system

Abstract

We provide the system. [Solution] Means for collecting disaster information in real time, A method for analyzing the extent of damage by comparing data from normal times with information from disasters, A means of collecting information on the status of evacuation shelters and proposing the most suitable evacuation location, Means of providing information to users across multiple platforms, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, 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 in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern times when the frequency of natural disasters is increasing, local governments and related institutions are required to collect and provide information quickly and accurately in the event of a disaster. However, with conventional methods, the labor required to handle information in real time is large, and necessary information is often not fully utilized. As a result, there are problems such as delays in appropriate evacuation instructions and the provision of supplies, and the safety of disaster victims cannot be ensured. In particular, it is necessary to diversify information provision for the elderly and information-disadvantaged people, but in reality, effective means are limited.

Means for Solving the Problems

[0005] This invention is a system equipped with means for collecting disaster information in real time and analyzing the damage situation by comparing normal data with disaster information. Furthermore, it collects information on the status of evacuation centers and proposes the optimal evacuation location to the user. It also includes means for calculating and guiding the optimal evacuation route based on the user's location information, thereby supporting rapid evacuation. In addition, it includes means for identifying the level of disaster risk using AI based on the collected data, providing more accurate disaster information. This enables rapid and effective disaster response and, in particular, enables the provision of information across multiple platforms to vulnerable groups.

[0006] "Means of collecting disaster information in real time" refers to technologies and methods for immediately gathering information from the scene of a disaster, and this includes data collection using the internet and sensors.

[0007] "Normal-time data" refers to environmental data and infrastructure status information collected during normal times when no disaster has occurred, and is intended to serve as a baseline.

[0008] "Means for analyzing disaster damage" refer to systems and methods for evaluating and judging the damage and situation on-site based on information collected during a disaster.

[0009] "Shelter status information" refers to data such as the number of people a shelter can accommodate, its capacity, and any supplies that are lacking.

[0010] A "means for proposing the optimal evacuation site" is a system that selects and displays the most suitable location for users to evacuate safely, based on existing information.

[0011] "User location information" refers to data indicating the user's current location, and is obtained using technologies such as GPS.

[0012] A "means for calculating and guiding to the optimal evacuation route" is a system that calculates the best route a user should take when evacuating and provides instructions accordingly.

[0013] "A method for identifying disaster risk levels using AI based on collected data" refers to a method that utilizes artificial intelligence technology to determine the predicted level of disaster risk from current data.

[0014] "Providing information across multiple platforms" refers to a method of providing information widely by utilizing multiple communication methods and media, including smartphone apps and public broadcasting. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This 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] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This 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 combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0016] 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.

[0017] First, the language used in the following description will be explained.

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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).

[0022] 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."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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".

[0036] This invention aims to build a system that highly automates information gathering, analysis, and provision during disasters. This system consists of server, terminal, and user elements, and each element works in cooperation to enable the understanding of the disaster situation and support for disaster victims.

[0037] The server first collects real-time data during normal times and during disasters. Normal data includes weather information, topographic images, and river water levels, while during disasters, it obtains information from social media posts and news articles. This information is stored in a database and used with AI to analyze the extent of the damage.

[0038] The terminal provides navigation and notifications to the user based on information provided by the server. Based on user input, it suggests the most suitable evacuation shelter and route to the user's current location. It also displays information on the capacity of evacuation shelters and shortages of supplies, prompting the user to take appropriate action.

[0039] Users receive evacuation information via their smartphones or tablets and begin using the system by entering their location information into their devices as needed. In particular, even if users are unable to access the internet, the server will widely disseminate information using disaster prevention radio and local broadcasting.

[0040] As a concrete example, when a river floods, the server detects posts from social media indicating rising water levels, and the AI ​​analyzes the level of danger by comparing it with normal water level data. As a result, it recommends evacuation locations to the user's device and guides them along safe routes. In this way, the present invention can provide rapid and practical information to disaster victims, supporting disaster response. Furthermore, considering situations where the impact of a disaster affects infrastructure and smartphones become unusable, the system is designed to provide information across multiple platforms, thus addressing all possible situations.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The server performs routine data collection. This involves obtaining weather information using a weather data API and downloading images of terrain and infrastructure from the internet. This data is stored in a static database.

[0044] Step 2:

[0045] The server collects real-time data when a disaster occurs. It gathers disaster-related posts from social media and news feeds on the internet and updates a dynamic database based on that information. APIs and scraping techniques are used to collect the information.

[0046] Step 3:

[0047] The server analyzes collected real-time data and normal-time data using an AI engine. By evaluating the extent of the damage and risk levels, and visualizing the data, it helps formulate emergency response strategies.

[0048] Step 4:

[0049] The device receives disaster information and analysis results transmitted from the server and displays them to the user. This is done through an app or web interface, and information on evacuation shelters and recommended routes is presented based on the user's location.

[0050] Step 5:

[0051] Users enter their location information into their device to receive evacuation shelter guidance and route suggestions from the system. They can also check information about supplies needed by evacuation shelters and arrange for their delivery as needed.

[0052] Step 6:

[0053] The terminal will deliver important notifications via disaster prevention radio and local broadcasting equipment in case smartphone use is difficult. This will ensure that information reaches users even when internet connectivity is lost.

[0054] Step 7:

[0055] The server periodically updates the entire database and compares and optimizes past disaster data with new data to improve the system's learning model. This prepares the system for future disasters and improves the accuracy of information provided.

[0056] (Example 1)

[0057] 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."

[0058] In recent years, damage from natural disasters has increased, creating a demand for rapid and accurate information provision. However, conventional information provision systems lack real-time capabilities and accuracy, resulting in situations where users are unable to take appropriate action. Therefore, the challenge lies in achieving rapid and accurate situation assessment and the provision of appropriate information to users during disasters.

[0059] 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.

[0060] In this invention, the server includes means for collecting disaster data in real time, means for analyzing the disaster situation by comparing normal information with disaster data, and means for collecting status information of evacuation sites and suggesting appropriate evacuation sites. This enables users to take accurate and rapid evacuation actions.

[0061] "Methods for collecting disaster data in real time" refers to technical methods that continuously collect data on the current disaster situation in real time.

[0062] "Methods for analyzing disaster situations by comparing normal-time information with disaster-time data" refers to methods for analyzing the situation and impact of a disaster by comparing standard data from normal times with data from the time of a disaster.

[0063] "Methods for gathering information on the status of evacuation sites and proposing appropriate evacuation sites" refers to the process of collecting information on evacuation sites and their occupancy rates, and presenting the most suitable evacuation site to users.

[0064] "Means of providing information to users through diverse media" refers to methods of transmitting disaster information to users in various formats, including smartphone apps and radio broadcasts.

[0065] "Methods for applying natural language processing techniques using artificial intelligence models" refer to methods of analyzing information by utilizing AI technology to understand and process natural language.

[0066] "Methods for identifying disaster risk levels using machine learning technology" refers to methods that utilize machine learning algorithms to evaluate and determine the degree of disaster risk from collected data.

[0067] This invention aims to build a system that can quickly collect and analyze information during a disaster and provide users with accurate evacuation instructions and support information. The system operates through the coordinated efforts of a server, terminals, and users.

[0068] The server plays a central role in data management during disasters. It collects disaster data in real time from weather sensors, social media, and news sources. This process involves retrieving data using APIs and storing it in a database. The collected data is analyzed using AI models, particularly those employing natural language processing and deep learning. This AI model performs its analysis based on the prompt: "Extract disaster-related information from social media posts in the specified area and assess the level of risk based on that information."

[0069] The terminal operates based on analysis results transmitted from the server. The terminal resides within the user's smartphone or tablet and provides real-time evacuation instructions, optimal shelters, and evacuation routes. Furthermore, the terminal also displays information on shelter occupancy rates and shortages of supplies. This allows users to quickly decide on and execute safe actions.

[0070] Users receive information via their devices to help them make appropriate decisions during a disaster. Even when communication environments are limited, the server provides information through local broadcasts and disaster prevention radio systems, ensuring users can obtain the necessary information.

[0071] This system is designed to reduce risks and support victims during disasters, enabling a rapid and effective disaster response by supporting optimal evacuation actions.

[0072] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0073] Step 1:

[0074] The server will begin collecting disaster information in real time. Input data will come from sources such as weather sensors, social media, and news APIs. This data will then be stored in a database. Specifically, the server will retrieve the latest information from each platform via API calls and filter it to select the most important data.

[0075] Step 2:

[0076] The server inputs the collected data into an AI model for analysis. The input data includes posts and articles from the disaster, as well as baseline data from normal times. A generative AI model is used to apply natural language processing techniques and assess the level of risk. Specifically, the model uses prompt sentences to understand the context and outputs an analysis of the disaster situation. This process utilizes deep learning algorithms to classify the content of the text data by topic.

[0077] Step 3:

[0078] The server sends the analysis results to the terminal. Specifically, this includes information on evacuation advisories, the capacity of evacuation shelters, and safe evacuation routes. Based on these results, the terminal prepares a notification message for the user. As output, the system processes the information to reflect real-time evacuation information, including location information, in the user interface.

[0079] Step 4:

[0080] The device receives data from the server and presents that information to the user. Input data includes analyzed information from the server. The device integrates with a map application to visually display evacuation routes. It also prompts users to take appropriate action through voice and push notifications.

[0081] Step 5:

[0082] Users begin evacuation actions based on information from their devices. Specific actions include moving to the nearest evacuation center and selecting a suggested safe route. Further support information is provided through location feedback from the user.

[0083] (Application Example 1)

[0084] 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."

[0085] In recent years, the need for rapid and accurate information provision during disasters has become increasingly important. However, conventional disaster information systems have limitations in real-time data collection and information provision across diverse platforms, resulting in delays in providing appropriate support in response to the disaster situation. As a result, victims may not be able to take appropriate evacuation actions. Therefore, there is a need for a more efficient and reliable disaster information system.

[0086] 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.

[0087] In this invention, the server includes means for collecting disaster information in real time from various sources during a disaster, means for automatically analyzing the degree of risk by comparing normal data with disaster information and assessing the risk of damage, and means for aggregating the capacity and inventory information of evacuation centers and selecting the optimal evacuation site. This makes it possible to provide disaster victims with quick and appropriate information and encourage prompt evacuation.

[0088] "Means for collecting disaster information in real time from diverse sources during a disaster" refers to technologies for instantly acquiring data from multiple sources such as social media, news feeds, and various sensors when a disaster occurs.

[0089] "A method for automatically analyzing the degree of risk by comparing normal data with disaster information and assessing the risk of disaster" refers to a process that compares normal weather data and geographic information with disaster information and uses artificial intelligence to assess the degree of risk.

[0090] "A means of aggregating information on the capacity and inventory of evacuation shelters and selecting the most suitable evacuation location" refers to a function that summarizes the capacity and supply status of each evacuation shelter and determines the most appropriate evacuation shelter for the user.

[0091] "A multi-platform solution that utilizes the diverse output functions of users' mobile information processing devices to provide disaster information to users" refers to a system that provides disaster information tailored to the situation through different devices such as smartphones and tablets.

[0092] "A means of calculating a safe evacuation route based on the user's location data on the ground and guiding the user along that route" refers to a technology that uses GPS or other means to determine the user's current location, calculate the safest and most efficient evacuation route, and direct the user along it.

[0093] "A means of dynamically identifying disaster risk levels using artificial intelligence based on accumulated data" refers to a mechanism that uses artificial intelligence to analyze collected data and evaluate the impact of an ongoing disaster under constantly changing circumstances.

[0094] This invention is a system for collecting, analyzing, and providing information during disasters. By coordinating the server, terminal, and user elements, it enables the rapid and appropriate provision of information.

[0095] The server collects data in real time from various sources such as social media, news sites, and sensor devices when a disaster occurs. High-performance servers are used as hardware, and artificial intelligence platforms (e.g., Google Cloud AI, Amazon SageMaker) are used for data analysis. The server assesses the risk of disaster based on both normal data and disaster information. This allows it to analyze the severity of the situation on the ground and provide customized evacuation instructions to each user.

[0096] The device utilizes smartphones and tablets to calculate the optimal evacuation route based on the user's location. Real-time evacuation information is sent to the device via push notifications, and thanks to multi-platform compatibility, the information can be received on a variety of devices. For example, when a user is heading to a designated evacuation center, the device guides them along a safe route that takes into account dangerous areas to avoid and road closures.

[0097] Users can take swift action based on the information provided through their devices. Furthermore, users can input their own safety status and provide feedback to the server, which further improves the accuracy of the information.

[0098] As a concrete example, if heavy rainfall in a certain area is expected to cause river flooding, the server analyzes water level data and historical disaster data to assess the risk of flooding. Based on the results, a safe evacuation route is provided to the user's terminal. This process is supported by using a prompt message that says, "Analyze evacuation information for the target area based on real-time collected data and propose a recommended route."

[0099] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0100] Step 1:

[0101] The server collects disaster information in real time from social media, news feeds, and various sensors. It uses data obtained from these sources as input. The collected data is organized and stored in a database to prepare for subsequent analysis.

[0102] Step 2:

[0103] The server compares normal weather and geographical data with collected disaster data. The input consists of normal data and collected disaster data. Using AI technology, it analyzes the data differences and calculates the level of risk. The output is the disaster risk assessment result, which is then passed on to the next processing step.

[0104] Step 3:

[0105] The server executes a process to aggregate information on the capacity and inventory of evacuation shelters based on the disaster risk assessment results. Using the collected data and shelter information as input, it determines the optimal evacuation location. This information is output to the user's device as a push notification.

[0106] Step 4:

[0107] The device obtains the user's current location using location services such as GPS. The input is the user's location data. Based on this, it calculates a safe evacuation route and displays it on a map. The output is the recommended evacuation route guided to the user.

[0108] Step 5:

[0109] The user initiates evacuation procedures following route instructions provided through the terminal. Safety status feedback from the user is sent to the server. This feedback is used by the system to continuously update information and provide more accurate information to other users. Input is user feedback information, and output is system-wide information updates.

[0110] 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.

[0111] This invention aims to construct an advanced disaster response system that integrates an emotion engine to recognize user emotions in addition to information gathering, analysis, and provision during disasters. This system consists of a server, terminals, and users, each operating with its own specific role.

[0112] The server first collects information necessary during normal times and in the event of a disaster. It aggregates historical data and real-time data from sensors, and extracts situational information from internet and social media posts. This data is analyzed by an AI engine to assess the extent of the disaster. In addition, an emotion engine analyzes the user's emotional state from voice and behavioral data to determine the degree of stress and anxiety.

[0113] The terminal suggests the optimal evacuation shelter and route to the user based on analyzed data sent from the server. An emotion engine assesses the user's mental state and provides individualized support according to the level of emergency stress as needed. Furthermore, notifications and guidance to the user are provided across multiple platforms, including smartphones, tablets, and disaster prevention radio systems, ensuring that information is never interrupted.

[0114] Users can input their location information using their device and initiate evacuation actions without delay based on the information received from the system. For example, if the emotional engine detects a high-stress state, specific actions such as breathing exercises and mental support will be provided to the user. As a result, users can understand the information more effectively and evacuate safely.

[0115] As a concrete example, suppose a major earthquake occurs and the server recognizes the risk of a tsunami and immediately instructs the user to evacuate. At that time, the emotion engine detects the user's state of tension, and the terminal provides an alert along with guidance to help the user regain composure. In this way, the present invention also plays a role in making it easier for users to take appropriate actions during a disaster and reducing the psychological burden caused by the disaster. The system as a whole is designed to function as an integrated whole, enabling safe and rapid disaster response.

[0116] The following describes the processing flow.

[0117] Step 1:

[0118] The server collects various types of data under normal circumstances and stores them in a database. This includes data from weather sensors, topographic images, and information on evacuation shelters.

[0119] Step 2:

[0120] The server monitors social media posts and news feeds in real time during a disaster, extracting posts containing important keywords. It then analyzes location information and damage assessments from these posts.

[0121] Step 3:

[0122] The server uses an AI model to compare real-time data collected during disasters with data from normal times to assess the extent of the damage. This assessment includes detecting rising water levels and information on road closures.

[0123] Step 4:

[0124] The terminal receives status information about evacuation shelters from the server and suggests the most suitable evacuation location and route to the user. Information on the shelter's occupancy rate and the shortage of supplies is also presented.

[0125] Step 5:

[0126] Users enter their current location into the device and begin taking action based on the provided evacuation information. The device can also provide personalized evacuation route guidance and voice prompts.

[0127] Step 6:

[0128] The server uses an emotion engine to analyze user behavior logs and voice data. This allows it to assess the level of stress and anxiety the user is experiencing and send instructions to the device if necessary.

[0129] Step 7:

[0130] Based on the analysis results of its emotion engine, the device provides users with mental support and stress reduction advice. This includes simple breathing exercises and relaxing music playback.

[0131] Step 8:

[0132] The server regularly updates all data and learns to improve the accuracy of the AI ​​model and emotion engine in preparation for the next disaster. This ensures that the overall system performance is always optimized.

[0133] (Example 2)

[0134] 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".

[0135] In modern times, there is a need for rapid and appropriate responses during disasters, but many conventional technologies are insufficient in terms of information gathering and analysis, and in particular, they do not provide support that takes into account the emotional state of users. As a result, there are delays in appropriate evacuation actions and increased anxiety among disaster victims, leaving challenges in terms of safety and psychological care.

[0136] 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.

[0137] In this invention, the server includes means for collecting disaster information in real time, means for analyzing the disaster situation by comparing data from normal times and disaster times, means for acquiring status information of evacuation facilities and proposing the optimal evacuation location, means for providing information to users through different information processing terminals, and means for analyzing voice and behavioral data to determine the user's emotional state and evaluate the degree of stress and anxiety. This enables evacuation routes and emotional support based on an accurate understanding of the disaster situation.

[0138] "Means of collecting disaster information in real time" refers to technologies for immediately gathering the latest data when a disaster occurs, utilizing sensors and online information sources.

[0139] "Time-series data" refers to a collection of data that changes over time, and is information that is analyzed in chronological order.

[0140] "Means of analyzing the damage situation" refers to technologies used to evaluate the impact and scale of a disaster based on collected data, and to identify anomalies compared to normal conditions.

[0141] "Evacuation facilities" refer to buildings or locations designated to ensure safety during disasters.

[0142] "Methods of distributing information through different information processing terminals" refers to technologies that use multiple devices, such as smartphones, tablets, and disaster prevention radio systems, to distribute information.

[0143] "Analyzing voice and behavioral data" refers to the process of analyzing a person's voice tone and body movements to estimate their emotions and state of mind.

[0144] "Means for determining a user's emotional state" refers to technologies for evaluating a user's psychological condition, such as measuring the degree of stress and anxiety.

[0145] "Assessing the level of stress and anxiety" refers to the process of measuring and analyzing how much tension or worry a user is experiencing.

[0146] To implement this invention, three main components are required: a server, a terminal, and a user. The server is responsible for collecting necessary information in real time during a disaster and analyzing the collected data. This information collection utilizes hardware such as seismometers and weather sensors, as well as databases and SNS analysis software accessed via the internet. The AI ​​engine uses a generally available machine learning library, and the data analysis is performed using an application on the server with big data processing capabilities.

[0147] The server analyzes the user's emotions using an emotion engine based on collected voice and behavioral data. This analysis utilizes voice recognition software and behavioral pattern analysis software. This allows the server to assess the user's stress and anxiety levels.

[0148] The terminal suggests the most suitable evacuation shelter and route to the user based on analyzed data sent from the server. The terminal uses a mobile device such as a smartphone or tablet and receives information via an internet connection. It also utilizes a map service API for navigation. In addition, the terminal assesses the user's mental state and provides audio guidance on relaxation techniques if a high-stress state is detected.

[0149] Users can voluntarily input their location information via their device and quickly initiate evacuation actions based on information from the server. The information provided by the server includes the location of evacuation shelters and the safety of evacuation routes, and users make decisions about their actions accordingly. Users can also receive instructions on breathing techniques and simple exercises to reduce stress and stabilize their mental state.

[0150] As a concrete example, in the event of a major earthquake, the server can immediately analyze disaster information and instruct the user on the optimal evacuation route. At the same time, an emotion engine can sense the user's level of anxiety and provide support messages through the device, such as, "Take a deep breath and check this link for guidance on how to stay calm."

[0151] An example of a prompt for the generating AI model would include instructions such as, "A major earthquake has occurred, and a tsunami warning has been issued. Analyze the user's emotional state and generate action guidelines for safe evacuation."

[0152] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0153] Step 1:

[0154] The server collects disaster information in real time using sensors and internet data sources. Inputs include data from seismometers and weather sensors, as well as social media posts. This data is aggregated and stored in a database. The output is a collection of raw data ready for analysis.

[0155] Step 2:

[0156] The server sends the collected time-series data to the AI ​​engine, which then analyzes the damage situation by comparing data from normal and disaster periods. Specifically, the AI ​​engine analyzes the location, scale, and extent of impact of the disaster and generates the results. The input is raw time-series data, and the output is an analyzed disaster situation report.

[0157] Step 3:

[0158] The server sends voice and behavioral data to the emotion engine to determine the user's emotional state. The emotion engine uses speech recognition and pattern analysis to assess the user's stress and anxiety levels. The input is voice and behavioral data, and the output is a user emotion assessment report.

[0159] Step 4:

[0160] The terminal receives analyzed data from the server and suggests the most suitable evacuation shelters and routes to the user. The terminal calculates location information and updates the map in real time via the internet connection, presenting it visually to the user. The input is the user's location and analyzed data from the server, and the output is an easy-to-read map including guidance information.

[0161] Step 5:

[0162] The device provides a relaxation guide if high stress is detected based on the user's emotional assessment report. Specifically, it introduces breathing techniques and mental stabilization skills to the user through audio and video. The input is the emotional assessment report, and the output is viewable guide content.

[0163] Step 6:

[0164] Based on the evacuation information and support received from the device, the user initiates safe evacuation actions. The user follows the designated evacuation route and evacuates while carrying necessary supplies. The input is information from the device, and the output is the user's specific actions and movements.

[0165] (Application Example 2)

[0166] 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".

[0167] In the event of a disaster, real-time information gathering and analysis are necessary to provide swift and appropriate support to victims. Even more important is reducing the psychological burden on victims and supporting their safe evacuation. However, current systems do not take into account the emotional state of users, and currently lack consideration for users experiencing stress and anxiety.

[0168] 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.

[0169] In this invention, the server includes means for collecting disaster information in real time, means for analyzing the damage situation by comparing normal data with disaster information, means for collecting information on the status of evacuation centers and suggesting the optimal evacuation location, means for analyzing the user's emotional state and providing support according to that state, and means for providing the user with psychological support information in an emergency. This enables rapid information dissemination, reduces the psychological stress of disaster victims, and allows for safer and more secure evacuation.

[0170] "Methods for collecting disaster information in real time" refer to technologies for collecting necessary information immediately during a disaster and analyzing it accurately and quickly.

[0171] "Methods for analyzing the extent of damage by comparing normal data with disaster information" refers to technologies that evaluate the degree and impact of damage by comparing data from normal times with data from the current disaster and analyzing the changes.

[0172] "A means of collecting information on the status of evacuation shelters and proposing the most suitable evacuation location" refers to a technology that acquires information such as the capacity, safety, and location of each evacuation shelter and then presents the most suitable evacuation shelter to the user.

[0173] "A means of providing information to users across multiple platforms" refers to technology that enables users to receive necessary information regardless of their circumstances, using various devices and communication methods.

[0174] "Means of analyzing a user's emotional state and providing support tailored to that state" refers to technology that understands a user's emotions based on voice and behavioral data and provides psychological or physical support that is appropriate to that state.

[0175] "Means of providing users with psychological support information in emergencies" refers to technologies that provide users with appropriate psychological support information in order to alleviate anxiety and stress during disasters.

[0176] The system for realizing this invention consists of a server, terminals, and users, each playing a specific role. The server collects critical information in real time during a disaster and analyzes the disaster situation by comparing it with data from normal times. This involves high-performance communication methods, computers for data analysis, and artificial intelligence software. Specifically, it uses an AI engine and database running on a cloud platform (e.g., Amazon Web Services or Microsoft Azure).

[0177] Furthermore, the server can acquire status information about evacuation shelters and suggest the most suitable shelter. This information is collected from local government agencies and NGOs and processed by algorithms on the server. The suggested shelters are then notified to the user via smartphone or tablet.

[0178] The device displays analysis results sent from the server and implements an emotion engine to evaluate the user's emotional state. The user's emotional state is analyzed in conjunction with speech recognition software, and psychological support is provided according to the user's stress level. This utilizes the smartphone's built-in microphone and camera.

[0179] Users can make decisions based on real-time updated evacuation information by operating the device. The information displayed on the device also includes psychological support information generated by an emotion engine, which offers advice to help users stay calm in critical situations.

[0180] As a concrete example, in the event of a major earthquake, the server immediately analyzes the information and sends immediate evacuation instructions to users in areas at risk of tsunamis. Simultaneously, the emotion engine detects the user's high stress level and provides guidance on relaxation techniques and reassuring messages. Using a generative AI model, an example of a prompt might be: "The user is in a very anxious state. Please generate suggestions for calming him, along with evacuation locations."

[0181] In this way, this invention goes beyond mere information transmission and enables advanced disaster response that also takes into account the user's psychological well-being.

[0182] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0183] Step 1:

[0184] The server collects disaster information in real time from the internet and various sensors. This information includes satellite data and sensor data from seismometers. This data is integrated by a data collection module and stored in a database. The input is raw disaster-related data, and the output is an integrated data stream.

[0185] Step 2:

[0186] The server analyzes the extent of the damage by comparing normal data with disaster-related information. This analysis utilizes an AI engine and implements machine learning algorithms. The input is an integrated data stream, and the output is data evaluating the damage situation. Specifically, change detection technology is used to analyze the impact of the disaster.

[0187] Step 3:

[0188] The server collects local evacuation shelter information and suggests the most suitable shelters based on the analyzed disaster situation. An optimization algorithm is used that considers the capacity and safety of the shelters. The input is evacuation shelter status data, and the output is a list of recommended shelters.

[0189] Step 4:

[0190] The terminal receives analysis results sent from the server and provides information to the user. The terminal is equipped with a multi-platform information provision system, and displays information via smartphones, tablets, etc. The input is the analysis results from the server, and the output is a visual evacuation instruction shown to the user.

[0191] Step 5:

[0192] The device collects user voice and facial expression data and analyzes the user's emotional state using an emotion engine. This analysis utilizes the smartphone's built-in microphone and camera, and applies speech recognition technology. The input is voice and visual data, and the output is the evaluation result of the emotional state.

[0193] Step 6:

[0194] The device provides appropriate psychological support information based on the user's emotional state. Specifically, it provides guidance on breathing exercises and plays relaxation music. The input is the result of an assessment of the emotional state, and the output is psychological support content for the user.

[0195] Step 7:

[0196] The user makes decisions based on evacuation information and psychological support presented by the device. User input includes their location and decision-making, while the expected output is safe evacuation. At this stage, interaction through the user interface is crucial.

[0197] This series of processes allows users to take evacuation actions quickly and accurately, while also providing a sense of psychological security. This invention is realized through advanced data analysis and prompt message design using a generative AI model.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] [Second Embodiment]

[0202] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0203] 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.

[0204] 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).

[0205] 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.

[0206] 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.

[0207] 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).

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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".

[0214] This invention aims to build a system that highly automates information gathering, analysis, and provision during disasters. This system consists of server, terminal, and user elements, and each element works in cooperation to enable the understanding of the disaster situation and support for disaster victims.

[0215] The server first collects real-time data during normal times and during disasters. Normal data includes weather information, topographic images, and river water levels, while during disasters, it obtains information from social media posts and news articles. This information is stored in a database and used with AI to analyze the extent of the damage.

[0216] The terminal provides navigation and notifications to the user based on information provided by the server. Based on user input, it suggests the most suitable evacuation shelter and route to the user's current location. It also displays information on the capacity of evacuation shelters and shortages of supplies, prompting the user to take appropriate action.

[0217] Users receive evacuation information via their smartphones or tablets and begin using the system by entering their location information into their devices as needed. In particular, even if users are unable to access the internet, the server will widely disseminate information using disaster prevention radio and local broadcasting.

[0218] As a concrete example, when a river floods, the server detects posts from social media indicating rising water levels, and the AI ​​analyzes the level of danger by comparing it with normal water level data. As a result, it recommends evacuation locations to the user's device and guides them along safe routes. In this way, the present invention can provide rapid and practical information to disaster victims, supporting disaster response. Furthermore, considering situations where the impact of a disaster affects infrastructure and smartphones become unusable, the system is designed to provide information across multiple platforms, thus addressing all possible situations.

[0219] The following describes the processing flow.

[0220] Step 1:

[0221] The server performs routine data collection. This involves obtaining weather information using a weather data API and downloading images of terrain and infrastructure from the internet. This data is stored in a static database.

[0222] Step 2:

[0223] The server collects real-time data when a disaster occurs. It gathers disaster-related posts from social media and news feeds on the internet and updates a dynamic database based on that information. APIs and scraping techniques are used to collect the information.

[0224] Step 3:

[0225] The server analyzes collected real-time data and normal-time data using an AI engine. By evaluating the extent of the damage and risk levels, and visualizing the data, it helps formulate emergency response strategies.

[0226] Step 4:

[0227] The device receives disaster information and analysis results transmitted from the server and displays them to the user. This is done through an app or web interface, and information on evacuation shelters and recommended routes is presented based on the user's location.

[0228] Step 5:

[0229] Users enter their location information into their device to receive evacuation shelter guidance and route suggestions from the system. They can also check information about supplies needed by evacuation shelters and arrange for their delivery as needed.

[0230] Step 6:

[0231] The terminal will deliver important notifications via disaster prevention radio and local broadcasting equipment in case smartphone use is difficult. This will ensure that information reaches users even when internet connectivity is lost.

[0232] Step 7:

[0233] The server periodically updates the entire database and compares and optimizes past disaster data with new data to improve the system's learning model. This prepares the system for future disasters and improves the accuracy of information provided.

[0234] (Example 1)

[0235] 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."

[0236] In recent years, damage from natural disasters has increased, creating a demand for rapid and accurate information provision. However, conventional information provision systems lack real-time capabilities and accuracy, resulting in situations where users are unable to take appropriate action. Therefore, the challenge lies in achieving rapid and accurate situation assessment and the provision of appropriate information to users during disasters.

[0237] 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.

[0238] In this invention, the server includes means for collecting disaster data in real time, means for analyzing the disaster situation by comparing normal information with disaster data, and means for collecting status information of evacuation sites and suggesting appropriate evacuation sites. This enables users to take accurate and rapid evacuation actions.

[0239] "Methods for collecting disaster data in real time" refers to technical methods that continuously collect data on the current disaster situation in real time.

[0240] "Methods for analyzing disaster situations by comparing normal-time information with disaster-time data" refers to methods for analyzing the situation and impact of a disaster by comparing standard data from normal times with data from the time of a disaster.

[0241] "Methods for gathering information on the status of evacuation sites and proposing appropriate evacuation sites" refers to the process of collecting information on evacuation sites and their occupancy rates, and presenting the most suitable evacuation site to users.

[0242] "Means of providing information to users through diverse media" refers to methods of transmitting disaster information to users in various formats, including smartphone apps and radio broadcasts.

[0243] "Methods for applying natural language processing techniques using artificial intelligence models" refer to methods of analyzing information by utilizing AI technology to understand and process natural language.

[0244] "Methods for identifying disaster risk levels using machine learning technology" refers to methods that utilize machine learning algorithms to evaluate and determine the degree of disaster risk from collected data.

[0245] This invention aims to build a system that can quickly collect and analyze information during a disaster and provide users with accurate evacuation instructions and support information. The system operates through the coordinated efforts of a server, terminals, and users.

[0246] The server plays a central role in data management during disasters. It collects disaster data in real time from weather sensors, social media, and news sources. This process involves retrieving data using APIs and storing it in a database. The collected data is analyzed using AI models, particularly those employing natural language processing and deep learning. This AI model performs its analysis based on the prompt: "Extract disaster-related information from social media posts in the specified area and assess the level of risk based on that information."

[0247] The terminal operates based on analysis results transmitted from the server. The terminal resides within the user's smartphone or tablet and provides real-time evacuation instructions, optimal shelters, and evacuation routes. Furthermore, the terminal also displays information on shelter occupancy rates and shortages of supplies. This allows users to quickly decide on and execute safe actions.

[0248] Users receive information via their devices to help them make appropriate decisions during a disaster. Even when communication environments are limited, the server provides information through local broadcasts and disaster prevention radio systems, ensuring users can obtain the necessary information.

[0249] This system is designed to reduce risks and support victims during disasters, enabling a rapid and effective disaster response by supporting optimal evacuation actions.

[0250] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0251] Step 1:

[0252] The server will begin collecting disaster information in real time. Input data will come from sources such as weather sensors, social media, and news APIs. This data will then be stored in a database. Specifically, the server will retrieve the latest information from each platform via API calls and filter it to select the most important data.

[0253] Step 2:

[0254] The server inputs the collected data into an AI model for analysis. The input data includes posts and articles from the disaster, as well as baseline data from normal times. A generative AI model is used to apply natural language processing techniques and assess the level of risk. Specifically, the model uses prompt sentences to understand the context and outputs an analysis of the disaster situation. This process utilizes deep learning algorithms to classify the content of the text data by topic.

[0255] Step 3:

[0256] The server sends the analysis results to the terminal. Specifically, this includes information on evacuation advisories, the capacity of evacuation shelters, and safe evacuation routes. Based on these results, the terminal prepares a notification message for the user. As output, the system processes the information to reflect real-time evacuation information, including location information, in the user interface.

[0257] Step 4:

[0258] The device receives data from the server and presents that information to the user. Input data includes analyzed information from the server. The device integrates with a map application to visually display evacuation routes. It also prompts users to take appropriate action through voice and push notifications.

[0259] Step 5:

[0260] Users begin evacuation actions based on information from their devices. Specific actions include moving to the nearest evacuation center and selecting a suggested safe route. Further support information is provided through location feedback from the user.

[0261] (Application Example 1)

[0262] 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."

[0263] In recent years, the need for rapid and accurate information provision during disasters has become increasingly important. However, conventional disaster information systems have limitations in real-time data collection and information provision across diverse platforms, resulting in delays in providing appropriate support in response to the disaster situation. As a result, victims may not be able to take appropriate evacuation actions. Therefore, there is a need for a more efficient and reliable disaster information system.

[0264] 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.

[0265] In this invention, the server includes means for collecting disaster information in real time from various sources during a disaster, means for automatically analyzing the degree of risk by comparing normal data with disaster information and assessing the risk of damage, and means for aggregating the capacity and inventory information of evacuation centers and selecting the optimal evacuation site. This makes it possible to provide disaster victims with quick and appropriate information and encourage prompt evacuation.

[0266] "Means for collecting disaster information in real time from diverse sources during a disaster" refers to technologies for instantly acquiring data from multiple sources such as social media, news feeds, and various sensors when a disaster occurs.

[0267] "A method for automatically analyzing the degree of risk by comparing normal data with disaster information and assessing the risk of disaster" refers to a process that compares normal weather data and geographic information with disaster information and uses artificial intelligence to assess the degree of risk.

[0268] "A means of aggregating information on the capacity and inventory of evacuation shelters and selecting the most suitable evacuation location" refers to a function that summarizes the capacity and supply status of each evacuation shelter and determines the most appropriate evacuation shelter for the user.

[0269] "A multi-platform solution that utilizes the diverse output functions of users' mobile information processing devices to provide disaster information to users" refers to a system that provides disaster information tailored to the situation through different devices such as smartphones and tablets.

[0270] "A means of calculating a safe evacuation route based on the user's location data on the ground and guiding the user along that route" refers to a technology that uses GPS or other means to determine the user's current location, calculate the safest and most efficient evacuation route, and direct the user along it.

[0271] "A means of dynamically identifying disaster risk levels using artificial intelligence based on accumulated data" refers to a mechanism that uses artificial intelligence to analyze collected data and evaluate the impact of an ongoing disaster under constantly changing circumstances.

[0272] This invention is a system for collecting, analyzing, and providing information during disasters. By coordinating the server, terminal, and user elements, it enables the rapid and appropriate provision of information.

[0273] The server collects data in real time from various sources such as social media, news sites, and sensor devices when a disaster occurs. High-performance servers are used as hardware, and artificial intelligence platforms (e.g., Google Cloud AI, Amazon SageMaker) are used for data analysis. Based on normal data and disaster information, the server assesses the risk of damage. This allows it to analyze the severity of the situation on the ground and provide customized evacuation instructions to each user.

[0274] The device utilizes smartphones and tablets to calculate the optimal evacuation route based on the user's location. Real-time evacuation information is sent to the device via push notifications, and thanks to multi-platform compatibility, the information can be received on a variety of devices. For example, when a user is heading to a designated evacuation center, the device guides them along a safe route that takes into account dangerous areas to avoid and road closures.

[0275] Users can take swift action based on the information provided through their devices. Furthermore, users can input their own safety status and provide feedback to the server, which further improves the accuracy of the information.

[0276] As a specific example, when heavy rain is predicted to cause a river flood in a certain area, the server analyzes the water level data and past disaster data to evaluate the flood risk. Based on the results, a safe evacuation route is provided to the user's terminal. This process is supported by using a prompt sentence such as "Analyze the evacuation information of the target area based on the data collected in real time and propose a recommended route."

[0277] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0278] Step 1:

[0279] The server collects disaster information in real time from SNS, news feeds, and various sensors. As input, the data obtained from those information sources is used. By organizing the collected data and storing it in a database, it is prepared for subsequent analysis processing.

[0280] Step 2:

[0281] The server compares the normal weather data and geographical information with the information collected during the disaster. The input is the normal data and the collected disaster data. Using AI technology, the differences in the data are analyzed and the risk level is calculated. The output is the disaster risk assessment result, which is passed to the next processing step.

[0282] Step 3:

[0283] The server executes a process of aggregating the accommodation capacity and inventory information of shelters based on the disaster risk assessment result. Using the collected data and the information of the shelters as input, the optimal evacuation location is determined. This information is output as a push notification to the user's terminal.

[0284] Step 4:

[0285] The terminal obtains the user's current location using a location information service such as GPS. The input is the user's location data. Based on this, a safe evacuation route is calculated and displayed on the map. The output is the recommended evacuation route for guiding the user.

[0286] Step 5:

[0287] The user starts the evacuation action according to the route instructions provided through the terminal. The safety status feedback from the user is sent to the server. This feedback is used for the system to continuously update information and provide more accurate information to other users. The input is the feedback information from the user, and the output is the information update of the entire system.

[0288] Furthermore, an emotion engine for estimating the user's emotions may be combined. That is, the specific processing unit 290 may estimate the user's emotions using the emotion recognition model 59 and perform specific processing using the user's emotions.

[0289] The purpose of the present invention is to construct an advanced disaster response system that integrates an emotion engine for recognizing the user's emotions in addition to information collection, analysis, and provision during disasters. This system consists of a server, a terminal, and a user, and each operates with its own role.

[0290] The server first collects the information necessary during normal times and during disasters. It accumulates performance data and real-time data from sensors, and extracts situation information from Internet and SNS posts. This data is analyzed by the AI engine to evaluate the disaster situation. Also, the emotion engine analyzes the user's emotional state from voice and action data to determine the degree of stress and anxiety.

[0291] The terminal suggests the optimal evacuation shelter and route to the user based on analyzed data sent from the server. An emotion engine assesses the user's mental state and provides individualized support according to the level of emergency stress as needed. Furthermore, notifications and guidance to the user are provided across multiple platforms, including smartphones, tablets, and disaster prevention radio systems, ensuring that information is never interrupted.

[0292] Users can input their location information using their device and initiate evacuation actions without delay based on the information received from the system. For example, if the emotional engine detects a high-stress state, specific actions such as breathing exercises and mental support will be provided to the user. As a result, users can understand the information more effectively and evacuate safely.

[0293] As a concrete example, suppose a major earthquake occurs and the server recognizes the risk of a tsunami and immediately instructs the user to evacuate. At that time, the emotion engine detects the user's state of tension, and the terminal provides an alert along with guidance to help the user regain composure. In this way, the present invention also plays a role in making it easier for users to take appropriate actions during a disaster and reducing the psychological burden caused by the disaster. The system as a whole is designed to function as an integrated whole, enabling safe and rapid disaster response.

[0294] The following describes the processing flow.

[0295] Step 1:

[0296] The server collects various types of data under normal circumstances and stores them in a database. This includes data from weather sensors, topographic images, and information on evacuation shelters.

[0297] Step 2:

[0298] The server monitors social media posts and news feeds in real time during a disaster, extracting posts containing important keywords. It then analyzes location information and damage assessments from these posts.

[0299] Step 3:

[0300] The server compares the collected real-time disaster data with the normal-time data using an AI model to evaluate the disaster situation. This evaluation includes the detection of rising water levels and road closure information.

[0301] Step 4:

[0302] The terminal receives the shelter status information provided by the server and proposes the most suitable evacuation location and route for the user. The occupancy status of the shelter and the shortage status of supplies are presented together.

[0303] Step 5:

[0304] The user inputs their current location information into the terminal and starts acting based on the provided evacuation information. The terminal can also show the evacuation route on a one-on-one basis and provide voice guidance.

[0305] Step 6:

[0306] The server analyzes the user's behavior logs and voice data using an emotion engine. This evaluates the level of stress and anxiety felt by the user and sends instructions to the terminal if action is required.

[0307] Step 7:

[0308] Based on the analysis results of the emotion engine, the terminal provides the user with advice for mental support and stress reduction. This includes simple breathing exercises and music playback for relaxation.

[0309] Step 8:

[0310] The server periodically updates all the data and conducts learning to improve the accuracy of the AI model and the emotion engine in preparation for the next disaster. This optimizes the overall performance of the system at all times.

[0311] (Example 2)

[0312] 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".

[0313] In modern times, there is a need for rapid and appropriate responses during disasters, but many conventional technologies are insufficient in terms of information gathering and analysis, and in particular, they do not provide support that takes into account the emotional state of users. As a result, there are delays in appropriate evacuation actions and increased anxiety among disaster victims, leaving challenges in terms of safety and psychological care.

[0314] 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.

[0315] In this invention, the server includes means for collecting disaster information in real time, means for analyzing the disaster situation by comparing data from normal times and disaster times, means for acquiring status information of evacuation facilities and proposing the optimal evacuation location, means for providing information to users through different information processing terminals, and means for analyzing voice and behavioral data to determine the user's emotional state and evaluate the degree of stress and anxiety. This enables evacuation routes and emotional support based on an accurate understanding of the disaster situation.

[0316] "Means of collecting disaster information in real time" refers to technologies for immediately gathering the latest data when a disaster occurs, utilizing sensors and online information sources.

[0317] "Time-series data" refers to a collection of data that changes over time, and is information that is analyzed in chronological order.

[0318] "Means of analyzing the damage situation" refers to technologies used to evaluate the impact and scale of a disaster based on collected data, and to identify anomalies compared to normal conditions.

[0319] "Evacuation facilities" refer to buildings or locations designated to ensure safety during disasters.

[0320] "Methods of distributing information through different information processing terminals" refers to technologies that use multiple devices, such as smartphones, tablets, and disaster prevention radio systems, to distribute information.

[0321] "Analyzing voice and behavioral data" refers to the process of analyzing a person's voice tone and body movements to estimate their emotions and state of mind.

[0322] "Means for determining a user's emotional state" refers to technologies for evaluating a user's psychological condition, such as measuring the degree of stress and anxiety.

[0323] "Assessing the level of stress and anxiety" refers to the process of measuring and analyzing how much tension or worry a user is experiencing.

[0324] To implement this invention, three main components are required: a server, a terminal, and a user. The server is responsible for collecting necessary information in real time during a disaster and analyzing the collected data. This information collection utilizes hardware such as seismometers and weather sensors, as well as databases and SNS analysis software accessed via the internet. The AI ​​engine uses a generally available machine learning library, and the data analysis is performed using an application on the server with big data processing capabilities.

[0325] The server analyzes the user's emotions using an emotion engine based on collected voice and behavioral data. This analysis utilizes voice recognition software and behavioral pattern analysis software. This allows the server to assess the user's stress and anxiety levels.

[0326] The terminal suggests the most suitable evacuation shelter and route to the user based on analyzed data sent from the server. The terminal uses a mobile device such as a smartphone or tablet and receives information via an internet connection. It also utilizes a map service API for navigation. In addition, the terminal assesses the user's mental state and provides audio guidance on relaxation techniques if a high-stress state is detected.

[0327] Users can voluntarily input their location information via their device and quickly initiate evacuation actions based on information from the server. The information provided by the server includes the location of evacuation shelters and the safety of evacuation routes, and users make decisions about their actions accordingly. Users can also receive instructions on breathing techniques and simple exercises to reduce stress and stabilize their mental state.

[0328] As a concrete example, in the event of a major earthquake, the server can immediately analyze disaster information and instruct the user on the optimal evacuation route. At the same time, an emotion engine can sense the user's level of anxiety and provide support messages through the device, such as, "Take a deep breath and check this link for guidance on how to stay calm."

[0329] An example of a prompt for the generating AI model would include instructions such as, "A major earthquake has occurred, and a tsunami warning has been issued. Analyze the user's emotional state and generate action guidelines for safe evacuation."

[0330] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0331] Step 1:

[0332] The server collects disaster information in real time using sensors and internet data sources. Inputs include data from seismometers and weather sensors, as well as social media posts. This data is aggregated and stored in a database. The output is a collection of raw data ready for analysis.

[0333] Step 2:

[0334] The server sends the collected time-series data to the AI ​​engine, which then analyzes the damage situation by comparing data from normal and disaster periods. Specifically, the AI ​​engine analyzes the location, scale, and extent of impact of the disaster and generates the results. The input is raw time-series data, and the output is an analyzed disaster situation report.

[0335] Step 3:

[0336] The server sends voice and behavioral data to the emotion engine to determine the user's emotional state. The emotion engine uses speech recognition and pattern analysis to assess the user's stress and anxiety levels. The input is voice and behavioral data, and the output is a user emotion assessment report.

[0337] Step 4:

[0338] The terminal receives analyzed data from the server and suggests the most suitable evacuation shelters and routes to the user. The terminal calculates location information and updates the map in real time via the internet connection, presenting it visually to the user. The input is the user's location and analyzed data from the server, and the output is an easy-to-read map including guidance information.

[0339] Step 5:

[0340] The device provides a relaxation guide if high stress is detected based on the user's emotional assessment report. Specifically, it introduces breathing techniques and mental stabilization skills to the user through audio and video. The input is the emotional assessment report, and the output is viewable guide content.

[0341] Step 6:

[0342] Based on the evacuation information and support received from the device, the user initiates safe evacuation actions. The user follows the designated evacuation route and evacuates while carrying necessary supplies. The input is information from the device, and the output is the user's specific actions and movements.

[0343] (Application Example 2)

[0344] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0345] In the event of a disaster, real-time information gathering and analysis are necessary to provide swift and appropriate support to victims. Even more important is reducing the psychological burden on victims and supporting their safe evacuation. However, current systems do not take into account the emotional state of users, and currently lack consideration for users experiencing stress and anxiety.

[0346] 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.

[0347] In this invention, the server includes means for collecting disaster information in real time, means for analyzing the damage situation by comparing normal data with disaster information, means for collecting information on the status of evacuation centers and suggesting the optimal evacuation location, means for analyzing the user's emotional state and providing support according to that state, and means for providing the user with psychological support information in an emergency. This enables rapid information dissemination, reduces the psychological stress of disaster victims, and allows for safer and more secure evacuation.

[0348] "Methods for collecting disaster information in real time" refer to technologies for collecting necessary information immediately during a disaster and analyzing it accurately and quickly.

[0349] "Methods for analyzing the extent of damage by comparing normal data with disaster information" refers to technologies that evaluate the degree and impact of damage by comparing data from normal times with data from the current disaster and analyzing the changes.

[0350] "A means of collecting information on the status of evacuation shelters and proposing the most suitable evacuation location" refers to a technology that acquires information such as the capacity, safety, and location of each evacuation shelter and then presents the most suitable evacuation shelter to the user.

[0351] "A means of providing information to users across multiple platforms" refers to technology that enables users to receive necessary information regardless of their circumstances, using various devices and communication methods.

[0352] "Means of analyzing a user's emotional state and providing support tailored to that state" refers to technology that understands a user's emotions based on voice and behavioral data and provides psychological or physical support that is appropriate to that state.

[0353] "Means of providing users with psychological support information in emergencies" refers to technologies that provide users with appropriate psychological support information in order to alleviate anxiety and stress during disasters.

[0354] The system for realizing this invention consists of a server, terminals, and users, each playing a specific role. The server collects critical information in real time during a disaster and analyzes the damage situation by comparing it with data from normal times. This involves high-performance communication methods, computers for data analysis, and artificial intelligence software. Specifically, it uses an AI engine and database running on a cloud platform (e.g., Amazon Web Services or Microsoft Azure).

[0355] Furthermore, the server can acquire status information about evacuation shelters and suggest the most suitable shelter. This information is collected from local government agencies and NGOs and processed by algorithms on the server. The suggested shelters are then notified to the user via smartphone or tablet.

[0356] The device displays analysis results sent from the server and implements an emotion engine to evaluate the user's emotional state. The user's emotional state is analyzed in conjunction with speech recognition software, and psychological support is provided according to the user's stress level. This utilizes the smartphone's built-in microphone and camera.

[0357] Users can make decisions based on real-time updated evacuation information by operating the device. The information displayed on the device also includes psychological support information generated by an emotion engine, which offers advice to help users stay calm in critical situations.

[0358] As a concrete example, in the event of a major earthquake, the server immediately analyzes the information and sends immediate evacuation instructions to users in areas at risk of tsunamis. Simultaneously, the emotion engine detects the user's high stress level and provides guidance on relaxation techniques and reassuring messages. Using a generative AI model, an example of a prompt might be: "The user is in a very anxious state. Please generate suggestions for calming him, along with evacuation locations."

[0359] In this way, this invention goes beyond mere information transmission and enables advanced disaster response that also takes into account the user's psychological well-being.

[0360] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0361] Step 1:

[0362] The server collects disaster information in real time from the internet and various sensors. This information includes satellite data and sensor data from seismometers. This data is integrated by a data collection module and stored in a database. The input is raw disaster-related data, and the output is an integrated data stream.

[0363] Step 2:

[0364] The server analyzes the extent of the damage by comparing normal data with disaster-related information. This analysis utilizes an AI engine and implements machine learning algorithms. The input is an integrated data stream, and the output is data evaluating the damage situation. Specifically, change detection technology is used to analyze the impact of the disaster.

[0365] Step 3:

[0366] The server collects local evacuation shelter information and suggests the most suitable shelters based on the analyzed disaster situation. An optimization algorithm is used that considers the capacity and safety of the shelters. The input is evacuation shelter status data, and the output is a list of recommended shelters.

[0367] Step 4:

[0368] The terminal receives analysis results sent from the server and provides information to the user. The terminal is equipped with a multi-platform information provision system, and displays information via smartphones, tablets, etc. The input is the analysis results from the server, and the output is a visual evacuation instruction shown to the user.

[0369] Step 5:

[0370] The device collects user voice and facial expression data and analyzes the user's emotional state using an emotion engine. This analysis utilizes the smartphone's built-in microphone and camera, and applies speech recognition technology. The input is voice and visual data, and the output is the evaluation result of the emotional state.

[0371] Step 6:

[0372] The device provides appropriate psychological support information based on the user's emotional state. Specifically, it provides guidance on breathing exercises and plays relaxation music. The input is the result of an assessment of the emotional state, and the output is psychological support content for the user.

[0373] Step 7:

[0374] The user makes decisions based on evacuation information and psychological support presented by the device. User input includes their location and decision-making, while the expected output is safe evacuation. At this stage, interaction through the user interface is crucial.

[0375] This series of processes allows users to take evacuation actions quickly and accurately, while also providing a sense of psychological security. This invention is realized through advanced data analysis and prompt message design using a generative AI model.

[0376] 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.

[0377] 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.

[0378] 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.

[0379] [Third Embodiment]

[0380] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0381] 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.

[0382] 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).

[0383] 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.

[0384] 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.

[0385] 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).

[0386] 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.

[0387] 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.

[0388] 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.

[0389] 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.

[0390] 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.

[0391] 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".

[0392] This invention aims to build a system that highly automates information gathering, analysis, and provision during disasters. This system consists of server, terminal, and user elements, and each element works in cooperation to enable the understanding of the disaster situation and support for disaster victims.

[0393] The server first collects real-time data during normal times and during disasters. Normal data includes weather information, topographic images, and river water levels, while during disasters, it obtains information from social media posts and news articles. This information is stored in a database and used with AI to analyze the extent of the damage.

[0394] The terminal provides navigation and notifications to the user based on information provided by the server. Based on user input, it suggests the most suitable evacuation shelter and route to the user's current location. It also displays information on the capacity of evacuation shelters and shortages of supplies, prompting the user to take appropriate action.

[0395] Users receive evacuation information via their smartphones or tablets and begin using the system by entering their location information into their devices as needed. In particular, even if users are unable to access the internet, the server will widely disseminate information using disaster prevention radio and local broadcasting.

[0396] As a concrete example, when a river floods, the server detects posts from social media indicating rising water levels, and the AI ​​analyzes the level of danger by comparing it with normal water level data. As a result, it recommends evacuation locations to the user's device and guides them along safe routes. In this way, the present invention can provide rapid and practical information to disaster victims, supporting disaster response. Furthermore, considering situations where the impact of a disaster affects infrastructure and smartphones become unusable, the system is designed to provide information across multiple platforms, thus addressing all possible situations.

[0397] The following describes the processing flow.

[0398] Step 1:

[0399] The server performs routine data collection. This involves obtaining weather information using a weather data API and downloading images of terrain and infrastructure from the internet. This data is stored in a static database.

[0400] Step 2:

[0401] The server collects real-time data when a disaster occurs. It gathers disaster-related posts from social media and news feeds on the internet and updates a dynamic database based on that information. APIs and scraping techniques are used to collect the information.

[0402] Step 3:

[0403] The server analyzes collected real-time data and normal-time data using an AI engine. By evaluating the extent of the damage and risk levels, and visualizing the data, it helps formulate emergency response strategies.

[0404] Step 4:

[0405] The device receives disaster information and analysis results transmitted from the server and displays them to the user. This is done through an app or web interface, and information on evacuation shelters and recommended routes is presented based on the user's location.

[0406] Step 5:

[0407] Users enter their location information into their device to receive evacuation shelter guidance and route suggestions from the system. They can also check information about supplies needed by evacuation shelters and arrange for their delivery as needed.

[0408] Step 6:

[0409] The terminal will deliver important notifications via disaster prevention radio and local broadcasting equipment in case smartphone use is difficult. This will ensure that information reaches users even when internet connectivity is lost.

[0410] Step 7:

[0411] The server periodically updates the entire database and compares and optimizes past disaster data with new data to improve the system's learning model. This prepares the system for future disasters and improves the accuracy of information provided.

[0412] (Example 1)

[0413] 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."

[0414] In recent years, damage from natural disasters has increased, creating a demand for rapid and accurate information provision. However, conventional information provision systems lack real-time capabilities and accuracy, resulting in situations where users are unable to take appropriate action. Therefore, the challenge lies in achieving rapid and accurate situation assessment and the provision of appropriate information to users during disasters.

[0415] 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.

[0416] In this invention, the server includes means for collecting disaster data in real time, means for analyzing the disaster situation by comparing normal information with disaster data, and means for collecting status information of evacuation sites and suggesting appropriate evacuation sites. This enables users to take accurate and rapid evacuation actions.

[0417] "Methods for collecting disaster data in real time" refers to technical methods that continuously collect data on the current disaster situation in real time.

[0418] "Methods for analyzing disaster situations by comparing normal-time information with disaster-time data" refers to methods for analyzing the situation and impact of a disaster by comparing standard data from normal times with data from the time of a disaster.

[0419] "Methods for gathering information on the status of evacuation sites and proposing appropriate evacuation sites" refers to the process of collecting information on evacuation sites and their occupancy rates, and presenting the most suitable evacuation site to users.

[0420] "Means of providing information to users through diverse media" refers to methods of transmitting disaster information to users in various formats, including smartphone apps and radio broadcasts.

[0421] "Methods for applying natural language processing techniques using artificial intelligence models" refer to methods of analyzing information by utilizing AI technology to understand and process natural language.

[0422] "Methods for identifying disaster risk levels using machine learning technology" refers to methods that utilize machine learning algorithms to evaluate and determine the degree of disaster risk from collected data.

[0423] This invention aims to build a system that can quickly collect and analyze information during a disaster and provide users with accurate evacuation instructions and support information. The system operates through the coordinated efforts of a server, terminals, and users.

[0424] The server plays a central role in data management during disasters. It collects disaster data in real time from weather sensors, social media, and news sources. This process involves retrieving data using APIs and storing it in a database. The collected data is analyzed using AI models, particularly those employing natural language processing and deep learning. This AI model performs its analysis based on the prompt: "Extract disaster-related information from social media posts in the specified area and assess the level of risk based on that information."

[0425] The terminal operates based on analysis results transmitted from the server. The terminal resides within the user's smartphone or tablet and provides real-time evacuation instructions, optimal shelters, and evacuation routes. Furthermore, the terminal also displays information on shelter occupancy rates and shortages of supplies. This allows users to quickly decide on and execute safe actions.

[0426] Users receive information via their devices to help them make appropriate decisions during a disaster. Even when communication environments are limited, the server provides information through local broadcasts and disaster prevention radio systems, ensuring users can obtain the necessary information.

[0427] This system is designed to reduce risks and support victims during disasters, enabling a rapid and effective disaster response by supporting optimal evacuation actions.

[0428] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0429] Step 1:

[0430] The server will begin collecting disaster information in real time. Input data will come from sources such as weather sensors, social media, and news APIs. This data will then be stored in a database. Specifically, the server will retrieve the latest information from each platform via API calls and filter it to select the most important data.

[0431] Step 2:

[0432] The server inputs the collected data into an AI model for analysis. The input data includes posts and articles from the disaster, as well as baseline data from normal times. A generative AI model is used to apply natural language processing techniques and assess the level of risk. Specifically, the model uses prompt sentences to understand the context and outputs an analysis of the disaster situation. This process utilizes deep learning algorithms to classify the content of the text data by topic.

[0433] Step 3:

[0434] The server sends the analysis results to the terminal. Specifically, this includes information on evacuation advisories, the capacity of evacuation shelters, and safe evacuation routes. Based on these results, the terminal prepares a notification message for the user. As output, the system processes the information to reflect real-time evacuation information, including location information, in the user interface.

[0435] Step 4:

[0436] The device receives data from the server and presents that information to the user. Input data includes analyzed information from the server. The device integrates with a map application to visually display evacuation routes. It also prompts users to take appropriate action through voice and push notifications.

[0437] Step 5:

[0438] Users begin evacuation actions based on information from their devices. Specific actions include moving to the nearest evacuation center and selecting a suggested safe route. Further support information is provided through location feedback from the user.

[0439] (Application Example 1)

[0440] 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."

[0441] In recent years, the need for rapid and accurate information provision during disasters has become increasingly important. However, conventional disaster information systems have limitations in real-time data collection and information provision across diverse platforms, resulting in delays in providing appropriate support in response to the disaster situation. As a result, victims may not be able to take appropriate evacuation actions. Therefore, there is a need for a more efficient and reliable disaster information system.

[0442] 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.

[0443] In this invention, the server includes means for collecting disaster information in real time from various sources during a disaster, means for automatically analyzing the degree of risk by comparing normal data with disaster information and assessing the risk of damage, and means for aggregating the capacity and inventory information of evacuation centers and selecting the optimal evacuation site. This makes it possible to provide disaster victims with quick and appropriate information and encourage prompt evacuation.

[0444] "Means for collecting disaster information in real time from diverse sources during a disaster" refers to technologies for instantly acquiring data from multiple sources such as social media, news feeds, and various sensors when a disaster occurs.

[0445] "A method for automatically analyzing the degree of risk by comparing normal data with disaster information and assessing the risk of disaster" refers to a process that compares normal weather data and geographic information with disaster information and uses artificial intelligence to assess the degree of risk.

[0446] "A means of aggregating information on the capacity and inventory of evacuation shelters and selecting the most suitable evacuation location" refers to a function that summarizes the capacity and supply status of each evacuation shelter and determines the most appropriate evacuation shelter for the user.

[0447] "A multi-platform solution that utilizes the diverse output functions of users' mobile information processing devices to provide disaster information to users" refers to a system that provides disaster information tailored to the situation through different devices such as smartphones and tablets.

[0448] "A means of calculating a safe evacuation route based on the user's location data on the ground and guiding the user along that route" refers to a technology that uses GPS or other means to determine the user's current location, calculate the safest and most efficient evacuation route, and direct the user along it.

[0449] "A means of dynamically identifying disaster risk levels using artificial intelligence based on accumulated data" refers to a mechanism that uses artificial intelligence to analyze collected data and evaluate the impact of an ongoing disaster under constantly changing circumstances.

[0450] This invention is a system for collecting, analyzing, and providing information during disasters. By coordinating the server, terminal, and user elements, it enables the rapid and appropriate provision of information.

[0451] The server collects data in real time from various sources such as social media, news sites, and sensor devices when a disaster occurs. High-performance servers are used as hardware, and artificial intelligence platforms (e.g., Google Cloud AI, Amazon SageMaker) are used for data analysis. Based on normal data and disaster information, the server assesses the risk of damage. This allows it to analyze the severity of the situation on the ground and provide customized evacuation instructions to each user.

[0452] The device utilizes smartphones and tablets to calculate the optimal evacuation route based on the user's location. Real-time evacuation information is sent to the device via push notifications, and thanks to multi-platform compatibility, the information can be received on a variety of devices. For example, when a user is heading to a designated evacuation center, the device guides them along a safe route that takes into account dangerous areas to avoid and road closures.

[0453] Users can take swift action based on the information provided through their devices. Furthermore, users can input their own safety status and provide feedback to the server, which further improves the accuracy of the information.

[0454] As a concrete example, if heavy rainfall in a certain area is expected to cause river flooding, the server analyzes water level data and historical disaster data to assess the risk of flooding. Based on the results, a safe evacuation route is provided to the user's terminal. This process is supported by using a prompt message that says, "Analyze evacuation information for the target area based on real-time collected data and propose a recommended route."

[0455] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0456] Step 1:

[0457] The server collects disaster information in real time from social media, news feeds, and various sensors. It uses data obtained from these sources as input. The collected data is organized and stored in a database to prepare for subsequent analysis.

[0458] Step 2:

[0459] The server compares normal weather and geographical data with collected disaster data. The input consists of normal data and collected disaster data. Using AI technology, it analyzes the data differences and calculates the level of risk. The output is the disaster risk assessment result, which is then passed on to the next processing step.

[0460] Step 3:

[0461] The server executes a process to aggregate information on the capacity and inventory of evacuation shelters based on the disaster risk assessment results. Using the collected data and shelter information as input, it determines the optimal evacuation location. This information is output to the user's device as a push notification.

[0462] Step 4:

[0463] The device obtains the user's current location using location services such as GPS. The input is the user's location data. Based on this, it calculates a safe evacuation route and displays it on a map. The output is the recommended evacuation route guided to the user.

[0464] Step 5:

[0465] The user initiates evacuation procedures following route instructions provided through the terminal. Safety status feedback from the user is sent to the server. This feedback is used by the system to continuously update information and provide more accurate information to other users. Input is user feedback information, and output is system-wide information updates.

[0466] 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.

[0467] This invention aims to construct an advanced disaster response system that integrates an emotion engine to recognize user emotions in addition to information gathering, analysis, and provision during disasters. This system consists of a server, terminals, and users, each operating with its own specific role.

[0468] The server first collects information necessary during normal times and in the event of a disaster. It aggregates historical data and real-time data from sensors, and extracts situational information from internet and social media posts. This data is analyzed by an AI engine to assess the extent of the disaster. In addition, an emotion engine analyzes the user's emotional state from voice and behavioral data to determine the degree of stress and anxiety.

[0469] The terminal suggests the optimal evacuation shelter and route to the user based on analyzed data sent from the server. An emotion engine assesses the user's mental state and provides individualized support according to the level of emergency stress as needed. Furthermore, notifications and guidance to the user are provided across multiple platforms, including smartphones, tablets, and disaster prevention radio systems, ensuring that information is never interrupted.

[0470] Users can input their location information using their device and initiate evacuation actions without delay based on the information received from the system. For example, if the emotional engine detects a high-stress state, specific actions such as breathing exercises and mental support will be provided to the user. As a result, users can understand the information more effectively and evacuate safely.

[0471] As a concrete example, suppose a major earthquake occurs and the server recognizes the risk of a tsunami and immediately instructs the user to evacuate. At that time, the emotion engine detects the user's state of tension, and the terminal provides an alert along with guidance to help the user regain composure. In this way, the present invention also plays a role in making it easier for users to take appropriate actions during a disaster and reducing the psychological burden caused by the disaster. The system as a whole is designed to function as an integrated whole, enabling safe and rapid disaster response.

[0472] The following describes the processing flow.

[0473] Step 1:

[0474] The server collects various types of data under normal circumstances and stores them in a database. This includes data from weather sensors, topographic images, and information on evacuation shelters.

[0475] Step 2:

[0476] The server monitors social media posts and news feeds in real time during a disaster, extracting posts containing important keywords. It then analyzes location information and damage assessments from these posts.

[0477] Step 3:

[0478] The server uses an AI model to compare real-time data collected during disasters with data from normal times to assess the extent of the damage. This assessment includes detecting rising water levels and information on road closures.

[0479] Step 4:

[0480] The terminal receives status information about evacuation shelters from the server and suggests the most suitable evacuation location and route to the user. Information on the shelter's occupancy rate and the shortage of supplies is also presented.

[0481] Step 5:

[0482] Users enter their current location into the device and begin taking action based on the provided evacuation information. The device can also provide personalized evacuation route guidance and voice prompts.

[0483] Step 6:

[0484] The server uses an emotion engine to analyze user behavior logs and voice data. This allows it to assess the level of stress and anxiety the user is experiencing and send instructions to the device if necessary.

[0485] Step 7:

[0486] Based on the analysis results of its emotion engine, the device provides users with mental support and stress reduction advice. This includes simple breathing exercises and relaxing music playback.

[0487] Step 8:

[0488] The server regularly updates all data and learns to improve the accuracy of the AI ​​model and emotion engine in preparation for the next disaster. This ensures that the overall system performance is always optimized.

[0489] (Example 2)

[0490] 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."

[0491] In modern times, there is a need for rapid and appropriate responses during disasters, but many conventional technologies are insufficient in terms of information gathering and analysis, and in particular, they do not provide support that takes into account the emotional state of users. As a result, there are delays in appropriate evacuation actions and increased anxiety among disaster victims, leaving challenges in terms of safety and psychological care.

[0492] 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.

[0493] In this invention, the server includes means for collecting disaster information in real time, means for analyzing the disaster situation by comparing data from normal times and disaster times, means for acquiring status information of evacuation facilities and proposing the optimal evacuation location, means for providing information to users through different information processing terminals, and means for analyzing voice and behavioral data to determine the user's emotional state and evaluate the degree of stress and anxiety. This enables evacuation routes and emotional support based on an accurate understanding of the disaster situation.

[0494] "Means of collecting disaster information in real time" refers to technologies for immediately gathering the latest data when a disaster occurs, utilizing sensors and online information sources.

[0495] "Time-series data" refers to a collection of data that changes over time, and is information that is analyzed in chronological order.

[0496] "Means of analyzing the damage situation" refers to technologies used to evaluate the impact and scale of a disaster based on collected data, and to identify anomalies compared to normal conditions.

[0497] "Evacuation facilities" refer to buildings or locations designated to ensure safety during disasters.

[0498] "Methods of distributing information through different information processing terminals" refers to technologies that use multiple devices, such as smartphones, tablets, and disaster prevention radio systems, to distribute information.

[0499] "Analyzing voice and behavioral data" refers to the process of analyzing a person's voice tone and body movements to estimate their emotions and state of mind.

[0500] "Means for determining a user's emotional state" refers to technologies for evaluating a user's psychological condition, such as measuring the degree of stress and anxiety.

[0501] "Assessing the level of stress and anxiety" refers to the process of measuring and analyzing how much tension or worry a user is experiencing.

[0502] To implement this invention, three main components are required: a server, a terminal, and a user. The server is responsible for collecting necessary information in real time during a disaster and analyzing the collected data. This information collection utilizes hardware such as seismometers and weather sensors, as well as databases and SNS analysis software accessed via the internet. The AI ​​engine uses a generally available machine learning library, and the data analysis is performed using an application on the server with big data processing capabilities.

[0503] The server analyzes the user's emotions using an emotion engine based on collected voice and behavioral data. This analysis utilizes voice recognition software and behavioral pattern analysis software. This allows the server to assess the user's stress and anxiety levels.

[0504] The terminal suggests the most suitable evacuation shelter and route to the user based on analyzed data sent from the server. The terminal uses a mobile device such as a smartphone or tablet and receives information via an internet connection. It also utilizes a map service API for navigation. In addition, the terminal assesses the user's mental state and provides audio guidance on relaxation techniques if a high-stress state is detected.

[0505] Users can voluntarily input their location information via their device and quickly initiate evacuation actions based on information from the server. The information provided by the server includes the location of evacuation shelters and the safety of evacuation routes, and users make decisions about their actions accordingly. Users can also receive instructions on breathing techniques and simple exercises to reduce stress and stabilize their mental state.

[0506] As a concrete example, in the event of a major earthquake, the server can immediately analyze disaster information and instruct the user on the optimal evacuation route. At the same time, an emotion engine can sense the user's level of anxiety and provide support messages through the device, such as, "Take a deep breath and check this link for guidance on how to stay calm."

[0507] An example of a prompt for the generating AI model would include instructions such as, "A major earthquake has occurred, and a tsunami warning has been issued. Analyze the user's emotional state and generate action guidelines for safe evacuation."

[0508] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0509] Step 1:

[0510] The server collects disaster information in real time using sensors and internet data sources. Inputs include data from seismometers and weather sensors, as well as social media posts. This data is aggregated and stored in a database. The output is a collection of raw data ready for analysis.

[0511] Step 2:

[0512] The server sends the collected time-series data to the AI ​​engine, which then analyzes the damage situation by comparing data from normal and disaster periods. Specifically, the AI ​​engine analyzes the location, scale, and extent of impact of the disaster and generates the results. The input is raw time-series data, and the output is an analyzed disaster situation report.

[0513] Step 3:

[0514] The server sends voice and behavioral data to the emotion engine to determine the user's emotional state. The emotion engine uses speech recognition and pattern analysis to assess the user's stress and anxiety levels. The input is voice and behavioral data, and the output is a user emotion assessment report.

[0515] Step 4:

[0516] The terminal receives analyzed data from the server and suggests the most suitable evacuation shelters and routes to the user. The terminal calculates location information and updates the map in real time via the internet connection, presenting it visually to the user. The input is the user's location and analyzed data from the server, and the output is an easy-to-read map including guidance information.

[0517] Step 5:

[0518] The device provides a relaxation guide if high stress is detected based on the user's emotional assessment report. Specifically, it introduces breathing techniques and mental stabilization skills to the user through audio and video. The input is the emotional assessment report, and the output is viewable guide content.

[0519] Step 6:

[0520] Based on the evacuation information and support received from the device, the user initiates safe evacuation actions. The user follows the designated evacuation route and evacuates while carrying necessary supplies. The input is information from the device, and the output is the user's specific actions and movements.

[0521] (Application Example 2)

[0522] 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."

[0523] In the event of a disaster, real-time information gathering and analysis are necessary to provide swift and appropriate support to victims. Even more important is reducing the psychological burden on victims and supporting their safe evacuation. However, current systems do not take into account the emotional state of users, and currently lack consideration for users experiencing stress and anxiety.

[0524] 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.

[0525] In this invention, the server includes means for collecting disaster information in real time, means for analyzing the damage situation by comparing normal data with disaster information, means for collecting information on the status of evacuation centers and suggesting the optimal evacuation location, means for analyzing the user's emotional state and providing support according to that state, and means for providing the user with psychological support information in an emergency. This enables rapid information dissemination, reduces the psychological stress of disaster victims, and allows for safer and more secure evacuation.

[0526] "Methods for collecting disaster information in real time" refer to technologies for collecting necessary information immediately during a disaster and analyzing it accurately and quickly.

[0527] "Methods for analyzing the extent of damage by comparing normal data with disaster information" refers to technologies that evaluate the degree and impact of damage by comparing data from normal times with data from the current disaster and analyzing the changes.

[0528] "A means of collecting information on the status of evacuation shelters and proposing the most suitable evacuation location" refers to a technology that acquires information such as the capacity, safety, and location of each evacuation shelter and then presents the most suitable evacuation shelter to the user.

[0529] "A means of providing information to users across multiple platforms" refers to technology that enables users to receive necessary information regardless of their circumstances, using various devices and communication methods.

[0530] "Means of analyzing a user's emotional state and providing support tailored to that state" refers to technology that understands a user's emotions based on voice and behavioral data and provides psychological or physical support that is appropriate to that state.

[0531] "Means of providing users with psychological support information in emergencies" refers to technologies that provide users with appropriate psychological support information in order to alleviate anxiety and stress during disasters.

[0532] The system for realizing this invention consists of a server, terminals, and users, each playing a specific role. The server collects critical information in real time during a disaster and analyzes the damage situation by comparing it with data from normal times. This involves high-performance communication methods, computers for data analysis, and artificial intelligence software. Specifically, it uses an AI engine and database running on a cloud platform (e.g., Amazon Web Services or Microsoft Azure).

[0533] Furthermore, the server can acquire status information about evacuation shelters and suggest the most suitable shelter. This information is collected from local government agencies and NGOs and processed by algorithms on the server. The suggested shelters are then notified to the user via smartphone or tablet.

[0534] The device displays analysis results sent from the server and implements an emotion engine to evaluate the user's emotional state. The user's emotional state is analyzed in conjunction with speech recognition software, and psychological support is provided according to the user's stress level. This utilizes the smartphone's built-in microphone and camera.

[0535] Users can make decisions based on real-time updated evacuation information by operating the device. The information displayed on the device also includes psychological support information generated by an emotion engine, which offers advice to help users stay calm in critical situations.

[0536] As a concrete example, in the event of a major earthquake, the server immediately analyzes the information and sends immediate evacuation instructions to users in areas at risk of tsunamis. Simultaneously, the emotion engine detects the user's high stress level and provides guidance on relaxation techniques and reassuring messages. Using a generative AI model, an example of a prompt might be: "The user is in a very anxious state. Please generate suggestions for calming him, along with evacuation locations."

[0537] In this way, this invention goes beyond mere information transmission and enables advanced disaster response that also takes into account the user's psychological well-being.

[0538] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0539] Step 1:

[0540] The server collects disaster information in real time from the internet and various sensors. This information includes satellite data and sensor data from seismometers. This data is integrated by a data collection module and stored in a database. The input is raw disaster-related data, and the output is an integrated data stream.

[0541] Step 2:

[0542] The server analyzes the extent of the damage by comparing normal data with disaster-related information. This analysis utilizes an AI engine and implements machine learning algorithms. The input is an integrated data stream, and the output is data evaluating the damage situation. Specifically, change detection technology is used to analyze the impact of the disaster.

[0543] Step 3:

[0544] The server collects local evacuation shelter information and suggests the most suitable shelters based on the analyzed disaster situation. An optimization algorithm is used that considers the capacity and safety of the shelters. The input is evacuation shelter status data, and the output is a list of recommended shelters.

[0545] Step 4:

[0546] The terminal receives analysis results sent from the server and provides information to the user. The terminal is equipped with a multi-platform information provision system, and displays information via smartphones, tablets, etc. The input is the analysis results from the server, and the output is a visual evacuation instruction shown to the user.

[0547] Step 5:

[0548] The device collects user voice and facial expression data and analyzes the user's emotional state using an emotion engine. This analysis utilizes the smartphone's built-in microphone and camera, and applies speech recognition technology. The input is voice and visual data, and the output is the evaluation result of the emotional state.

[0549] Step 6:

[0550] The device provides appropriate psychological support information based on the user's emotional state. Specifically, it provides guidance on breathing exercises and plays relaxation music. The input is the result of an assessment of the emotional state, and the output is psychological support content for the user.

[0551] Step 7:

[0552] The user makes decisions based on evacuation information and psychological support presented by the device. User input includes their location and decision-making, while the expected output is safe evacuation. At this stage, interaction through the user interface is crucial.

[0553] This series of processes allows users to take evacuation actions quickly and accurately, while also providing a sense of psychological security. This invention is realized through advanced data analysis and prompt message design using a generative AI model.

[0554] 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.

[0555] 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.

[0556] 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.

[0557] [Fourth Embodiment]

[0558] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0559] 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.

[0560] 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).

[0561] 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.

[0562] 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.

[0563] 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).

[0564] 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.

[0565] 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.

[0566] 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.

[0567] 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.

[0568] 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.

[0569] 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.

[0570] 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".

[0571] This invention aims to build a system that highly automates information gathering, analysis, and provision during disasters. This system consists of server, terminal, and user elements, and each element works in cooperation to enable the understanding of the disaster situation and support for disaster victims.

[0572] The server first collects real-time data during normal times and during disasters. Normal data includes weather information, topographic images, and river water levels, while during disasters, it obtains information from social media posts and news articles. This information is stored in a database and used with AI to analyze the extent of the damage.

[0573] The terminal provides navigation and notifications to the user based on information provided by the server. Based on user input, it suggests the most suitable evacuation shelter and route to the user's current location. It also displays information on the capacity of evacuation shelters and shortages of supplies, prompting the user to take appropriate action.

[0574] Users receive evacuation information via their smartphones or tablets and begin using the system by entering their location information into their devices as needed. In particular, even if users are unable to access the internet, the server will widely disseminate information using disaster prevention radio and local broadcasting.

[0575] As a concrete example, when a river floods, the server detects posts from social media indicating rising water levels, and the AI ​​analyzes the level of danger by comparing it with normal water level data. As a result, it recommends evacuation locations to the user's device and guides them along safe routes. In this way, the present invention can provide rapid and practical information to disaster victims, supporting disaster response. Furthermore, considering situations where the impact of a disaster affects infrastructure and smartphones become unusable, the system is designed to provide information across multiple platforms, thus addressing all possible situations.

[0576] The following describes the processing flow.

[0577] Step 1:

[0578] The server performs routine data collection. This involves obtaining weather information using a weather data API and downloading images of terrain and infrastructure from the internet. This data is stored in a static database.

[0579] Step 2:

[0580] The server collects real-time data when a disaster occurs. It gathers disaster-related posts from social media and news feeds on the internet and updates a dynamic database based on that information. APIs and scraping techniques are used to collect the information.

[0581] Step 3:

[0582] The server analyzes collected real-time data and normal-time data using an AI engine. By evaluating the extent of the damage and risk levels, and visualizing the data, it helps formulate emergency response strategies.

[0583] Step 4:

[0584] The device receives disaster information and analysis results transmitted from the server and displays them to the user. This is done through an app or web interface, and information on evacuation shelters and recommended routes is presented based on the user's location.

[0585] Step 5:

[0586] Users enter their location information into their device to receive evacuation shelter guidance and route suggestions from the system. They can also check information about supplies needed by evacuation shelters and arrange for their delivery as needed.

[0587] Step 6:

[0588] The terminal will deliver important notifications via disaster prevention radio and local broadcasting equipment in case smartphone use is difficult. This will ensure that information reaches users even when internet connectivity is lost.

[0589] Step 7:

[0590] The server periodically updates the entire database and compares and optimizes past disaster data with new data to improve the system's learning model. This prepares the system for future disasters and improves the accuracy of information provided.

[0591] (Example 1)

[0592] 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".

[0593] In recent years, damage from natural disasters has increased, creating a demand for rapid and accurate information provision. However, conventional information provision systems lack real-time capabilities and accuracy, resulting in situations where users are unable to take appropriate action. Therefore, the challenge lies in achieving rapid and accurate situation assessment and the provision of appropriate information to users during disasters.

[0594] 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.

[0595] In this invention, the server includes means for collecting disaster data in real time, means for analyzing the disaster situation by comparing normal information with disaster data, and means for collecting status information of evacuation sites and suggesting appropriate evacuation sites. This enables users to take accurate and rapid evacuation actions.

[0596] "Methods for collecting disaster data in real time" refers to technical methods that continuously collect data on the current disaster situation in real time.

[0597] "Methods for analyzing disaster situations by comparing normal-time information with disaster-time data" refers to methods for analyzing the situation and impact of a disaster by comparing standard data from normal times with data from the time of a disaster.

[0598] "Methods for gathering information on the status of evacuation sites and proposing appropriate evacuation sites" refers to the process of collecting information on evacuation sites and their occupancy rates, and presenting the most suitable evacuation site to users.

[0599] "Means of providing information to users through diverse media" refers to methods of transmitting disaster information to users in various formats, including smartphone apps and radio broadcasts.

[0600] "Methods for applying natural language processing techniques using artificial intelligence models" refer to methods of analyzing information by utilizing AI technology to understand and process natural language.

[0601] "Methods for identifying disaster risk levels using machine learning technology" refers to methods that utilize machine learning algorithms to evaluate and determine the degree of disaster risk from collected data.

[0602] This invention aims to build a system that can quickly collect and analyze information during a disaster and provide users with accurate evacuation instructions and support information. The system operates through the coordinated efforts of a server, terminals, and users.

[0603] The server plays a central role in data management during disasters. It collects disaster data in real time from weather sensors, social media, and news sources. This process involves retrieving data using APIs and storing it in a database. The collected data is analyzed using AI models, particularly those employing natural language processing and deep learning. This AI model performs its analysis based on the prompt: "Extract disaster-related information from social media posts in the specified area and assess the level of risk based on that information."

[0604] The terminal operates based on analysis results transmitted from the server. The terminal resides within the user's smartphone or tablet and provides real-time evacuation instructions, optimal shelters, and evacuation routes. Furthermore, the terminal also displays information on shelter occupancy rates and shortages of supplies. This allows users to quickly decide on and execute safe actions.

[0605] Users receive information via their devices to help them make appropriate decisions during a disaster. Even when communication environments are limited, the server provides information through local broadcasts and disaster prevention radio systems, ensuring users can obtain the necessary information.

[0606] This system is designed to reduce risks and support victims during disasters, enabling a rapid and effective disaster response by supporting optimal evacuation actions.

[0607] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0608] Step 1:

[0609] The server will begin collecting disaster information in real time. Input data will come from sources such as weather sensors, social media, and news APIs. This data will then be stored in a database. Specifically, the server will retrieve the latest information from each platform via API calls and filter it to select the most important data.

[0610] Step 2:

[0611] The server inputs the collected data into an AI model for analysis. The input data includes posts and articles from the disaster, as well as baseline data from normal times. A generative AI model is used to apply natural language processing techniques and assess the level of risk. Specifically, the model uses prompt sentences to understand the context and outputs an analysis of the disaster situation. This process utilizes deep learning algorithms to classify the content of the text data by topic.

[0612] Step 3:

[0613] The server sends the analysis results to the terminal. Specifically, this includes information on evacuation advisories, the capacity of evacuation shelters, and safe evacuation routes. Based on these results, the terminal prepares a notification message for the user. As output, the system processes the information to reflect real-time evacuation information, including location information, in the user interface.

[0614] Step 4:

[0615] The device receives data from the server and presents that information to the user. Input data includes analyzed information from the server. The device integrates with a map application to visually display evacuation routes. It also prompts users to take appropriate action through voice and push notifications.

[0616] Step 5:

[0617] Users begin evacuation actions based on information from their devices. Specific actions include moving to the nearest evacuation center and selecting a suggested safe route. Further support information is provided through location feedback from the user.

[0618] (Application Example 1)

[0619] 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".

[0620] In recent years, the need for rapid and accurate information provision during disasters has become increasingly important. However, conventional disaster information systems have limitations in real-time data collection and information provision across diverse platforms, resulting in delays in providing appropriate support in response to the disaster situation. As a result, victims may not be able to take appropriate evacuation actions. Therefore, there is a need for a more efficient and reliable disaster information system.

[0621] 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.

[0622] In this invention, the server includes means for collecting disaster information in real time from various sources during a disaster, means for automatically analyzing the degree of risk by comparing normal data with disaster information and assessing the risk of damage, and means for aggregating the capacity and inventory information of evacuation centers and selecting the optimal evacuation site. This makes it possible to provide disaster victims with quick and appropriate information and encourage prompt evacuation.

[0623] "Means for collecting disaster information in real time from diverse sources during a disaster" refers to technologies for instantly acquiring data from multiple sources such as social media, news feeds, and various sensors when a disaster occurs.

[0624] "A method for automatically analyzing the degree of risk by comparing normal data with disaster information and assessing the risk of disaster" refers to a process that compares normal weather data and geographic information with disaster information and uses artificial intelligence to assess the degree of risk.

[0625] "A means of aggregating information on the capacity and inventory of evacuation shelters and selecting the most suitable evacuation location" refers to a function that summarizes the capacity and supply status of each evacuation shelter and determines the most appropriate evacuation shelter for the user.

[0626] "A multi-platform solution that utilizes the diverse output functions of users' mobile information processing devices to provide disaster information to users" refers to a system that provides disaster information tailored to the situation through different devices such as smartphones and tablets.

[0627] "A means of calculating a safe evacuation route based on the user's location data on the ground and guiding the user along that route" refers to a technology that uses GPS or other means to determine the user's current location, calculate the safest and most efficient evacuation route, and direct the user along it.

[0628] "A means of dynamically identifying disaster risk levels using artificial intelligence based on accumulated data" refers to a mechanism that uses artificial intelligence to analyze collected data and evaluate the impact of an ongoing disaster under constantly changing circumstances.

[0629] This invention is a system for collecting, analyzing, and providing information during disasters. By coordinating the server, terminal, and user elements, it enables the rapid and appropriate provision of information.

[0630] The server collects data in real time from various sources such as social media, news sites, and sensor devices when a disaster occurs. High-performance servers are used as hardware, and artificial intelligence platforms (e.g., Google Cloud AI, Amazon SageMaker) are used for data analysis. Based on normal data and disaster information, the server assesses the risk of damage. This allows it to analyze the severity of the situation on the ground and provide customized evacuation instructions to each user.

[0631] The device utilizes smartphones and tablets to calculate the optimal evacuation route based on the user's location. Real-time evacuation information is sent to the device via push notifications, and thanks to multi-platform compatibility, the information can be received on a variety of devices. For example, when a user is heading to a designated evacuation center, the device guides them along a safe route that takes into account dangerous areas to avoid and road closures.

[0632] Users can take swift action based on the information provided through their devices. Furthermore, users can input their own safety status and provide feedback to the server, which further improves the accuracy of the information.

[0633] As a concrete example, if heavy rainfall in a certain area is expected to cause river flooding, the server analyzes water level data and historical disaster data to assess the risk of flooding. Based on the results, a safe evacuation route is provided to the user's terminal. This process is supported by using a prompt message that says, "Analyze evacuation information for the target area based on real-time collected data and propose a recommended route."

[0634] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0635] Step 1:

[0636] The server collects disaster information in real time from social media, news feeds, and various sensors. It uses data obtained from these sources as input. The collected data is organized and stored in a database to prepare for subsequent analysis.

[0637] Step 2:

[0638] The server compares normal weather and geographical data with collected disaster data. The input consists of normal data and collected disaster data. Using AI technology, it analyzes the data differences and calculates the level of risk. The output is the disaster risk assessment result, which is then passed on to the next processing step.

[0639] Step 3:

[0640] The server executes a process to aggregate information on the capacity and inventory of evacuation shelters based on the disaster risk assessment results. Using the collected data and shelter information as input, it determines the optimal evacuation location. This information is output to the user's device as a push notification.

[0641] Step 4:

[0642] The device obtains the user's current location using location services such as GPS. The input is the user's location data. Based on this, it calculates a safe evacuation route and displays it on a map. The output is the recommended evacuation route guided to the user.

[0643] Step 5:

[0644] The user initiates evacuation procedures following route instructions provided through the terminal. Safety status feedback from the user is sent to the server. This feedback is used by the system to continuously update information and provide more accurate information to other users. Input is user feedback information, and output is system-wide information updates.

[0645] 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.

[0646] This invention aims to construct an advanced disaster response system that integrates an emotion engine to recognize user emotions in addition to information gathering, analysis, and provision during disasters. This system consists of a server, terminals, and users, each operating with its own specific role.

[0647] The server first collects information necessary during normal times and in the event of a disaster. It aggregates historical data and real-time data from sensors, and extracts situational information from internet and social media posts. This data is analyzed by an AI engine to assess the extent of the disaster. In addition, an emotion engine analyzes the user's emotional state from voice and behavioral data to determine the degree of stress and anxiety.

[0648] The terminal suggests the optimal evacuation shelter and route to the user based on analyzed data sent from the server. An emotion engine assesses the user's mental state and provides individualized support according to the level of emergency stress as needed. Furthermore, notifications and guidance to the user are provided across multiple platforms, including smartphones, tablets, and disaster prevention radio systems, ensuring that information is never interrupted.

[0649] Users can input their location information using their device and initiate evacuation actions without delay based on the information received from the system. For example, if the emotional engine detects a high-stress state, specific actions such as breathing exercises and mental support will be provided to the user. As a result, users can understand the information more effectively and evacuate safely.

[0650] As a concrete example, suppose a major earthquake occurs and the server recognizes the risk of a tsunami and immediately instructs the user to evacuate. At that time, the emotion engine detects the user's state of tension, and the terminal provides an alert along with guidance to help the user regain composure. In this way, the present invention also plays a role in making it easier for users to take appropriate actions during a disaster and reducing the psychological burden caused by the disaster. The system as a whole is designed to function as an integrated whole, enabling safe and rapid disaster response.

[0651] The following describes the processing flow.

[0652] Step 1:

[0653] The server collects various types of data under normal circumstances and stores them in a database. This includes data from weather sensors, topographic images, and information on evacuation shelters.

[0654] Step 2:

[0655] The server monitors social media posts and news feeds in real time during a disaster, extracting posts containing important keywords. It then analyzes location information and damage assessments from these posts.

[0656] Step 3:

[0657] The server uses an AI model to compare real-time data collected during disasters with data from normal times to assess the extent of the damage. This assessment includes detecting rising water levels and information on road closures.

[0658] Step 4:

[0659] The terminal receives status information about evacuation shelters from the server and suggests the most suitable evacuation location and route to the user. Information on the shelter's occupancy rate and the shortage of supplies is also presented.

[0660] Step 5:

[0661] Users enter their current location into the device and begin taking action based on the provided evacuation information. The device can also provide personalized evacuation route guidance and voice prompts.

[0662] Step 6:

[0663] The server uses an emotion engine to analyze user behavior logs and voice data. This allows it to assess the level of stress and anxiety the user is experiencing and send instructions to the device if necessary.

[0664] Step 7:

[0665] Based on the analysis results of its emotion engine, the device provides users with mental support and stress reduction advice. This includes simple breathing exercises and relaxing music playback.

[0666] Step 8:

[0667] The server regularly updates all data and learns to improve the accuracy of the AI ​​model and emotion engine in preparation for the next disaster. This ensures that the overall system performance is always optimized.

[0668] (Example 2)

[0669] 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".

[0670] In modern times, there is a need for rapid and appropriate responses during disasters, but many conventional technologies are insufficient in terms of information gathering and analysis, and in particular, they do not provide support that takes into account the emotional state of users. As a result, there are delays in appropriate evacuation actions and increased anxiety among disaster victims, leaving challenges in terms of safety and psychological care.

[0671] 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.

[0672] In this invention, the server includes means for collecting disaster information in real time, means for analyzing the disaster situation by comparing data from normal times and disaster times, means for acquiring status information of evacuation facilities and proposing the optimal evacuation location, means for providing information to users through different information processing terminals, and means for analyzing voice and behavioral data to determine the user's emotional state and evaluate the degree of stress and anxiety. This enables evacuation routes and emotional support based on an accurate understanding of the disaster situation.

[0673] "Means of collecting disaster information in real time" refers to technologies for immediately gathering the latest data when a disaster occurs, utilizing sensors and online information sources.

[0674] "Time-series data" refers to a collection of data that changes over time, and is information that is analyzed in chronological order.

[0675] "Means of analyzing the damage situation" refers to technologies used to evaluate the impact and scale of a disaster based on collected data, and to identify anomalies compared to normal conditions.

[0676] "Evacuation facilities" refer to buildings or locations designated to ensure safety during disasters.

[0677] "Methods of distributing information through different information processing terminals" refers to technologies that use multiple devices, such as smartphones, tablets, and disaster prevention radio systems, to distribute information.

[0678] "Analyzing voice and behavioral data" refers to the process of analyzing a person's voice tone and body movements to estimate their emotions and state of mind.

[0679] "Means for determining a user's emotional state" refers to technologies for evaluating a user's psychological condition, such as measuring the degree of stress and anxiety.

[0680] "Assessing the level of stress and anxiety" refers to the process of measuring and analyzing how much tension or worry a user is experiencing.

[0681] To implement this invention, three main components are required: a server, a terminal, and a user. The server is responsible for collecting necessary information in real time during a disaster and analyzing the collected data. This information collection utilizes hardware such as seismometers and weather sensors, as well as databases and SNS analysis software accessed via the internet. The AI ​​engine uses a generally available machine learning library, and the data analysis is performed using an application on the server with big data processing capabilities.

[0682] The server analyzes the user's emotions using an emotion engine based on collected voice and behavioral data. This analysis utilizes voice recognition software and behavioral pattern analysis software. This allows the server to assess the user's stress and anxiety levels.

[0683] The terminal suggests the most suitable evacuation shelter and route to the user based on analyzed data sent from the server. The terminal uses a mobile device such as a smartphone or tablet and receives information via an internet connection. It also utilizes a map service API for navigation. In addition, the terminal assesses the user's mental state and provides audio guidance on relaxation techniques if a high-stress state is detected.

[0684] Users can voluntarily input their location information via their device and quickly initiate evacuation actions based on information from the server. The information provided by the server includes the location of evacuation shelters and the safety of evacuation routes, and users make decisions about their actions accordingly. Users can also receive instructions on breathing techniques and simple exercises to reduce stress and stabilize their mental state.

[0685] As a concrete example, in the event of a major earthquake, the server can immediately analyze disaster information and instruct the user on the optimal evacuation route. At the same time, an emotion engine can sense the user's level of anxiety and provide support messages through the device, such as, "Take a deep breath and check this link for guidance on how to stay calm."

[0686] An example of a prompt for the generating AI model would include instructions such as, "A major earthquake has occurred, and a tsunami warning has been issued. Analyze the user's emotional state and generate action guidelines for safe evacuation."

[0687] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0688] Step 1:

[0689] The server collects disaster information in real time using sensors and internet data sources. Inputs include data from seismometers and weather sensors, as well as social media posts. This data is aggregated and stored in a database. The output is a collection of raw data ready for analysis.

[0690] Step 2:

[0691] The server sends the collected time-series data to the AI ​​engine, which then analyzes the damage situation by comparing data from normal and disaster periods. Specifically, the AI ​​engine analyzes the location, scale, and extent of impact of the disaster and generates the results. The input is raw time-series data, and the output is an analyzed disaster situation report.

[0692] Step 3:

[0693] The server sends voice and behavioral data to the emotion engine to determine the user's emotional state. The emotion engine uses speech recognition and pattern analysis to assess the user's stress and anxiety levels. The input is voice and behavioral data, and the output is a user emotion assessment report.

[0694] Step 4:

[0695] The terminal receives analyzed data from the server and suggests the most suitable evacuation shelters and routes to the user. The terminal calculates location information and updates the map in real time via the internet connection, presenting it visually to the user. The input is the user's location and analyzed data from the server, and the output is an easy-to-read map including guidance information.

[0696] Step 5:

[0697] The device provides a relaxation guide if high stress is detected based on the user's emotional assessment report. Specifically, it introduces breathing techniques and mental stabilization skills to the user through audio and video. The input is the emotional assessment report, and the output is viewable guide content.

[0698] Step 6:

[0699] Based on the evacuation information and support received from the device, the user initiates safe evacuation actions. The user follows the designated evacuation route and evacuates while carrying necessary supplies. The input is information from the device, and the output is the user's specific actions and movements.

[0700] (Application Example 2)

[0701] 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".

[0702] In the event of a disaster, real-time information gathering and analysis are necessary to provide swift and appropriate support to victims. Even more important is reducing the psychological burden on victims and supporting their safe evacuation. However, current systems do not take into account the emotional state of users, and currently lack consideration for users experiencing stress and anxiety.

[0703] 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.

[0704] In this invention, the server includes means for collecting disaster information in real time, means for analyzing the damage situation by comparing normal data with disaster information, means for collecting information on the status of evacuation centers and suggesting the optimal evacuation location, means for analyzing the user's emotional state and providing support according to that state, and means for providing the user with psychological support information in an emergency. This enables rapid information dissemination, reduces the psychological stress of disaster victims, and allows for safer and more secure evacuation.

[0705] "Methods for collecting disaster information in real time" refer to technologies for collecting necessary information immediately during a disaster and analyzing it accurately and quickly.

[0706] "Methods for analyzing the extent of damage by comparing normal data with disaster information" refers to technologies that evaluate the degree and impact of damage by comparing data from normal times with data from the current disaster and analyzing the changes.

[0707] "A means of collecting information on the status of evacuation shelters and proposing the most suitable evacuation location" refers to a technology that acquires information such as the capacity, safety, and location of each evacuation shelter and then presents the most suitable evacuation shelter to the user.

[0708] "A means of providing information to users across multiple platforms" refers to technology that enables users to receive necessary information regardless of their circumstances, using various devices and communication methods.

[0709] "Means of analyzing a user's emotional state and providing support tailored to that state" refers to technology that understands a user's emotions based on voice and behavioral data and provides psychological or physical support that is appropriate to that state.

[0710] "Means of providing users with psychological support information in emergencies" refers to technologies that provide users with appropriate psychological support information in order to alleviate anxiety and stress during disasters.

[0711] The system for realizing this invention consists of a server, terminals, and users, each playing a specific role. The server collects critical information in real time during a disaster and analyzes the damage situation by comparing it with data from normal times. This involves high-performance communication methods, computers for data analysis, and artificial intelligence software. Specifically, it uses an AI engine and database running on a cloud platform (e.g., Amazon Web Services or Microsoft Azure).

[0712] Furthermore, the server can acquire status information about evacuation shelters and suggest the most suitable shelter. This information is collected from local government agencies and NGOs and processed by algorithms on the server. The suggested shelters are then notified to the user via smartphone or tablet.

[0713] The device displays analysis results sent from the server and implements an emotion engine to evaluate the user's emotional state. The user's emotional state is analyzed in conjunction with speech recognition software, and psychological support is provided according to the user's stress level. This utilizes the smartphone's built-in microphone and camera.

[0714] Users can make decisions based on real-time updated evacuation information by operating the device. The information displayed on the device also includes psychological support information generated by an emotion engine, which offers advice to help users stay calm in critical situations.

[0715] As a concrete example, in the event of a major earthquake, the server immediately analyzes the information and sends immediate evacuation instructions to users in areas at risk of tsunamis. Simultaneously, the emotion engine detects the user's high stress level and provides guidance on relaxation techniques and reassuring messages. Using a generative AI model, an example of a prompt might be: "The user is in a very anxious state. Please generate suggestions for calming him, along with evacuation locations."

[0716] In this way, this invention goes beyond mere information transmission and enables advanced disaster response that also takes into account the user's psychological well-being.

[0717] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0718] Step 1:

[0719] The server collects disaster information in real time from the internet and various sensors. This information includes satellite data and sensor data from seismometers. This data is integrated by a data collection module and stored in a database. The input is raw disaster-related data, and the output is an integrated data stream.

[0720] Step 2:

[0721] The server analyzes the extent of the damage by comparing normal data with disaster-related information. This analysis utilizes an AI engine and implements machine learning algorithms. The input is an integrated data stream, and the output is data evaluating the damage situation. Specifically, change detection technology is used to analyze the impact of the disaster.

[0722] Step 3:

[0723] The server collects local evacuation shelter information and suggests the most suitable shelters based on the analyzed disaster situation. An optimization algorithm is used that considers the capacity and safety of the shelters. The input is evacuation shelter status data, and the output is a list of recommended shelters.

[0724] Step 4:

[0725] The terminal receives analysis results sent from the server and provides information to the user. The terminal is equipped with a multi-platform information provision system, and displays information via smartphones, tablets, etc. The input is the analysis results from the server, and the output is a visual evacuation instruction shown to the user.

[0726] Step 5:

[0727] The device collects user voice and facial expression data and analyzes the user's emotional state using an emotion engine. This analysis utilizes the smartphone's built-in microphone and camera, and applies speech recognition technology. The input is voice and visual data, and the output is the evaluation result of the emotional state.

[0728] Step 6:

[0729] The device provides appropriate psychological support information based on the user's emotional state. Specifically, it provides guidance on breathing exercises and plays relaxation music. The input is the result of an assessment of the emotional state, and the output is psychological support content for the user.

[0730] Step 7:

[0731] The user makes decisions based on evacuation information and psychological support presented by the device. User input includes their location and decision-making, while the expected output is safe evacuation. At this stage, interaction through the user interface is crucial.

[0732] This series of processes allows users to take evacuation actions quickly and accurately, while also providing a sense of psychological security. This invention is realized through advanced data analysis and prompt message design using a generative AI model.

[0733] 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.

[0734] 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.

[0735] 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.

[0736] 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.

[0737] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0738] 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.

[0739] 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.

[0740] 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.

[0741] 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."

[0742] 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.

[0743] 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.

[0744] 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.

[0745] 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.

[0746] 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.

[0747] 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.

[0748] 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.

[0749] 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.

[0750] 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.

[0751] 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.

[0752] 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.

[0753] 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.

[0754] The following is further disclosed regarding the embodiments described above.

[0755] (Claim 1)

[0756] Means for collecting disaster information in real time,

[0757] A method for analyzing the extent of damage by comparing data from normal times with information from disasters,

[0758] A means of collecting information on the status of evacuation shelters and proposing the most suitable evacuation location,

[0759] Means of providing information to users across multiple platforms,

[0760] A system that includes this.

[0761] (Claim 2)

[0762] The system according to claim 1, characterized by comprising means for calculating and guiding the user to the optimal evacuation route based on the user's location information.

[0763] (Claim 3)

[0764] The system according to claim 1, characterized in that it includes means for identifying the level of disaster risk using AI based on collected data.

[0765] "Example 1"

[0766] (Claim 1)

[0767] A means of collecting disaster data in real time,

[0768] A method for analyzing disaster situations by comparing normal-time information with disaster-time data,

[0769] A means of collecting information on the status of evacuation sites and proposing appropriate evacuation shelters,

[0770] Means of providing information to users through various media,

[0771] A means of applying natural language processing technology using an artificial intelligence model,

[0772] A system that includes this.

[0773] (Claim 2)

[0774] The system according to claim 1, characterized by comprising means for calculating and guiding users to the optimal evacuation route based on their location data.

[0775] (Claim 3)

[0776] The system according to claim 1, characterized in that it includes means for identifying disaster risk levels using machine learning technology based on collected data.

[0777] "Application Example 1"

[0778] (Claim 1)

[0779] In times of disaster, means of collecting disaster information in real time from various sources,

[0780] A means of automatically analyzing the degree of risk by comparing normal data with information from disasters, and assessing the risk of disaster,

[0781] A means of selecting the optimal evacuation location by aggregating information on the capacity and inventory of evacuation shelters,

[0782] A multi-platform compatible method for providing disaster information to users by utilizing the diverse output functions of the user's portable information processing device,

[0783] A system that includes this.

[0784] (Claim 2)

[0785] The system according to claim 1, characterized in that it includes means for calculating a safe avoidance route based on the user's position data on the ground and guiding the user along that route.

[0786] (Claim 3)

[0787] The system according to claim 1, characterized in that it includes means for dynamically identifying disaster risk levels using artificial intelligence based on accumulated data.

[0788] "Example 2 of combining an emotion engine"

[0789] (Claim 1)

[0790] Means for collecting disaster information in real time,

[0791] A method for analyzing the extent of damage by comparing time-series data from normal conditions with time-series data from disasters,

[0792] A means of obtaining status information on evacuation facilities and proposing the most suitable evacuation location,

[0793] A means of providing information to users through different information processing terminals,

[0794] A method for analyzing voice and behavioral data to determine the user's emotional state and evaluate the degree of stress and anxiety,

[0795] A system that includes this.

[0796] (Claim 2)

[0797] The system according to claim 1, characterized by comprising means for calculating and guiding the user to the optimal evacuation route based on the user's current location information, and means for providing personalized response information based on the user's emotional state.

[0798] (Claim 3)

[0799] The system according to claim 1, characterized by comprising means for identifying the level of disaster risk using artificial intelligence based on collected data, and means for processing the user's emotional state and providing support measures.

[0800] "Application example 2 of combining emotional engines"

[0801] (Claim 1)

[0802] Means for collecting disaster information in real time,

[0803] A method for analyzing the extent of damage by comparing data from normal times with information from disasters,

[0804] A means of collecting information on the status of evacuation shelters and proposing the most suitable evacuation location,

[0805] Means of providing information to users across multiple platforms,

[0806] A means of analyzing the user's emotional state and providing support tailored to that state,

[0807] A means of providing users with information on psychological support in emergencies,

[0808] A system that includes this.

[0809] (Claim 2)

[0810] The system according to claim 1, characterized by comprising means for calculating and guiding the user to the optimal evacuation route based on the user's location information.

[0811] (Claim 3)

[0812] The system according to claim 1, characterized in that it includes means for identifying the level of disaster risk using artificial intelligence based on collected data. [Explanation of Symbols]

[0813] 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. Means for collecting disaster information in real time, A method for analyzing the extent of damage by comparing data from normal times with information from disasters, A means of collecting information on the status of evacuation shelters and proposing the most suitable evacuation location, Means of providing information to users across multiple platforms, A system that includes this.

2. The system according to claim 1, characterized by comprising means for calculating and guiding the user to the optimal evacuation route based on the user's location information.

3. The system according to claim 1, characterized in that it includes means for identifying the level of disaster risk using AI based on collected data.

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