1224] The server sends the generated notification to the appropriate department. It sends an email to the emergency services via the mail server and a POST request to the support organization's API. The input is the notification content, and the output is the status of successful transmission.

[1225] Step 8:

[1226] The device uses its built-in GPS sensor to obtain the user's current location. The obtained location information is converted to JSON format and sent to the server. The input is GPS data, and the output is a packet of location data.

[1227] Step 9:

[1228] Based on location information acquired by the server, the system calculates a safe route to the nearest evacuation shelter. It uses the Google Maps API to determine the specific evacuation route. It also generates reassuring guidance messages based on the analysis results of an emotion engine. Input is location information, and output is evacuation route information.

[1229] Step 10:

[1230] The terminal displays evacuation routes received from the server on a map application, guiding the user safely. The input is evacuation route information, and the output is the route display on the map application.

[1231] Step 11:

[1232] The user enters their safety status. For example, they might enter, "I'm safe. I'm heading to the evacuation center." This completes the entry of their safety information.

[1233] Step 12:

[1234] The terminal organizes the safety information entered and sends it to the server. The input is the safety information, and the output is the data sent to the server.

[1235] Step 13:

[1236] The server analyzes the safety information it receives and sends a POST request to the API to post it on the disaster message board. The input is the safety information, and the output is the information posted on the disaster message board.

[1237] This processing flow ensures that information gathering, analysis, notification, evacuation guidance, and safety confirmation from disaster victims are carried out consistently.

[1238] (Application Example 2)

[1239] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1240] During disasters, it is crucial to quickly and accurately assess the situation of victims and provide appropriate support. However, conventional systems lack sufficient psychological support that takes into account the emotional state of victims. Furthermore, systems for promptly notifying the appropriate departments of analysis results and conducting effective rescue and support are not adequately developed, and accurate guidance to ensure the safe evacuation of victims is insufficient. These challenges need to be addressed.

[1241] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1242] In this invention, the server includes an input means for disaster victims to input disaster conditions, rescue requests, and necessary supplies information; an analysis means using a generative AI model to analyze the input information and identify the extent of the damage and the type and quantity of necessary supplies and rescue; an emotion analysis means to analyze the emotional data of disaster victims and identify whether psychological support is needed; a notification generation means to generate and send notifications to the appropriate departments based on the analysis results; a support provision means to provide appropriate psychological support to disaster victims based on the emotion analysis results; a route guidance means to acquire the current location of disaster victims using GPS functionality and guide them to the nearest evacuation center; and a safety information means to receive information on the safety of disaster victims and post it on a disaster message board. This enables a comprehensive understanding of the situation of disaster victims and allows for prompt and accurate support and guidance.

[1243] - "Input means" refers to devices or functions that allow disaster victims to input information about the disaster situation, rescue requests, and necessary supplies.

[1244] "Analysis means" refers to devices or functions that use a generative AI model to analyze input information and identify the extent of the damage and the types and quantities of necessary supplies and rescue efforts.

[1245] A "notification generation means" refers to a device or function that generates and sends notifications to the appropriate department based on the analysis results.

[1246] "Emotional analysis tools" refer to devices or functions that analyze the emotional data of disaster victims to determine whether or not they require psychological support.

[1247] "Means of providing support" refers to devices and functions that provide appropriate psychological support to disaster victims based on the results of emotion analysis.

[1248] A "route guidance system" refers to a device or function that uses GPS functionality to obtain the current location of disaster victims and guides them to the nearest evacuation shelter.

[1249] A "safety information system" refers to a device or function that receives information about the safety of disaster victims and posts it on a disaster message board.

[1250] A "generative AI model" is an artificial intelligence model that analyzes disaster situations, rescue requests, and necessary supplies to identify appropriate responses.

[1251] A "data communication network" is a network of communications used to transmit input information to a server.

[1252] A "server" is a computer system that receives, processes, and analyzes information sent by disaster victims.

[1253] overview

[1254] This invention is a system for more accurately understanding the situation of disaster victims and providing prompt and appropriate support. Disaster victims input information about the disaster situation, rescue requests, and necessary supplies from a device such as a smartphone or tablet, and transmit this information to a server via a data communication network. This server analyzes the information using a generative AI model and sentiment analysis means, generates appropriate notifications, and sends them. It also obtains the current location of disaster victims, guides them to the nearest evacuation center, and receives information on the safety status of disaster victims and posts it on a disaster message board.

[1255] Hardware and software to be used

[1256] Devices: Smartphones, tablets, smart glasses, head-mounted displays, robots

[1257] Server: High-performance computer system, Django framework (Python)

[1258] Database: PostgreSQL

[1259] Generative AI model: GPT-4 (OpenAI)

[1260] Emotion analysis software: Affectiva

[1261] Location services: Google Maps API

[1262] Program processing

[1263] Data entry and transmission

[1264] Users input disaster information, rescue requests, and necessary supplies using their devices. Simultaneously, emotional data such as audio and video is also input. This data is transmitted to a server via a data communication network.

[1265] Data Analysis

[1266] The server sends the received data to the GPT-4 AI model for analysis of the disaster situation and necessary supplies. It also uses Affectiva, emotion analysis software, to analyze the user's emotional state.

[1267] Generating and sending notifications

[1268] The server generates and sends notifications to the appropriate departments based on the analysis results. For example, it notifies emergency services of emergency requests and aid organizations of requests for the delivery of necessary supplies. Based on the emotion analysis results, it also generates notifications that take into account the mental state of the victims.

[1269] Route guidance

[1270] The system uses the GPS function of the device to obtain the current location of the disaster victim. The server uses the Google Maps API to calculate a safe route to the nearest evacuation center and sends that information to the device. The device displays this route information on a map to guide the disaster victim.

[1271] Confirmation of safety

[1272] Users input safety information using their devices and send it to the server. The server analyzes this information and posts it on a disaster message board. It also notifies relevant parties of the safety status.

[1273] Specific example

[1274] If a disaster victim, Person A, is at home, they will use their smartphone to input information such as, "My house is half-destroyed. I injured my leg. I need drinking water and a first-aid kit." Voice and facial expression data of Person A will also be input. This data is sent to a server and analyzed by a generative AI model and emotion analysis software. The server will notify emergency services to request rescue and aid organizations to deliver necessary supplies. Furthermore, if the analysis indicates that Person A's stress level is high, a notification offering psychological support will be generated. Person A's current location will be obtained, and a safe route to the nearest evacuation center will be provided. If Person A inputs a safety status message such as, "I'm safe. I'm heading to the evacuation center," this will be posted on the disaster message board and notified to relevant parties.

[1275] Example of a prompt

[1276] Please analyze the following disaster situation and determine whether assistance is needed.

[1277] Disaster situation: My house is partially destroyed. I injured my leg. I need drinking water and a first-aid kit.

[1278] Emotion data: emotion_audio_file.mp3

[1279] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1280] Step 1:

[1281] Information input processing

[1282] Users who are victims of the disaster use their smartphones or tablets to input information about the disaster situation, rescue requests, and necessary supplies. Specifically, they input text information such as, "My house is half destroyed. I injured my leg. I need drinking water and a first-aid kit," as well as voice and emotional data such as facial expressions. This information is taken into the device as input. The device organizes this information and converts it into data packets in formats such as JSON.

[1283] Step 2:

[1284] Information transmission processing

[1285] The terminal sends organized information packets to the server via the data communication network. Specifically, the terminal sends a POST request using the HTTPS protocol. In this case, the data packet containing input data (disaster situation, emotional data, etc.) becomes the input amount. This allows the necessary information to be instantly transmitted from the terminal to the server.

[1286] Step 3:

[1287] Information reception and analysis processing

[1288] The server receives information packets sent from the terminal. It receives the information at the receiving port, decodes it, and prepares it for analysis. The analysis is based on the generative AI model GPT-4 and the sentiment analysis software Affectiva. The data received as input is processed by the server in the preliminary stages of analysis.

[1289] Step 4:

[1290] Analysis using generative AI models

[1291] The received data is sent to the GPT-4 generation AI model to analyze the disaster situation and necessary supplies. A prompt message is generated and data is passed for analysis. Based on text information such as "My house is half-destroyed. I injured my leg. I need drinking water and a first-aid kit," the generation AI model understands the situation and identifies the necessary assistance. The output is a disaster situation report and a list of necessary supplies.

[1292] Step 5:

[1293] Emotion analysis

[1294] The server sends emotional data to Affectiva, which analyzes the user's emotional state. It takes audio and facial expression data as input for the analysis. Affectiva identifies the user's emotional state from the audio and video, and extracts information such as stress levels and tension. The output is the emotional analysis result.

[1295] Step 6:

[1296] Notification generation process

[1297] The server generates and sends notifications to the appropriate departments based on the analysis results. Specifically, it creates notification content based on the disaster situation and required supplies identified by the generation AI model, and the emotional state identified by Affectiva. It notifies emergency services of emergency requests and aid organizations of the delivery of necessary supplies. In addition, if the emotional analysis results indicate that psychological support is needed, it generates a customized notification. The output is the notification sent to each relevant party.

[1298] Step 7:

[1299] Route guidance processing

[1300] The server uses GPS to obtain the current location of disaster victims and guides them to the nearest evacuation shelter. Using the Google Maps API, it calculates specific evacuation routes and uses the current location as input to calculate the safest evacuation route. The output is the calculated route information. The server sends the route information to the terminal, which displays it on a map to guide the disaster victims.

[1301] Step 8:

[1302] Safety confirmation process

[1303] The user enters safety status information using a terminal and sends it to the server. The input data is information such as "I'm safe. I'm heading to the evacuation center." The server analyzes the received safety status information and posts it on the disaster message board. Furthermore, it notifies relevant parties of the safety status. The output is the safety status information posted on the disaster message board and the notifications sent to relevant parties.

[1304] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1305] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1306] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1307] [Fourth Embodiment]

[1308] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1309] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1310] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1311] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1312] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1313] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1314] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1315] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1316] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1317] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1318] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1319] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1320] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1321] Modes for carrying out the invention

[1322] This invention utilizes data communication to centrally manage disaster relief requests, requests for necessary supplies, safety checks, and route guidance to evacuation centers, and implements a system that promptly notifies relevant parties using a generation AI model. A specific embodiment of this system is described in detail below.

[1323] (System Overview)

[1324] This system consists of terminals, servers, and generative AI models. Disaster victims (users) input data from terminals such as smartphones and tablets, and the server analyzes and notifies them of this information. Furthermore, the use of generative AI models ensures that the input data is processed efficiently and accurately.

[1325] (Detailed system processing flow)

[1326] 1. Information input processing

[1327] Users: Disaster victims use smartphones or tablets to input information about the disaster situation, rescue requests, necessary supplies, and their safety status. For example, they might input specific information such as, "My house is half-destroyed. I injured my leg. I need drinking water and a first-aid kit."

[1328] Terminal: The terminal organizes the input information and prepares it to be sent to the server via the data communication network.

[1329] 2. Information transmission process

[1330] Terminal: The organized information is sent to the server via the data communication network. Here, the information is converted to, for example, JSON format and sent using the HTTPS protocol.

[1331] 3. Information reception and analysis processing

[1332] Server: The server receives information sent from the user and passes it to the generating AI model. The server analyzes the information to identify the extent of the damage and the type and quantity of supplies and rescue needed.

[1333] Generative AI Model: This model analyzes the input data and identifies information such as "house partially destroyed," "foot injury," and "drinking water and first-aid kit."

[1334] 4. Notification generation process

[1335] Server: Based on the analysis results, it generates and sends necessary notifications to the relevant departments. For example, it creates and sends a "request for rescue of a foot injury" to emergency services and a "request for delivery of drinking water and first-aid kits" to support organizations.

[1336] 5. Route guidance processing

[1337] Device: Obtains the current location of disaster victims via GPS.

[1338] Server: Based on the acquired location information, it calculates a safe route to the nearest evacuation shelter. This is done by using a map service API to calculate the specific evacuation route.

[1339] Terminal: Displays routing information received from the server and guides disaster victims to evacuate safely.

[1340] 6. Safety confirmation process

[1341] User: Disaster victims enter their own safety information, for example, "I'm safe. I'm heading to the evacuation center."

[1342] Terminal: Send this safety information to the server.

[1343] Server: Posts received safety information to the disaster message board so that relevant parties can check it.

[1344] (Specific example)

[1345] Let's consider a scenario where victim A is at home during a disaster. A uses their smartphone to type, "My house is half-destroyed. I've injured my leg. I need drinking water and a first-aid kit." The device sends this information to a server, which analyzes it using a generative AI model. The server notifies emergency services of a "request for assistance regarding the leg injury" and aid organizations of a "request for delivery of drinking water and a first-aid kit." Furthermore, it obtains A's current location and guides them to the nearest evacuation center. When A enters a safety update such as, "I'm safe. I'm heading to the evacuation center," this information is posted on a disaster message board, allowing family and friends to see it.

[1346] In this way, this system comprehensively supports disaster relief efforts, including information dissemination, supply of relief goods, guidance on evacuation routes, and confirmation of the safety of disaster victims, thereby supporting their safety and rapid evacuation.

[1347] The following describes the processing flow.

[1348] (Specific processing flow of the program)

[1349] Step 1: Enter Information

[1350] Users: Disaster victims use smartphones or tablets to input information about the disaster situation, rescue requests, necessary supplies, and their safety status. They input specific information such as, "My house is half destroyed. I injured my leg. I need drinking water and a first-aid kit."

[1351] Terminal: The terminal organizes the input information and generates data packets.

[1352] Step 2: Send Information

[1353] Terminal: Sends organized information packets to the server via the data communication network. The information is converted to, for example, JSON format, and a POST request is sent to the server using the HTTPS protocol.

[1354] Step 3: Information Reception

[1355] Server: Receives information packets sent by the user on the receiving port. Decodes the received information for analysis.

[1356] Step 4: Information Analysis

[1357] Server: Passes the received information to the generating AI model for analysis. The generating AI model identifies the disaster situation, the extent of injuries, and the types and quantities of supplies needed.

[1358] Generative AI model: Based on input data, it identifies information such as "house partially destroyed," "foot injury," and "drinking water and first-aid kit."

[1359] Step 5: Generate Notification

[1360] Server: Based on the analysis results, it generates notifications for the relevant departments. For example, it creates a "request for assistance for a foot injury" for paramedics and a "request for delivery of drinking water and first-aid kits" for aid organizations.

[1361] Server: Sends generated notifications to the appropriate departments. This includes sending emails to emergency services via the mail server and sending POST requests to support organization APIs.

[1362] Step 6: Get current location

[1363] Terminal: Uses GPS functionality to obtain the current location of disaster victims. This utilizes the terminal's built-in GPS sensor.

[1364] Terminal: Transmits acquired location information to the server in real time.

[1365] Step 7: Route generation

[1366] Server: Based on acquired location information, it calculates a safe route to the nearest evacuation shelter. It uses a map service API to calculate the specific evacuation route.

[1367] Server: Converts secure routing information into data packets and sends them to the terminal.

[1368] Step 8: Route guidance display

[1369] Terminal: Displays route information received from the server on a map. This guides disaster victims so they can evacuate safely.

[1370] Step 9: Enter safety information

[1371] User: The disaster victim enters their own safety information. They report, "I'm safe. I'm heading to the evacuation center."

[1372] Terminal: Organizes safety information and sends it to the server.

[1373] Step 10: Posting safety information

[1374] Server: Analyzes received safety information and posts it on the disaster message board. It sends a POST request to the disaster message board API and posts the entered information.

[1375] Server: Notifies relevant parties of the posted safety information.

[1376] The above outlines the specific processing flow of this invention. This system enables rapid and accurate information transmission, supply of relief goods, guidance of evacuation routes, and confirmation of safety during disasters.

[1377] (Example 1)

[1378] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1379] During disasters, there is a need for a system that allows victims to quickly and efficiently request rescue, request necessary supplies, confirm their safety, and receive directions to evacuation centers, while appropriately notifying relevant organizations. However, existing systems have fragmented processes, from information input to notification, safety confirmation, and evacuation route guidance, making a rapid and efficient response difficult. Furthermore, information about victims has not always been accurately transmitted, sometimes leading to delays in providing appropriate assistance.

[1380] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1381] In this invention, the server includes an input device for disaster victims to input disaster conditions, rescue requests, and necessary supplies information; a transmission device that organizes the input information and transmits it to the server via a data communication network; an analysis device that receives the information transmitted by the user and performs analysis using a generation AI model; a notification generation device that generates and transmits notifications to the appropriate departments based on the analysis results; a route guidance device that acquires the current location of disaster victims using GPS functionality and guides them to the nearest evacuation center; and a safety information device that receives information on the safety of disaster victims and posts it on a disaster message board. This makes it possible to quickly and efficiently collect, analyze, and notify disaster victims' information during a disaster, thereby realizing appropriate support and safe evacuation.

[1382] An "input device" is a device used by disaster victims to input information about the disaster situation, rescue requests, and necessary supplies.

[1383] A "transmission device" is a device that organizes the input information and transmits it to a server via a data communication network.

[1384] An "analysis device" is a device that receives information sent from a user via a server and performs analysis using a generated AI model.

[1385] A "notification generation device" is a device that generates and sends notifications to the appropriate department based on the analysis results.

[1386] A "route guidance device" is a device that uses GPS functionality to obtain the current location of disaster victims and guides them to the nearest evacuation shelter.

[1387] A "safety information device" is a device that receives information about the safety of disaster victims and posts it on a disaster message board.

[1388] A "generative AI model" is an artificial intelligence model that analyzes input data and extracts necessary information.

[1389] A "data communication network" is a network used to transmit information from a terminal to a server.

[1390] A "server" is a device that analyzes received information and performs processing such as generating notifications and providing route guidance.

[1391] This invention realizes a system that uses a generative AI model to centrally manage information such as rescue requests, requests for necessary supplies, safety confirmations, and route guidance to evacuation shelters during disasters, and to quickly notify relevant organizations. This system mainly consists of terminals, servers, and generative AI models.

[1392] Hardware and software to be used

[1393] Device: Smartphone, tablet, or other mobile device

[1394] Servers: Cloud services and dedicated servers

[1395] Generative AI models: Generative AI models that perform natural language processing.

[1396] Data communication networks: Internet, mobile networks

[1397] GPS function: GPS module built into the device

[1398] Map Service API: A map service API for calculating evacuation routes.

[1399] Overview of Functions and Data Processing

[1400] Information entry

[1401] Users: Disaster victims use the terminal to input information about the current state of the disaster, requests for rescue, necessary supplies, and their safety status. Specifically, they might enter "My house is partially destroyed. I injured my leg. I need drinking water and a first-aid kit" into a form on the application.

[1402] Terminal: The terminal checks the format of the input information and converts it to an appropriate data format (e.g., JSON format).

[1403] Information transmission

[1404] Terminal: The formatted data is sent to the server using the HTTPS protocol. Encryption is also performed as needed.

[1405] Information reception and analysis

[1406] Server: The server passes the received data to the generating AI model. The AI ​​model analyzes the input language data and extracts important information (e.g., "house is partially destroyed," "leg injury," "drinking water and first-aid kit needed").

[1407] Notification generation

[1408] Server: Based on the analysis results, it generates and sends notifications to the appropriate relevant organizations. For example, it notifies emergency services of a "request for assistance for a leg injury" and aid organizations of a "request for delivery of drinking water and first-aid kits."

[1409] Route guidance

[1410] Terminal: The device uses GPS to obtain the current location of the disaster victim. The obtained location information is then sent to the server.

[1411] Server: Uses a map service API to calculate a safe route to the nearest evacuation shelter. The calculated route information is returned to the terminal.

[1412] Terminal: The terminal displays route information to the user and guides them along evacuation routes using a voice assistant and map display.

[1413] Confirmation of safety

[1414] User: Disaster victims enter their own safety information. For example, they might enter "I'm safe. I'm heading to the evacuation center."

[1415] Terminal: Safety information is sent to the server.

[1416] Server: Posts received safety information to the disaster message board so that relevant parties can check it.

[1417] Specific example

[1418] If person A is at home during a disaster, they would use their smartphone to input, "My house is half-destroyed. I've injured my leg. I need drinking water and a first-aid kit." The device would then organize this information and send it to a server, which would analyze it using a generative AI model. Based on the analysis, the server would notify emergency services of a "request for rescue due to leg injury" and support organizations of a "request for delivery of drinking water and a first-aid kit." Furthermore, the system would obtain A's current location and guide them to the nearest evacuation center via a safe route. If A then inputs a message confirming their safety, such as "I'm safe. I'm heading to the evacuation center," that information would be posted on the disaster message board.

[1419] Example of a prompt

[1420] "I entered specific information about the disaster, such as 'My house is half-destroyed,' 'I injured my leg,' and 'I need drinking water and a first-aid kit.' How will this information be organized and sent to the server?"

[1421] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1422] Step 1:

[1423] Users input information about the disaster situation, rescue requests, and necessary supplies. Using a smartphone or tablet application, users fill in specific information in designated input forms. For example, they might enter, "My house is partially destroyed. I injured my leg. I need drinking water and a first-aid kit." This input registers the specific needs and circumstances of the disaster victim.

[1424] Step 2:

[1425] The terminal retrieves information entered by the user. The terminal checks the format of the entered information and verifies the data's integrity. Information entered in the correct format is stored in a temporary database on the terminal.

[1426] Step 3:

[1427] The terminal organizes the verified information and converts it into JSON format. Data encryption is performed as needed during this process. The converted information is then sent to the server using the HTTPS protocol. This transmission allows the server to receive information about the victims' situations.

[1428] Step 4:

[1429] The server receives data in JSON format from the terminal. The server checks the integrity and completeness of the received data to ensure there is no invalid data. After verification, the server passes the data to the generating AI model.

[1430] Step 5:

[1431] The AI ​​model on the server analyzes the input data. The AI ​​model uses natural language processing techniques to categorize the information entered by disaster victims. For example, it extracts information such as "house partially destroyed," "leg injury," and "drinking water and first-aid kit." These analysis results are then used for the following processes on the server.

[1432] Step 6:

[1433] The server generates notifications based on the analysis results. The server generates notifications to be sent to relevant organizations according to the disaster situation and rescue requests. For example, based on the analysis results, it generates a notification for emergency services of "request for rescue of a leg injury" and a notification for aid organizations of "request for delivery of drinking water and first-aid kits."

[1434] Step 7:

[1435] The server sends the generated notification to the appropriate relevant organizations. The notification is sent using the appropriate protocol (e.g., email, SMS, API call). This allows the relevant organizations to quickly initiate appropriate action.

[1436] Step 8:

[1437] The device uses GPS to obtain the victim's current location. The obtained location information is sent to the server. This location information allows the victim's precise location to be determined.

[1438] Step 9:

[1439] Based on the acquired location information, the server uses a map service API to calculate a safe route to the nearest evacuation shelter. The calculated route information is then sent from the server to the terminal.

[1440] Step 10:

[1441] The terminal displays route information received from the server to the user. Based on this route information, the user can evacuate to the nearest shelter using voice and visual guides.

[1442] Step 11:

[1443] The user enters safety information. For example, they might enter, "I'm safe. I'm heading to the evacuation center." The device then sends this safety information to the server.

[1444] Step 12:

[1445] The server posts the received safety information to the disaster message board. This allows family members and other concerned parties to check the safety status of the victims. At the same time, it also sends notifications to relevant organizations.

[1446] (Application Example 1)

[1447] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1448] During disasters, it is crucial to efficiently and quickly transmit rescue requests and supply requests needed by victims to the relevant parties and ensure they receive appropriate support. Furthermore, while knowing the current location of victims, guiding them to evacuation routes, and confirming their safety are important, the lack of integrated management can lead to delays and confusion in information. Similarly, in food and supply deliveries during disasters, prompt guidance of delivery routes and reporting of delivery completion are essential. To address these challenges, a centralized management system is necessary.

[1449] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1450] In this invention, the server includes an input means for disaster victims to input disaster conditions, rescue requests, and necessary supplies information; an analysis means using a generative AI model to analyze the input information and identify the extent of the damage and the type and quantity of necessary supplies and rescue; a notification generation means to generate and send notifications to the appropriate departments based on the analysis results; a route guidance means to acquire the current location of disaster victims using GPS functionality and guide them to the nearest evacuation center; a safety information means to receive information on the safety of disaster victims and post it on a disaster message board; a route guidance means to acquire the current location of delivery personnel via GPS functionality and calculate a safe delivery route; and a delivery reporting means to report the completion of delivery. This enables centralized management of everything from disaster victim rescue requests to supply delivery, evacuation route guidance, and safety confirmation, allowing for a quick and efficient response.

[1451] "Disaster victims" refers to people who are facing emergencies such as natural disasters.

[1452] "Disaster situation" refers to the specific conditions and damage at a site during an emergency such as a natural disaster.

[1453] A "rescue request" refers to a request for rescue when a disaster victim needs assistance.

[1454] "Information on necessary supplies" refers to information about specific supplies such as food, water, and medical supplies that disaster victims need during a disaster.

[1455] "Input means" refers to devices or methods for disaster victims to input information about the disaster situation, rescue requests, and necessary supplies.

[1456] "Analysis means" refers to devices and methods for processing input data to identify the extent of damage and the types and quantities of necessary supplies.

[1457] A "generative AI model" refers to an artificial intelligence model designed to efficiently and accurately analyze input data.

[1458] "Notification generation means" refers to a device or method that generates and sends notifications to the appropriate relevant departments based on the analysis results.

[1459] "GPS function" refers to the function of a global positioning system used to acquire location information.

[1460] "Route guidance means" refers to devices or methods that guide users to the nearest evacuation center or delivery location based on acquired location information.

[1461] "Safety information means" refers to devices and methods for receiving information on the safety status of disaster victims and posting it on disaster message boards or similar platforms.

[1462] "Transmission means" refers to devices or methods for transmitting input information to a server via a data communication network.

[1463] "Delivery reporting means" refers to a device or method used to report the completion of a delivery after it has been completed.

[1464] This invention is a system for efficiently managing and rapidly transmitting information and supplies needed by disaster victims and related parties during a disaster. Specific embodiments of this system are described below.

[1465] System Overview

[1466] The system consists of terminals where disaster victims input information, a server that analyzes the information and sends notifications, and a generation AI model. Disaster victims input data from smartphones or tablets, and the server analyzes that information and takes appropriate action.

[1467] Hardware and software to be used

[1468] Smartphones and tablets: Devices used by disaster victims and delivery personnel.

[1469] Server (cloud service): Performs information analysis and notification processing.

[1470] GPS module: Obtains the current location of disaster victims and delivery personnel.

[1471] Generative AI model: Analyzes the input data.

[1472] Map service APIs (Google Maps API, Apple Maps API, etc.): Used to calculate evacuation routes and delivery routes.

[1473] Information input and transmission process

[1474] Disaster victims use smartphones or tablets to input information about the disaster situation, rescue requests, and necessary supplies. The entered information is organized by the device, converted to JSON format via the data communication network, and sent to the server using the HTTPS protocol.

[1475] Information reception and analysis processing

[1476] The server receives information sent from disaster victims and passes it to a generative AI model. The generative AI model analyzes the input data and identifies specific information such as "house partially destroyed," "leg injury," and "drinking water and first-aid kit." Based on the identified information, the server generates and sends appropriate notifications to the necessary departments.

[1477] Guide to evacuation routes and delivery routes

[1478] The server uses GPS functionality to obtain the current locations of disaster victims and delivery personnel. Based on the acquired location information, it calculates a safe route to the nearest evacuation center or delivery location. This involves using a map service API to calculate the specific route. The calculated route information is sent to the terminal to guide users so that they can evacuate or make deliveries safely.

[1479] Delivery completion and safety confirmation.

[1480] After delivery personnel successfully deliver supplies, they send information via an application to report the completion of the delivery. The server receives this information and notifies the victims and relevant departments. Similarly, victims enter their own safety information, and the server posts it on a disaster message board.

[1481] Specific example

[1482] Example input from disaster victims

[1483] Person A, a victim of the disaster, is at home and uses their smartphone to input, "My house is half-destroyed. I've injured my leg. I need drinking water and a first-aid kit." The device organizes this information and sends it to a server. The server analyzes it using a generative AI model, sends appropriate notifications to the relevant departments, and guides Person A to the nearest evacuation center.

[1484] Examples of actions taken by delivery personnel

[1485] Delivery person B uses the app to receive a delivery request for "water, bread, rice, and a first-aid kit," and uses GPS to confirm their current location. The server calculates the optimal delivery route and sends that information to delivery person B's app. After completing the delivery, B reports completion via the app, and the server sends a notification.

[1486] Example of a prompt

[1487] (Prompt example 1):

[1488] User A has requested "rice, bread, water, and a first-aid kit" during a disaster. The server should analyze the received information and send a notification to the appropriate delivery department. Additionally, create a program to notify User A and the administrator once the delivery personnel have successfully delivered the supplies.

[1489] (Prompt example 2):

[1490] Please create a program that obtains the delivery person's current location using GPS, calculates a safe delivery route on the server, and displays it on the delivery person's device. Use a map service API for the calculation.

[1491] Thus, this invention enables a swift and efficient response by centrally managing everything from disaster victims' requests for rescue to the delivery of supplies, guidance on evacuation routes, and confirmation of their safety.

[1492] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1493] Step 1:

[1494] Information input processing

[1495] Users use smartphones or tablets to input information about the disaster situation, rescue requests, and necessary supplies. This input includes specific text information such as, "My house is partially destroyed. I injured my leg. I need drinking water and a first-aid kit." The entered information is organized by category depending on the device.

[1496] Input: Disaster situation, rescue request, and necessary supplies information entered by the user.

[1497] Output: Organized text data (JSON format)

[1498] Step 2:

[1499] Information transmission processing

[1500] The terminal sends the organized information to the server via the data communication network. In this process, the information is converted to JSON format and transmitted securely using the HTTPS protocol.

[1501] Input: Organized text data (JSON format)

[1502] Output: Information sent to the server

[1503] Specific operation: The device prepares to send data, and tapping the send button sends the information to the server.

[1504] Step 3:

[1505] Information reception and analysis processing

[1506] The server receives information sent by the user. The received data is passed to a generating AI model for analysis. Through the analysis, information is categorized, such as "house partially destroyed," "foot injury," and "drinking water and first-aid kit."

[1507] Input: Information sent to the server

[1508] Output: Analyzed information (categorized data)

[1509] Specific operation: The server receives data, inputs it into the AI ​​model, and receives the analysis results.

[1510] Step 4:

[1511] Notification generation process

[1512] Based on the analysis results, the server generates and sends notifications to the appropriate departments. For example, it notifies emergency services of a "request for assistance for a foot injury" and aid organizations of a "request for delivery of drinking water and first-aid kits."

[1513] Input: Analyzed information

[1514] Output: Notification to relevant departments

[1515] Specific operation: The server generates a notification message based on the analyzed data and sends it to the designated relevant department.

[1516] Step 5:

[1517] Evacuation route guidance processing

[1518] The server uses GPS to obtain the current location of disaster victims. Based on the obtained location information, it calculates a safe route to the nearest evacuation center. A map service API is used for this route calculation. The calculated route information is sent to the terminal and displayed to the user.

[1519] Input: Current location information of disaster victims

[1520] Output: Route information to the nearest evacuation shelter

[1521] Specific operation: The server acquires GPS data, calls a map service API to calculate the route, and then sends the route information to the terminal.

[1522] Step 6:

[1523] Delivery route guidance processing

[1524] The server obtains the delivery person's current location via GPS. Based on the obtained location information, it calculates a safe route to the delivery destination. This also utilizes a map service API. The calculated route information is sent to the delivery person's terminal and displayed to them.

[1525] Input: Delivery driver's current location information

[1526] Output: Route information to the delivery address

[1527] Specific operation: The server acquires GPS data, calls a map service API to calculate the route, and then sends the route information to the terminal.

[1528] Step 7:

[1529] Delivery completion report processing

[1530] After the delivery person safely delivers the supplies, they report the completion of the delivery through the application. The terminal sends this information to the server. The server notifies the disaster victims and relevant departments of the received completion information.

[1531] Input: Delivery completion information

[1532] Output: Notification to disaster victims and relevant departments

[1533] Specific operation: The delivery person taps the completion report button, and the device sends that information to the server. The server generates and sends a notification based on the received information.

[1534] Step 8:

[1535] Safety information processing

[1536] Disaster victims enter their safety status information using their smartphones. The device then sends this safety information to a server. The server posts the received safety information on a disaster message board so that relevant parties can check it.

[1537] Input: Information on the safety status of disaster victims

[1538] Output: Posting to the disaster message board

[1539] Specific operation: Disaster victims enter information into a safety status input form, and the terminal sends that information to the server. The server then posts the received information on a message board.

[1540] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1541] Modes for carrying out the invention

[1542] This invention provides a system that utilizes data communication to request rescue during disasters, request necessary supplies, confirm the safety of victims, provide route guidance to evacuation centers, and recognize the emotions of disaster victims to provide appropriate responses. Specifically, the system aims to more accurately understand the situation of disaster victims and provide appropriate support and guidance by combining a generative AI model and an emotion engine. The specific embodiments of this system are described in detail below.

[1543] (System Overview)

[1544] This system consists of terminals, servers, a generative AI model, and an emotion engine. Disaster victims (users) input data from terminals such as smartphones and tablets, and the server analyzes and notifies them of this information. Furthermore, the use of a generative AI model and an emotion engine ensures that the input data is processed efficiently and accurately.

[1545] (Detailed system processing flow)

[1546] 1. Information input processing

[1547] Users: Disaster victims use smartphones or tablets to input information about the disaster situation, rescue requests, necessary supplies, and their safety status. They input specific information such as, "My house is half-destroyed. I injured my leg. I need drinking water and a first-aid kit." User emotional data (voice, text, facial expressions, etc.) is also input.

[1548] Terminal: The terminal organizes the input information and generates data packets.

[1549] 2. Information transmission process

[1550] Terminal: Sends organized information packets to the server via the data communication network. The information is converted to, for example, JSON format, and a POST request is sent to the server using the HTTPS protocol.

[1551] 3. Information reception and analysis processing

[1552] Server: Receives information packets sent by the user on the receiving port. Decodes the received information for analysis.

[1553] Server: Passes the received information to the generative AI model and emotion engine for analysis. The generative AI model identifies the disaster situation, the extent of injuries, and the types and quantities of necessary supplies, while the emotion engine analyzes the user's emotional state.

[1554] 4. Notification generation process

[1555] Server: Generates notifications to relevant departments based on the analysis results. For example, it creates a "request for assistance for a leg injury" for paramedics and a "request for delivery of drinking water and first-aid kits" for aid organizations. It also generates notifications that take into account the mental state of the disaster victims based on the analysis results of the emotion engine.

[1556] Server: Sends generated notifications to the appropriate departments. This includes sending emails to emergency services via the mail server and sending POST requests to support organization APIs.

[1557] 5. Route guidance processing

[1558] Terminal: Uses GPS functionality to obtain the current location of disaster victims. This utilizes the terminal's built-in GPS sensor.

[1559] Server: Based on acquired location information, it calculates a safe route to the nearest evacuation shelter. It uses a map service API to calculate the specific evacuation route. In addition, it generates more reassuring guidance text according to the user's emotional state analyzed by the emotion engine.

[1560] Terminal: Displays route information received from the server on a map. This guides disaster victims so they can evacuate safely.

[1561] 6. Safety confirmation process

[1562] User: The disaster victim enters their own safety information. They report, "I'm safe. I'm heading to the evacuation center."

[1563] Terminal: Organizes safety information and sends it to the server.

[1564] Server: Analyzes received safety information and posts it on the disaster message board. It sends a POST request to the disaster message board API and posts the entered information.

[1565] Server: Notifies relevant parties of the posted safety information.

[1566] (Specific example)

[1567] Let's consider a scenario where victim A is at home during a disaster. A uses their smartphone to type, "My house is half-destroyed. I've injured my leg. I need drinking water and a first-aid kit." The device sends this information to a server, which analyzes it using a generative AI model and an emotion engine. The server notifies emergency services of a "request for assistance regarding the leg injury" and aid organizations of a "request for delivery of drinking water and a first-aid kit." Furthermore, if the emotion engine analyzes A's stress level as high, it sends a notification offering more detailed guidance and additional emotional support.

[1568] The system also obtains A's current location and guides them to the nearest evacuation center. When A enters a message indicating their safety, such as "I'm safe. I'm heading to the evacuation center," that information is posted on a disaster message board, allowing family and friends to see it.

[1569] In this way, this system comprehensively supports the safety of disaster victims and their rapid evacuation by providing information dissemination, supply of relief goods, guidance on evacuation routes, confirmation of safety, and support based on emotional state during disasters.

[1570] The following describes the processing flow.

[1571] (Specific processing flow of the program)

[1572] Step 1: Enter Information

[1573] Users: Disaster victims use smartphones or tablets to input information about the disaster situation, rescue requests, necessary supplies, and their safety status. For example, they might input specific information such as, "My house is half destroyed. I injured my leg. I need drinking water and a first-aid kit." They can also input emotions through voice data, text data, and facial image data.

[1574] Terminal: The terminal organizes this input information and generates data packets.

[1575] Step 2: Send Information

[1576] Terminal: Sends organized information packets to the server via the data communication network. The information is converted to, for example, JSON format, and a POST request is sent to the server using the HTTPS protocol.

[1577] Step 3: Information Reception

[1578] Server: Receives information packets sent from the user on the receiving port. Decodes the received information for analysis and passes it to the generative AI model and emotion engine.

[1579] Step 4: Information Analysis

[1580] Generative AI Model: Based on received data, it identifies disaster conditions, the extent of injuries, and the types and quantities of necessary supplies. For example, it identifies information such as "house partially destroyed," "leg injury," and "drinking water and first-aid kit."

[1581] Emotion Engine: Analyzes emotional data (voice, text, facial expressions) to identify the user's emotional state. For example, it evaluates stress levels, feelings of security, and feelings of anxiety.

[1582] Step 5: Generate Notification

[1583] Server: Based on the analysis results of the generation AI model and the emotion engine, it generates appropriate notifications. For example, it creates a "request for assistance for a leg injury" for paramedics and a "request for delivery of drinking water and first-aid kits" for aid organizations. Depending on the results of the emotion engine, it may also generate notifications that provide emotional support if the stress level of the disaster victim is high.

[1584] Server: Sends generated notifications to the appropriate departments. This includes sending emails to emergency services via a mail server or sending POST requests to support organization APIs.

[1585] Step 6: Get current location

[1586] Terminal: Uses GPS functionality to obtain the current location of disaster victims. This utilizes the terminal's built-in GPS sensor.

[1587] Terminal: Transmits acquired location information to the server in real time.

[1588] Step 7: Route generation

[1589] Server: Based on acquired location information, it calculates a safe route to the nearest evacuation shelter. It uses a map service API to calculate the specific evacuation route. In addition, it may generate more reassuring guidance messages based on the user's emotional state analyzed by the emotion engine.

[1590] Server: Converts secure routing information into data packets and sends them to the terminal.

[1591] Step 8: Route guidance display

[1592] Terminal: Displays route information received from the server on a map. This guides disaster victims so they can evacuate safely.

[1593] Step 9: Enter safety information

[1594] User: The disaster victim enters their own safety information. They report, "I'm safe. I'm heading to the evacuation center."

[1595] Terminal: Organizes safety information and sends it to the server.

[1596] Step 10: Posting safety information

[1597] Server: Analyzes received safety information and posts it on the disaster message board. It sends a POST request to the disaster message board API and posts the entered information.

[1598] Server: Notifies relevant parties of the posted safety information.

[1599] As a concrete example, consider a case where victim B experiences an earthquake and their home is partially destroyed. B inputs, "My house is partially destroyed. I injured my leg. I need drinking water and a first-aid kit," and also voice-inputs the emotion, "I'm very scared." The device sends this to a server, where a generative AI model and emotion engine analyze it. Then, a notification is sent to emergency services requesting assistance for a leg injury, to aid organizations requesting delivery of drinking water and a first-aid kit, and to psychiatric support teams that B is a victim at a high stress level. In addition, based on B's current location, directions to the nearest evacuation center are provided.

[1600] This system allows for a comprehensive understanding of the circumstances and mental state of disaster victims, enabling the provision of more appropriate support.

[1601] (Example 2)

[1602] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1603] Conventional disaster relief systems have faced challenges, particularly in providing support that takes into account the emotions and psychological state of victims, due to the limited information that victims can input. Furthermore, the entire process from information input to analysis, notification, and support is not carried out efficiently, and a rapid and appropriate response is required. As a result, there are situations where both the physical safety and psychological support of victims are insufficient.

[1604] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1605] In this invention, the server includes an input means for disaster victims to input disaster conditions, rescue requests, and necessary supplies information; an emotion data input means for inputting emotion data from voice data and facial expressions; an analysis means using a generative AI model to analyze the input information and identify the damage situation and the type and quantity of necessary supplies and rescue; an analysis means using an emotion engine to analyze the input emotion data and evaluate the emotional state of the disaster victim; a notification generation means to generate and send notifications to the appropriate departments based on the analysis results; a route guidance means to obtain the current location of the disaster victim using GPS functionality and guide them to the nearest evacuation center; and a safety information means to receive information on the safety status of the disaster victim and post it on a disaster message board. This enables support that takes into account the emotional state of the disaster victim, and realizes a swift and appropriate response.

[1606] "Disaster victims" refers to individuals or groups who have experienced and been affected by a disaster.

[1607] "Input means" refers to devices or software that users use to input information. Specifically, this refers to applications on smartphones and tablets.

[1608] "Emotional data input means" refers to devices or software used to input information related to a user's emotions, such as voice data or facial expressions.

[1609] A "generative AI model" refers to an algorithm that uses machine learning and data analysis techniques to identify the extent of damage and the types and quantities of necessary supplies from the input information.

[1610] "Analysis means" refers to devices or software that analyze and interpret input data.

[1611] An "emotion engine" refers to an algorithm or software that analyzes emotional data and evaluates the user's emotional state.

[1612] "Notification generation means" refers to a device or software that generates and sends notifications to the appropriate department based on the analysis results.

[1613] "Transmission means" refers to devices or software used to transmit input information to a server via a data communication network.

[1614] "GPS function" refers to the function of obtaining the current physical location using a global positioning system.

[1615] "Route guidance means" refers to devices or software that calculate the route to the destination based on acquired location information and guide the user.

[1616] "Safety information means" refers to devices or software that receive information on the safety status of disaster victims and post it on disaster message boards or similar platforms.

[1617] This invention is a system for effectively and quickly providing support to disaster victims, and is comprised of a terminal, a server, a generative AI model, and an emotion engine. Specific embodiments of this system are described in detail below.

[1618] (Terminal and user operation)

[1619] Users: Disaster victims use smartphones or tablets to input information about the disaster situation, rescue requests, and necessary supplies. For example, they might input specific information such as, "My house is half-destroyed. I injured my leg. I need drinking water and a first-aid kit." They also input emotional data through voice and facial expressions. The devices are equipped with emotional data input mechanisms, which allows for the collection of emotional data as well.

[1620] Terminal: Receives user input and organizes it into data packets. Voice data is converted into text data using a speech recognition engine. The organized data packets are converted into JSON format and sent to the server using the HTTPS protocol.

[1621] (Server analysis and notification)

[1622] Server: Receives data packets from users via the receiving port and decodes them. The decoded data is passed to the generative AI model and the emotion engine. The generative AI model analyzes the input text data to identify the disaster situation and the types and quantities of supplies needed. The emotion engine analyzes the emotion data to assess the emotional state of the victims.

[1623] Server: Based on the analysis results, it generates notifications for the appropriate departments (e.g., paramedics, aid organizations). The generation AI model creates notifications such as "request for assistance for a leg injury," and the emotion engine creates notifications such as "request for delivery of drinking water and a first-aid kit," taking into account the emotional state. The notifications are sent to the mail server or the aid organization's API.

[1624] (GPS and route guidance)

[1625] Device: Uses the built-in GPS sensor to obtain the user's current location. GPS data is converted to JSON format and sent to the server.

[1626] Server: Based on acquired location information, it calculates a safe route to the nearest evacuation shelter. It uses map services such as the Google Maps API to calculate a specific evacuation route and generates reassuring guidance text based on the analysis results of the emotion engine.

[1627] Terminal: Displays evacuation routes received from the server on a map, guiding users safely.

[1628] (Confirmation of safety)

[1629] User: Disaster victims enter their safety status. For example, they might enter, "I'm safe. I'm heading to the evacuation center."

[1630] Terminal: Organizes the entered safety information and sends it to the server.

[1631] Server: Analyzes received safety information and sends a POST request to the API to post it on the disaster message board. This allows family and friends to check on the safety of disaster victims.

[1632] (Specific example)

[1633] Person A, a victim of a disaster, is at home and uses their smartphone to type, "My house is half-destroyed. I've injured my leg. I need drinking water and a first-aid kit." This information is compiled on the device and sent to a server. On the server, a generative AI model analyzes the disaster situation and necessary supplies, and an emotion engine evaluates Person A's emotional state. The server then notifies emergency services of a "request for assistance regarding the leg injury" and aid organizations of a "request for delivery of drinking water and a first-aid kit." It also guides Person A along an evacuation route based on their GPS information, and when Person A enters their safety status, it is posted on a disaster message board, allowing family and friends to check on their safety.

[1634] (Example of prompt text to input to the generated AI model)

[1635] Please provide a detailed report of the disaster situation. Include specific information about the damage to your home, the extent of your injuries, and the types and quantities of supplies you need. Additionally, please describe your current emotional state.

[1636] In this way, the system can effectively ensure the safety and provide psychological support to disaster victims by allowing them to input information and quickly providing appropriate support that takes their emotional state into consideration.

[1637] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1638] Step 1:

[1639] Users use smartphones or tablets to input information about the disaster situation, rescue requests, and necessary supplies. For example, they might input specific information such as, "My house is half-destroyed. I injured my leg. I need drinking water and a first-aid kit." They also input emotional data through voice and facial expressions. This allows for the acquisition of both text data and emotional data through the input method.

[1640] Step 2:

[1641] The terminal receives input information from the user, organizes it, and converts it into data packets. Voice data is converted into text data by a speech recognition engine. The organized data packets are converted into JSON format and sent to the server via the data communication network. The input is the user's text and voice, and the output is data packets in JSON format.

[1642] Step 3:

[1643] The server receives data packets from the user via the receiving port and decodes them. The decoded data is passed to the generating AI model and emotion engine. Data ready for analysis becomes input, and data in an analyzable state becomes output.

[1644] Step 4:

[1645] The server decodes the data, which is then analyzed by a generating AI model to identify the disaster situation and the types and quantities of supplies needed. The generating AI model takes text data as input and outputs the identified disaster situation and supply information. Specifically, it extracts information such as "partially destroyed," "leg injury," and "drinking water and first-aid kit needed."

[1646] Step 5:

[1647] The server passes the decoded emotional data to the emotion engine, which analyzes the emotional state of the disaster victims. The emotion engine takes voice and facial expression data as input and evaluates stress levels and emotional states. The analysis results output emotional evaluations such as "high stress" or "fearful state."

[1648] Step 6:

[1649] The server generates notifications to the appropriate departments based on the analysis results of the generated AI model and the emotion engine. Specifically, it notifies emergency services of a "request for assistance for a leg injury" and aid organizations of a "request for delivery of drinking water and a first-aid kit." Furthermore, based on the results evaluated by the emotion engine, it generates notification content in more polite language. The input is the analysis results, and the output is the notification content.

[1650] Step 7:

[1651] The server sends the generated notification to the appropriate department. It sends an email to the emergency services via the mail server and a POST request to the support organization's API. The input is the notification content, and the output is the status of successful transmission.

[1652] Step 8:

[1653] The device uses its built-in GPS sensor to obtain the user's current location. The obtained location information is converted to JSON format and sent to the server. The input is GPS data, and the output is a packet of location data.

[1654] Step 9:

[1655] Based on location information acquired by the server, the system calculates a safe route to the nearest evacuation shelter. It uses the Google Maps API to determine the specific evacuation route. It also generates reassuring guidance messages based on the analysis results of an emotion engine. Input is location information, and output is evacuation route information.

[1656] Step 10:

[1657] The terminal displays evacuation routes received from the server on a map application, guiding the user safely. The input is evacuation route information, and the output is the route display on the map application.

[1658] Step 11:

[1659] The user enters their safety status. For example, they might enter, "I'm safe. I'm heading to the evacuation center." This completes the entry of their safety information.

[1660] Step 12:

[1661] The terminal organizes the safety information entered and sends it to the server. The input is the safety information, and the output is the data sent t...

Claims

1. An input method for disaster victims to enter information on the disaster situation, rescue requests, and necessary supplies, An analysis method using a generative AI model that analyzes input information to identify the extent of the damage and the type and quantity of necessary supplies and rescue efforts, A notification generation means that generates and sends notifications to the appropriate departments based on the analysis results, A route guidance system that uses GPS functionality to obtain the current location of disaster victims and guides them to the nearest evacuation shelter, A means of receiving information on the safety status of disaster victims and posting it on a disaster message board, A system that includes this.

2. The system according to claim 1, further comprising a transmission means for transmitting the entered disaster situation, rescue request, and necessary supplies information to a server via a data communication network.

3. The system according to claim 1, comprising a notification generation means and a route guidance means for analyzing information received by a server, generating a notification, and sending it.

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