system

Abstract

We provide the system. [Solution] A means of integrating and analyzing disaster information collected in real time, A means for optimally allocating rescue resources based on the analysis results, Means for transmitting the relevant information to administrative agencies and rescue organizations, A means of obtaining a user's location information using a portable information terminal, A means of displaying evacuation routes overlaid in space using augmented reality technology, Means to automate communications in emergencies, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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] During an earthquake, disaster information becomes complicated and excessive, while there may also be a lack of information, making it difficult to make quick and appropriate judgments. In addition, there are problems such as a decline in judgment due to panic and the existence of language barriers, which prevent appropriate evacuation actions from being taken. As a result, there is a risk of causing great damage to human lives and property.

Means for Solving the Problems

[0005] This invention solves the above problems by providing a system that integrates and analyzes disaster information collected in real time. This system is designed to optimally allocate rescue resources based on the analysis results, enabling rapid and effective rescue operations. It also has a function to transmit relevant information to local governments and rescue teams, supporting coordinated disaster response. Furthermore, by providing information in multiple languages ​​and displaying evacuation routes visually using augmented reality technology, it can provide accurate evacuation instructions to users, overcoming language barriers.

[0006] "Real-time" is a term that refers to an environment where information is processed and used immediately the moment it is generated.

[0007] "Disaster information" refers to data and reports related to natural disasters such as earthquakes, weather events, and floods.

[0008] "Integration" refers to the process of bringing together data obtained from different sources and ensuring consistency.

[0009] "Analysis" is the act of thoroughly examining collected data to find meaning and patterns.

[0010] "Rescue resources" is a term that refers to the personnel, supplies, equipment, etc., needed in the event of a disaster.

[0011] "Optimal allocation" refers to the process of allocating limited resources to the most effective locations and activities in order to make the most of them.

[0012] A "local government" is a local government that manages a specific area and provides public services.

[0013] A "rescue team" refers to a specially trained organization that responds immediately to disasters and rescues victims.

[0014] "Multilingualism" refers to a situation or ability to use multiple languages ​​simultaneously.

[0015] "Augmented reality technology" is a technology that overlays computer-generated images on real-world videos to create an environment where reality and virtuality are fused.

[0016] "Visual display" refers to a method of visually presenting information using diagrams, images, videos, etc.

[0017] "Evacuation route" refers to a pre-planned route for moving to a safe place during a disaster.

Brief Explanation of Drawings

[0018] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple 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 Embodiment 2 when the 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 the emotion engine is combined.

Mode for Carrying Out the Invention

[0019] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0020] First, the language used in the following description will be explained.

[0021] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0022] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0023] 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.

[0024] 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).

[0025] 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."

[0026] [First Embodiment]

[0027] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0028] 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.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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".

[0039] This invention provides a system for collecting and analyzing disaster information in real time during disasters such as earthquakes. This system enables the optimal allocation of rescue resources and the provision of effective evacuation instructions. Specific embodiments of each element are described below.

[0040] Server Role

[0041] The server is responsible for collecting disaster information in real time from multiple external sources. This includes earthquake intensity and location information, as well as damage reports. The server integrates the collected data, analyzes it using AI algorithms, and identifies high-risk areas. Based on the analysis results, it calculates the optimal allocation of rescue resources and transmits this information to local governments and rescue teams.

[0042] Terminal role

[0043] The device is used to transmit location information and personal health status from the user to a server. It also provides the user with evacuation instructions and safety information received from the server, both visually and audibly. In particular, it utilizes augmented reality technology to display evacuation routes on a map, supporting users in moving safely. Furthermore, it includes a multilingual display function, allowing users to receive information in their preferred language.

[0044] User roles

[0045] Users need to check the evacuation instructions displayed on their devices and evacuate to a safe place as quickly as possible. For example, after an earthquake, users register their location information with the system via a smartphone app. Based on this, they use augmented reality (AR) technology to confirm the route to the nearest safe evacuation center and begin evacuating according to the instructions. Furthermore, registration to confirm their safety is performed via the device, and notifications are automatically sent to pre-registered emergency contacts.

[0046] By having servers, terminals, and users work together in this way, it is possible to provide effective support during disasters and minimize human casualties. This system enables safe and smooth disaster response through a rapid and accurate flow of information and user-friendly evacuation support.

[0047] The following describes the processing flow.

[0048] Step 1:

[0049] The server collects data in real time from external sources when an earthquake occurs. This includes earthquake intensity, epicenter, and damage reports. The data obtained from each source is integrated and stored in a database.

[0050] Step 2:

[0051] The server analyzes the collected data using AI algorithms to identify high-risk areas. During the analysis process, it refers to past data and patterns to predict areas where damage is expected to spread.

[0052] Step 3:

[0053] The server calculates the optimal allocation of rescue resources based on the analysis results. In this calculation, priorities are set according to the level of danger in each area to ensure the efficient use of resources.

[0054] Step 4:

[0055] The server transmits the optimal allocation plan and analysis results to local governments and rescue teams. This enables the rapid and organized deployment of rescue operations on the ground.

[0056] Step 5:

[0057] The terminal notifies the user of evacuation instructions received from the server. The notification includes a visual display of evacuation routes using augmented reality technology and audio guidance.

[0058] Step 6:

[0059] The device displays notifications in multiple languages ​​based on the user's selected language. This feature ensures that information is provided across language barriers.

[0060] Step 7:

[0061] The user follows the instructions on the device and follows the designated evacuation route. The user updates their location information via the device and checks the progress of the evacuation.

[0062] Step 8:

[0063] After completing the evacuation, users register their safety status on their device. This information is automatically sent to emergency contacts via the server.

[0064] (Example 1)

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

[0066] In recent years, the frequency of natural disasters has increased, and there is a growing need for rapid and effective responses during disasters. However, conventional disaster information management systems have faced challenges such as the time required for information collection and analysis, making it difficult to optimally allocate rescue resources. Furthermore, multilingual support and real-time user assistance have been insufficient, and support for foreigners and people with disabilities, in particular, has been inadequate.

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

[0068] In this invention, the server includes means for integrating and analyzing disaster-related data acquired using collection means, means for evaluating the degree of risk using a generated AI model and calculating the optimal allocation of rescue resources, and means for providing relevant information to local governments and rescue departments. This enables rapid and accurate information processing and rescue operations during a disaster.

[0069] "Collection methods" refer to methods and devices for obtaining disaster-related data from external sources.

[0070] A "generative AI model" refers to an artificial intelligence model that is trained based on machine learning algorithms and used for analyzing and predicting disaster data.

[0071] "Risk assessment" refers to the process of measuring and determining the risks of a particular area or situation based on collected data.

[0072] "Rescue resource allocation" refers to planning how to effectively allocate available personnel, equipment, and other resources to optimize rescue operations.

[0073] "Multilingual conversion function" refers to translation technology that displays or converts information into audio in multiple languages.

[0074] Augmented reality technology refers to a technology that overlays digital information onto the real environment, enabling users to obtain additional information in real-world situations.

[0075] "Registered emergency contacts" refer to contacts that the user has designated in advance and to whom they should be automatically contacted in the event of an emergency.

[0076] "Dynamic updates" refer to the process of immediately changing or adjusting plans and instructions based on new information that becomes available.

[0077] This invention is a system that functions as infrastructure during disasters and is realized by combining various devices and AI technology. Specific embodiments are shown below.

[0078] Server functions and operation

[0079] The server is equipped with means to collect disaster-related data from external sources. These external sources include APIs from earthquake observation agencies and meteorological agencies, and data from these sources is collected in real time. After collection, the server integrates the data and stores it in a database. Database systems such as PostgreSQL and MongoDB are used for this purpose. Based on this integrated data, data analysis is performed using a generative AI model to assess the level of risk. Machine learning frameworks such as TENSORFLOW® and PyTorch are used, and the AI ​​model has been trained on past disaster data. Based on the analysis results, calculations are performed to optimally allocate rescue resources, and the necessary information is sent to local governments and rescue departments.

[0080] Device functions and operation

[0081] The terminal consists of smart devices (smartphones and tablets) used by the user. It has the function of acquiring location information and health status from the user and transmitting this information to the server. The terminal utilizes augmented reality technology to notify the user of instructions received from the server visually and audibly. Using ARKit (iOS) and ARCore (ANDROID®), it provides evacuation routes to the user and guides them to a safe route. Furthermore, it has a multilingual translation function, and information is provided in the language selected by the user. This is achieved by a translation function using the Google Translate API®.

[0082] User behavior and roles

[0083] Users must receive disaster information and evacuation orders from the system via their devices and initiate appropriate evacuation actions. During evacuation, they must follow real-time AR guidance provided by their devices to move to a safe evacuation center. Once evacuation is complete, users perform an action on their devices to confirm their safety, and this is automatically notified to their emergency contacts. This enables rapid communication of safety to family and friends during large-scale disasters.

[0084] Specific example

[0085] For example, in the event of an earthquake, the server immediately collects information, assesses the level of risk, and determines the optimal allocation of rescue resources. On the device, users are provided with real-time guidance to the nearest evacuation shelter using augmented reality (AR). Furthermore, evacuation information is displayed in the language selected on the smartphone for users who do not speak Japanese.

[0086] Example of a prompt

[0087] By entering "Please tell me how to safely guide people to the nearest evacuation center after an earthquake," the AI ​​will begin analyzing the data and providing guidance.

[0088] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0089] Step 1:

[0090] The server collects disaster-related data from external sources. Input includes real-time data obtained from APIs of earthquake observation and meteorological agencies. This data is provided in JSON format and includes information on epicenters and seismic intensity. The server receives this data and performs initial formatting for storage in the database. Specifically, the server requests data via HTTP requests, parses the received response, and extracts the necessary information.

[0091] Step 2:

[0092] The server stores the integrated data in a database. The input is initially formatted disaster data, and the output is information stored in the database in a structured format. Specifically, the server connects to PostgreSQL or MongoDB and inserts the data into the appropriate fields. To do this, it maps values ​​to each field according to the data schema.

[0093] Step 3:

[0094] On the server, data analysis is performed using a generative AI model. The input is current disaster information from a database. Based on this information, the AI ​​model evaluates the level of risk and generates a risk score for each region as output. Specifically, the AI ​​model, which has been trained using TensorFlow or PyTorch, is executed, and the results are presented through visualization and report output.

[0095] Step 4:

[0096] The server calculates the optimal allocation of rescue resources based on the analysis results from the AI ​​model. The input is the risk score for each region, and the output is a specific rescue resource allocation plan. Specifically, the server uses linear programming techniques to calculate the optimal deployment of fire brigades and medical teams, and generates a message to send that information to the local government.

[0097] Step 5:

[0098] The device transmits location information and health status from the user to the server. Input is location information entered by the user or automatically acquired by the device, while output is user status information updated by transmission to the server. Specifically, the device acquires location using a GPS sensor, and health information is entered via the app.

[0099] Step 6:

[0100] The device receives evacuation instructions from the server and uses augmented reality technology to visually guide the user. The input is evacuation route information sent from the server, and the output is an AR display and audio guidance for the user. Specifically, the device uses ARKit or ARCore to overlay the evacuation route onto a map and provides audio guidance through its speaker.

[0101] Step 7:

[0102] The user uses their device to follow a designated evacuation route and begin the evacuation process. The input is AR navigation displayed on the device, and the output is the user's movement to a safe location. Specifically, the user moves along the evacuation route, and after safely arriving, performs a safety check operation within the app and automatically notifies emergency contacts.

[0103] (Application Example 1)

[0104] 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."

[0105] In the event of a disaster, obtaining information and efficiently allocating rescue resources are crucial for saving lives. However, conventional systems have limitations in real-time information integration, multilingual support, and the clear presentation of evacuation orders, hindering swift and accurate evacuation guidance and rescue operations. Solving this problem is essential.

[0106] 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.

[0107] In this invention, the server includes means for integrating and analyzing disaster information collected in real time, means for optimally allocating rescue resources based on the analysis results, and means for transmitting the relevant information to administrative agencies and rescue organizations. This enables rapid and comprehensive information provision and the efficient deployment of rescue operations. Furthermore, effective evacuation support for users is realized through means for acquiring user location information using a portable information terminal, means for displaying evacuation routes overlaid in space using augmented reality technology, and means for automating communication in emergencies. This makes it possible to promote rapid and safe evacuation during disasters through the provision of multilingual instructions and visual route guidance.

[0108] "Real-time" refers to a method where information and data are processed instantly, and results are provided immediately.

[0109] "Disaster information" refers to detailed data on natural and man-made disasters, including earthquake intensity, location, and extent of damage.

[0110] "Integration" is the process of gathering data from multiple sources and analyzing it in a consistent format.

[0111] "Analysis" is the process of examining data in detail, understanding its content, and finding meaning in it.

[0112] "Rescue resources" refer to the resources necessary to carry out life-saving and support activities during a disaster, including personnel, equipment, and supplies.

[0113] "Optimal allocation" refers to adopting the most effective allocation method in order to make the most of limited resources.

[0114] An "administrative agency" is an organization of the government or local authorities that provides public services and is responsible for tasks such as disaster response.

[0115] A "rescue organization" refers to a group or institution formed to carry out rescue operations during a disaster.

[0116] A "portable information terminal" refers to a portable device used for accessing geographical information and communication.

[0117] "Location information" refers to data that indicates the current location of an object or person, and is generally expressed using geographic coordinates.

[0118] Augmented reality technology is a technique that overlays computer-generated images and information onto real-world footage.

[0119] An "evacuation route" refers to the recommended path or method for moving to a safe place during a disaster.

[0120] "Overlaying information into space" means overlaying additional information onto real-world landscapes or images to visually represent them.

[0121] "Automating communication" refers to automatically executing a process that performs communication based on pre-set conditions.

[0122] "Multilingual" means supporting multiple different languages ​​and being able to provide information in each of those languages.

[0123] "Providing instructions" means providing information that advises someone to take a specific action.

[0124] The system implementing this invention mainly consists of three elements: a server, a terminal, and a user.

[0125] The server receives disaster information in real time and integrates this data. Specifically, it acquires and analyzes data such as seismic intensity and location of earthquakes, and damage reports from various sources. The analysis utilizes machine learning algorithms to identify high-risk areas. A database management system is used for information collection and storage during this process. Based on the analysis results, the optimal allocation of rescue resources is calculated, and relevant information is sent to government agencies and rescue organizations.

[0126] The terminals used are primarily portable information terminals and similar devices. These terminals have the functionality to acquire the user's current location information and transmit it to a server. Furthermore, augmented reality technology can be used to visually display evacuation routes. Libraries such as OpenCV and ARCore are used as the platform for this. Disaster information is translated into multiple languages, providing users with instructions through various senses. This functionality allows users to evacuate quickly to a safe location while receiving visual and auditory guidance.

[0127] Users need to check evacuation instructions provided through their devices and move quickly to a safe place. Based on the AR display generated on their devices, they begin moving to a nearby safe area. For example, when an earthquake occurs, users can check the shortest route to a nearby park or evacuation center on an AR map and follow the instructions. An example of a prompt message in this case would be: "An earthquake has occurred. The location of the evacuation center and a safe route to it are shown in AR. Please check the route to the evacuation center on your smartphone and move safely."

[0128] By implementing this invention, it becomes possible to obtain information immediately during disasters, efficiently allocate resources, and provide smooth evacuation support for users, thereby realizing a swift and safe response.

[0129] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0130] Step 1:

[0131] The server collects disaster information in real time from multiple external sources. These sources include seismometer networks, the Japan Meteorological Agency database, and damage reports from local governments. Input data includes earthquake intensity, location, and damage status. The server receives this data, stores it in a database, and then converts it into a unified format.

[0132] Step 2:

[0133] The server uses machine learning algorithms based on collected disaster information to identify high-risk areas. The input is the disaster information integrated in Step 1, which is then analyzed to assess the level of risk. As a result, a list of identified high-risk areas is output. Furthermore, the optimal allocation of rescue resources is also calculated.

[0134] Step 3:

[0135] The device sends location information from the user to the server. The input includes location data obtained from the device's GPS sensor. This allows the server to determine the user's current location. The output is the user's location data.

[0136] Step 4:

[0137] The server generates evacuation instructions translated into multiple languages ​​based on the user's current location and the results of a risk assessment. The input consists of the user's location and a list of risk levels, which are used to identify an appropriate evacuation route for the user. As a result, evacuation instructions are generated and sent to the terminal.

[0138] Step 5:

[0139] The terminal displays evacuation routes using augmented reality technology based on received evacuation instructions. The input is the evacuation instructions sent from the server, which are used to overlay route information onto the camera feed. As output, the evacuation route is displayed on the screen using AR, guiding the user.

[0140] Step 6:

[0141] The user follows the evacuation instructions displayed on their device and moves to a safe location. Input includes visual information from AR route display. Based on this, the user follows the correct route and evacuates to the designated shelter.

[0142] Step 7:

[0143] The device confirms that the user has safely arrived at the designated location and automatically sends a notification to pre-configured emergency contacts. The input is the user's arrival information, which then notifies the contacts of the user's safety status. As output, a message confirming safe arrival is sent.

[0144] 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.

[0145] This invention is a system that collects and analyzes real-time disaster information during earthquakes and other disasters, and provides dynamic evacuation instructions based on the user's emotional state. This system, by combining an emotion engine, understands the user's psychological state and provides information accordingly. Specific embodiments are described below.

[0146] Server Role

[0147] The server collects disaster information in real time from external sources, integrates and stores that data. This includes detailed earthquake location information, seismic intensity, and damage status. The collected data is analyzed using AI algorithms to identify high-risk areas. Based on the analysis, the server calculates the optimal allocation of rescue resources and transmits the information to local governments and rescue teams. The server also receives user emotion data from terminals and notifies experts so that psychological support can be provided as needed.

[0148] Terminal role

[0149] The device is equipped with an emotion engine that recognizes emotions from the user's facial expressions and voice. It has the ability to transmit the user's emotional state to a server in real time. It also receives evacuation instructions from the server and presents them to the user visually and audibly in multiple languages. Evacuation routes are displayed using augmented reality technology to support the user's safe evacuation. If the user's emotional state is determined to be anxiety or panic, the frequency and format of notifications are dynamically adjusted.

[0150] User roles

[0151] Users take appropriate evacuation actions based on information provided by their devices. An emotion engine analyzes the user's psychological state, and evacuation instructions are adjusted accordingly, allowing users to act in a way that aligns with their emotions. After completing the evacuation, users confirm their safety and register this information with the server via their devices. This information is automatically sent to emergency contacts.

[0152] As a concrete example, if a user shows signs of surprise or anxiety during an earthquake, the device sends emotional data to a server, which then provides the device with a more reassuring notification format. The user then moves to a safe location following evacuation instructions tailored to provide a sense of security. In this way, the entire system operates consistently, enhancing safety during disasters and reducing people's mental burden.

[0153] The following describes the processing flow.

[0154] Step 1:

[0155] The server collects data in real time from external sources when an earthquake occurs. This includes earthquake intensity, epicenter information, and damage reports. The collected data is integrated and stored in a database.

[0156] Step 2:

[0157] The server analyzes the integrated data using AI algorithms to identify high-risk areas. Based on the analysis results, it calculates the optimal allocation of rescue resources.

[0158] Step 3:

[0159] The server transmits analysis results and resource allocation plans to local governments and rescue teams, thereby supporting rapid rescue operations on the ground.

[0160] Step 4:

[0161] The terminal receives evacuation instructions from the server and notifies the user. The notification is displayed in multiple languages. Evacuation routes are displayed visually using augmented reality technology.

[0162] Step 5:

[0163] The device uses an emotion engine that recognizes emotions from the user's facial expressions and voice. This data represents the user's real-time emotional state and is sent to the server.

[0164] Step 6:

[0165] The server analyzes user emotion data and dynamically changes the frequency and format of notifications based on the results. For example, if a user is in a state of panic, it will provide messages and guidance that offer greater reassurance.

[0166] Step 7:

[0167] Users review evacuation instructions optimized to their emotions and begin moving along safe evacuation routes.

[0168] Step 8:

[0169] After completing their evacuation, users register their safety status with the server via their device. This automatically notifies designated emergency contacts that the user is safe.

[0170] (Example 2)

[0171] 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".

[0172] When a disaster strikes, it is crucial to quickly collect, analyze, and provide appropriate information to relevant organizations and victims. However, conventional systems are not capable of providing dynamic information that takes emotional states into account. As a result, victims may experience excessive anxiety and confusion in response to the information they receive, and traditional evacuation orders provide insufficient emotional support.

[0173] 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.

[0174] In this invention, the server includes means for integrating and analyzing disaster information collected in real time, means for optimally allocating rescue resources based on the analysis results, means for analyzing the user's emotional state from an external detection device and providing the relevant information, and means for dynamically adjusting the tone and format of evacuation orders based on emotional data. This enables the provision of real-time information that takes emotions into account, reducing the mental burden on disaster victims while allowing for efficient evacuation and rescue operations.

[0175] "Real-time" refers to a state in which processing and response can be carried out immediately at the moment an event occurs.

[0176] "Disaster information" refers to all data related to disasters, such as location information, seismic intensity, and damage status, concerning earthquakes and natural disasters.

[0177] "Analysis" refers to the process of analyzing collected data and transforming it into more meaningful information.

[0178] "Rescue resources" refers to the total amount of personnel, equipment, supplies, etc., necessary for rescue operations.

[0179] "Optimal allocation" means distributing limited resources in a way that allows them to be used most effectively.

[0180] A "detection device" refers to a device or sensor used to collect information about a user's emotional state or environmental conditions.

[0181] "Emotional data" refers to information that indicates the user's psychological state, and includes analysis results of facial expressions, voice, and other data.

[0182] "Dynamic adjustment" refers to changing the content and format in real time in response to changes in circumstances and conditions.

[0183] A "local government" refers to a local public entity that exists under the national or local government and is responsible for the administration of a specific region.

[0184] An "evacuation order" is an instruction or recommendation to take safe actions in the event of a disaster.

[0185] This invention is a system designed to streamline emergency disaster response and enable dynamic responses tailored to the user's psychological state. The system is primarily composed of a server, terminals, and users, with each element working in coordination to function.

[0186] The server collects disaster information in real time from multiple external sources during natural disasters such as earthquakes. Data obtained from sources such as the Japan Meteorological Agency and earthquake databases is analyzed using AI algorithms. This analysis identifies high-risk areas and calculates the optimal allocation of rescue resources. It also has a system that receives user emotional data via an emotion engine and notifies experts. This enables a rapid response if psychological support is needed.

[0187] The device is equipped with an emotion engine that analyzes the user's facial expressions and voice to recognize emotions. This data is transmitted to a server in real time. Furthermore, the device receives evacuation instructions from the server and uses AR technology to visually present evacuation routes. This allows users to receive instructions adapted to their environment. It also supports multiple languages, making it applicable to users worldwide.

[0188] By utilizing this system, users can enhance their sense of security and safety during disasters. Based on the information provided, they can take appropriate evacuation actions to ensure their own safety. Once evacuation is complete, they report their safety to the server via their device. This information is automatically notified to emergency contacts, enabling rapid communication.

[0189] As a concrete example, if an earthquake occurs and the user expresses anxiety, the device sends emotional data to the server. The server generates customized evacuation instructions designed to provide reassurance and provides them to the user through the device. The user can then follow the instructions and move to a safe location. An example of a prompt using the generative AI model might be, "Generate evacuation instructions that provide reassurance based on the user's emotional state."

[0190] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0191] Step 1:

[0192] The server collects disaster information. The server connects to external data sources such as meteorological agencies and earthquake databases to obtain real-time earthquake information. Inputs include location information, seismic intensity, and damage status at the time of the disaster. This data is integrated and stored in the system's internal database.

[0193] Step 2:

[0194] The server analyzes the data. Based on the collected disaster information, an AI algorithm operates to identify areas where damage is expected. The input here is integrated earthquake data, and the output is a list of high-risk areas. The server then calculates the optimal allocation of rescue resources as needed.

[0195] Step 3:

[0196] The server generates evacuation orders. Based on the analysis results, it creates appropriate evacuation orders for users. The input is a list of high-risk areas, and the output is individual evacuation order information. This includes safe evacuation routes, recommended evacuation locations, and information on what to bring.

[0197] Step 4:

[0198] The device collects emotional data. Sensors detect the user's facial expressions and voice, and an emotion engine analyzes their psychological state. The input is the user's visual and auditory data, and the output is the analyzed emotional state.

[0199] Step 5:

[0200] The device sends emotional data to the server. The analyzed user emotional data is sent to the server in real time. The input is the user's emotional state, which is processed by the server and becomes the basis for adjusting the tone and format of evacuation instructions.

[0201] Step 6:

[0202] The terminal displays evacuation instructions. The terminal provides the user with evacuation instructions received from the server, both visually and audibly. The input is evacuation instruction information from the server, and the output is a visual and audible presentation to the user. Augmented reality technology is used to overlay evacuation routes onto the real world, allowing the user to intuitively identify safe paths.

[0203] Step 7:

[0204] The user evacuates. Based on the information provided, the user takes appropriate evacuation actions. If the user's psychological state is anxious or panicked, the tone and frequency of notifications are adjusted, and evacuation support that provides a sense of security is provided as output.

[0205] Step 8:

[0206] The user reports their safety. After evacuation is complete, the user reports their safety to the server via their device. The input is the user's safety information, which is sent to emergency contacts via the server, enabling rapid notification as output.

[0207] (Application Example 2)

[0208] 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".

[0209] During disasters, there is a challenge in providing effective and reassuring evacuation instructions in real time. Conventional systems issue uniform instructions without considering the user's feelings, which can lead to panic and hinder proper evacuation. Furthermore, the lack of multilingual information provision and insufficient visual displays using augmented reality technology makes it difficult to provide smooth evacuation support.

[0210] 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.

[0211] In this invention, the server includes means for integrating and analyzing disaster information collected in real time, means for optimally allocating support resources based on the analysis results, and means for recognizing the user's emotional state and dynamically adjusting evacuation orders based on that state. This makes it possible to provide optimal evacuation orders that respond to the user's emotions so that they can evacuate with a sense of security.

[0212] "Real-time disaster information" refers to all data continuously acquired from the moment a disaster occurs, including earthquake location information, seismic intensity, and damage status.

[0213] "Optimally allocating support resources based on analysis results" refers to the process where an AI algorithm uses disaster information to perform calculations and efficiently allocate necessary supplies and human resources to high-risk areas.

[0214] "Public institutions and support teams" refers to various government organizations and private support groups that operate during disasters.

[0215] "Recognizing the user's emotional state and dynamically adjusting evacuation instructions based on it" refers to a function where the device analyzes the user's facial expressions and voice to identify their psychological state and appropriately changes the notification method and content to provide a sense of security.

[0216] "Visually displaying the optimal evacuation route using augmented reality technology" means utilizing technology that overlays digital information onto the real world via smart devices to clearly show users safe evacuation routes.

[0217] This system is designed to provide real-time situational awareness and appropriate evacuation instructions during disasters. The server continuously collects disaster information from various sensors and external databases, and uses AI algorithms to analyze the integrated data. Specifically, it identifies earthquake location and intensity information, as well as risk areas affected, and efficiently allocates support resources. This enables the rapid and appropriate provision of information to government agencies and support organizations.

[0218] The device is equipped with an emotion analysis engine to understand the user's emotions, recognizing them in real time from facial expressions and voice. This data is sent to a server, and evacuation instructions that take the user's psychological burden into consideration are generated. The device also has a function that displays evacuation routes using augmented reality technology, providing route guidance in an intuitive and reassuring way for the user.

[0219] The device also utilizes multilingual translation capabilities to provide appropriate information to a variety of users. As a result, it helps users from diverse backgrounds, including foreign residents and tourists, to evacuate safely.

[0220] As a concrete example, consider a scenario where a family with children uses the system during a disaster. The platform could present simplified visual guidance that is easy for children to understand, and could even emit encouraging voice messages to alleviate anxiety. A possible prompt for the generating AI model could be something like, "Please suggest a guide message for children to help them cope with anxiety during a disaster."

[0221] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0222] Step 1:

[0223] The server acquires disaster information in real time from various data sources. This process involves inputting information from seismometers, weather observation devices, and other sources. The server integrates this information and processes it into an analyzable format by storing it in a database.

[0224] Step 2:

[0225] The server analyzes the collected disaster information using an AI algorithm. Based on the input data, it identifies high-risk areas and calculates the allocation of support resources. The output generates a plan outlining areas requiring rescue and the resources needed.

[0226] Step 3:

[0227] The device uses an emotion analysis engine to recognize the user's emotions by taking the user's facial expressions and voice data as input. This data is sent to a server in real time, and it is determined whether the user's psychological state is one of temporary anxiety or panic.

[0228] Step 4:

[0229] When the server receives emotional state data, it dynamically adjusts evacuation instructions based on its assessment. Specifically, this includes changing the tone and frequency of voice messages, for example. Based on this, the optimal evacuation route is determined.

[0230] Step 5:

[0231] The terminal uses augmented reality technology to visually display evacuation routes to the user based on evacuation instructions received from the server. In this step, the input route data is used, and intuitive directional guidance appears in the user's field of view as output.

[0232] Step 6:

[0233] Users can begin evacuating with a sense of security based on the information presented on their devices. In some cases, the server can further improve the accuracy of the information throughout the evacuation process by allowing users to provide simple feedback to their devices.

[0234] 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.

[0235] 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.

[0236] 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.

[0237] [Second Embodiment]

[0238] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0239] 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.

[0240] 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).

[0241] 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.

[0242] 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.

[0243] 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).

[0244] 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.

[0245] 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.

[0246] 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.

[0247] 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.

[0248] 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.

[0249] 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".

[0250] This invention provides a system for collecting and analyzing disaster information in real time during disasters such as earthquakes. This system enables the optimal allocation of rescue resources and the provision of effective evacuation instructions. Specific embodiments of each element are described below.

[0251] Server Role

[0252] The server is responsible for collecting disaster information in real time from multiple external sources. This includes earthquake intensity and location information, as well as damage reports. The server integrates the collected data, analyzes it using AI algorithms, and identifies high-risk areas. Based on the analysis results, it calculates the optimal allocation of rescue resources and transmits this information to local governments and rescue teams.

[0253] Terminal role

[0254] The device is used to transmit location information and personal health status from the user to a server. It also provides the user with evacuation instructions and safety information received from the server, both visually and audibly. In particular, it utilizes augmented reality technology to display evacuation routes on a map, supporting users in moving safely. Furthermore, it includes a multilingual display function, allowing users to receive information in their preferred language.

[0255] User roles

[0256] Users need to check the evacuation instructions displayed on their devices and evacuate to a safe place as quickly as possible. For example, after an earthquake, users register their location information with the system via a smartphone app. Based on this, they use augmented reality (AR) technology to confirm the route to the nearest safe evacuation center and begin evacuating according to the instructions. Furthermore, registration to confirm their safety is performed via the device, and notifications are automatically sent to pre-registered emergency contacts.

[0257] By having servers, terminals, and users work together in this way, it is possible to provide effective support during disasters and minimize human casualties. This system enables safe and smooth disaster response through a rapid and accurate flow of information and user-friendly evacuation support.

[0258] The following describes the processing flow.

[0259] Step 1:

[0260] The server collects data in real time from external sources when an earthquake occurs. This includes earthquake intensity, epicenter, and damage reports. The data obtained from each source is integrated and stored in a database.

[0261] Step 2:

[0262] The server analyzes the collected data using AI algorithms to identify high-risk areas. During the analysis process, it refers to past data and patterns to predict areas where damage is expected to spread.

[0263] Step 3:

[0264] The server calculates the optimal allocation of rescue resources based on the analysis results. In this calculation, priorities are set according to the level of danger in each area to ensure the efficient use of resources.

[0265] Step 4:

[0266] The server transmits the optimal allocation plan and analysis results to local governments and rescue teams. This enables the rapid and organized deployment of rescue operations on the ground.

[0267] Step 5:

[0268] The terminal notifies the user of evacuation instructions received from the server. The notification includes a visual display of evacuation routes using augmented reality technology and audio guidance.

[0269] Step 6:

[0270] The device displays notifications in multiple languages ​​based on the user's selected language. This feature ensures that information is provided across language barriers.

[0271] Step 7:

[0272] The user follows the instructions on the device and follows the designated evacuation route. The user updates their location information via the device and checks the progress of the evacuation.

[0273] Step 8:

[0274] After completing the evacuation, users register their safety status on their device. This information is automatically sent to emergency contacts via the server.

[0275] (Example 1)

[0276] 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."

[0277] In recent years, the frequency of natural disasters has increased, and there is a growing need for rapid and effective responses during disasters. However, conventional disaster information management systems have faced challenges such as the time required for information collection and analysis, making it difficult to optimally allocate rescue resources. Furthermore, multilingual support and real-time user assistance have been insufficient, and support for foreigners and people with disabilities, in particular, has been inadequate.

[0278] 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.

[0279] In this invention, the server includes means for integrating and analyzing disaster-related data acquired using collection means, means for evaluating the degree of risk using a generated AI model and calculating the optimal allocation of rescue resources, and means for providing relevant information to local governments and rescue departments. This enables rapid and accurate information processing and rescue operations during a disaster.

[0280] "Collection methods" refer to methods and devices for obtaining disaster-related data from external sources.

[0281] A "generative AI model" refers to an artificial intelligence model that is trained based on machine learning algorithms and used for analyzing and predicting disaster data.

[0282] "Risk assessment" refers to the process of measuring and judging the risks of a specific region or situation based on the collected data.

[0283] "Allocation of rescue resources" refers to the formulation of a plan to effectively allocate available personnel, equipment, and other resources to optimize rescue activities.

[0284] "Multilingual conversion function" refers to the translation technology for displaying or vocalizing information in multiple languages.

[0285] "Augmented reality technology" refers to the technology of overlaying digital information on the real environment for display, enabling users to obtain additional information in real scenarios.

[0286] "Registered emergency contact" refers to the contact that the user has specified in advance and will be automatically contacted in case of emergency.

[0287] "Dynamic update" refers to the process of immediately changing or adjusting plans or instructions based on new information when new information is obtained.

[0288] This invention is a system that functions as an infrastructure during disasters and is realized by combining various devices and AI technologies. Specific embodiments are shown below.

[0289] Functions and Operations of the Server

[0290] The server is equipped with means to collect disaster-related data from external sources. These external sources include APIs from earthquake observation agencies and meteorological agencies, and data from these sources is collected in real time. After collection, the server integrates the data and stores it in a database. Database systems such as PostgreSQL and MongoDB are used for this purpose. Based on this integrated data, data analysis is performed using a generative AI model to assess the level of risk. Machine learning frameworks such as TensorFlow and PyTorch are used, and the AI ​​model has been trained on past disaster data. Based on the analysis results, calculations are performed to optimally allocate rescue resources, and the necessary information is sent to local governments and rescue departments.

[0291] Device functions and operation

[0292] The terminal consists of smart devices (smartphones and tablets) used by the user. It has the function of acquiring location information and health status from the user and transmitting this information to the server. The terminal utilizes augmented reality technology to notify the user of instructions received from the server visually and audibly. Using ARKit (iOS) or ARCore (Android), it provides evacuation routes to the user and guides them to a safe route. Furthermore, it has a multilingual translation function, and information is provided in the language selected by the user. This is achieved by a translation function using the Google Translate API.

[0293] User behavior and roles

[0294] Users must receive disaster information and evacuation orders from the system via their devices and initiate appropriate evacuation actions. During evacuation, they must follow real-time AR guidance provided by their devices to move to a safe evacuation center. Once evacuation is complete, users perform an action on their devices to confirm their safety, and this is automatically notified to their emergency contacts. This enables rapid communication of safety to family and friends during large-scale disasters.

[0295] Specific example

[0296] For example, in the event of an earthquake, the server immediately collects information, assesses the level of risk, and determines the optimal allocation of rescue resources. On the device, users are provided with real-time guidance to the nearest evacuation shelter using augmented reality (AR). Furthermore, evacuation information is displayed in the language selected on the smartphone for users who do not speak Japanese.

[0297] Example of a prompt

[0298] By entering "Please tell me how to safely guide people to the nearest evacuation center after an earthquake," the AI ​​will begin analyzing the data and providing guidance.

[0299] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0300] Step 1:

[0301] The server collects disaster-related data from external sources. Input includes real-time data obtained from APIs of earthquake observation and meteorological agencies. This data is provided in JSON format and includes information on epicenters and seismic intensity. The server receives this data and performs initial formatting for storage in the database. Specifically, the server requests data via HTTP requests, parses the received response, and extracts the necessary information.

[0302] Step 2:

[0303] The server stores the integrated data in a database. The input is initially formatted disaster data, and the output is information stored in the database in a structured format. Specifically, the server connects to PostgreSQL or MongoDB and inserts the data into the appropriate fields. To do this, it maps values ​​to each field according to the data schema.

[0304] Step 3:

[0305] On the server, data analysis is performed using a generative AI model. As input, the current disaster information in the database is utilized. Based on this information, the AI model evaluates the risk level and generates a risk score for each region as output. The specific operation is to execute a pre-trained AI model using TensorFlow or PyTorch and present the results through visualization and report output.

[0306] Step 4:

[0307] The server calculates the optimal allocation of rescue resources based on the analysis results by the AI model. The input is the risk score for each region, and the output is a specific rescue resource allocation plan. As a specific operation, the server uses linear programming techniques to calculate the optimal deployment of fire brigades and medical teams and generates a message for transmitting that information to local governments.

[0308] Step 5:

[0309] The terminal transmits the location information and health status of the user to the server. The input is the location information that the user inputs or automatically obtains on the terminal, and the output is the user's status information updated by transmission to the server. The specific operation is that the terminal obtains the location using a GPS sensor, and the health information is input via an app.

[0310] Step 6:

[0311] The terminal receives an evacuation instruction from the server and visually guides the user using augmented reality technology. The input is the evacuation route information transmitted from the server, and the output is an AR display and voice guidance for the user. As a specific operation, the terminal uses ARKit or ARCore to overlay and display the evacuation route on the map and provides voice guidance through a speaker.

[0312] Step 7:

[0313] The user uses their device to follow a designated evacuation route and begin the evacuation process. The input is AR navigation displayed on the device, and the output is the user's movement to a safe location. Specifically, the user moves along the evacuation route, and after safely arriving, performs a safety check operation within the app and automatically notifies emergency contacts.

[0314] (Application Example 1)

[0315] 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."

[0316] In the event of a disaster, obtaining information and efficiently allocating rescue resources are crucial for saving lives. However, conventional systems have limitations in real-time information integration, multilingual support, and the clear presentation of evacuation orders, hindering swift and accurate evacuation guidance and rescue operations. Solving this problem is essential.

[0317] 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.

[0318] In this invention, the server includes means for integrating and analyzing disaster information collected in real time, means for optimally allocating rescue resources based on the analysis results, and means for transmitting the relevant information to administrative agencies and rescue organizations. This enables rapid and comprehensive information provision and the efficient deployment of rescue operations. Furthermore, effective evacuation support for users is realized through means for acquiring user location information using a portable information terminal, means for displaying evacuation routes overlaid in space using augmented reality technology, and means for automating communication in emergencies. This makes it possible to promote rapid and safe evacuation during disasters through the provision of multilingual instructions and visual route guidance.

[0319] "Real-time" refers to a method where information and data are processed instantly, and results are provided immediately.

[0320] "Disaster information" refers to detailed data on natural and man-made disasters, including earthquake intensity, location, and extent of damage.

[0321] "Integration" is the process of gathering data from multiple sources and analyzing it in a consistent format.

[0322] "Analysis" is the process of examining data in detail, understanding its content, and finding meaning in it.

[0323] "Rescue resources" refer to the resources necessary to carry out life-saving and support activities during a disaster, including personnel, equipment, and supplies.

[0324] "Optimal allocation" refers to adopting the most effective allocation method in order to make the most of limited resources.

[0325] An "administrative agency" is an organization of the government or local authorities that provides public services and is responsible for tasks such as disaster response.

[0326] A "rescue organization" refers to a group or institution formed to carry out rescue operations during a disaster.

[0327] A "portable information terminal" refers to a portable device used for accessing geographical information and communication.

[0328] "Location information" refers to data that indicates the current location of an object or person, and is generally expressed using geographic coordinates.

[0329] Augmented reality technology is a technique that overlays computer-generated images and information onto real-world footage.

[0330] An "evacuation route" refers to the recommended path or method for moving to a safe place during a disaster.

[0331] "Overlaying information into space" means overlaying additional information onto real-world landscapes or images to visually represent them.

[0332] "Automating communication" refers to automatically executing a process that performs communication based on pre-set conditions.

[0333] "Multilingual" means supporting multiple different languages ​​and being able to provide information in each of those languages.

[0334] "Providing instructions" means providing information that advises someone to take a specific action.

[0335] The system implementing this invention mainly consists of three elements: a server, a terminal, and a user.

[0336] The server receives disaster information in real time and integrates this data. Specifically, it acquires and analyzes data such as seismic intensity and location of earthquakes, and damage reports from various sources. The analysis utilizes machine learning algorithms to identify high-risk areas. A database management system is used for information collection and storage during this process. Based on the analysis results, the optimal allocation of rescue resources is calculated, and relevant information is sent to government agencies and rescue organizations.

[0337] The terminals used are primarily portable information terminals and similar devices. These terminals have the functionality to acquire the user's current location information and transmit it to a server. Furthermore, augmented reality technology can be used to visually display evacuation routes. Libraries such as OpenCV and ARCore are used as the platform for this. Disaster information is translated into multiple languages, providing users with instructions through various senses. This functionality allows users to evacuate quickly to a safe location while receiving visual and auditory guidance.

[0338] Users need to check evacuation instructions provided through their devices and move quickly to a safe place. Based on the AR display generated on their devices, they begin moving to a nearby safe area. For example, when an earthquake occurs, users can check the shortest route to a nearby park or evacuation center on an AR map and follow the instructions. An example of a prompt message in this case would be: "An earthquake has occurred. The location of the evacuation center and a safe route to it are shown in AR. Please check the route to the evacuation center on your smartphone and move safely."

[0339] By implementing this invention, it becomes possible to obtain information immediately during disasters, efficiently allocate resources, and provide smooth evacuation support for users, thereby realizing a swift and safe response.

[0340] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0341] Step 1:

[0342] The server collects disaster information in real time from multiple external sources. These sources include seismometer networks, the Japan Meteorological Agency database, and damage reports from local governments. Input data includes earthquake intensity, location, and damage status. The server receives this data, stores it in a database, and then converts it into a unified format.

[0343] Step 2:

[0344] The server uses machine learning algorithms based on collected disaster information to identify high-risk areas. The input is the disaster information integrated in Step 1, which is then analyzed to assess the level of risk. As a result, a list of identified high-risk areas is output. Furthermore, the optimal allocation of rescue resources is also calculated.

[0345] Step 3:

[0346] The device sends location information from the user to the server. The input includes location data obtained from the device's GPS sensor. This allows the server to determine the user's current location. The output is the user's location data.

[0347] Step 4:

[0348] The server generates evacuation instructions translated into multiple languages ​​based on the user's current location and the results of a risk assessment. The input consists of the user's location and a list of risk levels, which are used to identify an appropriate evacuation route for the user. As a result, evacuation instructions are generated and sent to the terminal.

[0349] Step 5:

[0350] The terminal displays evacuation routes using augmented reality technology based on received evacuation instructions. The input is the evacuation instructions sent from the server, which are used to overlay route information onto the camera feed. As output, the evacuation route is displayed on the screen using AR, guiding the user.

[0351] Step 6:

[0352] The user follows the evacuation instructions displayed on their device and moves to a safe location. Input includes visual information from AR route display. Based on this, the user follows the correct route and evacuates to the designated shelter.

[0353] Step 7:

[0354] The device confirms that the user has safely arrived at the designated location and automatically sends a notification to pre-configured emergency contacts. The input is the user's arrival information, which then notifies the contacts of the user's safety status. As output, a message confirming safe arrival is sent.

[0355] 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.

[0356] This invention is a system that collects and analyzes real-time disaster information during earthquakes and other disasters, and provides dynamic evacuation instructions based on the user's emotional state. This system, by combining an emotion engine, understands the user's psychological state and provides information accordingly. Specific embodiments are described below.

[0357] Server Role

[0358] The server collects disaster information in real time from external sources, integrates and stores that data. This includes detailed earthquake location information, seismic intensity, and damage status. The collected data is analyzed using AI algorithms to identify high-risk areas. Based on the analysis, the server calculates the optimal allocation of rescue resources and transmits the information to local governments and rescue teams. The server also receives user emotion data from terminals and notifies experts so that psychological support can be provided as needed.

[0359] Terminal role

[0360] The device is equipped with an emotion engine that recognizes emotions from the user's facial expressions and voice. It has the ability to transmit the user's emotional state to a server in real time. It also receives evacuation instructions from the server and presents them to the user visually and audibly in multiple languages. Evacuation routes are displayed using augmented reality technology to support the user's safe evacuation. If the user's emotional state is determined to be anxiety or panic, the frequency and format of notifications are dynamically adjusted.

[0361] User roles

[0362] Users take appropriate evacuation actions based on information provided by their devices. An emotion engine analyzes the user's psychological state, and evacuation instructions are adjusted accordingly, allowing users to act in a way that aligns with their emotions. After completing the evacuation, users confirm their safety and register this information with the server via their devices. This information is automatically sent to emergency contacts.

[0363] As a concrete example, if a user shows signs of surprise or anxiety during an earthquake, the device sends emotional data to a server, which then provides the device with a more reassuring notification format. The user then moves to a safe location following evacuation instructions tailored to provide a sense of security. In this way, the entire system operates consistently, enhancing safety during disasters and reducing people's mental burden.

[0364] The following describes the processing flow.

[0365] Step 1:

[0366] The server collects data in real time from external sources when an earthquake occurs. This includes earthquake intensity, epicenter information, and damage reports. The collected data is integrated and stored in a database.

[0367] Step 2:

[0368] The server analyzes the integrated data using AI algorithms to identify high-risk areas. Based on the analysis results, it calculates the optimal allocation of rescue resources.

[0369] Step 3:

[0370] The server transmits analysis results and resource allocation plans to local governments and rescue teams, thereby supporting rapid rescue operations on the ground.

[0371] Step 4:

[0372] The terminal receives evacuation instructions from the server and notifies the user. The notification is displayed in multiple languages. Evacuation routes are displayed visually using augmented reality technology.

[0373] Step 5:

[0374] The device uses an emotion engine that recognizes emotions from the user's facial expressions and voice. This data represents the user's real-time emotional state and is sent to the server.

[0375] Step 6:

[0376] The server analyzes user emotion data and dynamically changes the frequency and format of notifications based on the results. For example, if a user is in a state of panic, it will provide messages and guidance that offer greater reassurance.

[0377] Step 7:

[0378] Users review evacuation instructions optimized to their emotions and begin moving along safe evacuation routes.

[0379] Step 8:

[0380] After completing their evacuation, users register their safety status with the server via their device. This automatically notifies designated emergency contacts that the user is safe.

[0381] (Example 2)

[0382] 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".

[0383] When a disaster strikes, it is crucial to quickly collect, analyze, and provide appropriate information to relevant organizations and victims. However, conventional systems are not capable of providing dynamic information that takes emotional states into account. As a result, victims may experience excessive anxiety and confusion in response to the information they receive, and traditional evacuation orders provide insufficient emotional support.

[0384] 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.

[0385] In this invention, the server includes means for integrating and analyzing disaster information collected in real time, means for optimally allocating rescue resources based on the analysis results, means for analyzing the user's emotional state from an external detection device and providing the relevant information, and means for dynamically adjusting the tone and format of evacuation orders based on emotional data. This enables the provision of real-time information that takes emotions into account, reducing the mental burden on disaster victims while allowing for efficient evacuation and rescue operations.

[0386] "Real-time" refers to a state in which processing and response can be carried out immediately at the moment an event occurs.

[0387] "Disaster information" refers to all data related to disasters, such as location information, seismic intensity, and damage status, concerning earthquakes and natural disasters.

[0388] "Analysis" refers to the process of analyzing collected data and transforming it into more meaningful information.

[0389] "Rescue resources" refers to the total amount of personnel, equipment, supplies, etc., necessary for rescue operations.

[0390] "Optimal allocation" means distributing limited resources in a way that allows them to be used most effectively.

[0391] A "detection device" refers to a device or sensor used to collect information about a user's emotional state or environmental conditions.

[0392] "Emotional data" refers to information that indicates the user's psychological state, and includes analysis results of facial expressions, voice, and other data.

[0393] "Dynamic adjustment" refers to changing the content and format in real time in response to changes in circumstances and conditions.

[0394] A "local government" refers to a local public entity that exists under the national or local government and is responsible for the administration of a specific region.

[0395] An "evacuation order" is an instruction or recommendation to take safe actions in the event of a disaster.

[0396] This invention is a system designed to streamline emergency disaster response and enable dynamic responses tailored to the user's psychological state. The system is primarily composed of a server, terminals, and users, with each element working in coordination to function.

[0397] The server collects disaster information in real time from multiple external sources during natural disasters such as earthquakes. Data obtained from sources such as the Japan Meteorological Agency and earthquake databases is analyzed using AI algorithms. This analysis identifies high-risk areas and calculates the optimal allocation of rescue resources. It also has a system that receives user emotional data via an emotion engine and notifies experts. This enables a rapid response if psychological support is needed.

[0398] The device is equipped with an emotion engine that analyzes the user's facial expressions and voice to recognize emotions. This data is transmitted to a server in real time. Furthermore, the device receives evacuation instructions from the server and uses AR technology to visually present evacuation routes. This allows users to receive instructions adapted to their environment. It also supports multiple languages, making it applicable to users worldwide.

[0399] By utilizing this system, users can enhance their sense of security and safety during disasters. Based on the information provided, they can take appropriate evacuation actions to ensure their own safety. Once evacuation is complete, they report their safety to the server via their device. This information is automatically notified to emergency contacts, enabling rapid communication.

[0400] As a concrete example, if an earthquake occurs and the user expresses anxiety, the device sends emotional data to the server. The server generates customized evacuation instructions designed to provide reassurance and provides them to the user through the device. The user can then follow the instructions and move to a safe location. An example of a prompt using the generative AI model might be, "Generate evacuation instructions that provide reassurance based on the user's emotional state."

[0401] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0402] Step 1:

[0403] The server collects disaster information. The server connects to external data sources such as meteorological agencies and earthquake databases to obtain real-time earthquake information. Inputs include location information, seismic intensity, and damage status at the time of the disaster. This data is integrated and stored in the system's internal database.

[0404] Step 2:

[0405] The server analyzes the data. Based on the collected disaster information, an AI algorithm operates to identify areas where damage is expected. The input here is integrated earthquake data, and the output is a list of high-risk areas. The server then calculates the optimal allocation of rescue resources as needed.

[0406] Step 3:

[0407] The server generates evacuation orders. Based on the analysis results, it creates appropriate evacuation orders for users. The input is a list of high-risk areas, and the output is individual evacuation order information. This includes safe evacuation routes, recommended evacuation locations, and information on what to bring.

[0408] Step 4:

[0409] The device collects emotional data. Sensors detect the user's facial expressions and voice, and an emotion engine analyzes their psychological state. The input is the user's visual and auditory data, and the output is the analyzed emotional state.

[0410] Step 5:

[0411] The device sends emotional data to the server. The analyzed user emotional data is sent to the server in real time. The input is the user's emotional state, which is processed by the server and becomes the basis for adjusting the tone and format of evacuation instructions.

[0412] Step 6:

[0413] The terminal displays evacuation instructions. The terminal provides the user with evacuation instructions received from the server, both visually and audibly. The input is evacuation instruction information from the server, and the output is a visual and audible presentation to the user. Augmented reality technology is used to overlay evacuation routes onto the real world, allowing the user to intuitively identify safe paths.

[0414] Step 7:

[0415] The user evacuates. Based on the information provided, the user takes appropriate evacuation actions. If the user's psychological state is anxious or panicked, the tone and frequency of notifications are adjusted, and evacuation support that provides a sense of security is provided as output.

[0416] Step 8:

[0417] The user reports their safety. After evacuation is complete, the user reports their safety to the server via their device. The input is the user's safety information, which is sent to emergency contacts via the server, enabling rapid notification as output.

[0418] (Application Example 2)

[0419] 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 as the "terminal".

[0420] During disasters, there is a challenge in providing effective and reassuring evacuation instructions in real time. Conventional systems issue uniform instructions without considering the user's feelings, which can lead to panic and hinder proper evacuation. Furthermore, the lack of multilingual information provision and insufficient visual displays using augmented reality technology makes it difficult to provide smooth evacuation support.

[0421] 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.

[0422] In this invention, the server includes means for integrating and analyzing disaster information collected in real time, means for optimally allocating support resources based on the analysis results, and means for recognizing the user's emotional state and dynamically adjusting evacuation orders based on that state. This makes it possible to provide optimal evacuation orders that respond to the user's emotions so that they can evacuate with a sense of security.

[0423] "Real-time disaster information" refers to all data continuously acquired from the moment a disaster occurs, including earthquake location information, seismic intensity, and damage status.

[0424] "Optimally allocating support resources based on analysis results" refers to the process where an AI algorithm uses disaster information to perform calculations and efficiently allocate necessary supplies and human resources to high-risk areas.

[0425] "Public institutions and support teams" refers to various government organizations and private support groups that operate during disasters.

[0426] "Recognizing the user's emotional state and dynamically adjusting evacuation instructions based on it" refers to a function where the device analyzes the user's facial expressions and voice to identify their psychological state and appropriately changes the notification method and content to provide a sense of security.

[0427] "Visually displaying the optimal evacuation route using augmented reality technology" means utilizing technology that overlays digital information onto the real world via smart devices to clearly show users safe evacuation routes.

[0428] This system is designed to provide real-time situational awareness and appropriate evacuation instructions during disasters. The server continuously collects disaster information from various sensors and external databases, and uses AI algorithms to analyze the integrated data. Specifically, it identifies earthquake location and intensity information, as well as risk areas affected, and efficiently allocates support resources. This enables the rapid and appropriate provision of information to government agencies and support organizations.

[0429] The device is equipped with an emotion analysis engine to understand the user's emotions, recognizing them in real time from facial expressions and voice. This data is sent to a server, and evacuation instructions that take the user's psychological burden into consideration are generated. The device also has a function that displays evacuation routes using augmented reality technology, providing route guidance in an intuitive and reassuring way for the user.

[0430] The device also utilizes multilingual translation capabilities to provide appropriate information to a variety of users. As a result, it helps users from diverse backgrounds, including foreign residents and tourists, to evacuate safely.

[0431] As a concrete example, consider a scenario where a family with children uses the system during a disaster. The platform could present simplified visual guidance that is easy for children to understand, and could even emit encouraging voice messages to alleviate anxiety. A possible prompt for the generating AI model could be something like, "Please suggest a guide message for children to help them cope with anxiety during a disaster."

[0432] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0433] Step 1:

[0434] The server acquires disaster information in real time from various data sources. This process involves inputting information from seismometers, weather observation devices, and other sources. The server integrates this information and processes it into an analyzable format by storing it in a database.

[0435] Step 2:

[0436] The server analyzes the collected disaster information using an AI algorithm. Based on the input data, it identifies high-risk areas and calculates the allocation of support resources. The output generates a plan outlining areas requiring rescue and the resources needed.

[0437] Step 3:

[0438] The device uses an emotion analysis engine to recognize the user's emotions by taking the user's facial expressions and voice data as input. This data is sent to a server in real time, and it is determined whether the user's psychological state is one of temporary anxiety or panic.

[0439] Step 4:

[0440] When the server receives emotional state data, it dynamically adjusts evacuation instructions based on its assessment. Specifically, this includes changing the tone and frequency of voice messages, for example. Based on this, the optimal evacuation route is determined.

[0441] Step 5:

[0442] The terminal uses augmented reality technology to visually display evacuation routes to the user based on evacuation instructions received from the server. In this step, the input route data is used, and intuitive directional guidance appears in the user's field of view as output.

[0443] Step 6:

[0444] Users can begin evacuating with a sense of security based on the information presented on their devices. In some cases, the server can further improve the accuracy of the information throughout the evacuation process by allowing users to provide simple feedback to their devices.

[0445] 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.

[0446] 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.

[0447] 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.

[0448] [Third Embodiment]

[0449] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0450] 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.

[0451] 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).

[0452] 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.

[0453] 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.

[0454] 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).

[0455] 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.

[0456] 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.

[0457] 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.

[0458] 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.

[0459] 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.

[0460] 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".

[0461] This invention provides a system for collecting and analyzing disaster information in real time during disasters such as earthquakes. This system enables the optimal allocation of rescue resources and the provision of effective evacuation instructions. Specific embodiments of each element are described below.

[0462] Server Role

[0463] The server is responsible for collecting disaster information in real time from multiple external sources. This includes earthquake intensity and location information, as well as damage reports. The server integrates the collected data, analyzes it using AI algorithms, and identifies high-risk areas. Based on the analysis results, it calculates the optimal allocation of rescue resources and transmits this information to local governments and rescue teams.

[0464] Terminal role

[0465] The device is used to transmit location information and personal health status from the user to a server. It also provides the user with evacuation instructions and safety information received from the server, both visually and audibly. In particular, it utilizes augmented reality technology to display evacuation routes on a map, supporting users in moving safely. Furthermore, it includes a multilingual display function, allowing users to receive information in their preferred language.

[0466] User roles

[0467] Users need to check the evacuation instructions displayed on their devices and evacuate to a safe place as quickly as possible. For example, after an earthquake, users register their location information with the system via a smartphone app. Based on this, they use augmented reality (AR) technology to confirm the route to the nearest safe evacuation center and begin evacuating according to the instructions. Furthermore, registration to confirm their safety is performed via the device, and notifications are automatically sent to pre-registered emergency contacts.

[0468] By having servers, terminals, and users work together in this way, it is possible to provide effective support during disasters and minimize human casualties. This system enables safe and smooth disaster response through a rapid and accurate flow of information and user-friendly evacuation support.

[0469] The following describes the processing flow.

[0470] Step 1:

[0471] The server collects data in real time from external sources when an earthquake occurs. This includes earthquake intensity, epicenter, and damage reports. The data obtained from each source is integrated and stored in a database.

[0472] Step 2:

[0473] The server analyzes the collected data using AI algorithms to identify high-risk areas. During the analysis process, it refers to past data and patterns to predict areas where damage is expected to spread.

[0474] Step 3:

[0475] The server calculates the optimal allocation of rescue resources based on the analysis results. In this calculation, priorities are set according to the level of danger in each area to ensure the efficient use of resources.

[0476] Step 4:

[0477] The server transmits the optimal allocation plan and analysis results to local governments and rescue teams. This enables the rapid and organized deployment of rescue operations on the ground.

[0478] Step 5:

[0479] The terminal notifies the user of evacuation instructions received from the server. The notification includes a visual display of evacuation routes using augmented reality technology and audio guidance.

[0480] Step 6:

[0481] The device displays notifications in multiple languages ​​based on the user's selected language. This feature ensures that information is provided across language barriers.

[0482] Step 7:

[0483] The user follows the instructions on the device and follows the designated evacuation route. The user updates their location information via the device and checks the progress of the evacuation.

[0484] Step 8:

[0485] After completing the evacuation, users register their safety status on their device. This information is automatically sent to emergency contacts via the server.

[0486] (Example 1)

[0487] 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."

[0488] In recent years, the frequency of natural disasters has increased, and there is a growing need for rapid and effective responses during disasters. However, conventional disaster information management systems have faced challenges such as the time required for information collection and analysis, making it difficult to optimally allocate rescue resources. Furthermore, multilingual support and real-time user assistance have been insufficient, and support for foreigners and people with disabilities, in particular, has been inadequate.

[0489] 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.

[0490] In this invention, the server includes means for integrating and analyzing disaster-related data acquired using collection means, means for evaluating the degree of risk using a generated AI model and calculating the optimal allocation of rescue resources, and means for providing relevant information to local governments and rescue departments. This enables rapid and accurate information processing and rescue operations during a disaster.

[0491] "Collection methods" refer to methods and devices for obtaining disaster-related data from external sources.

[0492] A "generative AI model" refers to an artificial intelligence model that is trained based on machine learning algorithms and used for analyzing and predicting disaster data.

[0493] "Risk assessment" refers to the process of measuring and determining the risks of a particular area or situation based on collected data.

[0494] "Rescue resource allocation" refers to planning how to effectively allocate available personnel, equipment, and other resources to optimize rescue operations.

[0495] "Multilingual conversion function" refers to translation technology that displays or converts information into audio in multiple languages.

[0496] Augmented reality technology refers to a technology that overlays digital information onto the real environment, enabling users to obtain additional information in real-world situations.

[0497] "Registered emergency contacts" refer to contacts that the user has designated in advance and to whom they should be automatically contacted in the event of an emergency.

[0498] "Dynamic updates" refer to the process of immediately changing or adjusting plans and instructions based on new information that becomes available.

[0499] This invention is a system that functions as infrastructure during disasters and is realized by combining various devices and AI technology. Specific embodiments are shown below.

[0500] Server functions and operation

[0501] The server is equipped with means to collect disaster-related data from external sources. These external sources include APIs from earthquake observation agencies and meteorological agencies, and data from these sources is collected in real time. After collection, the server integrates the data and stores it in a database. Database systems such as PostgreSQL and MongoDB are used for this purpose. Based on this integrated data, data analysis is performed using a generative AI model to assess the level of risk. Machine learning frameworks such as TensorFlow and PyTorch are used, and the AI ​​model has been trained on past disaster data. Based on the analysis results, calculations are performed to optimally allocate rescue resources, and the necessary information is sent to local governments and rescue departments.

[0502] Device functions and operation

[0503] The terminal consists of smart devices (smartphones and tablets) used by the user. It has the function of acquiring location information and health status from the user and transmitting this information to the server. The terminal utilizes augmented reality technology to notify the user of instructions received from the server visually and audibly. Using ARKit (iOS) or ARCore (Android), it provides evacuation routes to the user and guides them to a safe route. Furthermore, it has a multilingual translation function, and information is provided in the language selected by the user. This is achieved by a translation function using the Google Translate API.

[0504] User behavior and roles

[0505] Users must receive disaster information and evacuation orders from the system via their devices and initiate appropriate evacuation actions. During evacuation, they must follow real-time AR guidance provided by their devices to move to a safe evacuation center. Once evacuation is complete, users perform an action on their devices to confirm their safety, and this is automatically notified to their emergency contacts. This enables rapid communication of safety to family and friends during large-scale disasters.

[0506] Specific example

[0507] For example, in the event of an earthquake, the server immediately collects information, assesses the level of risk, and determines the optimal allocation of rescue resources. On the device, users are provided with real-time guidance to the nearest evacuation shelter using augmented reality (AR). Furthermore, evacuation information is displayed in the language selected on the smartphone for users who do not speak Japanese.

[0508] Example of a prompt

[0509] By entering "Please tell me how to safely guide people to the nearest evacuation center after an earthquake," the AI ​​will begin analyzing the data and providing guidance.

[0510] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0511] Step 1:

[0512] The server collects disaster-related data from external sources. Input includes real-time data obtained from APIs of earthquake observation and meteorological agencies. This data is provided in JSON format and includes information on epicenters and seismic intensity. The server receives this data and performs initial formatting for storage in the database. Specifically, the server requests data via HTTP requests, parses the received response, and extracts the necessary information.

[0513] Step 2:

[0514] The server stores the integrated data in a database. The input is initially formatted disaster data, and the output is information stored in the database in a structured format. Specifically, the server connects to PostgreSQL or MongoDB and inserts the data into the appropriate fields. To do this, it maps values ​​to each field according to the data schema.

[0515] Step 3:

[0516] On the server, data analysis is performed using a generative AI model. The input is current disaster information from a database. Based on this information, the AI ​​model evaluates the level of risk and generates a risk score for each region as output. Specifically, the AI ​​model, which has been trained using TensorFlow or PyTorch, is executed, and the results are presented through visualization and report output.

[0517] Step 4:

[0518] The server calculates the optimal allocation of rescue resources based on the analysis results from the AI ​​model. The input is the risk score for each region, and the output is a specific rescue resource allocation plan. Specifically, the server uses linear programming techniques to calculate the optimal deployment of fire brigades and medical teams, and generates a message to send that information to the local government.

[0519] Step 5:

[0520] The device transmits location information and health status from the user to the server. Input is location information entered by the user or automatically acquired by the device, while output is user status information updated by transmission to the server. Specifically, the device acquires location using a GPS sensor, and health information is entered via the app.

[0521] Step 6:

[0522] The device receives evacuation instructions from the server and uses augmented reality technology to visually guide the user. The input is evacuation route information sent from the server, and the output is an AR display and audio guidance for the user. Specifically, the device uses ARKit or ARCore to overlay the evacuation route onto a map and provides audio guidance through its speaker.

[0523] Step 7:

[0524] The user uses their device to follow a designated evacuation route and begin the evacuation process. The input is AR navigation displayed on the device, and the output is the user's movement to a safe location. Specifically, the user moves along the evacuation route, and after safely arriving, performs a safety check operation within the app and automatically notifies emergency contacts.

[0525] (Application Example 1)

[0526] 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."

[0527] In the event of a disaster, obtaining information and efficiently allocating rescue resources are crucial for saving lives. However, conventional systems have limitations in real-time information integration, multilingual support, and the clear presentation of evacuation orders, hindering swift and accurate evacuation guidance and rescue operations. Solving this problem is essential.

[0528] 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.

[0529] In this invention, the server includes means for integrating and analyzing disaster information collected in real time, means for optimally allocating rescue resources based on the analysis results, and means for transmitting the relevant information to administrative agencies and rescue organizations. This enables rapid and comprehensive information provision and the efficient deployment of rescue operations. Furthermore, effective evacuation support for users is realized through means for acquiring user location information using a portable information terminal, means for displaying evacuation routes overlaid in space using augmented reality technology, and means for automating communication in emergencies. This makes it possible to promote rapid and safe evacuation during disasters through the provision of multilingual instructions and visual route guidance.

[0530] "Real-time" refers to a method where information and data are processed instantly, and results are provided immediately.

[0531] "Disaster information" refers to detailed data on natural and man-made disasters, including earthquake intensity, location, and extent of damage.

[0532] "Integration" is the process of gathering data from multiple sources and analyzing it in a consistent format.

[0533] "Analysis" is the process of examining data in detail, understanding its content, and finding meaning in it.

[0534] "Rescue resources" refer to the resources necessary to carry out life-saving and support activities during a disaster, including personnel, equipment, and supplies.

[0535] "Optimal allocation" refers to adopting the most effective allocation method in order to make the most of limited resources.

[0536] An "administrative agency" is an organization of the government or local authorities that provides public services and is responsible for tasks such as disaster response.

[0537] A "rescue organization" refers to a group or institution formed to carry out rescue operations during a disaster.

[0538] A "portable information terminal" refers to a portable device used for accessing geographical information and communication.

[0539] "Location information" refers to data that indicates the current location of an object or person, and is generally expressed using geographic coordinates.

[0540] Augmented reality technology is a technique that overlays computer-generated images and information onto real-world footage.

[0541] An "evacuation route" refers to the recommended path or method for moving to a safe place during a disaster.

[0542] "Overlaying information into space" means overlaying additional information onto real-world landscapes or images to visually represent them.

[0543] "Automating communication" refers to automatically executing a process that performs communication based on pre-set conditions.

[0544] "Multilingual" means supporting multiple different languages ​​and being able to provide information in each of those languages.

[0545] "Providing instructions" means providing information that advises someone to take a specific action.

[0546] The system implementing this invention mainly consists of three elements: a server, a terminal, and a user.

[0547] The server receives disaster information in real time and integrates this data. Specifically, it acquires and analyzes data such as seismic intensity and location of earthquakes, and damage reports from various sources. The analysis utilizes machine learning algorithms to identify high-risk areas. A database management system is used for information collection and storage during this process. Based on the analysis results, the optimal allocation of rescue resources is calculated, and relevant information is sent to government agencies and rescue organizations.

[0548] The terminals used are primarily portable information terminals and similar devices. These terminals have the functionality to acquire the user's current location information and transmit it to a server. Furthermore, augmented reality technology can be used to visually display evacuation routes. Libraries such as OpenCV and ARCore are used as the platform for this. Disaster information is translated into multiple languages, providing users with instructions through various senses. This functionality allows users to evacuate quickly to a safe location while receiving visual and auditory guidance.

[0549] Users need to check evacuation instructions provided through their devices and move quickly to a safe place. Based on the AR display generated on their devices, they begin moving to a nearby safe area. For example, when an earthquake occurs, users can check the shortest route to a nearby park or evacuation center on an AR map and follow the instructions. An example of a prompt message in this case would be: "An earthquake has occurred. The location of the evacuation center and a safe route to it are shown in AR. Please check the route to the evacuation center on your smartphone and move safely."

[0550] By implementing this invention, it becomes possible to obtain information immediately during disasters, efficiently allocate resources, and provide smooth evacuation support for users, thereby realizing a swift and safe response.

[0551] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0552] Step 1:

[0553] The server collects disaster information in real time from multiple external sources. These sources include seismometer networks, the Japan Meteorological Agency database, and damage reports from local governments. Input data includes earthquake intensity, location, and damage status. The server receives this data, stores it in a database, and then converts it into a unified format.

[0554] Step 2:

[0555] The server uses machine learning algorithms based on collected disaster information to identify high-risk areas. The input is the disaster information integrated in Step 1, which is then analyzed to assess the level of risk. As a result, a list of identified high-risk areas is output. Furthermore, the optimal allocation of rescue resources is also calculated.

[0556] Step 3:

[0557] The device sends location information from the user to the server. The input includes location data obtained from the device's GPS sensor. This allows the server to determine the user's current location. The output is the user's location data.

[0558] Step 4:

[0559] The server generates evacuation instructions translated into multiple languages ​​based on the user's current location and the results of a risk assessment. The input consists of the user's location and a list of risk levels, which are used to identify an appropriate evacuation route for the user. As a result, evacuation instructions are generated and sent to the terminal.

[0560] Step 5:

[0561] The terminal displays evacuation routes using augmented reality technology based on received evacuation instructions. The input is the evacuation instructions sent from the server, which are used to overlay route information onto the camera feed. As output, the evacuation route is displayed on the screen using AR, guiding the user.

[0562] Step 6:

[0563] The user follows the evacuation instructions displayed on their device and moves to a safe location. Input includes visual information from AR route display. Based on this, the user follows the correct route and evacuates to the designated shelter.

[0564] Step 7:

[0565] The device confirms that the user has safely arrived at the designated location and automatically sends a notification to pre-configured emergency contacts. The input is the user's arrival information, which then notifies the contacts of the user's safety status. As output, a message confirming safe arrival is sent.

[0566] 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.

[0567] This invention is a system that collects and analyzes real-time disaster information during earthquakes and other disasters, and provides dynamic evacuation instructions based on the user's emotional state. This system, by combining an emotion engine, understands the user's psychological state and provides information accordingly. Specific embodiments are described below.

[0568] Server Role

[0569] The server collects disaster information in real time from external sources, integrates and stores that data. This includes detailed earthquake location information, seismic intensity, and damage status. The collected data is analyzed using AI algorithms to identify high-risk areas. Based on the analysis, the server calculates the optimal allocation of rescue resources and transmits the information to local governments and rescue teams. The server also receives user emotion data from terminals and notifies experts so that psychological support can be provided as needed.

[0570] Terminal role

[0571] The device is equipped with an emotion engine that recognizes emotions from the user's facial expressions and voice. It has the ability to transmit the user's emotional state to a server in real time. It also receives evacuation instructions from the server and presents them to the user visually and audibly in multiple languages. Evacuation routes are displayed using augmented reality technology to support the user's safe evacuation. If the user's emotional state is determined to be anxiety or panic, the frequency and format of notifications are dynamically adjusted.

[0572] User roles

[0573] Users take appropriate evacuation actions based on information provided by their devices. An emotion engine analyzes the user's psychological state, and evacuation instructions are adjusted accordingly, allowing users to act in a way that aligns with their emotions. After completing the evacuation, users confirm their safety and register this information with the server via their devices. This information is automatically sent to emergency contacts.

[0574] As a concrete example, if a user shows signs of surprise or anxiety during an earthquake, the device sends emotional data to a server, which then provides the device with a more reassuring notification format. The user then moves to a safe location following evacuation instructions tailored to provide a sense of security. In this way, the entire system operates consistently, enhancing safety during disasters and reducing people's mental burden.

[0575] The following describes the processing flow.

[0576] Step 1:

[0577] The server collects data in real time from external sources when an earthquake occurs. This includes earthquake intensity, epicenter information, and damage reports. The collected data is integrated and stored in a database.

[0578] Step 2:

[0579] The server analyzes the integrated data using AI algorithms to identify high-risk areas. Based on the analysis results, it calculates the optimal allocation of rescue resources.

[0580] Step 3:

[0581] The server transmits analysis results and resource allocation plans to local governments and rescue teams, thereby supporting rapid rescue operations on the ground.

[0582] Step 4:

[0583] The terminal receives evacuation instructions from the server and notifies the user. The notification is displayed in multiple languages. Evacuation routes are displayed visually using augmented reality technology.

[0584] Step 5:

[0585] The device uses an emotion engine that recognizes emotions from the user's facial expressions and voice. This data represents the user's real-time emotional state and is sent to the server.

[0586] Step 6:

[0587] The server analyzes user emotion data and dynamically changes the frequency and format of notifications based on the results. For example, if a user is in a state of panic, it will provide messages and guidance that offer greater reassurance.

[0588] Step 7:

[0589] Users review evacuation instructions optimized to their emotions and begin moving along safe evacuation routes.

[0590] Step 8:

[0591] After completing their evacuation, users register their safety status with the server via their device. This automatically notifies designated emergency contacts that the user is safe.

[0592] (Example 2)

[0593] 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."

[0594] When a disaster strikes, it is crucial to quickly collect, analyze, and provide appropriate information to relevant organizations and victims. However, conventional systems are not capable of providing dynamic information that takes emotional states into account. As a result, victims may experience excessive anxiety and confusion in response to the information they receive, and traditional evacuation orders provide insufficient emotional support.

[0595] 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.

[0596] In this invention, the server includes means for integrating and analyzing disaster information collected in real time, means for optimally allocating rescue resources based on the analysis results, means for analyzing the user's emotional state from an external detection device and providing the relevant information, and means for dynamically adjusting the tone and format of evacuation orders based on emotional data. This enables the provision of real-time information that takes emotions into account, reducing the mental burden on disaster victims while allowing for efficient evacuation and rescue operations.

[0597] "Real-time" refers to a state in which processing and response can be carried out immediately at the moment an event occurs.

[0598] "Disaster information" refers to all data related to disasters, such as location information, seismic intensity, and damage status, concerning earthquakes and natural disasters.

[0599] "Analysis" refers to the process of analyzing collected data and transforming it into more meaningful information.

[0600] "Rescue resources" refers to the total amount of personnel, equipment, supplies, etc., necessary for rescue operations.

[0601] "Optimal allocation" means distributing limited resources in a way that allows them to be used most effectively.

[0602] A "detection device" refers to a device or sensor used to collect information about a user's emotional state or environmental conditions.

[0603] "Emotional data" refers to information that indicates the user's psychological state, and includes analysis results of facial expressions, voice, and other data.

[0604] "Dynamic adjustment" refers to changing the content and format in real time in response to changes in circumstances and conditions.

[0605] A "local government" refers to a local public entity that exists under the national or local government and is responsible for the administration of a specific region.

[0606] An "evacuation order" is an instruction or recommendation to take safe actions in the event of a disaster.

[0607] This invention is a system designed to streamline emergency disaster response and enable dynamic responses tailored to the user's psychological state. The system is primarily composed of a server, terminals, and users, with each element working in coordination to function.

[0608] The server collects disaster information in real time from multiple external sources during natural disasters such as earthquakes. Data obtained from sources such as the Japan Meteorological Agency and earthquake databases is analyzed using AI algorithms. This analysis identifies high-risk areas and calculates the optimal allocation of rescue resources. It also has a system that receives user emotional data via an emotion engine and notifies experts. This enables a rapid response if psychological support is needed.

[0609] The device is equipped with an emotion engine that analyzes the user's facial expressions and voice to recognize emotions. This data is transmitted to a server in real time. Furthermore, the device receives evacuation instructions from the server and uses AR technology to visually present evacuation routes. This allows users to receive instructions adapted to their environment. It also supports multiple languages, making it applicable to users worldwide.

[0610] By utilizing this system, users can enhance their sense of security and safety during disasters. Based on the information provided, they can take appropriate evacuation actions to ensure their own safety. Once evacuation is complete, they report their safety to the server via their device. This information is automatically notified to emergency contacts, enabling rapid communication.

[0611] As a concrete example, if an earthquake occurs and the user expresses anxiety, the device sends emotional data to the server. The server generates customized evacuation instructions designed to provide reassurance and provides them to the user through the device. The user can then follow the instructions and move to a safe location. An example of a prompt using the generative AI model might be, "Generate evacuation instructions that provide reassurance based on the user's emotional state."

[0612] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0613] Step 1:

[0614] The server collects disaster information. The server connects to external data sources such as meteorological agencies and earthquake databases to obtain real-time earthquake information. Inputs include location information, seismic intensity, and damage status at the time of the disaster. This data is integrated and stored in the system's internal database.

[0615] Step 2:

[0616] The server analyzes the data. Based on the collected disaster information, an AI algorithm operates to identify areas where damage is expected. The input here is integrated earthquake data, and the output is a list of high-risk areas. The server then calculates the optimal allocation of rescue resources as needed.

[0617] Step 3:

[0618] The server generates evacuation orders. Based on the analysis results, it creates appropriate evacuation orders for users. The input is a list of high-risk areas, and the output is individual evacuation order information. This includes safe evacuation routes, recommended evacuation locations, and information on what to bring.

[0619] Step 4:

[0620] The device collects emotional data. Sensors detect the user's facial expressions and voice, and an emotion engine analyzes their psychological state. The input is the user's visual and auditory data, and the output is the analyzed emotional state.

[0621] Step 5:

[0622] The device sends emotional data to the server. The analyzed user emotional data is sent to the server in real time. The input is the user's emotional state, which is processed by the server and becomes the basis for adjusting the tone and format of evacuation instructions.

[0623] Step 6:

[0624] The terminal displays evacuation instructions. The terminal provides the user with evacuation instructions received from the server, both visually and audibly. The input is evacuation instruction information from the server, and the output is a visual and audible presentation to the user. Augmented reality technology is used to overlay evacuation routes onto the real world, allowing the user to intuitively identify safe paths.

[0625] Step 7:

[0626] The user evacuates. Based on the information provided, the user takes appropriate evacuation actions. If the user's psychological state is anxious or panicked, the tone and frequency of notifications are adjusted, and evacuation support that provides a sense of security is provided as output.

[0627] Step 8:

[0628] The user reports their safety. After evacuation is complete, the user reports their safety to the server via their device. The input is the user's safety information, which is sent to emergency contacts via the server, enabling rapid notification as output.

[0629] (Application Example 2)

[0630] 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."

[0631] During disasters, there is a challenge in providing effective and reassuring evacuation instructions in real time. Conventional systems issue uniform instructions without considering the user's feelings, which can lead to panic and hinder proper evacuation. Furthermore, the lack of multilingual information provision and insufficient visual displays using augmented reality technology makes it difficult to provide smooth evacuation support.

[0632] 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.

[0633] In this invention, the server includes means for integrating and analyzing disaster information collected in real time, means for optimally allocating support resources based on the analysis results, and means for recognizing the user's emotional state and dynamically adjusting evacuation orders based on that state. This makes it possible to provide optimal evacuation orders that respond to the user's emotions so that they can evacuate with a sense of security.

[0634] "Real-time disaster information" refers to all data continuously acquired from the moment a disaster occurs, including earthquake location information, seismic intensity, and damage status.

[0635] "Optimally allocating support resources based on analysis results" refers to the process where an AI algorithm uses disaster information to perform calculations and efficiently allocate necessary supplies and human resources to high-risk areas.

[0636] "Public institutions and support teams" refers to various government organizations and private support groups that operate during disasters.

[0637] "Recognizing the user's emotional state and dynamically adjusting evacuation instructions based on it" refers to a function where the device analyzes the user's facial expressions and voice to identify their psychological state and appropriately changes the notification method and content to provide a sense of security.

[0638] "Visually displaying the optimal evacuation route using augmented reality technology" means utilizing technology that overlays digital information onto the real world via smart devices to clearly show users safe evacuation routes.

[0639] This system is designed to provide real-time situational awareness and appropriate evacuation instructions during disasters. The server continuously collects disaster information from various sensors and external databases, and uses AI algorithms to analyze the integrated data. Specifically, it identifies earthquake location and intensity information, as well as risk areas affected, and efficiently allocates support resources. This enables the rapid and appropriate provision of information to government agencies and support organizations.

[0640] The device is equipped with an emotion analysis engine to understand the user's emotions, recognizing them in real time from facial expressions and voice. This data is sent to a server, and evacuation instructions that take the user's psychological burden into consideration are generated. The device also has a function that displays evacuation routes using augmented reality technology, providing route guidance in an intuitive and reassuring way for the user.

[0641] The device also utilizes multilingual translation capabilities to provide appropriate information to a variety of users. As a result, it helps users from diverse backgrounds, including foreign residents and tourists, to evacuate safely.

[0642] As a concrete example, consider a scenario where a family with children uses the system during a disaster. The platform could present simplified visual guidance that is easy for children to understand, and could even emit encouraging voice messages to alleviate anxiety. A possible prompt for the generating AI model could be something like, "Please suggest a guide message for children to help them cope with anxiety during a disaster."

[0643] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0644] Step 1:

[0645] The server acquires disaster information in real time from various data sources. This process involves inputting information from seismometers, weather observation devices, and other sources. The server integrates this information and processes it into an analyzable format by storing it in a database.

[0646] Step 2:

[0647] The server analyzes the collected disaster information using an AI algorithm. Based on the input data, it identifies high-risk areas and calculates the allocation of support resources. The output generates a plan outlining areas requiring rescue and the resources needed.

[0648] Step 3:

[0649] The device uses an emotion analysis engine to recognize the user's emotions by taking the user's facial expressions and voice data as input. This data is sent to a server in real time, and it is determined whether the user's psychological state is one of temporary anxiety or panic.

[0650] Step 4:

[0651] When the server receives emotional state data, it dynamically adjusts evacuation instructions based on its assessment. Specifically, this includes changing the tone and frequency of voice messages, for example. Based on this, the optimal evacuation route is determined.

[0652] Step 5:

[0653] The terminal uses augmented reality technology to visually display evacuation routes to the user based on evacuation instructions received from the server. In this step, the input route data is used, and intuitive directional guidance appears in the user's field of view as output.

[0654] Step 6:

[0655] Users can begin evacuating with a sense of security based on the information presented on their devices. In some cases, the server can further improve the accuracy of the information throughout the evacuation process by allowing users to provide simple feedback to their devices.

[0656] 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.

[0657] 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.

[0658] 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.

[0659] [Fourth Embodiment]

[0660] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0661] 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.

[0662] 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).

[0663] 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.

[0664] 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.

[0665] 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).

[0666] 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.

[0667] 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.

[0668] 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.

[0669] 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.

[0670] 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.

[0671] 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.

[0672] 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".

[0673] This invention provides a system for collecting and analyzing disaster information in real time during disasters such as earthquakes. This system enables the optimal allocation of rescue resources and the provision of effective evacuation instructions. Specific embodiments of each element are described below.

[0674] Server Role

[0675] The server is responsible for collecting disaster information in real time from multiple external sources. This includes earthquake intensity and location information, as well as damage reports. The server integrates the collected data, analyzes it using AI algorithms, and identifies high-risk areas. Based on the analysis results, it calculates the optimal allocation of rescue resources and transmits this information to local governments and rescue teams.

[0676] Terminal role

[0677] The device is used to transmit location information and personal health status from the user to a server. It also provides the user with evacuation instructions and safety information received from the server, both visually and audibly. In particular, it utilizes augmented reality technology to display evacuation routes on a map, supporting users in moving safely. Furthermore, it includes a multilingual display function, allowing users to receive information in their preferred language.

[0678] User roles

[0679] Users need to check the evacuation instructions displayed on their devices and evacuate to a safe place as quickly as possible. For example, after an earthquake, users register their location information with the system via a smartphone app. Based on this, they use augmented reality (AR) technology to confirm the route to the nearest safe evacuation center and begin evacuating according to the instructions. Furthermore, registration to confirm their safety is performed via the device, and notifications are automatically sent to pre-registered emergency contacts.

[0680] By having servers, terminals, and users work together in this way, it is possible to provide effective support during disasters and minimize human casualties. This system enables safe and smooth disaster response through a rapid and accurate flow of information and user-friendly evacuation support.

[0681] The following describes the processing flow.

[0682] Step 1:

[0683] The server collects data in real time from external sources when an earthquake occurs. This includes earthquake intensity, epicenter, and damage reports. The data obtained from each source is integrated and stored in a database.

[0684] Step 2:

[0685] The server analyzes the collected data using AI algorithms to identify high-risk areas. During the analysis process, it refers to past data and patterns to predict areas where damage is expected to spread.

[0686] Step 3:

[0687] The server calculates the optimal allocation of rescue resources based on the analysis results. In this calculation, priorities are set according to the level of danger in each area to ensure the efficient use of resources.

[0688] Step 4:

[0689] The server transmits the optimal allocation plan and analysis results to local governments and rescue teams. This enables the rapid and organized deployment of rescue operations on the ground.

[0690] Step 5:

[0691] The terminal notifies the user of evacuation instructions received from the server. The notification includes a visual display of evacuation routes using augmented reality technology and audio guidance.

[0692] Step 6:

[0693] The device displays notifications in multiple languages ​​based on the user's selected language. This feature ensures that information is provided across language barriers.

[0694] Step 7:

[0695] The user follows the instructions on the device and follows the designated evacuation route. The user updates their location information via the device and checks the progress of the evacuation.

[0696] Step 8:

[0697] After completing the evacuation, users register their safety status on their device. This information is automatically sent to emergency contacts via the server.

[0698] (Example 1)

[0699] 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".

[0700] In recent years, the frequency of natural disasters has increased, and there is a growing need for rapid and effective responses during disasters. However, conventional disaster information management systems have faced challenges such as the time required for information collection and analysis, making it difficult to optimally allocate rescue resources. Furthermore, multilingual support and real-time user assistance have been insufficient, and support for foreigners and people with disabilities, in particular, has been inadequate.

[0701] 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.

[0702] In this invention, the server includes means for integrating and analyzing disaster-related data acquired using collection means, means for evaluating the degree of risk using a generated AI model and calculating the optimal allocation of rescue resources, and means for providing relevant information to local governments and rescue departments. This enables rapid and accurate information processing and rescue operations during a disaster.

[0703] "Collection methods" refer to methods and devices for obtaining disaster-related data from external sources.

[0704] A "generative AI model" refers to an artificial intelligence model that is trained based on machine learning algorithms and used for analyzing and predicting disaster data.

[0705] "Risk assessment" refers to the process of measuring and determining the risks of a particular area or situation based on collected data.

[0706] "Rescue resource allocation" refers to planning how to effectively allocate available personnel, equipment, and other resources to optimize rescue operations.

[0707] "Multilingual conversion function" refers to translation technology that displays or converts information into audio in multiple languages.

[0708] Augmented reality technology refers to a technology that overlays digital information onto the real environment, enabling users to obtain additional information in real-world situations.

[0709] "Registered emergency contacts" refer to contacts that the user has designated in advance and to whom they should be automatically contacted in the event of an emergency.

[0710] "Dynamic updates" refer to the process of immediately changing or adjusting plans and instructions based on new information that becomes available.

[0711] This invention is a system that functions as infrastructure during disasters and is realized by combining various devices and AI technology. Specific embodiments are shown below.

[0712] Server functions and operation

[0713] The server is equipped with means to collect disaster-related data from external sources. These external sources include APIs from earthquake observation agencies and meteorological agencies, and data from these sources is collected in real time. After collection, the server integrates the data and stores it in a database. Database systems such as PostgreSQL and MongoDB are used for this purpose. Based on this integrated data, data analysis is performed using a generative AI model to assess the level of risk. Machine learning frameworks such as TensorFlow and PyTorch are used, and the AI ​​model has been trained on past disaster data. Based on the analysis results, calculations are performed to optimally allocate rescue resources, and the necessary information is sent to local governments and rescue departments.

[0714] Device functions and operation

[0715] The terminal consists of smart devices (smartphones and tablets) used by the user. It has the function of acquiring location information and health status from the user and transmitting this information to the server. The terminal utilizes augmented reality technology to notify the user of instructions received from the server visually and audibly. Using ARKit (iOS) or ARCore (Android), it provides evacuation routes to the user and guides them to a safe route. Furthermore, it has a multilingual translation function, and information is provided in the language selected by the user. This is achieved by a translation function using the Google Translate API.

[0716] User behavior and roles

[0717] Users must receive disaster information and evacuation orders from the system via their devices and initiate appropriate evacuation actions. During evacuation, they must follow real-time AR guidance provided by their devices to move to a safe evacuation center. Once evacuation is complete, users perform an action on their devices to confirm their safety, and this is automatically notified to their emergency contacts. This enables rapid communication of safety to family and friends during large-scale disasters.

[0718] Specific example

[0719] For example, in the event of an earthquake, the server immediately collects information, assesses the level of risk, and determines the optimal allocation of rescue resources. On the device, users are provided with real-time guidance to the nearest evacuation shelter using augmented reality (AR). Furthermore, evacuation information is displayed in the language selected on the smartphone for users who do not speak Japanese.

[0720] Example of a prompt

[0721] By entering "Please tell me how to safely guide people to the nearest evacuation center after an earthquake," the AI ​​will begin analyzing the data and providing guidance.

[0722] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0723] Step 1:

[0724] The server collects disaster-related data from external sources. Input includes real-time data obtained from APIs of earthquake observation and meteorological agencies. This data is provided in JSON format and includes information on epicenters and seismic intensity. The server receives this data and performs initial formatting for storage in the database. Specifically, the server requests data via HTTP requests, parses the received response, and extracts the necessary information.

[0725] Step 2:

[0726] The server stores the integrated data in a database. The input is initially formatted disaster data, and the output is information stored in the database in a structured format. Specifically, the server connects to PostgreSQL or MongoDB and inserts the data into the appropriate fields. To do this, it maps values ​​to each field according to the data schema.

[0727] Step 3:

[0728] On the server, data analysis is performed using a generative AI model. The input is current disaster information from a database. Based on this information, the AI ​​model evaluates the level of risk and generates a risk score for each region as output. Specifically, the AI ​​model, which has been trained using TensorFlow or PyTorch, is executed, and the results are presented through visualization and report output.

[0729] Step 4:

[0730] The server calculates the optimal allocation of rescue resources based on the analysis results from the AI ​​model. The input is the risk score for each region, and the output is a specific rescue resource allocation plan. Specifically, the server uses linear programming techniques to calculate the optimal deployment of fire brigades and medical teams, and generates a message to send that information to the local government.

[0731] Step 5:

[0732] The device transmits location information and health status from the user to the server. Input is location information entered by the user or automatically acquired by the device, while output is user status information updated by transmission to the server. Specifically, the device acquires location using a GPS sensor, and health information is entered via the app.

[0733] Step 6:

[0734] The device receives evacuation instructions from the server and uses augmented reality technology to visually guide the user. The input is evacuation route information sent from the server, and the output is an AR display and audio guidance for the user. Specifically, the device uses ARKit or ARCore to overlay the evacuation route onto a map and provides audio guidance through its speaker.

[0735] Step 7:

[0736] The user uses their device to follow a designated evacuation route and begin the evacuation process. The input is AR navigation displayed on the device, and the output is the user's movement to a safe location. Specifically, the user moves along the evacuation route, and after safely arriving, performs a safety check operation within the app and automatically notifies emergency contacts.

[0737] (Application Example 1)

[0738] 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".

[0739] In the event of a disaster, obtaining information and efficiently allocating rescue resources are crucial for saving lives. However, conventional systems have limitations in real-time information integration, multilingual support, and the clear presentation of evacuation orders, hindering swift and accurate evacuation guidance and rescue operations. Solving this problem is essential.

[0740] 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.

[0741] In this invention, the server includes means for integrating and analyzing disaster information collected in real time, means for optimally allocating rescue resources based on the analysis results, and means for transmitting the relevant information to administrative agencies and rescue organizations. This enables rapid and comprehensive information provision and the efficient deployment of rescue operations. Furthermore, effective evacuation support for users is realized through means for acquiring user location information using a portable information terminal, means for displaying evacuation routes overlaid in space using augmented reality technology, and means for automating communication in emergencies. This makes it possible to promote rapid and safe evacuation during disasters through the provision of multilingual instructions and visual route guidance.

[0742] "Real-time" refers to a method where information and data are processed instantly, and results are provided immediately.

[0743] "Disaster information" refers to detailed data on natural and man-made disasters, including earthquake intensity, location, and extent of damage.

[0744] "Integration" is the process of gathering data from multiple sources and analyzing it in a consistent format.

[0745] "Analysis" is the process of examining data in detail, understanding its content, and finding meaning in it.

[0746] "Rescue resources" refer to the resources necessary to carry out life-saving and support activities during a disaster, including personnel, equipment, and supplies.

[0747] "Optimal allocation" refers to adopting the most effective allocation method in order to make the most of limited resources.

[0748] An "administrative agency" is an organization of the government or local authorities that provides public services and is responsible for tasks such as disaster response.

[0749] A "rescue organization" refers to a group or institution formed to carry out rescue operations during a disaster.

[0750] A "portable information terminal" refers to a portable device used for accessing geographical information and communication.

[0751] "Location information" refers to data that indicates the current location of an object or person, and is generally expressed using geographic coordinates.

[0752] Augmented reality technology is a technique that overlays computer-generated images and information onto real-world footage.

[0753] An "evacuation route" refers to the recommended path or method for moving to a safe place during a disaster.

[0754] "Overlaying information into space" means overlaying additional information onto real-world landscapes or images to visually represent them.

[0755] "Automating communication" refers to automatically executing a process that performs communication based on pre-set conditions.

[0756] "Multilingual" means supporting multiple different languages ​​and being able to provide information in each of those languages.

[0757] "Providing instructions" means providing information that advises someone to take a specific action.

[0758] The system implementing this invention mainly consists of three elements: a server, a terminal, and a user.

[0759] The server receives disaster information in real time and integrates this data. Specifically, it acquires and analyzes data such as seismic intensity and location of earthquakes, and damage reports from various sources. The analysis utilizes machine learning algorithms to identify high-risk areas. A database management system is used for information collection and storage during this process. Based on the analysis results, the optimal allocation of rescue resources is calculated, and relevant information is sent to government agencies and rescue organizations.

[0760] The terminals used are primarily portable information terminals and similar devices. These terminals have the functionality to acquire the user's current location information and transmit it to a server. Furthermore, augmented reality technology can be used to visually display evacuation routes. Libraries such as OpenCV and ARCore are used as the platform for this. Disaster information is translated into multiple languages, providing users with instructions through various senses. This functionality allows users to evacuate quickly to a safe location while receiving visual and auditory guidance.

[0761] Users need to check evacuation instructions provided through their devices and move quickly to a safe place. Based on the AR display generated on their devices, they begin moving to a nearby safe area. For example, when an earthquake occurs, users can check the shortest route to a nearby park or evacuation center on an AR map and follow the instructions. An example of a prompt message in this case would be: "An earthquake has occurred. The location of the evacuation center and a safe route to it are shown in AR. Please check the route to the evacuation center on your smartphone and move safely."

[0762] By implementing this invention, it becomes possible to obtain information immediately during disasters, efficiently allocate resources, and provide smooth evacuation support for users, thereby realizing a swift and safe response.

[0763] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0764] Step 1:

[0765] The server collects disaster information in real time from multiple external sources. These sources include seismometer networks, the Japan Meteorological Agency database, and damage reports from local governments. Input data includes earthquake intensity, location, and damage status. The server receives this data, stores it in a database, and then converts it into a unified format.

[0766] Step 2:

[0767] The server uses machine learning algorithms based on collected disaster information to identify high-risk areas. The input is the disaster information integrated in Step 1, which is then analyzed to assess the level of risk. As a result, a list of identified high-risk areas is output. Furthermore, the optimal allocation of rescue resources is also calculated.

[0768] Step 3:

[0769] The device sends location information from the user to the server. The input includes location data obtained from the device's GPS sensor. This allows the server to determine the user's current location. The output is the user's location data.

[0770] Step 4:

[0771] The server generates evacuation instructions translated into multiple languages ​​based on the user's current location and the results of a risk assessment. The input consists of the user's location and a list of risk levels, which are used to identify an appropriate evacuation route for the user. As a result, evacuation instructions are generated and sent to the terminal.

[0772] Step 5:

[0773] The terminal displays evacuation routes using augmented reality technology based on received evacuation instructions. The input is the evacuation instructions sent from the server, which are used to overlay route information onto the camera feed. As output, the evacuation route is displayed on the screen using AR, guiding the user.

[0774] Step 6:

[0775] The user follows the evacuation instructions displayed on their device and moves to a safe location. Input includes visual information from AR route display. Based on this, the user follows the correct route and evacuates to the designated shelter.

[0776] Step 7:

[0777] The device confirms that the user has safely arrived at the designated location and automatically sends a notification to pre-configured emergency contacts. The input is the user's arrival information, which then notifies the contacts of the user's safety status. As output, a message confirming safe arrival is sent.

[0778] 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.

[0779] This invention is a system that collects and analyzes real-time disaster information during earthquakes and other disasters, and provides dynamic evacuation instructions based on the user's emotional state. This system, by combining an emotion engine, understands the user's psychological state and provides information accordingly. Specific embodiments are described below.

[0780] Server Role

[0781] The server collects disaster information in real time from external sources, integrates and stores that data. This includes detailed earthquake location information, seismic intensity, and damage status. The collected data is analyzed using AI algorithms to identify high-risk areas. Based on the analysis, the server calculates the optimal allocation of rescue resources and transmits the information to local governments and rescue teams. The server also receives user emotion data from terminals and notifies experts so that psychological support can be provided as needed.

[0782] Terminal role

[0783] The device is equipped with an emotion engine that recognizes emotions from the user's facial expressions and voice. It has the ability to transmit the user's emotional state to a server in real time. It also receives evacuation instructions from the server and presents them to the user visually and audibly in multiple languages. Evacuation routes are displayed using augmented reality technology to support the user's safe evacuation. If the user's emotional state is determined to be anxiety or panic, the frequency and format of notifications are dynamically adjusted.

[0784] User roles

[0785] Users take appropriate evacuation actions based on information provided by their devices. An emotion engine analyzes the user's psychological state, and evacuation instructions are adjusted accordingly, allowing users to act in a way that aligns with their emotions. After completing the evacuation, users confirm their safety and register this information with the server via their devices. This information is automatically sent to emergency contacts.

[0786] As a concrete example, if a user shows signs of surprise or anxiety during an earthquake, the device sends emotional data to a server, which then provides the device with a more reassuring notification format. The user then moves to a safe location following evacuation instructions tailored to provide a sense of security. In this way, the entire system operates consistently, enhancing safety during disasters and reducing people's mental burden.

[0787] The following describes the processing flow.

[0788] Step 1:

[0789] The server collects data in real time from external sources when an earthquake occurs. This includes earthquake intensity, epicenter information, and damage reports. The collected data is integrated and stored in a database.

[0790] Step 2:

[0791] The server analyzes the integrated data using AI algorithms to identify high-risk areas. Based on the analysis results, it calculates the optimal allocation of rescue resources.

[0792] Step 3:

[0793] The server transmits analysis results and resource allocation plans to local governments and rescue teams, thereby supporting rapid rescue operations on the ground.

[0794] Step 4:

[0795] The terminal receives evacuation instructions from the server and notifies the user. The notification is displayed in multiple languages. Evacuation routes are displayed visually using augmented reality technology.

[0796] Step 5:

[0797] The device uses an emotion engine that recognizes emotions from the user's facial expressions and voice. This data represents the user's real-time emotional state and is sent to the server.

[0798] Step 6:

[0799] The server analyzes user emotion data and dynamically changes the frequency and format of notifications based on the results. For example, if a user is in a state of panic, it will provide messages and guidance that offer greater reassurance.

[0800] Step 7:

[0801] Users review evacuation instructions optimized to their emotions and begin moving along safe evacuation routes.

[0802] Step 8:

[0803] After completing their evacuation, users register their safety status with the server via their device. This automatically notifies designated emergency contacts that the user is safe.

[0804] (Example 2)

[0805] 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".

[0806] When a disaster strikes, it is crucial to quickly collect, analyze, and provide appropriate information to relevant organizations and victims. However, conventional systems are not capable of providing dynamic information that takes emotional states into account. As a result, victims may experience excessive anxiety and confusion in response to the information they receive, and traditional evacuation orders provide insufficient emotional support.

[0807] 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.

[0808] In this invention, the server includes means for integrating and analyzing disaster information collected in real time, means for optimally allocating rescue resources based on the analysis results, means for analyzing the user's emotional state from an external detection device and providing the relevant information, and means for dynamically adjusting the tone and format of evacuation orders based on emotional data. This enables the provision of real-time information that takes emotions into account, reducing the mental burden on disaster victims while allowing for efficient evacuation and rescue operations.

[0809] "Real-time" refers to a state in which processing and response can be carried out immediately at the moment an event occurs.

[0810] "Disaster information" refers to all data related to disasters, such as location information, seismic intensity, and damage status, concerning earthquakes and natural disasters.

[0811] "Analysis" refers to the process of analyzing collected data and transforming it into more meaningful information.

[0812] "Rescue resources" refers to the total amount of personnel, equipment, supplies, etc., necessary for rescue operations.

[0813] "Optimal allocation" means distributing limited resources in a way that allows them to be used most effectively.

[0814] A "detection device" refers to a device or sensor used to collect information about a user's emotional state or environmental conditions.

[0815] "Emotional data" refers to information that indicates the user's psychological state, and includes analysis results of facial expressions, voice, and other data.

[0816] "Dynamic adjustment" refers to changing the content and format in real time in response to changes in circumstances and conditions.

[0817] A "local government" refers to a local public entity that exists under the national or local government and is responsible for the administration of a specific region.

[0818] An "evacuation order" is an instruction or recommendation to take safe actions in the event of a disaster.

[0819] This invention is a system designed to streamline emergency disaster response and enable dynamic responses tailored to the user's psychological state. The system is primarily composed of a server, terminals, and users, with each element working in coordination to function.

[0820] The server collects disaster information in real time from multiple external sources during natural disasters such as earthquakes. Data obtained from sources such as the Japan Meteorological Agency and earthquake databases is analyzed using AI algorithms. This analysis identifies high-risk areas and calculates the optimal allocation of rescue resources. It also has a system that receives user emotional data via an emotion engine and notifies experts. This enables a rapid response if psychological support is needed.

[0821] The device is equipped with an emotion engine that analyzes the user's facial expressions and voice to recognize emotions. This data is transmitted to a server in real time. Furthermore, the device receives evacuation instructions from the server and uses AR technology to visually present evacuation routes. This allows users to receive instructions adapted to their environment. It also supports multiple languages, making it applicable to users worldwide.

[0822] By utilizing this system, users can enhance their sense of security and safety during disasters. Based on the information provided, they can take appropriate evacuation actions to ensure their own safety. Once evacuation is complete, they report their safety to the server via their device. This information is automatically notified to emergency contacts, enabling rapid communication.

[0823] As a concrete example, if an earthquake occurs and the user expresses anxiety, the device sends emotional data to the server. The server generates customized evacuation instructions designed to provide reassurance and provides them to the user through the device. The user can then follow the instructions and move to a safe location. An example of a prompt using the generative AI model might be, "Generate evacuation instructions that provide reassurance based on the user's emotional state."

[0824] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0825] Step 1:

[0826] The server collects disaster information. The server connects to external data sources such as meteorological agencies and earthquake databases to obtain real-time earthquake information. Inputs include location information, seismic intensity, and damage status at the time of the disaster. This data is integrated and stored in the system's internal database.

[0827] Step 2:

[0828] The server analyzes the data. Based on the collected disaster information, an AI algorithm operates to identify areas where damage is expected. The input here is integrated earthquake data, and the output is a list of high-risk areas. The server then calculates the optimal allocation of rescue resources as needed.

[0829] Step 3:

[0830] The server generates evacuation orders. Based on the analysis results, it creates appropriate evacuation orders for users. The input is a list of high-risk areas, and the output is individual evacuation order information. This includes safe evacuation routes, recommended evacuation locations, and information on what to bring.

[0831] Step 4:

[0832] The device collects emotional data. Sensors detect the user's facial expressions and voice, and an emotion engine analyzes their psychological state. The input is the user's visual and auditory data, and the output is the analyzed emotional state.

[0833] Step 5:

[0834] The device sends emotional data to the server. The analyzed user emotional data is sent to the server in real time. The input is the user's emotional state, which is processed by the server and becomes the basis for adjusting the tone and format of evacuation instructions.

[0835] Step 6:

[0836] The terminal displays evacuation instructions. The terminal provides the user with evacuation instructions received from the server, both visually and audibly. The input is evacuation instruction information from the server, and the output is a visual and audible presentation to the user. Augmented reality technology is used to overlay evacuation routes onto the real world, allowing the user to intuitively identify safe paths.

[0837] Step 7:

[0838] The user evacuates. Based on the information provided, the user takes appropriate evacuation actions. If the user's psychological state is anxious or panicked, the tone and frequency of notifications are adjusted, and evacuation support that provides a sense of security is provided as output.

[0839] Step 8:

[0840] The user reports their safety. After evacuation is complete, the user reports their safety to the server via their device. The input is the user's safety information, which is sent to emergency contacts via the server, enabling rapid notification as output.

[0841] (Application Example 2)

[0842] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0843] During disasters, there is a challenge in providing effective and reassuring evacuation instructions in real time. Conventional systems issue uniform instructions without considering the user's feelings, which can lead to panic and hinder proper evacuation. Furthermore, the lack of multilingual information provision and insufficient visual displays using augmented reality technology makes it difficult to provide smooth evacuation support.

[0844] 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.

[0845] In this invention, the server includes means for integrating and analyzing disaster information collected in real time, means for optimally allocating support resources based on the analysis results, and means for recognizing the user's emotional state and dynamically adjusting evacuation orders based on that state. This makes it possible to provide optimal evacuation orders that respond to the user's emotions so that they can evacuate with a sense of security.

[0846] "Real-time disaster information" refers to all data continuously acquired from the moment a disaster occurs, including earthquake location information, seismic intensity, and damage status.

[0847] "Optimally allocating support resources based on analysis results" refers to the process where an AI algorithm uses disaster information to perform calculations and efficiently allocate necessary supplies and human resources to high-risk areas.

[0848] "Public institutions and support teams" refers to various government organizations and private support groups that operate during disasters.

[0849] "Recognizing the user's emotional state and dynamically adjusting evacuation instructions based on it" refers to a function where the device analyzes the user's facial expressions and voice to identify their psychological state and appropriately changes the notification method and content to provide a sense of security.

[0850] "Visually displaying the optimal evacuation route using augmented reality technology" means utilizing technology that overlays digital information onto the real world via smart devices to clearly show users safe evacuation routes.

[0851] This system is designed to provide real-time situational awareness and appropriate evacuation instructions during disasters. The server continuously collects disaster information from various sensors and external databases, and uses AI algorithms to analyze the integrated data. Specifically, it identifies earthquake location and intensity information, as well as risk areas affected, and efficiently allocates support resources. This enables the rapid and appropriate provision of information to government agencies and support organizations.

[0852] The device is equipped with an emotion analysis engine to understand the user's emotions, recognizing them in real time from facial expressions and voice. This data is sent to a server, and evacuation instructions that take the user's psychological burden into consideration are generated. The device also has a function that displays evacuation routes using augmented reality technology, providing route guidance in an intuitive and reassuring way for the user.

[0853] The device also utilizes multilingual translation capabilities to provide appropriate information to a variety of users. As a result, it helps users from diverse backgrounds, including foreign residents and tourists, to evacuate safely.

[0854] As a concrete example, consider a scenario where a family with children uses the system during a disaster. The platform could present simplified visual guidance that is easy for children to understand, and could even emit encouraging voice messages to alleviate anxiety. A possible prompt for the generating AI model could be something like, "Please suggest a guide message for children to help them cope with anxiety during a disaster."

[0855] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0856] Step 1:

[0857] The server acquires disaster information in real time from various data sources. This process involves inputting information from seismometers, weather observation devices, and other sources. The server integrates this information and processes it into an analyzable format by storing it in a database.

[0858] Step 2:

[0859] The server analyzes the collected disaster information using an AI algorithm. Based on the input data, it identifies high-risk areas and calculates the allocation of support resources. The output generates a plan outlining areas requiring rescue and the resources needed.

[0860] Step 3:

[0861] The device uses an emotion analysis engine to recognize the user's emotions by taking the user's facial expressions and voice data as input. This data is sent to a server in real time, and it is determined whether the user's psychological state is one of temporary anxiety or panic.

[0862] Step 4:

[0863] When the server receives emotional state data, it dynamically adjusts evacuation instructions based on its assessment. Specifically, this includes changing the tone and frequency of voice messages, for example. Based on this, the optimal evacuation route is determined.

[0864] Step 5:

[0865] The terminal uses augmented reality technology to visually display evacuation routes to the user based on evacuation instructions received from the server. In this step, the input route data is used, and intuitive directional guidance appears in the user's field of view as output.

[0866] Step 6:

[0867] Users can begin evacuating with a sense of security based on the information presented on their devices. In some cases, the server can further improve the accuracy of the information throughout the evacuation process by allowing users to provide simple feedback to their devices.

[0868] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0869] 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.

[0870] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0871] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0872] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. In the upper and lower directions of the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. Also, the upper side of the concentric circles is where "pleasant" emotions are located, and the lower side is where "unpleasant" emotions are located. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0873] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0874] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0875] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0876] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0877] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0878] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0879] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0880] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0881] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0882] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0883] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0884] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0885] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0886] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0887] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0888] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0889] The following is further disclosed regarding the embodiments described above.

[0890] (Claim 1)

[0891] A means of integrating and analyzing disaster information collected in real time,

[0892] A means for optimally allocating rescue resources based on the analysis results,

[0893] A means of transmitting the relevant information to local governments and rescue teams,

[0894] A system that includes this.

[0895] (Claim 2)

[0896] The system according to claim 1, comprising means for translating collected disaster information into multiple languages ​​and providing it to users.

[0897] (Claim 3)

[0898] The system according to claim 1, comprising means for visually displaying evacuation routes to the user using augmented reality technology.

[0899] "Example 1"

[0900] (Claim 1)

[0901] A means for integrating and analyzing disaster-related data acquired using collection methods,

[0902] A means for evaluating the degree of risk using a generated AI model and calculating the optimal allocation of rescue resources,

[0903] Means of providing relevant information to local governments and rescue departments,

[0904] A means for acquiring and transmitting location information and health status from a user via a terminal,

[0905] A means of providing evacuation routes and guiding users using augmented reality technology,

[0906] A means of notifying users of information using a multilingual conversion function,

[0907] A system that includes this.

[0908] (Claim 2)

[0909] The system according to claim 1, comprising a function to verify the safety of the user via a terminal and to notify registered emergency contacts of the result via a communication means.

[0910] (Claim 3)

[0911] The system according to claim 1, which has the function of using a generative AI model to analyze disaster situations based on collected data and dynamically update response strategies.

[0912] "Application Example 1"

[0913] (Claim 1)

[0914] A means of integrating and analyzing disaster information collected in real time,

[0915] A means for optimally allocating rescue resources based on the analysis results,

[0916] Means for transmitting the relevant information to administrative agencies and rescue organizations,

[0917] A means of obtaining a user's location information using a portable information terminal,

[0918] A means of displaying evacuation routes overlaid in space using augmented reality technology,

[0919] Means to automate communications in emergencies,

[0920] A system that includes this.

[0921] (Claim 2)

[0922] The system according to claim 1, comprising means for translating collected disaster information into multiple languages ​​and providing it to the user along with optimized evacuation routes.

[0923] (Claim 3)

[0924] The system according to claim 1, comprising means for presenting evacuation instructions to the user using a variety of senses and emphasizing evacuation routes.

[0925] "Example 2 of combining an emotion engine"

[0926] (Claim 1)

[0927] A means of integrating and analyzing disaster information collected in real time,

[0928] A means for optimally allocating rescue resources based on the analysis results,

[0929] A means of analyzing the user's emotional state from an external detection device and providing the relevant information,

[0930] A means of dynamically adjusting the tone and format of evacuation orders based on emotional data,

[0931] A means of transmitting the relevant information to local governments and rescue teams,

[0932] A system that includes this.

[0933] (Claim 2)

[0934] The system according to claim 1, comprising means for translating collected disaster information and evacuation orders into multiple languages ​​and providing them to users.

[0935] (Claim 3)

[0936] The system according to claim 1, comprising means for visually displaying evacuation routes to the user using augmented reality technology and dynamically changing them according to the user's psychological state.

[0937] "Application example 2 when combining with an emotional engine"

[0938] (Claim 1)

[0939] A means of integrating and analyzing disaster information collected in real time,

[0940] A means for optimally allocating support resources based on the analysis results,

[0941] Means of transmitting the relevant information to public institutions and support teams,

[0942] A means of recognizing the user's emotional state and dynamically adjusting evacuation orders based on that state,

[0943] A means of visually displaying the optimal evacuation route using augmented reality technology,

[0944] A system that includes this.

[0945] (Claim 2)

[0946] The system according to claim 1, comprising means for translating collected disaster information into multiple languages ​​and providing it to the user in a format appropriate to their emotional state.

[0947] (Claim 3)

[0948] The system according to claim 1, further comprising means for adjusting the frequency and format of notifications based on the user's emotions. [Explanation of Symbols]

[0949] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of integrating and analyzing disaster information collected in real time, A means for optimally allocating rescue resources based on the analysis results, Means for transmitting the relevant information to administrative agencies and rescue organizations, A means of obtaining a user's location information using a portable information terminal, A means of displaying evacuation routes overlaid in space using augmented reality technology, Means to automate communications in emergencies, A system that includes this.

2. The system according to claim 1, comprising means for translating collected disaster information into multiple languages ​​and providing it to the user along with optimized evacuation routes.

3. The system according to claim 1, comprising means for presenting evacuation instructions to the user using a variety of senses and emphasizing evacuation routes.

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