system

Abstract

We provide the system. [Solution] A computer-readable programming means for collecting information from multiple sources, A natural language processing engine means for analyzing collected information and determining the extent of damage, A programming method for visualizing analysis results in real time using a geographic information system, A communication module means for sending warning notifications to users, A user interface means for users to manipulate a map and obtain information, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the event of a disaster, it is difficult to collect and provide prompt and accurate damage information, resulting in a lack of information for taking appropriate evacuation actions. Also, the reliability of information obtained from multiple information sources may be low, and it is required to make accurate judgments without being misled by false reports or rumors.

Means for Solving the Problems

[0005] This invention provides a computer-readable program for collecting information from multiple sources, analyzes the collected information using a natural language processing engine, and determines the extent of damage. It also proposes a system that visualizes the analysis results in real time using a geographic information system and includes a communication module for sending warning notifications to users. Furthermore, by providing a user interface for users to manipulate maps to obtain information and combining it with an evaluation module that assesses the reliability of the information and filters out low-reliability information, it supports rapid evacuation actions based on highly reliable information.

[0006] "Information sources" refers to multiple media or platforms that can provide information related to disasters and damages.

[0007] A "computer-readable program" refers to a set of instructions written in a format that a computer system can interpret and execute.

[0008] A "natural language processing engine" refers to a software module that uses technology to understand, interpret, and manipulate human natural language using a computer.

[0009] "Analysis results" refer to the information obtained after the collected data has been processed by a natural language processing engine.

[0010] A "Geographic Information System" refers to a technology platform for acquiring, managing, analyzing, and displaying geographic data.

[0011] "Real-time visualization" refers to the process of instantly displaying analyzed information visually.

[0012] A "communication module" refers to a unit that has the function of sending and receiving information and is used to exchange data between systems and devices located in different places.

[0013] A "user interface" refers to a set of components that provide a visual or haptic means for a user to interact with a software system.

[0014] "Evaluating the reliability of information" refers to the process of judging and measuring the accuracy and reliability of acquired information.

[0015] An "evaluation module" refers to a software component designed to analyze the quality and reliability of information and make decisions based on that analysis. [Brief explanation of the drawing]

[0016] [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 Example 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

[0017] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the language used in the following description will be explained.

[0019] In the following embodiments, a 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.

[0020] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] 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.

[0022] 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).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] The embodiment for carrying out this invention is described as an integrated system for collecting, analyzing, and visualizing disaster information. This system consists of multiple components having the roles of server, terminal, and user.

[0038] First, the server retrieves data from sources such as social media and news sites to collect information about disasters and damage. APIs and web scraping techniques are used to obtain this information, ensuring that data is updated in real time. This data is temporarily stored and then added to a database as needed.

[0039] Next, the server applies a natural language processing engine to the collected data to analyze the text data. This process extracts information such as the location of the damage, the type of damage, and its extent. During the analysis, the reliability of the information is evaluated, and low-reliability information is filtered out to ensure data quality.

[0040] The analyzed information is integrated into a geographic information system by a server, and visualization data is generated. This process allows users to intuitively understand, on a map, which areas are at risk and to what extent.

[0041] The visualized information is provided to the user via the device. The device, upon receiving the information, also sends a warning notification to the user. For example, if a certain area is determined to be at high risk of flooding, the user can immediately receive that notification on their device.

[0042] Users can then use the interface on their devices to view detailed information and make decisions regarding safe evacuation. They can manipulate maps to check the current situation and obtain detailed text information and damage predictions for specific areas.

[0043] This system aims to support safe evacuation actions during disasters by providing timely and accurate information about ongoing hazards. For example, if this system were used during a typhoon, people would be able to check areas where flooding is predicted in advance and prepare safe routes.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The server uses APIs and web scraping techniques to collect disaster-related data from multiple sources, such as social media and news sites. It filters information using specific keywords and hashtags and retrieves data that is updated in real time.

[0047] Step 2:

[0048] The server temporarily stores the collected raw data and performs data cleaning. It removes duplicate and noisy data and formats it to prepare it for subsequent analysis. The cleaned data is then stored in a database.

[0049] Step 3:

[0050] The server uses a natural language processing engine to analyze the collected data. This analysis extracts the type, location, and extent of the damage from the text. It also scores the reliability of the information and filters out information with low reliability.

[0051] Step 4:

[0052] The server integrates the analysis results into a geographic information system. This generates visualization data, including location information of the damage, and displays high-risk areas on a map in real time.

[0053] Step 5:

[0054] The terminal provides the user with an interactive map based on visualization data received from the server. The map displays dangerous areas in different colors, making it easy for the user to understand the current situation.

[0055] Step 6:

[0056] The device will immediately notify the user upon receiving a warning notification from the server. The notification will include specific details about potential damage and evacuation advisories to help the user take swift action.

[0057] Step 7:

[0058] Users operate the map interface on their device to confirm safe evacuation routes. They obtain detailed damage and prediction information from the map and decide on evacuation actions according to the situation.

[0059] This series of processes enables the system to provide users with fast and reliable disaster information.

[0060] (Example 1)

[0061] 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."

[0062] In disaster situations, inadequate information gathering, analysis, and visualization hinder citizens from taking effective actions to evacuate safely, even when rapid and accurate decision-making is required. In particular, there is a lack of a systematic mechanism for evaluating the reliability of data from multiple sources and providing appropriate warnings and evacuation guidance.

[0063] 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.

[0064] In this invention, the server includes means for acquiring data from a network to collect information, a natural language processing unit for analyzing the acquired data and extracting location information and impact levels, and a visualization unit for integrating the analyzed information into a geospatial system and providing information visually. This enables the rapid provision of highly reliable information based on the collected data, allowing users to take appropriate evacuation actions.

[0065] "Means of acquiring data from networks for collecting information" refers to methods for acquiring relevant data from various information sources via the internet or communication networks.

[0066] A "natural language processing unit that analyzes acquired data and extracts location information and impact" is a device that analyzes collected text data to understand geographical location information and the extent and degree of impact.

[0067] A "visualization unit that integrates analyzed information into a geospatial system to provide information visually" is a system that makes it easy for users to understand information by intuitively displaying the analysis results as geographic map information.

[0068] A "communication device that sends notifications to warn of an emergency" is a device that transmits information to quickly warn users when a disaster or critical situation occurs.

[0069] "Interaction methods for users to manipulate maps and access information" refers to methods that allow users to manipulate maps through an interface to obtain necessary information or check details.

[0070] A "quality evaluation system for assessing data reliability and removing low-reliability data" is a mechanism for ensuring the quality of information by evaluating the accuracy and validity of collected information and eliminating erroneous data.

[0071] A "route guidance system for providing evacuation routes in emergency situations, taking into account the user's current location" is a program with a navigation function that presents safe and efficient evacuation routes based on the user's location information.

[0072] This invention is embodied as a system for rapidly and accurately collecting, analyzing, and visualizing disaster information. This system mainly consists of server, terminal, and user components.

[0073] The server first retrieves data from multiple sources via the network. This process uses APIs or web scraping techniques. The retrieved data is temporarily stored in storage and may also be stored in an SQL database or similar.

[0074] Next, the server analyzes the data using a natural language processing engine. Specifically, open-source natural language processing libraries (e.g., NLTK and spaCy) are used. This analysis allows for the extraction of location information and the degree of disaster impact. Furthermore, a quality evaluation system is implemented to assess the reliability of the data and filter out inaccurate information.

[0075] The analyzed information is integrated into a geospatial system. GIS software (e.g., ArcGIS or QGIS) is used to visualize the information, allowing users to intuitively understand it.

[0076] The device receives visualization data from geospatial systems and sends warning notifications to the user in the event of an emergency. For example, if a designated area is determined to be dangerous, the device immediately sends a push notification. This process allows the user to take quick and appropriate action.

[0077] Users can utilize the provided interface to view detailed information. For example, they can check the extent of damage on a map and confirm evacuation routes. Based on the presented options, users can obtain information to plan safe evacuation actions.

[0078] For example, if a user receives a typhoon warning, they can check areas at risk of flooding in advance and prepare a safe route. An example of a prompt to the generative AI model related to this system would be, "Based on the latest disaster information for the specified area, please suggest the best evacuation location."

[0079] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0080] Step 1:

[0081] The server begins collecting information. It obtains data via the network using APIs from social media and news sites, or by employing web scraping techniques. The input is URLs and API endpoints, and the output is raw disaster-related data. Specifically, it performs regularly scheduled tasks and obtains data in real time.

[0082] Step 2:

[0083] The server temporarily stores the acquired data. The data is saved to storage in JSON or XML format. The input is raw, unprocessed data requiring immediate reporting, while the output is structured data. Specific data formatting operations include parsing the data into JSON format, extracting important fields, and saving them.

[0084] Step 3:

[0085] The server performs natural language processing to analyze the data. Using a natural language processing engine, it extracts location information and the type and extent of damage from the text. The input is structured text data, and the output is analyzed location and damage information. A concrete example is the use of open-source natural language processing tools to perform named entity recognition.

[0086] Step 4:

[0087] The server performs a reliability assessment. It scores the reliability of the acquired information and filters out information that does not meet a certain level of confidence. The input is already analyzed information, and the output is data containing only highly reliable information. Specifically, it may apply an algorithm that automatically excludes items with low confidence scores.

[0088] Step 5:

[0089] The server integrates the analyzed data into a geographic information system (GIP) to create visualization data. The input is highly reliable analyzed information, and the output is visualization data as geospatial information. A concrete example is using GIP software to map damaged areas onto a map.

[0090] Step 6:

[0091] The device receives visualization data and sends a notification to the user. The input is visualization data, and the output is a warning notification to the user. Specific actions include sending push notifications to smartphones and other devices.

[0092] Step 7:

[0093] The user manipulates the map on their device to obtain detailed information. Inputs are warning notifications and visualization data, and output is relevant information tailored to their situation. Specific actions include zooming and panning the map to analyze the current situation and find a safe evacuation route.

[0094] (Application Example 1)

[0095] 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."

[0096] In urban disaster situations, it is essential to obtain reliable, real-time damage information and guide residents along appropriate evacuation routes in order for them to evacuate quickly and safely. However, conventional technologies lacked the speed and reliability of information, making it difficult to adequately ensure the safety of residents.

[0097] 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.

[0098] In this invention, the server includes a computer-readable processing means for collecting information from multiple information sources, a natural language processing structure means for analyzing the collected information and determining the extent of damage, and a route guidance processing means for providing evacuation routes based on the analyzed earthquake information. This makes it possible to quickly grasp the dangers faced by residents and provide appropriate evacuation routes.

[0099] "Multiple sources of information" refers to multiple platforms or services that provide data of different natures or formats.

[0100] "Computer-readable processing" refers to a series of program code or algorithms written in a format that a computer can interpret.

[0101] "Natural language processing structures" refer to computer programs used to understand, interpret, and analyze human language.

[0102] A "spatial information system" refers to a technological system for managing, analyzing, and visualizing geographical data.

[0103] "Processing for immediate visualization" refers to the technical process of quickly converting data into a visual format and displaying it.

[0104] "Communication function" refers to the technical function for electronically sending and receiving data and information.

[0105] A "user interface" refers to an interface that provides visual and operational elements for users to directly interact with a system.

[0106] "Route guidance processing" refers to the process of providing instructions to help users find the optimal route to their destination.

[0107] "Distribution function" refers to information technology used to deliver notifications and messages to users' devices.

[0108] Embodiments of this invention include an integrated system in which a server, terminal, and user each perform their respective roles. This system is intended to support real-time information provision and safe evacuation during disasters.

[0109] The server collects necessary data from multiple sources, such as social media and news sites. This involves using APIs and web scraping techniques to organize the information in a way that is readable by computing power. The collected information is analyzed using natural language processing structures to extract location, type, and extent of damage. Furthermore, the reliability of the information is evaluated, and low-reliability information is removed. The analyzed data is then immediately visualized via a spatial information system, and route guidance processing is used to provide evacuation routes during disasters.

[0110] The terminal uses its communication capabilities to deliver information provided by the server to the user. Through the user interface, the user can manipulate maps and check real-time disaster information and safe evacuation routes. For example, in the event of an earthquake, a notification is immediately sent to the user's terminal, showing the need for evacuation and the route. This allows the user to understand the extent of damage to the land and buildings and move quickly to a safe location.

[0111] By utilizing generative AI models, it is possible to generate prompt messages for unexpected disaster scenarios. An example of a prompt message is, "Create a prompt to notify users' devices of evacuation guidance information in the event of urban flooding." This enables scenario analysis for a variety of disaster situations, leading to more efficient evacuation support.

[0112] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0113] Step 1:

[0114] The server collects data from multiple sources, such as social media and news sites. It uses APIs and web scraping techniques as input to obtain text data and metadata. The output is a dataset where this information is temporarily stored. At this stage, the server retrieves data in real time and efficiently organizes the retrieved data.

[0115] Step 2:

[0116] The server applies natural language processing structures to the collected data to analyze the information. The input is the dataset obtained in step 1. Data analysis extracts location, type, and extent of damage. The output is a database containing the analyzed information. At this stage, the server evaluates the reliability of the information and removes information with low reliability.

[0117] Step 3:

[0118] The server visualizes the analyzed information using a spatial information system. The input is the analyzed data obtained in step 2. Based on the geographic data, it generates visual data to display dangerous areas and evacuation routes on a map. The output is the visualized map information.

[0119] Step 4:

[0120] The terminal receives visualization data sent from the server. The input is the visual data generated in step 3. The terminal displays it on the user interface and provides it to the user in a viewable format. The output is map information that the user can access.

[0121] Step 5:

[0122] The user interacts with map information provided on their device to check the current danger situation and evacuation routes. Input consists of the map information displayed on the device and the user's selections. Output is the evacuation information the user has received and their decisions regarding the next action. At this stage, the user receives real-time information to determine appropriate evacuation actions.

[0123] 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.

[0124] Embodiments of this invention are described as an integrated system that combines a disaster information system with an emotion engine that recognizes user emotions, and includes psychological support for users during disasters. This system is based on the interaction between a server, a terminal, and the user.

[0125] First, the server collects information not only from social media and news sites, but also from user input and responses. This data is analyzed in real time via an emotion engine to evaluate the user's psychological state. This evaluation is performed using text-level analysis and sentiment analysis techniques, and a score is generated that reflects states such as reassurance and anxiety.

[0126] Based on the analysis results, the server utilizes a geographic information system to visualize the extent of the damage. This data is structured in multiple layers, providing not only general damage information but also an interface that shows the user's emotional state and how it changes. Furthermore, customized evacuation information tailored to the user's emotions is generated by an emotion engine.

[0127] The device, along with notifications from the server, presents the user with emotion-based messages. For example, if the device determines that the user is in an anxious state, it will prioritize displaying information that provides reassurance (e.g., distance to evacuation shelters, ongoing rescue operations, etc.). It will also display empowerment messages to the user, providing support to reduce their psychological burden.

[0128] Through an emotionally resonant interface, users can explore situation-appropriate solutions. For example, in emergencies, their location information is used to suggest appropriate evacuation routes, along with reassuring information. This system goes beyond mere information provision, offering disaster support in a way that also considers the user's mental well-being.

[0129] Thus, this system, which incorporates an emotion engine, aims to provide users with information and psychological support tailored to their individual needs. For example, even in the chaotic circumstances of a disaster, users will be able to maintain their composure and take safe evacuation actions.

[0130] The following describes the processing flow.

[0131] Step 1:

[0132] The server collects information from social media, news sites, and user input. This data includes not only text information about the disaster, but also comments and feedback posted by users.

[0133] Step 2:

[0134] The server applies a natural language processing engine and an emotion engine to the collected data. The natural language processing engine extracts the type, location, and severity of the disaster, while the emotion engine generates emotion indicators from user comments.

[0135] Step 3:

[0136] The server evaluates the reliability of information and filters out low-reliability information. This evaluation takes into account the reliability of the information source and the influence of user sentiment.

[0137] Step 4:

[0138] The server integrates the analysis results into a geographic information system and generates map data that includes damage assessment and emotional information. This map is updated in real time and provides visual information tailored to the user's psychological state.

[0139] Step 5:

[0140] The terminal receives map data and warning notifications sent from the server and displays them to the user. The terminal builds an interface that takes the user's emotional state into consideration and provides information that soothes the user's emotions along with the warning message.

[0141] Step 6:

[0142] Users use the map interface on their device to check disaster information and evacuation routes. They also receive situation-appropriate suggestions and encouraging messages to help them make decisions about evacuation.

[0143] Step 7:

[0144] The server collects user feedback and new inputs, updating the database and map information in response to changing circumstances. It also adjusts the content of the information and messages provided as needed to improve the user's emotional state.

[0145] (Example 2)

[0146] 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".

[0147] There is a need for a system that can address the psychological stress and anxiety experienced by users during disasters, providing appropriate information and psychological support simultaneously. Conventional disaster information systems only provide physical evacuation information and lack the nuanced support that can be tailored to the emotional state of users. As a result, it is difficult for users to take swift and accurate evacuation actions while maintaining peace of mind.

[0148] 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.

[0149] In this invention, the server includes computer-readable programming means for collecting information from multiple information sources, emotion analysis engine means for analyzing the collected information and evaluating the user's psychological state, and programming means for visualizing the analysis results in real time using a map information processing system. This makes it possible to provide customized information and psychological support according to the user's emotional state.

[0150] A "computer-readable program" is code that contains instructions that can be read and executed by a computer, and is designed to automate a specific task.

[0151] An "emotion analysis engine" refers to artificial intelligence technology that identifies a user's emotions from text and audio data and quantifies their psychological state, such as feeling safe or anxious.

[0152] A "geographic information processing system" refers to information technology that visualizes geographical information on a digital map and provides that information to users in an easy-to-understand manner.

[0153] A "communication module" is a component that includes the hardware and software functions necessary for a system to send and receive data over a network.

[0154] A "display interface" refers to a user interface provided through a screen or input device that allows users to visually acquire information and perform operations.

[0155] A "generative AI model" refers to an algorithm trained using machine learning, which is used to automatically generate text and information from various input data.

[0156] A "message generation program" is software designed to create appropriate content based on the user's situation and emotional state, thereby enabling effective information delivery.

[0157] In an embodiment of this invention, the system is based on the interaction between a server, a terminal, and a user. In this system, the server first collects information from multiple sources. These sources include social networking services (SNS), online news, and direct input from the user. Web scraping technology is used for information collection. The server then uses a natural language processing engine to analyze the collected data in real time and evaluate the user's psychological state. This evaluation is performed using open-source natural language processing libraries (e.g., TENSORFLOW®, PyTorch).

[0158] The analyzed information includes sentiment analysis results, which score psychological states such as feelings of security and anxiety. The server uses this data to visually visualize the damage situation and sentiment distribution on a map using a geographic information processing system. Commonly used geographic information system (GIS) tools are employed for the geographic information processing.

[0159] Furthermore, the server uses a generative AI model to generate personalized evacuation information and psychological support messages based on the analysis results. These generated messages are sent to the terminal via a communication module. After receiving this information, the terminal prioritizes its display according to the user and issues voice and visual alerts as needed. More specifically, if the user is feeling anxious, the mobile device will display a prompt such as, "This is the shortest route to the shelter. Your leader is checking on your safety."

[0160] Finally, users can take appropriate evacuation actions by operating the display interface provided by their device. This allows users to act based on appropriate information while maintaining a sense of psychological security. This system aims to promote immediate action while maintaining the mental health of users during disasters.

[0161] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0162] Step 1:

[0163] The server collects information. It uses web scraping techniques to gather data from sources such as social media, news sites, and direct user input. The collected information is stored in a database in text format. It receives diverse digital content as input, organizes it, and converts it into an analyzable format. The output is text data ready for analysis.

[0164] Step 2:

[0165] The server analyzes the collected data in real time using an emotion analysis engine. The input is the text data collected in step 1. Using natural language processing techniques, it performs data calculations to numerically evaluate psychological states such as reassurance and anxiety. The output is the analysis result with a score representing the user's emotions. Specifically, it is output in the form of, for example, "Anxiety score: 0.75".

[0166] Step 3:

[0167] The server visualizes the analyzed sentiment data and damage status using a geographic information processing system. Inputs are sentiment scores and geographical damage information. Using GIS tools, the multi-layered data is displayed on a map, visualizing the distribution of user sentiment and the extent of damage. The output is a graphical interface placed on the map.

[0168] Step 4:

[0169] The server uses a generative AI model to generate personalized evacuation information and psychological support messages for the user. The input consists of sentiment analysis data and the user's current location. The generative AI model uses prompts to create individualized content, such as "Generate a message including directions from the current location to the nearest evacuation shelter." The output is a specific, action-oriented message.

[0170] Step 5:

[0171] The terminal displays messages received from the server and issues alerts as needed. Input consists of customized messages provided by the server. The terminal uses additional notification methods, such as sound and vibration, to attract the user's attention. Output consists of visual notifications displayed on the user's screen.

[0172] Step 6:

[0173] The user operates the terminal's interface. The input is the displayed evacuation information. The user uses this to plan their evacuation actions and check for the latest information as needed. The output is the user's own evacuation movement, which is focused on moving to a safe place.

[0174] (Application Example 2)

[0175] 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 device 14 will be referred to as the "terminal."

[0176] During a disaster, accurate damage assessment based on vast amounts of information and prompt warning notifications to users are required. However, psychological support that takes into account the emotional state of users is not provided. Furthermore, the lack of a system for evaluating the reliability of information and providing information that instills a sense of security in users can lead to anxiety.

[0177] 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.

[0178] In this invention, the server includes digital program means for collecting information from multiple information sources, a natural language processing engine and sentiment analysis engine means for analyzing the collected information and determining the extent of damage and the emotional state of the user, and a display program means for visualizing the analysis results in real time using a geographic information system and providing an interface that reflects the emotional state. This enables accurate and rapid determination of the extent of damage and provision of appropriate information according to the user's emotions.

[0179] A "digital program" is software designed to run on electronic devices and perform specific functions or processes.

[0180] A "natural language processing engine" is an engine that uses technology to enable computers to understand and analyze human language and extract its meaning.

[0181] An "emotion analysis engine" is an engine that uses technology to identify and evaluate emotions from text and audio data.

[0182] A "Geographic Information System" is a system for collecting, managing, and visually displaying geographical data.

[0183] A "display program" is a program designed to visualize data and provide information in a format that is easy for users to understand.

[0184] A "communication module" is software or hardware that has the function of exchanging information bidirectionally between the user and the system.

[0185] An "interface" is a point of contact or means for humans and machines to interact and exchange information, and is designed to improve the user experience.

[0186] An "analysis module" is a component that has the functionality to analyze collected data and extract useful information and patterns.

[0187] A "navigation program" is a program that calculates a route based on location information and guides the user in the direction of travel.

[0188] This system's program is designed for information gathering and user support during disasters. The server uses digital programs to collect data in real time from diverse sources such as social media and news sites. The collected information is then analyzed by a natural language processing engine and an emotion analysis engine to determine the extent of the damage and the emotional state of users. This analysis is further visualized using a geographic information system, and a customized interface based on the emotional state is displayed.

[0189] The terminal uses a communication module from the server to send out warning notifications. This ensures that users receive not only important information about the disaster, but also messages to help them maintain emotional stability. As a result, users can gain a sense of security even in anxious situations and deepen their understanding of the current situation.

[0190] The user interface is designed to interactively display information as users manipulate the map and to exchange special messages tailored to the user's emotions. The navigation program suggests evacuation routes that take into account location and emotional state in emergencies, allowing users to choose the optimal evacuation course.

[0191] As a concrete example, a device might display information about ongoing rescue operations to help users feel more secure when heading to a shelter. An example of a prompt message in this case might be: "As a citizen of a smart city, you will receive information to help you feel secure during a disaster in the following format. Based on your current emotional state, what information will calm you the most?" This system provides information tailored to individual needs, improving both psychological and physical safety during disasters.

[0192] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0193] Step 1:

[0194] The server collects data from social media and news sites. It receives text data from diverse sources as input and integrates it using digital programs. As output, it generates a set of integrated raw data. Specifically, it retrieves necessary data via APIs and stores it in a database.

[0195] Step 2:

[0196] The server analyzes the collected data using a natural language processing engine and an emotion analysis engine. The input is the raw dataset obtained in step 1. Data processing involves extracting the intent and emotions of the text and interpreting its meaning. The output includes a damage assessment result and the user's emotional state. Specifically, the analysis algorithm is applied to perform scoring.

[0197] Step 3:

[0198] The server uses a geographic information system to visualize the analysis results. The judgment results and sentiment scores obtained in step 2 are used as input. The data calculations geographically map the damage situation and generate an interface based on the sentiment state. The output is visualized map information. Specifically, a map API is used to perform multi-layered map display.

[0199] Step 4:

[0200] The device receives notifications from the server and presents the user with warning and emotionally reassuring messages. The input is the map and message information generated in step 3. The output is a visual or auditory notification displayed to the end user. Specifically, a push notification is sent to the user's screen.

[0201] Step 5:

[0202] The user interacts with the map using the device's user interface to view detailed information and evacuation routes. The input is the user manipulating map information presented by the device. The output is customized instructions tailored to the user's requests. Specific actions include using gestures such as tapping and swiping to zoom in on the map and display evacuation routes.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] [Second Embodiment]

[0207] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0208] 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.

[0209] 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).

[0210] 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.

[0211] 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.

[0212] 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).

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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".

[0219] The embodiment for carrying out this invention is described as an integrated system for collecting, analyzing, and visualizing disaster information. This system consists of multiple components having the roles of server, terminal, and user.

[0220] First, the server retrieves data from sources such as social media and news sites to collect information about disasters and damage. APIs and web scraping techniques are used to obtain this information, ensuring that data is updated in real time. This data is temporarily stored and then added to a database as needed.

[0221] Next, the server applies a natural language processing engine to the collected data to analyze the text data. This process extracts information such as the location of the damage, the type of damage, and its extent. During the analysis, the reliability of the information is evaluated, and low-reliability information is filtered out to ensure data quality.

[0222] The analyzed information is integrated into a geographic information system by a server, and visualization data is generated. This process allows users to intuitively understand, on a map, which areas are at risk and to what extent.

[0223] The visualized information is provided to the user via the device. The device, upon receiving the information, also sends a warning notification to the user. For example, if a certain area is determined to be at high risk of flooding, the user can immediately receive that notification on their device.

[0224] Users can then use the interface on their devices to view detailed information and make decisions regarding safe evacuation. They can manipulate maps to check the current situation and obtain detailed text information and damage predictions for specific areas.

[0225] This system aims to support safe evacuation actions during disasters by providing timely and accurate information about ongoing hazards. For example, if this system were used during a typhoon, people would be able to check areas where flooding is predicted in advance and prepare safe routes.

[0226] The following describes the processing flow.

[0227] Step 1:

[0228] The server uses APIs and web scraping techniques to collect disaster-related data from multiple sources, such as social media and news sites. It filters information using specific keywords and hashtags and retrieves data that is updated in real time.

[0229] Step 2:

[0230] The server temporarily stores the collected raw data and performs data cleaning. It removes duplicate and noisy data and formats it to prepare it for subsequent analysis. The cleaned data is then stored in a database.

[0231] Step 3:

[0232] The server uses a natural language processing engine to analyze the collected data. This analysis extracts the type, location, and extent of the damage from the text. It also scores the reliability of the information and filters out information with low reliability.

[0233] Step 4:

[0234] The server integrates the analysis results into a geographic information system. This generates visualization data, including location information of the damage, and displays high-risk areas on a map in real time.

[0235] Step 5:

[0236] The terminal provides the user with an interactive map based on visualization data received from the server. The map displays dangerous areas in different colors, making it easy for the user to understand the current situation.

[0237] Step 6:

[0238] The device will immediately notify the user upon receiving a warning notification from the server. The notification will include specific details about potential damage and evacuation advisories to help the user take swift action.

[0239] Step 7:

[0240] Users operate the map interface on their device to confirm safe evacuation routes. They obtain detailed damage and prediction information from the map and decide on evacuation actions according to the situation.

[0241] This series of processes enables the system to provide users with fast and reliable disaster information.

[0242] (Example 1)

[0243] 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."

[0244] In disaster situations, inadequate information gathering, analysis, and visualization hinder citizens from taking effective actions to evacuate safely, even when rapid and accurate decision-making is required. In particular, there is a lack of a systematic mechanism for evaluating the reliability of data from multiple sources and providing appropriate warnings and evacuation guidance.

[0245] 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.

[0246] In this invention, the server includes means for acquiring data from a network to collect information, a natural language processing unit for analyzing the acquired data and extracting location information and impact levels, and a visualization unit for integrating the analyzed information into a geospatial system and providing information visually. This enables the rapid provision of highly reliable information based on the collected data, allowing users to take appropriate evacuation actions.

[0247] "Means of acquiring data from networks for collecting information" refers to methods for acquiring relevant data from various information sources via the internet or communication networks.

[0248] A "natural language processing unit that analyzes acquired data and extracts location information and impact" is a device that analyzes collected text data to understand geographical location information and the extent and degree of impact.

[0249] A "visualization unit that integrates analyzed information into a geospatial system to provide information visually" is a system that makes it easy for users to understand information by intuitively displaying the analysis results as geographic map information.

[0250] A "communication device that sends notifications to warn of an emergency" is a device that transmits information to quickly warn users when a disaster or critical situation occurs.

[0251] "Interaction methods for users to manipulate maps and access information" refers to methods that allow users to manipulate maps through an interface to obtain necessary information or check details.

[0252] A "quality evaluation system for assessing data reliability and removing low-reliability data" is a mechanism for ensuring the quality of information by evaluating the accuracy and validity of collected information and eliminating erroneous data.

[0253] A "route guidance system for providing evacuation routes in emergency situations, taking into account the user's current location" is a program with a navigation function that presents safe and efficient evacuation routes based on the user's location information.

[0254] This invention is embodied as a system for rapidly and accurately collecting, analyzing, and visualizing disaster information. This system mainly consists of server, terminal, and user components.

[0255] The server first retrieves data from multiple sources via the network. This process uses APIs or web scraping techniques. The retrieved data is temporarily stored in storage and may also be stored in an SQL database or similar.

[0256] Next, the server analyzes the data using a natural language processing engine. Specifically, open-source natural language processing libraries (e.g., NLTK and spaCy) are used. This analysis allows for the extraction of location information and the degree of disaster impact. Furthermore, a quality evaluation system is implemented to assess the reliability of the data and filter out inaccurate information.

[0257] The analyzed information is integrated into a geospatial system. GIS software (e.g., ArcGIS or QGIS) is used to visualize the information, allowing users to intuitively understand it.

[0258] The device receives visualization data from geospatial systems and sends warning notifications to the user in the event of an emergency. For example, if a designated area is determined to be dangerous, the device immediately sends a push notification. This process allows the user to take quick and appropriate action.

[0259] Users can utilize the provided interface to view detailed information. For example, they can check the extent of damage on a map and confirm evacuation routes. Based on the presented options, users can obtain information to plan safe evacuation actions.

[0260] For example, if a user receives a typhoon warning, they can check areas at risk of flooding in advance and prepare a safe route. An example of a prompt to the generative AI model related to this system would be, "Based on the latest disaster information for the specified area, please suggest the best evacuation location."

[0261] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0262] Step 1:

[0263] The server begins collecting information. It obtains data via the network using APIs from social media and news sites, or by employing web scraping techniques. The input is URLs and API endpoints, and the output is raw disaster-related data. Specifically, it performs regularly scheduled tasks and obtains data in real time.

[0264] Step 2:

[0265] The server temporarily stores the acquired data. The data is saved to storage in JSON or XML format. The input is raw, unprocessed data requiring immediate reporting, while the output is structured data. Specific data formatting operations include parsing the data into JSON format, extracting important fields, and saving them.

[0266] Step 3:

[0267] The server performs natural language processing to analyze the data. Using a natural language processing engine, it extracts location information and the type and extent of damage from the text. The input is structured text data, and the output is analyzed location and damage information. A concrete example is the use of open-source natural language processing tools to perform named entity recognition.

[0268] Step 4:

[0269] The server performs a reliability assessment. It scores the reliability of the acquired information and filters out information that does not meet a certain level of confidence. The input is already analyzed information, and the output is data containing only highly reliable information. Specifically, it may apply an algorithm that automatically excludes items with low confidence scores.

[0270] Step 5:

[0271] The server integrates the analyzed data into a geographic information system (GIP) to create visualization data. The input is highly reliable analyzed information, and the output is visualization data as geospatial information. A concrete example is using GIP software to map damaged areas onto a map.

[0272] Step 6:

[0273] The device receives visualization data and sends a notification to the user. The input is visualization data, and the output is a warning notification to the user. Specific actions include sending push notifications to smartphones and other devices.

[0274] Step 7:

[0275] The user manipulates the map on their device to obtain detailed information. Inputs are warning notifications and visualization data, and output is relevant information tailored to their situation. Specific actions include zooming and panning the map to analyze the current situation and find a safe evacuation route.

[0276] (Application Example 1)

[0277] 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."

[0278] In urban disaster situations, it is essential to obtain reliable, real-time damage information and guide residents along appropriate evacuation routes in order for them to evacuate quickly and safely. However, conventional technologies lacked the speed and reliability of information, making it difficult to adequately ensure the safety of residents.

[0279] 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.

[0280] In this invention, the server includes a processing means readable by a computing device for collecting information from a plurality of information sources, a natural language processing structure means for analyzing the collected information and determining the damage situation, and a route guidance processing means for providing an evacuation route based on the analyzed earthquake information. Thereby, it becomes possible to quickly grasp the risks faced by residents and provide appropriate evacuation routes.

[0281] The "plurality of information sources" refers to a plurality of platforms and services that provide data of different natures and formats.

[0282] The "processing readable by a computing device" refers to a series of program codes and algorithms described in a form interpretable by a computer.

[0283] The "natural language processing structure" refers to a computer program for understanding, interpreting, and analyzing human language.

[0284] The "spatial information system" refers to a technical system for managing, analyzing, and visualizing geographical data.

[0285] The "processing for immediate visualization" refers to a technical process for quickly converting data into a visual form and displaying it.

[0286] The "communication function" refers to a technical function for electronically transmitting and receiving data and information.

[0287] The "user interface" refers to an interface equipped with visual and operational elements for the user to directly interact with the system.

[0288] The "route guidance processing" refers to a process for providing instructions so that the user can find the optimal route to the destination.

[0289] The "delivery function" refers to information technology for delivering notifications and messages to the user's device.

[0290] Embodiments of this invention include an integrated system in which a server, terminal, and user each perform their respective roles. This system is intended to support real-time information provision and safe evacuation during disasters.

[0291] The server collects necessary data from multiple sources, such as social media and news sites. This involves using APIs and web scraping techniques to organize the information in a way that is readable by computing power. The collected information is analyzed using natural language processing structures to extract location, type, and extent of damage. Furthermore, the reliability of the information is evaluated, and low-reliability information is removed. The analyzed data is then immediately visualized via a spatial information system, and route guidance processing is used to provide evacuation routes during disasters.

[0292] The terminal uses its communication capabilities to deliver information provided by the server to the user. Through the user interface, the user can manipulate maps and check real-time disaster information and safe evacuation routes. For example, in the event of an earthquake, a notification is immediately sent to the user's terminal, showing the need for evacuation and the route. This allows the user to understand the extent of damage to the land and buildings and move quickly to a safe location.

[0293] By utilizing generative AI models, it is possible to generate prompt messages for unexpected disaster scenarios. An example of a prompt message is, "Create a prompt to notify users' devices of evacuation guidance information in the event of urban flooding." This enables scenario analysis for a variety of disaster situations, leading to more efficient evacuation support.

[0294] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0295] Step 1:

[0296] The server collects data from multiple sources, such as social media and news sites. It uses APIs and web scraping techniques as input to obtain text data and metadata. The output is a dataset where this information is temporarily stored. At this stage, the server retrieves data in real time and efficiently organizes the retrieved data.

[0297] Step 2:

[0298] The server applies natural language processing structures to the collected data to analyze the information. The input is the dataset obtained in step 1. Data analysis extracts location, type, and extent of damage. The output is a database containing the analyzed information. At this stage, the server evaluates the reliability of the information and removes information with low reliability.

[0299] Step 3:

[0300] The server visualizes the analyzed information using a spatial information system. The input is the analyzed data obtained in step 2. Based on the geographic data, it generates visual data to display dangerous areas and evacuation routes on a map. The output is the visualized map information.

[0301] Step 4:

[0302] The terminal receives visualization data sent from the server. The input is the visual data generated in step 3. The terminal displays it on the user interface and provides it to the user in a viewable format. The output is map information that the user can access.

[0303] Step 5:

[0304] The user operates the map information provided on the terminal to check the current danger situation and evacuation route. The input is the map information displayed on the terminal and the user's selection. The output is the evacuation information obtained by the user and the judgment regarding the next action. At this stage, the user obtains information in real time to determine appropriate evacuation behavior.

[0305] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion specific model 59 and perform specific processing using the user's emotion.

[0306] The mode for implementing this invention will be described as an integrated system that combines an emotion engine for recognizing the user's emotion with a disaster information system and includes psychological support for the user during a disaster. This system is based on the interaction between the server, the terminal, and the user.

[0307] First, the server not only collects information from SNS and news sites but also collects the user's input and reactions. These data are analyzed in real time via the emotion engine to evaluate the user's psychological state. This evaluation is performed using text-level analysis and emotion analysis techniques, and a score reflecting states such as calmness and anxiety is generated.

[0308] Based on the analysis results, the server utilizes a geographic information system to visualize the damage situation. This data is composed in multiple layers, and in addition to general damage information, an interface showing the user's emotional state and its changes is also provided. Furthermore, customized evacuation information according to the user's emotion is generated by the emotion engine.

[0309] The device, along with notifications from the server, presents the user with emotion-based messages. For example, if the device determines that the user is in an anxious state, it will prioritize displaying information that provides reassurance (e.g., distance to evacuation shelters, ongoing rescue operations, etc.). It will also display empowerment messages to the user, providing support to reduce their psychological burden.

[0310] Through an emotionally resonant interface, users can explore situation-appropriate solutions. For example, in emergencies, their location information is used to suggest appropriate evacuation routes, along with reassuring information. This system goes beyond mere information provision, offering disaster support in a way that also considers the user's mental well-being.

[0311] Thus, this system, which incorporates an emotion engine, aims to provide users with information and psychological support tailored to their individual needs. For example, even in the chaotic circumstances of a disaster, users will be able to maintain their composure and take safe evacuation actions.

[0312] The following describes the processing flow.

[0313] Step 1:

[0314] The server collects information from social media, news sites, and user input. This data includes not only text information about the disaster, but also comments and feedback posted by users.

[0315] Step 2:

[0316] The server applies a natural language processing engine and an emotion engine to the collected data. The natural language processing engine extracts the type, location, and severity of the disaster, while the emotion engine generates emotion indicators from user comments.

[0317] Step 3:

[0318] The server evaluates the reliability of information and filters out low-reliability information. This evaluation takes into account the reliability of the information source and the influence of user sentiment.

[0319] Step 4:

[0320] The server integrates the analysis results into a geographic information system and generates map data that includes damage assessment and emotional information. This map is updated in real time and provides visual information tailored to the user's psychological state.

[0321] Step 5:

[0322] The terminal receives map data and warning notifications sent from the server and displays them to the user. The terminal builds an interface that takes the user's emotional state into consideration and provides information that soothes the user's emotions along with the warning message.

[0323] Step 6:

[0324] Users use the map interface on their device to check disaster information and evacuation routes. They also receive situation-appropriate suggestions and encouraging messages to help them make decisions about evacuation.

[0325] Step 7:

[0326] The server collects user feedback and new inputs, updating the database and map information in response to changing circumstances. It also adjusts the content of the information and messages provided as needed to improve the user's emotional state.

[0327] (Example 2)

[0328] 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".

[0329] There is a need for a system that can address the psychological stress and anxiety experienced by users during disasters, providing appropriate information and psychological support simultaneously. Conventional disaster information systems only provide physical evacuation information and lack the nuanced support that can be tailored to the emotional state of users. As a result, it is difficult for users to take swift and accurate evacuation actions while maintaining peace of mind.

[0330] 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.

[0331] In this invention, the server includes computer-readable programming means for collecting information from multiple information sources, emotion analysis engine means for analyzing the collected information and evaluating the user's psychological state, and programming means for visualizing the analysis results in real time using a map information processing system. This makes it possible to provide customized information and psychological support according to the user's emotional state.

[0332] A "computer-readable program" is code that contains instructions that can be read and executed by a computer, and is designed to automate a specific task.

[0333] An "emotion analysis engine" refers to artificial intelligence technology that identifies a user's emotions from text and audio data and quantifies their psychological state, such as feeling safe or anxious.

[0334] A "geographic information processing system" refers to information technology that visualizes geographical information on a digital map and provides that information to users in an easy-to-understand manner.

[0335] A "communication module" is a component that includes the hardware and software functions necessary for a system to send and receive data over a network.

[0336] A "display interface" refers to a user interface provided through a screen or input device that allows users to visually acquire information and perform operations.

[0337] A "generative AI model" refers to an algorithm trained using machine learning, which is used to automatically generate text and information from various input data.

[0338] A "message generation program" is software designed to create appropriate content based on the user's situation and emotional state, thereby enabling effective information delivery.

[0339] In an embodiment of this invention, the system is based on the interaction between a server, a terminal, and a user. In this system, the server first collects information from multiple sources. These sources include social networking services (SNS), online news, and direct input from the user. Web scraping technology is used for information collection. The server then uses a natural language processing engine to analyze the collected data in real time and evaluate the user's psychological state. This evaluation is performed using open-source natural language processing libraries (e.g., TensorFlow, PyTorch).

[0340] The analyzed information includes sentiment analysis results, which score psychological states such as feelings of security and anxiety. The server uses this data to visually visualize the damage situation and sentiment distribution on a map using a geographic information processing system. Commonly used geographic information system (GIS) tools are employed for the geographic information processing.

[0341] Furthermore, the server uses a generative AI model to generate personalized evacuation information and psychological support messages based on the analysis results. These generated messages are sent to the terminal via a communication module. After receiving this information, the terminal prioritizes its display according to the user and issues voice and visual alerts as needed. More specifically, if the user is feeling anxious, the mobile device will display a prompt such as, "This is the shortest route to the shelter. Your leader is checking on your safety."

[0342] Finally, users can take appropriate evacuation actions by operating the display interface provided by their device. This allows users to act based on appropriate information while maintaining a sense of psychological security. This system aims to promote immediate action while maintaining the mental health of users during disasters.

[0343] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0344] Step 1:

[0345] The server collects information. It uses web scraping techniques to gather data from sources such as social media, news sites, and direct user input. The collected information is stored in a database in text format. It receives diverse digital content as input, organizes it, and converts it into an analyzable format. The output is text data ready for analysis.

[0346] Step 2:

[0347] The server analyzes the collected data in real time using an emotion analysis engine. The input is the text data collected in step 1. Using natural language processing techniques, it performs data calculations to numerically evaluate psychological states such as reassurance and anxiety. The output is the analysis result with a score representing the user's emotions. Specifically, it is output in the form of, for example, "Anxiety score: 0.75".

[0348] Step 3:

[0349] The server visualizes the analyzed sentiment data and damage status using a geographic information processing system. Inputs are sentiment scores and geographical damage information. Using GIS tools, the multi-layered data is displayed on a map, visualizing the distribution of user sentiment and the extent of damage. The output is a graphical interface placed on the map.

[0350] Step 4:

[0351] The server uses a generative AI model to generate personalized evacuation information and psychological support messages for the user. The input consists of sentiment analysis data and the user's current location. The generative AI model uses prompts to create individualized content, such as "Generate a message including directions from the current location to the nearest evacuation shelter." The output is a specific, action-oriented message.

[0352] Step 5:

[0353] The terminal displays messages received from the server and issues alerts as needed. Input consists of customized messages provided by the server. The terminal uses additional notification methods, such as sound and vibration, to attract the user's attention. Output consists of visual notifications displayed on the user's screen.

[0354] Step 6:

[0355] The user operates the terminal's interface. The input is the displayed evacuation information. The user uses this to plan their evacuation actions and check for the latest information as needed. The output is the user's own evacuation movement, which is focused on moving to a safe place.

[0356] (Application Example 2)

[0357] 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."

[0358] During a disaster, accurate damage assessment based on vast amounts of information and prompt warning notifications to users are required. However, psychological support that takes into account the emotional state of users is not provided. Furthermore, the lack of a system for evaluating the reliability of information and providing information that instills a sense of security in users can lead to anxiety.

[0359] 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.

[0360] In this invention, the server includes digital program means for collecting information from multiple information sources, a natural language processing engine and sentiment analysis engine means for analyzing the collected information and determining the extent of damage and the emotional state of the user, and a display program means for visualizing the analysis results in real time using a geographic information system and providing an interface that reflects the emotional state. This enables accurate and rapid determination of the extent of damage and provision of appropriate information according to the user's emotions.

[0361] A "digital program" is software designed to run on electronic devices and perform specific functions or processes.

[0362] A "natural language processing engine" is an engine that uses technology to enable computers to understand and analyze human language and extract its meaning.

[0363] An "emotion analysis engine" is an engine that uses technology to identify and evaluate emotions from text and audio data.

[0364] A "Geographic Information System" is a system for collecting, managing, and visually displaying geographical data.

[0365] A "display program" is a program designed to visualize data and provide information in a format that is easy for users to understand.

[0366] A "communication module" is software or hardware that has the function of exchanging information bidirectionally between the user and the system.

[0367] An "interface" is a point of contact or means for humans and machines to interact and exchange information, and is designed to improve the user experience.

[0368] An "analysis module" is a component that has the functionality to analyze collected data and extract useful information and patterns.

[0369] A "navigation program" is a program that calculates a route based on location information and guides the user in the direction of travel.

[0370] This system's program is designed for information gathering and user support during disasters. The server uses digital programs to collect data in real time from diverse sources such as social media and news sites. The collected information is then analyzed by a natural language processing engine and an emotion analysis engine to determine the extent of the damage and the emotional state of users. This analysis is further visualized using a geographic information system, and a customized interface based on the emotional state is displayed.

[0371] The terminal uses a communication module from the server to send out warning notifications. This ensures that users receive not only important information about the disaster, but also messages to help them maintain emotional stability. As a result, users can gain a sense of security even in anxious situations and deepen their understanding of the current situation.

[0372] The user interface is designed to interactively display information as users manipulate the map and to exchange special messages tailored to the user's emotions. The navigation program suggests evacuation routes that take into account location and emotional state in emergencies, allowing users to choose the optimal evacuation course.

[0373] As a concrete example, a device might display information about ongoing rescue operations to help users feel more secure when heading to a shelter. An example of a prompt message in this case might be: "As a citizen of a smart city, you will receive information to help you feel secure during a disaster in the following format. Based on your current emotional state, what information will calm you the most?" This system provides information tailored to individual needs, improving both psychological and physical safety during disasters.

[0374] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0375] Step 1:

[0376] The server collects data from social media and news sites. It receives text data from diverse sources as input and integrates it using digital programs. As output, it generates a set of integrated raw data. Specifically, it retrieves necessary data via APIs and stores it in a database.

[0377] Step 2:

[0378] The server analyzes the collected data using a natural language processing engine and an emotion analysis engine. The input is the raw dataset obtained in step 1. Data processing involves extracting the intent and emotions of the text and interpreting its meaning. The output includes a damage assessment result and the user's emotional state. Specifically, the analysis algorithm is applied to perform scoring.

[0379] Step 3:

[0380] The server uses a geographic information system to visualize the analysis results. The judgment results and sentiment scores obtained in step 2 are used as input. The data calculations geographically map the damage situation and generate an interface based on the sentiment state. The output is visualized map information. Specifically, a map API is used to perform multi-layered map display.

[0381] Step 4:

[0382] The device receives notifications from the server and presents the user with warning and emotionally reassuring messages. The input is the map and message information generated in step 3. The output is a visual or auditory notification displayed to the end user. Specifically, a push notification is sent to the user's screen.

[0383] Step 5:

[0384] The user interacts with the map using the device's user interface to view detailed information and evacuation routes. The input is the user manipulating map information presented by the device. The output is customized instructions tailored to the user's requests. Specific actions include using gestures such as tapping and swiping to zoom in on the map and display evacuation routes.

[0385] 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.

[0386] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0387] 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.

[0388] [Third Embodiment]

[0389] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0390] 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.

[0391] 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).

[0392] 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.

[0393] 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.

[0394] 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).

[0395] 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.

[0396] 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.

[0397] 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.

[0398] 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.

[0399] 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.

[0400] 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".

[0401] The embodiment for carrying out this invention is described as an integrated system for collecting, analyzing, and visualizing disaster information. This system consists of multiple components having the roles of server, terminal, and user.

[0402] First, the server retrieves data from sources such as social media and news sites to collect information about disasters and damage. APIs and web scraping techniques are used to obtain this information, ensuring that data is updated in real time. This data is temporarily stored and then added to a database as needed.

[0403] Next, the server applies a natural language processing engine to the collected data to analyze the text data. This process extracts information such as the location of the damage, the type of damage, and its extent. During the analysis, the reliability of the information is evaluated, and low-reliability information is filtered out to ensure data quality.

[0404] The analyzed information is integrated into a geographic information system by a server, and visualization data is generated. This process allows users to intuitively understand, on a map, which areas are at risk and to what extent.

[0405] The visualized information is provided to the user via the device. The device, upon receiving the information, also sends a warning notification to the user. For example, if a certain area is determined to be at high risk of flooding, the user can immediately receive that notification on their device.

[0406] Users can then use the interface on their devices to view detailed information and make decisions regarding safe evacuation. They can manipulate maps to check the current situation and obtain detailed text information and damage predictions for specific areas.

[0407] This system aims to support safe evacuation actions during disasters by providing timely and accurate information about ongoing hazards. For example, if this system were used during a typhoon, people would be able to check areas where flooding is predicted in advance and prepare safe routes.

[0408] The following describes the processing flow.

[0409] Step 1:

[0410] The server uses APIs and web scraping techniques to collect disaster-related data from multiple sources, such as social media and news sites. It filters information using specific keywords and hashtags and retrieves data that is updated in real time.

[0411] Step 2:

[0412] The server temporarily stores the collected raw data and performs data cleaning. It removes duplicate and noisy data and formats it to prepare it for subsequent analysis. The cleaned data is then stored in a database.

[0413] Step 3:

[0414] The server uses a natural language processing engine to analyze the collected data. This analysis extracts the type, location, and extent of the damage from the text. It also scores the reliability of the information and filters out information with low reliability.

[0415] Step 4:

[0416] The server integrates the analysis results into a geographic information system. This generates visualization data, including location information of the damage, and displays high-risk areas on a map in real time.

[0417] Step 5:

[0418] The terminal provides the user with an interactive map based on visualization data received from the server. The map displays dangerous areas in different colors, making it easy for the user to understand the current situation.

[0419] Step 6:

[0420] The device will immediately notify the user upon receiving a warning notification from the server. The notification will include specific details about potential damage and evacuation advisories to help the user take swift action.

[0421] Step 7:

[0422] Users operate the map interface on their device to confirm safe evacuation routes. They obtain detailed damage and prediction information from the map and decide on evacuation actions according to the situation.

[0423] This series of processes enables the system to provide users with fast and reliable disaster information.

[0424] (Example 1)

[0425] 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."

[0426] In disaster situations, inadequate information gathering, analysis, and visualization hinder citizens from taking effective actions to evacuate safely, even when rapid and accurate decision-making is required. In particular, there is a lack of a systematic mechanism for evaluating the reliability of data from multiple sources and providing appropriate warnings and evacuation guidance.

[0427] 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.

[0428] In this invention, the server includes means for acquiring data from a network to collect information, a natural language processing unit for analyzing the acquired data and extracting location information and impact levels, and a visualization unit for integrating the analyzed information into a geospatial system and providing information visually. This enables the rapid provision of highly reliable information based on the collected data, allowing users to take appropriate evacuation actions.

[0429] "Means of acquiring data from networks for collecting information" refers to methods for acquiring relevant data from various information sources via the internet or communication networks.

[0430] A "natural language processing unit that analyzes acquired data and extracts location information and impact" is a device that analyzes collected text data to understand geographical location information and the extent and degree of impact.

[0431] A "visualization unit that integrates analyzed information into a geospatial system to provide information visually" is a system that makes it easy for users to understand information by intuitively displaying the analysis results as geographic map information.

[0432] A "communication device that sends notifications to warn of an emergency" is a device that transmits information to quickly warn users when a disaster or critical situation occurs.

[0433] "Interaction methods for users to manipulate maps and access information" refers to methods that allow users to manipulate maps through an interface to obtain necessary information or check details.

[0434] A "quality evaluation system for assessing data reliability and removing low-reliability data" is a mechanism for ensuring the quality of information by evaluating the accuracy and validity of collected information and eliminating erroneous data.

[0435] A "route guidance system for providing evacuation routes in emergency situations, taking into account the user's current location" is a program with a navigation function that presents safe and efficient evacuation routes based on the user's location information.

[0436] This invention is embodied as a system for rapidly and accurately collecting, analyzing, and visualizing disaster information. This system mainly consists of server, terminal, and user components.

[0437] The server first retrieves data from multiple sources via the network. This process uses APIs or web scraping techniques. The retrieved data is temporarily stored in storage and may also be stored in an SQL database or similar.

[0438] Next, the server analyzes the data using a natural language processing engine. Specifically, open-source natural language processing libraries (e.g., NLTK and spaCy) are used. This analysis allows for the extraction of location information and the degree of disaster impact. Furthermore, a quality evaluation system is implemented to assess the reliability of the data and filter out inaccurate information.

[0439] The analyzed information is integrated into a geospatial system. GIS software (e.g., ArcGIS or QGIS) is used to visualize the information, allowing users to intuitively understand it.

[0440] The device receives visualization data from geospatial systems and sends warning notifications to the user in the event of an emergency. For example, if a designated area is determined to be dangerous, the device immediately sends a push notification. This process allows the user to take quick and appropriate action.

[0441] Users can utilize the provided interface to view detailed information. For example, they can check the extent of damage on a map and confirm evacuation routes. Based on the presented options, users can obtain information to plan safe evacuation actions.

[0442] For example, if a user receives a typhoon warning, they can check areas at risk of flooding in advance and prepare a safe route. An example of a prompt to the generative AI model related to this system would be, "Based on the latest disaster information for the specified area, please suggest the best evacuation location."

[0443] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0444] Step 1:

[0445] The server begins collecting information. It obtains data via the network using APIs from social media and news sites, or by employing web scraping techniques. The input is URLs and API endpoints, and the output is raw disaster-related data. Specifically, it performs regularly scheduled tasks and obtains data in real time.

[0446] Step 2:

[0447] The server temporarily stores the acquired data. The data is saved to storage in JSON or XML format. The input is raw, unprocessed data requiring immediate reporting, while the output is structured data. Specific data formatting operations include parsing the data into JSON format, extracting important fields, and saving them.

[0448] Step 3:

[0449] The server performs natural language processing to analyze the data. Using a natural language processing engine, it extracts location information and the type and extent of damage from the text. The input is structured text data, and the output is analyzed location and damage information. A concrete example is the use of open-source natural language processing tools to perform named entity recognition.

[0450] Step 4:

[0451] The server performs a reliability assessment. It scores the reliability of the acquired information and filters out information that does not meet a certain level of confidence. The input is already analyzed information, and the output is data containing only highly reliable information. Specifically, it may apply an algorithm that automatically excludes items with low confidence scores.

[0452] Step 5:

[0453] The server integrates the analyzed data into a geographic information system (GIP) to create visualization data. The input is highly reliable analyzed information, and the output is visualization data as geospatial information. A concrete example is using GIP software to map damaged areas onto a map.

[0454] Step 6:

[0455] The device receives visualization data and sends a notification to the user. The input is visualization data, and the output is a warning notification to the user. Specific actions include sending push notifications to smartphones and other devices.

[0456] Step 7:

[0457] The user manipulates the map on their device to obtain detailed information. Inputs are warning notifications and visualization data, and output is relevant information tailored to their situation. Specific actions include zooming and panning the map to analyze the current situation and find a safe evacuation route.

[0458] (Application Example 1)

[0459] 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."

[0460] In urban disaster situations, it is essential to obtain reliable, real-time damage information and guide residents along appropriate evacuation routes in order for them to evacuate quickly and safely. However, conventional technologies lacked the speed and reliability of information, making it difficult to adequately ensure the safety of residents.

[0461] 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.

[0462] In this invention, the server includes a computer-readable processing means for collecting information from multiple information sources, a natural language processing structure means for analyzing the collected information and determining the extent of damage, and a route guidance processing means for providing evacuation routes based on the analyzed earthquake information. This makes it possible to quickly grasp the dangers faced by residents and provide appropriate evacuation routes.

[0463] "Multiple sources of information" refers to multiple platforms or services that provide data of different natures or formats.

[0464] "Computer-readable processing" refers to a series of program code or algorithms written in a format that a computer can interpret.

[0465] "Natural language processing structures" refer to computer programs used to understand, interpret, and analyze human language.

[0466] A "spatial information system" refers to a technological system for managing, analyzing, and visualizing geographical data.

[0467] "Processing for immediate visualization" refers to the technical process of quickly converting data into a visual format and displaying it.

[0468] "Communication function" refers to the technical function for electronically sending and receiving data and information.

[0469] A "user interface" refers to an interface that provides visual and operational elements for users to directly interact with a system.

[0470] "Route guidance processing" refers to the process of providing instructions to help users find the optimal route to their destination.

[0471] "Distribution function" refers to information technology used to deliver notifications and messages to users' devices.

[0472] Embodiments of this invention include an integrated system in which a server, terminal, and user each perform their respective roles. This system is intended to support real-time information provision and safe evacuation during disasters.

[0473] The server collects necessary data from multiple sources, such as social media and news sites. This involves using APIs and web scraping techniques to organize the information in a way that is readable by computing power. The collected information is analyzed using natural language processing structures to extract location, type, and extent of damage. Furthermore, the reliability of the information is evaluated, and low-reliability information is removed. The analyzed data is then immediately visualized via a spatial information system, and route guidance processing is used to provide evacuation routes during disasters.

[0474] The terminal uses its communication capabilities to deliver information provided by the server to the user. Through the user interface, the user can manipulate maps and check real-time disaster information and safe evacuation routes. For example, in the event of an earthquake, a notification is immediately sent to the user's terminal, showing the need for evacuation and the route. This allows the user to understand the extent of damage to the land and buildings and move quickly to a safe location.

[0475] By utilizing generative AI models, it is possible to generate prompt messages for unexpected disaster scenarios. An example of a prompt message is, "Create a prompt to notify users' devices of evacuation guidance information in the event of urban flooding." This enables scenario analysis for a variety of disaster situations, leading to more efficient evacuation support.

[0476] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0477] Step 1:

[0478] The server collects data from multiple sources, such as social media and news sites. It uses APIs and web scraping techniques as input to obtain text data and metadata. The output is a dataset where this information is temporarily stored. At this stage, the server retrieves data in real time and efficiently organizes the retrieved data.

[0479] Step 2:

[0480] The server applies natural language processing structures to the collected data to analyze the information. The input is the dataset obtained in step 1. Data analysis extracts location, type, and extent of damage. The output is a database containing the analyzed information. At this stage, the server evaluates the reliability of the information and removes information with low reliability.

[0481] Step 3:

[0482] The server visualizes the analyzed information using a spatial information system. The input is the analyzed data obtained in step 2. Based on the geographic data, it generates visual data to display dangerous areas and evacuation routes on a map. The output is the visualized map information.

[0483] Step 4:

[0484] The terminal receives visualization data sent from the server. The input is the visual data generated in step 3. The terminal displays it on the user interface and provides it to the user in a viewable format. The output is map information that the user can access.

[0485] Step 5:

[0486] The user interacts with map information provided on their device to check the current danger situation and evacuation routes. Input consists of the map information displayed on the device and the user's selections. Output is the evacuation information the user has received and their decisions regarding the next action. At this stage, the user receives real-time information to determine appropriate evacuation actions.

[0487] 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.

[0488] Embodiments of this invention are described as an integrated system that combines a disaster information system with an emotion engine that recognizes user emotions, and includes psychological support for users during disasters. This system is based on the interaction between a server, a terminal, and the user.

[0489] First, the server collects information not only from social media and news sites, but also from user input and responses. This data is analyzed in real time via an emotion engine to evaluate the user's psychological state. This evaluation is performed using text-level analysis and sentiment analysis techniques, and a score is generated that reflects states such as reassurance and anxiety.

[0490] Based on the analysis results, the server utilizes a geographic information system to visualize the extent of the damage. This data is structured in multiple layers, providing not only general damage information but also an interface that shows the user's emotional state and how it changes. Furthermore, customized evacuation information tailored to the user's emotions is generated by an emotion engine.

[0491] The device, along with notifications from the server, presents the user with emotion-based messages. For example, if the device determines that the user is in an anxious state, it will prioritize displaying information that provides reassurance (e.g., distance to evacuation shelters, ongoing rescue operations, etc.). It will also display empowerment messages to the user, providing support to reduce their psychological burden.

[0492] Through an emotionally resonant interface, users can explore situation-appropriate solutions. For example, in emergencies, their location information is used to suggest appropriate evacuation routes, along with reassuring information. This system goes beyond mere information provision, offering disaster support in a way that also considers the user's mental well-being.

[0493] Thus, this system, which incorporates an emotion engine, aims to provide users with information and psychological support tailored to their individual needs. For example, even in the chaotic circumstances of a disaster, users will be able to maintain their composure and take safe evacuation actions.

[0494] The following describes the processing flow.

[0495] Step 1:

[0496] The server collects information from social media, news sites, and user input. This data includes not only text information about the disaster, but also comments and feedback posted by users.

[0497] Step 2:

[0498] The server applies a natural language processing engine and an emotion engine to the collected data. The natural language processing engine extracts the type, location, and severity of the disaster, while the emotion engine generates emotion indicators from user comments.

[0499] Step 3:

[0500] The server evaluates the reliability of information and filters out low-reliability information. This evaluation takes into account the reliability of the information source and the influence of user sentiment.

[0501] Step 4:

[0502] The server integrates the analysis results into a geographic information system and generates map data that includes damage assessment and emotional information. This map is updated in real time and provides visual information tailored to the user's psychological state.

[0503] Step 5:

[0504] The terminal receives map data and warning notifications sent from the server and displays them to the user. The terminal builds an interface that takes the user's emotional state into consideration and provides information that soothes the user's emotions along with the warning message.

[0505] Step 6:

[0506] Users use the map interface on their device to check disaster information and evacuation routes. They also receive situation-appropriate suggestions and encouraging messages to help them make decisions about evacuation.

[0507] Step 7:

[0508] The server collects user feedback and new inputs, updating the database and map information in response to changing circumstances. It also adjusts the content of the information and messages provided as needed to improve the user's emotional state.

[0509] (Example 2)

[0510] 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."

[0511] There is a need for a system that can address the psychological stress and anxiety experienced by users during disasters, providing appropriate information and psychological support simultaneously. Conventional disaster information systems only provide physical evacuation information and lack the nuanced support that can be tailored to the emotional state of users. As a result, it is difficult for users to take swift and accurate evacuation actions while maintaining peace of mind.

[0512] 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.

[0513] In this invention, the server includes computer-readable programming means for collecting information from multiple information sources, emotion analysis engine means for analyzing the collected information and evaluating the user's psychological state, and programming means for visualizing the analysis results in real time using a map information processing system. This makes it possible to provide customized information and psychological support according to the user's emotional state.

[0514] A "computer-readable program" is code that contains instructions that can be read and executed by a computer, and is designed to automate a specific task.

[0515] An "emotion analysis engine" refers to artificial intelligence technology that identifies a user's emotions from text and audio data and quantifies their psychological state, such as feeling safe or anxious.

[0516] A "geographic information processing system" refers to information technology that visualizes geographical information on a digital map and provides that information to users in an easy-to-understand manner.

[0517] A "communication module" is a component that includes the hardware and software functions necessary for a system to send and receive data over a network.

[0518] A "display interface" refers to a user interface provided through a screen or input device that allows users to visually acquire information and perform operations.

[0519] A "generative AI model" refers to an algorithm trained using machine learning, which is used to automatically generate text and information from various input data.

[0520] A "message generation program" is software designed to create appropriate content based on the user's situation and emotional state, thereby enabling effective information delivery.

[0521] In an embodiment of this invention, the system is based on the interaction between a server, a terminal, and a user. In this system, the server first collects information from multiple sources. These sources include social networking services (SNS), online news, and direct input from the user. Web scraping technology is used for information collection. The server then uses a natural language processing engine to analyze the collected data in real time and evaluate the user's psychological state. This evaluation is performed using open-source natural language processing libraries (e.g., TensorFlow, PyTorch).

[0522] The analyzed information includes sentiment analysis results, which score psychological states such as feelings of security and anxiety. The server uses this data to visually visualize the damage situation and sentiment distribution on a map using a geographic information processing system. Commonly used geographic information system (GIS) tools are employed for the geographic information processing.

[0523] Furthermore, the server uses a generative AI model to generate personalized evacuation information and psychological support messages based on the analysis results. These generated messages are sent to the terminal via a communication module. After receiving this information, the terminal prioritizes its display according to the user and issues voice and visual alerts as needed. More specifically, if the user is feeling anxious, the mobile device will display a prompt such as, "This is the shortest route to the shelter. Your leader is checking on your safety."

[0524] Finally, users can take appropriate evacuation actions by operating the display interface provided by their device. This allows users to act based on appropriate information while maintaining a sense of psychological security. This system aims to promote immediate action while maintaining the mental health of users during disasters.

[0525] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0526] Step 1:

[0527] The server collects information. It uses web scraping techniques to gather data from sources such as social media, news sites, and direct user input. The collected information is stored in a database in text format. It receives diverse digital content as input, organizes it, and converts it into an analyzable format. The output is text data ready for analysis.

[0528] Step 2:

[0529] The server analyzes the collected data in real time using an emotion analysis engine. The input is the text data collected in step 1. Using natural language processing techniques, it performs data calculations to numerically evaluate psychological states such as reassurance and anxiety. The output is the analysis result with a score representing the user's emotions. Specifically, it is output in the form of, for example, "Anxiety score: 0.75".

[0530] Step 3:

[0531] The server visualizes the analyzed sentiment data and damage status using a geographic information processing system. Inputs are sentiment scores and geographical damage information. Using GIS tools, the multi-layered data is displayed on a map, visualizing the distribution of user sentiment and the extent of damage. The output is a graphical interface placed on the map.

[0532] Step 4:

[0533] The server uses a generative AI model to generate personalized evacuation information and psychological support messages for the user. The input consists of sentiment analysis data and the user's current location. The generative AI model uses prompts to create individualized content, such as "Generate a message including directions from the current location to the nearest evacuation shelter." The output is a specific, action-oriented message.

[0534] Step 5:

[0535] The terminal displays messages received from the server and issues alerts as needed. Input consists of customized messages provided by the server. The terminal uses additional notification methods, such as sound and vibration, to attract the user's attention. Output consists of visual notifications displayed on the user's screen.

[0536] Step 6:

[0537] The user operates the terminal's interface. The input is the displayed evacuation information. The user uses this to plan their evacuation actions and check for the latest information as needed. The output is the user's own evacuation movement, which is focused on moving to a safe place.

[0538] (Application Example 2)

[0539] 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."

[0540] During a disaster, accurate damage assessment based on vast amounts of information and prompt warning notifications to users are required. However, psychological support that takes into account the emotional state of users is not provided. Furthermore, the lack of a system for evaluating the reliability of information and providing information that instills a sense of security in users can lead to anxiety.

[0541] 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.

[0542] In this invention, the server includes digital program means for collecting information from multiple information sources, a natural language processing engine and sentiment analysis engine means for analyzing the collected information and determining the extent of damage and the emotional state of the user, and a display program means for visualizing the analysis results in real time using a geographic information system and providing an interface that reflects the emotional state. This enables accurate and rapid determination of the extent of damage and provision of appropriate information according to the user's emotions.

[0543] A "digital program" is software designed to run on electronic devices and perform specific functions or processes.

[0544] A "natural language processing engine" is an engine that uses technology to enable computers to understand and analyze human language and extract its meaning.

[0545] An "emotion analysis engine" is an engine that uses technology to identify and evaluate emotions from text and audio data.

[0546] A "Geographic Information System" is a system for collecting, managing, and visually displaying geographical data.

[0547] A "display program" is a program designed to visualize data and provide information in a format that is easy for users to understand.

[0548] A "communication module" is software or hardware that has the function of exchanging information bidirectionally between the user and the system.

[0549] An "interface" is a point of contact or means for humans and machines to interact and exchange information, and is designed to improve the user experience.

[0550] An "analysis module" is a component that has the functionality to analyze collected data and extract useful information and patterns.

[0551] A "navigation program" is a program that calculates a route based on location information and guides the user in the direction of travel.

[0552] This system's program is designed for information gathering and user support during disasters. The server uses digital programs to collect data in real time from diverse sources such as social media and news sites. The collected information is then analyzed by a natural language processing engine and an emotion analysis engine to determine the extent of the damage and the emotional state of users. This analysis is further visualized using a geographic information system, and a customized interface based on the emotional state is displayed.

[0553] The terminal uses a communication module from the server to send out warning notifications. This ensures that users receive not only important information about the disaster, but also messages to help them maintain emotional stability. As a result, users can gain a sense of security even in anxious situations and deepen their understanding of the current situation.

[0554] The user interface is designed to interactively display information as users manipulate the map and to exchange special messages tailored to the user's emotions. The navigation program suggests evacuation routes that take into account location and emotional state in emergencies, allowing users to choose the optimal evacuation course.

[0555] As a concrete example, a device might display information about ongoing rescue operations to help users feel more secure when heading to a shelter. An example of a prompt message in this case might be: "As a citizen of a smart city, you will receive information to help you feel secure during a disaster in the following format. Based on your current emotional state, what information will calm you the most?" This system provides information tailored to individual needs, improving both psychological and physical safety during disasters.

[0556] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0557] Step 1:

[0558] The server collects data from social media and news sites. It receives text data from diverse sources as input and integrates it using digital programs. As output, it generates a set of integrated raw data. Specifically, it retrieves necessary data via APIs and stores it in a database.

[0559] Step 2:

[0560] The server analyzes the collected data using a natural language processing engine and an emotion analysis engine. The input is the raw dataset obtained in step 1. Data processing involves extracting the intent and emotions of the text and interpreting its meaning. The output includes a damage assessment result and the user's emotional state. Specifically, the analysis algorithm is applied to perform scoring.

[0561] Step 3:

[0562] The server uses a geographic information system to visualize the analysis results. The judgment results and sentiment scores obtained in step 2 are used as input. The data calculations geographically map the damage situation and generate an interface based on the sentiment state. The output is visualized map information. Specifically, a map API is used to perform multi-layered map display.

[0563] Step 4:

[0564] The device receives notifications from the server and presents the user with warning and emotionally reassuring messages. The input is the map and message information generated in step 3. The output is a visual or auditory notification displayed to the end user. Specifically, a push notification is sent to the user's screen.

[0565] Step 5:

[0566] The user interacts with the map using the device's user interface to view detailed information and evacuation routes. The input is the user manipulating map information presented by the device. The output is customized instructions tailored to the user's requests. Specific actions include using gestures such as tapping and swiping to zoom in on the map and display evacuation routes.

[0567] 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.

[0568] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0569] 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.

[0570] [Fourth Embodiment]

[0571] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0572] 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.

[0573] 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).

[0574] 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.

[0575] 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.

[0576] 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).

[0577] 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.

[0578] 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.

[0579] 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.

[0580] 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.

[0581] 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.

[0582] 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.

[0583] 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".

[0584] The embodiment for carrying out this invention is described as an integrated system for collecting, analyzing, and visualizing disaster information. This system consists of multiple components having the roles of server, terminal, and user.

[0585] First, the server retrieves data from sources such as social media and news sites to collect information about disasters and damage. APIs and web scraping techniques are used to obtain this information, ensuring that data is updated in real time. This data is temporarily stored and then added to a database as needed.

[0586] Next, the server applies a natural language processing engine to the collected data to analyze the text data. This process extracts information such as the location of the damage, the type of damage, and its extent. During the analysis, the reliability of the information is evaluated, and low-reliability information is filtered out to ensure data quality.

[0587] The analyzed information is integrated into a geographic information system by a server, and visualization data is generated. This process allows users to intuitively understand, on a map, which areas are at risk and to what extent.

[0588] The visualized information is provided to the user via the device. The device, upon receiving the information, also sends a warning notification to the user. For example, if a certain area is determined to be at high risk of flooding, the user can immediately receive that notification on their device.

[0589] Users can then use the interface on their devices to view detailed information and make decisions regarding safe evacuation. They can manipulate maps to check the current situation and obtain detailed text information and damage predictions for specific areas.

[0590] This system aims to support safe evacuation actions during disasters by providing timely and accurate information about ongoing hazards. For example, if this system were used during a typhoon, people would be able to check areas where flooding is predicted in advance and prepare safe routes.

[0591] The following describes the processing flow.

[0592] Step 1:

[0593] The server uses APIs and web scraping techniques to collect disaster-related data from multiple sources, such as social media and news sites. It filters information using specific keywords and hashtags and retrieves data that is updated in real time.

[0594] Step 2:

[0595] The server temporarily stores the collected raw data and performs data cleaning. It removes duplicate and noisy data and formats it to prepare it for subsequent analysis. The cleaned data is then stored in a database.

[0596] Step 3:

[0597] The server uses a natural language processing engine to analyze the collected data. This analysis extracts the type, location, and extent of the damage from the text. It also scores the reliability of the information and filters out information with low reliability.

[0598] Step 4:

[0599] The server integrates the analysis results into a geographic information system. This generates visualization data, including location information of the damage, and displays high-risk areas on a map in real time.

[0600] Step 5:

[0601] The terminal provides the user with an interactive map based on visualization data received from the server. The map displays dangerous areas in different colors, making it easy for the user to understand the current situation.

[0602] Step 6:

[0603] The device will immediately notify the user upon receiving a warning notification from the server. The notification will include specific details about potential damage and evacuation advisories to help the user take swift action.

[0604] Step 7:

[0605] Users operate the map interface on their device to confirm safe evacuation routes. They obtain detailed damage and prediction information from the map and decide on evacuation actions according to the situation.

[0606] This series of processes enables the system to provide users with fast and reliable disaster information.

[0607] (Example 1)

[0608] 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".

[0609] In disaster situations, inadequate information gathering, analysis, and visualization hinder citizens from taking effective actions to evacuate safely, even when rapid and accurate decision-making is required. In particular, there is a lack of a systematic mechanism for evaluating the reliability of data from multiple sources and providing appropriate warnings and evacuation guidance.

[0610] 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.

[0611] In this invention, the server includes means for acquiring data from a network to collect information, a natural language processing unit for analyzing the acquired data and extracting location information and impact levels, and a visualization unit for integrating the analyzed information into a geospatial system and providing information visually. This enables the rapid provision of highly reliable information based on the collected data, allowing users to take appropriate evacuation actions.

[0612] "Means of acquiring data from networks for collecting information" refers to methods for acquiring relevant data from various information sources via the internet or communication networks.

[0613] A "natural language processing unit that analyzes acquired data and extracts location information and impact" is a device that analyzes collected text data to understand geographical location information and the extent and degree of impact.

[0614] A "visualization unit that integrates analyzed information into a geospatial system to provide information visually" is a system that makes it easy for users to understand information by intuitively displaying the analysis results as geographic map information.

[0615] A "communication device that sends notifications to warn of an emergency" is a device that transmits information to quickly warn users when a disaster or critical situation occurs.

[0616] "Interaction methods for users to manipulate maps and access information" refers to methods that allow users to manipulate maps through an interface to obtain necessary information or check details.

[0617] A "quality evaluation system for assessing data reliability and removing low-reliability data" is a mechanism for ensuring the quality of information by evaluating the accuracy and validity of collected information and eliminating erroneous data.

[0618] A "route guidance system for providing evacuation routes in emergency situations, taking into account the user's current location" is a program with a navigation function that presents safe and efficient evacuation routes based on the user's location information.

[0619] This invention is embodied as a system for rapidly and accurately collecting, analyzing, and visualizing disaster information. This system mainly consists of server, terminal, and user components.

[0620] The server first retrieves data from multiple sources via the network. This process uses APIs or web scraping techniques. The retrieved data is temporarily stored in storage and may also be stored in an SQL database or similar.

[0621] Next, the server analyzes the data using a natural language processing engine. Specifically, open-source natural language processing libraries (e.g., NLTK and spaCy) are used. This analysis allows for the extraction of location information and the degree of disaster impact. Furthermore, a quality evaluation system is implemented to assess the reliability of the data and filter out inaccurate information.

[0622] The analyzed information is integrated into a geospatial system. GIS software (e.g., ArcGIS or QGIS) is used to visualize the information, allowing users to intuitively understand it.

[0623] The device receives visualization data from geospatial systems and sends warning notifications to the user in the event of an emergency. For example, if a designated area is determined to be dangerous, the device immediately sends a push notification. This process allows the user to take quick and appropriate action.

[0624] Users can utilize the provided interface to view detailed information. For example, they can check the extent of damage on a map and confirm evacuation routes. Based on the presented options, users can obtain information to plan safe evacuation actions.

[0625] For example, if a user receives a typhoon warning, they can check areas at risk of flooding in advance and prepare a safe route. An example of a prompt to the generative AI model related to this system would be, "Based on the latest disaster information for the specified area, please suggest the best evacuation location."

[0626] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0627] Step 1:

[0628] The server begins collecting information. It obtains data via the network using APIs from social media and news sites, or by employing web scraping techniques. The input is URLs and API endpoints, and the output is raw disaster-related data. Specifically, it performs regularly scheduled tasks and obtains data in real time.

[0629] Step 2:

[0630] The server temporarily stores the acquired data. The data is saved to storage in JSON or XML format. The input is raw, unprocessed data requiring immediate reporting, while the output is structured data. Specific data formatting operations include parsing the data into JSON format, extracting important fields, and saving them.

[0631] Step 3:

[0632] The server performs natural language processing to analyze the data. Using a natural language processing engine, it extracts location information and the type and extent of damage from the text. The input is structured text data, and the output is analyzed location and damage information. A concrete example is the use of open-source natural language processing tools to perform named entity recognition.

[0633] Step 4:

[0634] The server performs a reliability assessment. It scores the reliability of the acquired information and filters out information that does not meet a certain level of confidence. The input is already analyzed information, and the output is data containing only highly reliable information. Specifically, it may apply an algorithm that automatically excludes items with low confidence scores.

[0635] Step 5:

[0636] The server integrates the analyzed data into a geographic information system (GIP) to create visualization data. The input is highly reliable analyzed information, and the output is visualization data as geospatial information. A concrete example is using GIP software to map damaged areas onto a map.

[0637] Step 6:

[0638] The device receives visualization data and sends a notification to the user. The input is visualization data, and the output is a warning notification to the user. Specific actions include sending push notifications to smartphones and other devices.

[0639] Step 7:

[0640] The user manipulates the map on their device to obtain detailed information. Inputs are warning notifications and visualization data, and output is relevant information tailored to their situation. Specific actions include zooming and panning the map to analyze the current situation and find a safe evacuation route.

[0641] (Application Example 1)

[0642] 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".

[0643] In urban disaster situations, it is essential to obtain reliable, real-time damage information and guide residents along appropriate evacuation routes in order for them to evacuate quickly and safely. However, conventional technologies lacked the speed and reliability of information, making it difficult to adequately ensure the safety of residents.

[0644] 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.

[0645] In this invention, the server includes a computer-readable processing means for collecting information from multiple information sources, a natural language processing structure means for analyzing the collected information and determining the extent of damage, and a route guidance processing means for providing evacuation routes based on the analyzed earthquake information. This makes it possible to quickly grasp the dangers faced by residents and provide appropriate evacuation routes.

[0646] "Multiple sources of information" refers to multiple platforms or services that provide data of different natures or formats.

[0647] "Computer-readable processing" refers to a series of program code or algorithms written in a format that a computer can interpret.

[0648] "Natural language processing structures" refer to computer programs used to understand, interpret, and analyze human language.

[0649] A "spatial information system" refers to a technological system for managing, analyzing, and visualizing geographical data.

[0650] "Processing for immediate visualization" refers to the technical process of quickly converting data into a visual format and displaying it.

[0651] "Communication function" refers to the technical function for electronically sending and receiving data and information.

[0652] A "user interface" refers to an interface that provides visual and operational elements for users to directly interact with a system.

[0653] "Route guidance processing" refers to the process of providing instructions to help users find the optimal route to their destination.

[0654] "Distribution function" refers to information technology used to deliver notifications and messages to users' devices.

[0655] Embodiments of this invention include an integrated system in which a server, terminal, and user each perform their respective roles. This system is intended to support real-time information provision and safe evacuation during disasters.

[0656] The server collects necessary data from multiple sources, such as social media and news sites. This involves using APIs and web scraping techniques to organize the information in a way that is readable by computing power. The collected information is analyzed using natural language processing structures to extract location, type, and extent of damage. Furthermore, the reliability of the information is evaluated, and low-reliability information is removed. The analyzed data is then immediately visualized via a spatial information system, and route guidance processing is used to provide evacuation routes during disasters.

[0657] The terminal uses its communication capabilities to deliver information provided by the server to the user. Through the user interface, the user can manipulate maps and check real-time disaster information and safe evacuation routes. For example, in the event of an earthquake, a notification is immediately sent to the user's terminal, showing the need for evacuation and the route. This allows the user to understand the extent of damage to the land and buildings and move quickly to a safe location.

[0658] By utilizing generative AI models, it is possible to generate prompt messages for unexpected disaster scenarios. An example of a prompt message is, "Create a prompt to notify users' devices of evacuation guidance information in the event of urban flooding." This enables scenario analysis for a variety of disaster situations, leading to more efficient evacuation support.

[0659] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0660] Step 1:

[0661] The server collects data from multiple sources, such as social media and news sites. It uses APIs and web scraping techniques as input to obtain text data and metadata. The output is a dataset where this information is temporarily stored. At this stage, the server retrieves data in real time and efficiently organizes the retrieved data.

[0662] Step 2:

[0663] The server applies natural language processing structures to the collected data to analyze the information. The input is the dataset obtained in step 1. Data analysis extracts location, type, and extent of damage. The output is a database containing the analyzed information. At this stage, the server evaluates the reliability of the information and removes information with low reliability.

[0664] Step 3:

[0665] The server visualizes the analyzed information using a spatial information system. The input is the analyzed data obtained in step 2. Based on the geographic data, it generates visual data to display dangerous areas and evacuation routes on a map. The output is the visualized map information.

[0666] Step 4:

[0667] The terminal receives visualization data sent from the server. The input is the visual data generated in step 3. The terminal displays it on the user interface and provides it to the user in a viewable format. The output is map information that the user can access.

[0668] Step 5:

[0669] The user interacts with map information provided on their device to check the current danger situation and evacuation routes. Input consists of the map information displayed on the device and the user's selections. Output is the evacuation information the user has received and their decisions regarding the next action. At this stage, the user receives real-time information to determine appropriate evacuation actions.

[0670] 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.

[0671] Embodiments of this invention are described as an integrated system that combines a disaster information system with an emotion engine that recognizes user emotions, and includes psychological support for users during disasters. This system is based on the interaction between a server, a terminal, and the user.

[0672] First, the server collects information not only from social media and news sites, but also from user input and responses. This data is analyzed in real time via an emotion engine to evaluate the user's psychological state. This evaluation is performed using text-level analysis and sentiment analysis techniques, and a score is generated that reflects states such as reassurance and anxiety.

[0673] Based on the analysis results, the server utilizes a geographic information system to visualize the extent of the damage. This data is structured in multiple layers, providing not only general damage information but also an interface that shows the user's emotional state and how it changes. Furthermore, customized evacuation information tailored to the user's emotions is generated by an emotion engine.

[0674] The device, along with notifications from the server, presents the user with emotion-based messages. For example, if the device determines that the user is in an anxious state, it will prioritize displaying information that provides reassurance (e.g., distance to evacuation shelters, ongoing rescue operations, etc.). It will also display empowerment messages to the user, providing support to reduce their psychological burden.

[0675] Through an emotionally resonant interface, users can explore situation-appropriate solutions. For example, in emergencies, their location information is used to suggest appropriate evacuation routes, along with reassuring information. This system goes beyond mere information provision, offering disaster support in a way that also considers the user's mental well-being.

[0676] Thus, this system, which incorporates an emotion engine, aims to provide users with information and psychological support tailored to their individual needs. For example, even in the chaotic circumstances of a disaster, users will be able to maintain their composure and take safe evacuation actions.

[0677] The following describes the processing flow.

[0678] Step 1:

[0679] The server collects information from social media, news sites, and user input. This data includes not only text information about the disaster, but also comments and feedback posted by users.

[0680] Step 2:

[0681] The server applies a natural language processing engine and an emotion engine to the collected data. The natural language processing engine extracts the type, location, and severity of the disaster, while the emotion engine generates emotion indicators from user comments.

[0682] Step 3:

[0683] The server evaluates the reliability of information and filters out low-reliability information. This evaluation takes into account the reliability of the information source and the influence of user sentiment.

[0684] Step 4:

[0685] The server integrates the analysis results into a geographic information system and generates map data that includes damage assessment and emotional information. This map is updated in real time and provides visual information tailored to the user's psychological state.

[0686] Step 5:

[0687] The terminal receives map data and warning notifications sent from the server and displays them to the user. The terminal builds an interface that takes the user's emotional state into consideration and provides information that soothes the user's emotions along with the warning message.

[0688] Step 6:

[0689] Users use the map interface on their device to check disaster information and evacuation routes. They also receive situation-appropriate suggestions and encouraging messages to help them make decisions about evacuation.

[0690] Step 7:

[0691] The server collects user feedback and new inputs, updating the database and map information in response to changing circumstances. It also adjusts the content of the information and messages provided as needed to improve the user's emotional state.

[0692] (Example 2)

[0693] 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".

[0694] There is a need for a system that can address the psychological stress and anxiety experienced by users during disasters, providing appropriate information and psychological support simultaneously. Conventional disaster information systems only provide physical evacuation information and lack the nuanced support that can be tailored to the emotional state of users. As a result, it is difficult for users to take swift and accurate evacuation actions while maintaining peace of mind.

[0695] 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.

[0696] In this invention, the server includes computer-readable programming means for collecting information from multiple information sources, emotion analysis engine means for analyzing the collected information and evaluating the user's psychological state, and programming means for visualizing the analysis results in real time using a map information processing system. This makes it possible to provide customized information and psychological support according to the user's emotional state.

[0697] A "computer-readable program" is code that contains instructions that can be read and executed by a computer, and is designed to automate a specific task.

[0698] An "emotion analysis engine" refers to artificial intelligence technology that identifies a user's emotions from text and audio data and quantifies their psychological state, such as feeling safe or anxious.

[0699] A "geographic information processing system" refers to information technology that visualizes geographical information on a digital map and provides that information to users in an easy-to-understand manner.

[0700] A "communication module" is a component that includes the hardware and software functions necessary for a system to send and receive data over a network.

[0701] A "display interface" refers to a user interface provided through a screen or input device that allows users to visually acquire information and perform operations.

[0702] A "generative AI model" refers to an algorithm trained using machine learning, which is used to automatically generate text and information from various input data.

[0703] A "message generation program" is software designed to create appropriate content based on the user's situation and emotional state, thereby enabling effective information delivery.

[0704] In an embodiment of this invention, the system is based on the interaction between a server, a terminal, and a user. In this system, the server first collects information from multiple sources. These sources include social networking services (SNS), online news, and direct input from the user. Web scraping technology is used for information collection. The server then uses a natural language processing engine to analyze the collected data in real time and evaluate the user's psychological state. This evaluation is performed using open-source natural language processing libraries (e.g., TensorFlow, PyTorch).

[0705] The analyzed information includes sentiment analysis results, which score psychological states such as feelings of security and anxiety. The server uses this data to visually visualize the damage situation and sentiment distribution on a map using a geographic information processing system. Commonly used geographic information system (GIS) tools are employed for the geographic information processing.

[0706] Furthermore, the server uses a generative AI model to generate personalized evacuation information and psychological support messages based on the analysis results. These generated messages are sent to the terminal via a communication module. After receiving this information, the terminal prioritizes its display according to the user and issues voice and visual alerts as needed. More specifically, if the user is feeling anxious, the mobile device will display a prompt such as, "This is the shortest route to the shelter. Your leader is checking on your safety."

[0707] Finally, users can take appropriate evacuation actions by operating the display interface provided by their device. This allows users to act based on appropriate information while maintaining a sense of psychological security. This system aims to promote immediate action while maintaining the mental health of users during disasters.

[0708] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0709] Step 1:

[0710] The server collects information. It uses web scraping techniques to gather data from sources such as social media, news sites, and direct user input. The collected information is stored in a database in text format. It receives diverse digital content as input, organizes it, and converts it into an analyzable format. The output is text data ready for analysis.

[0711] Step 2:

[0712] The server analyzes the collected data in real time using an emotion analysis engine. The input is the text data collected in step 1. Using natural language processing techniques, it performs data calculations to numerically evaluate psychological states such as reassurance and anxiety. The output is the analysis result with a score representing the user's emotions. Specifically, it is output in the form of, for example, "Anxiety score: 0.75".

[0713] Step 3:

[0714] The server visualizes the analyzed sentiment data and damage status using a geographic information processing system. Inputs are sentiment scores and geographical damage information. Using GIS tools, the multi-layered data is displayed on a map, visualizing the distribution of user sentiment and the extent of damage. The output is a graphical interface placed on the map.

[0715] Step 4:

[0716] The server uses a generative AI model to generate personalized evacuation information and psychological support messages for the user. The input consists of sentiment analysis data and the user's current location. The generative AI model uses prompts to create individualized content, such as "Generate a message including directions from the current location to the nearest evacuation shelter." The output is a specific, action-oriented message.

[0717] Step 5:

[0718] The terminal displays messages received from the server and issues alerts as needed. Input consists of customized messages provided by the server. The terminal uses additional notification methods, such as sound and vibration, to attract the user's attention. Output consists of visual notifications displayed on the user's screen.

[0719] Step 6:

[0720] The user operates the terminal's interface. The input is the displayed evacuation information. The user uses this to plan their evacuation actions and check for the latest information as needed. The output is the user's own evacuation movement, which is focused on moving to a safe place.

[0721] (Application Example 2)

[0722] 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".

[0723] During a disaster, accurate damage assessment based on vast amounts of information and prompt warning notifications to users are required. However, psychological support that takes into account the emotional state of users is not provided. Furthermore, the lack of a system for evaluating the reliability of information and providing information that instills a sense of security in users can lead to anxiety.

[0724] 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.

[0725] In this invention, the server includes digital program means for collecting information from multiple information sources, a natural language processing engine and sentiment analysis engine means for analyzing the collected information and determining the extent of damage and the emotional state of the user, and a display program means for visualizing the analysis results in real time using a geographic information system and providing an interface that reflects the emotional state. This enables accurate and rapid determination of the extent of damage and provision of appropriate information according to the user's emotions.

[0726] A "digital program" is software designed to run on electronic devices and perform specific functions or processes.

[0727] A "natural language processing engine" is an engine that uses technology to enable computers to understand and analyze human language and extract its meaning.

[0728] An "emotion analysis engine" is an engine that uses technology to identify and evaluate emotions from text and audio data.

[0729] A "Geographic Information System" is a system for collecting, managing, and visually displaying geographical data.

[0730] A "display program" is a program designed to visualize data and provide information in a format that is easy for users to understand.

[0731] A "communication module" is software or hardware that has the function of exchanging information bidirectionally between the user and the system.

[0732] An "interface" is a point of contact or means for humans and machines to interact and exchange information, and is designed to improve the user experience.

[0733] An "analysis module" is a component that has the functionality to analyze collected data and extract useful information and patterns.

[0734] A "navigation program" is a program that calculates a route based on location information and guides the user in the direction of travel.

[0735] This system's program is designed for information gathering and user support during disasters. The server uses digital programs to collect data in real time from diverse sources such as social media and news sites. The collected information is then analyzed by a natural language processing engine and an emotion analysis engine to determine the extent of the damage and the emotional state of users. This analysis is further visualized using a geographic information system, and a customized interface based on the emotional state is displayed.

[0736] The terminal uses a communication module from the server to send out warning notifications. This ensures that users receive not only important information about the disaster, but also messages to help them maintain emotional stability. As a result, users can gain a sense of security even in anxious situations and deepen their understanding of the current situation.

[0737] The user interface is designed to interactively display information as users manipulate the map and to exchange special messages tailored to the user's emotions. The navigation program suggests evacuation routes that take into account location and emotional state in emergencies, allowing users to choose the optimal evacuation course.

[0738] As a concrete example, a device might display information about ongoing rescue operations to help users feel more secure when heading to a shelter. An example of a prompt message in this case might be: "As a citizen of a smart city, you will receive information to help you feel secure during a disaster in the following format. Based on your current emotional state, what information will calm you the most?" This system provides information tailored to individual needs, improving both psychological and physical safety during disasters.

[0739] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0740] Step 1:

[0741] The server collects data from social media and news sites. It receives text data from diverse sources as input and integrates it using digital programs. As output, it generates a set of integrated raw data. Specifically, it retrieves necessary data via APIs and stores it in a database.

[0742] Step 2:

[0743] The server analyzes the collected data using a natural language processing engine and an emotion analysis engine. The input is the raw dataset obtained in step 1. Data processing involves extracting the intent and emotions of the text and interpreting its meaning. The output includes a damage assessment result and the user's emotional state. Specifically, the analysis algorithm is applied to perform scoring.

[0744] Step 3:

[0745] The server uses a geographic information system to visualize the analysis results. The judgment results and sentiment scores obtained in step 2 are used as input. The data calculations geographically map the damage situation and generate an interface based on the sentiment state. The output is visualized map information. Specifically, a map API is used to perform multi-layered map display.

[0746] Step 4:

[0747] The device receives notifications from the server and presents the user with warning and emotionally reassuring messages. The input is the map and message information generated in step 3. The output is a visual or auditory notification displayed to the end user. Specifically, a push notification is sent to the user's screen.

[0748] Step 5:

[0749] The user interacts with the map using the device's user interface to view detailed information and evacuation routes. The input is the user manipulating map information presented by the device. The output is customized instructions tailored to the user's requests. Specific actions include using gestures such as tapping and swiping to zoom in on the map and display evacuation routes.

[0750] 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.

[0751] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0752] 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.

[0753] 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.

[0754] 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.

[0755] 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.

[0756] 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.

[0757] 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.

[0758] 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."

[0759] 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.

[0760] 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.

[0761] 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.

[0762] 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.

[0763] 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.

[0764] 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.

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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.

[0769] 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.

[0770] 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.

[0771] The following is further disclosed regarding the embodiments described above.

[0772] (Claim 1)

[0773] A computer-readable programming means for collecting information from multiple sources,

[0774] A natural language processing engine means for analyzing collected information and determining the extent of damage,

[0775] A programming method for visualizing analysis results in real time using a geographic information system,

[0776] A communication module means for sending warning notifications to users,

[0777] A user interface means for users to manipulate a map and obtain information,

[0778] A system that includes this.

[0779] (Claim 2)

[0780] An evaluation module for evaluating the reliability of information and filtering out low-reliability information, according to claim 1.

[0781] (Claim 3)

[0782] A navigation program for suggesting evacuation routes based on the user's location information in an emergency, according to claim 1.

[0783] "Example 1"

[0784] (Claim 1)

[0785] A means of acquiring data from a network for collecting information,

[0786] A natural language processing unit analyzes the acquired data and extracts location information and impact level,

[0787] A visualization unit that integrates the analyzed information into a geospatial system to provide information visually,

[0788] A communication device that sends notifications to warn of an emergency,

[0789] Interaction methods for users to manipulate maps and access information.

[0790] A system that includes this.

[0791] (Claim 2)

[0792] A quality evaluation system for evaluating the reliability of data and removing low-reliability data, as described in claim 1.

[0793] (Claim 3)

[0794] A route guidance system for providing an evacuation route in an emergency, taking into account the user's current location, according to claim 1.

[0795] "Application Example 1"

[0796] (Claim 1)

[0797] A computing device for collecting information from multiple sources includes a readable processing means,

[0798] A natural language processing structure for analyzing collected information and determining the extent of damage,

[0799] Processing means for immediately visualizing analysis results using a spatial information system,

[0800] A communication function means for sending warning notifications to users,

[0801] A user interface means for users to manipulate a map and obtain information,

[0802] Route guidance processing means for providing evacuation routes based on analyzed earthquake information,

[0803] A distribution function means for sending real-time warning information to the user's mobile device,

[0804] A system that includes this.

[0805] (Claim 2)

[0806] The system according to claim 1, which has an information analysis function for evaluating the reliability of information and removing information with low reliability.

[0807] (Claim 3)

[0808] The system according to claim 1 for guidance processing to present evacuation routes based on the user's spatial information in an emergency.

[0809] "Example 2 of combining an emotion engine"

[0810] (Claim 1)

[0811] A computer-readable programming means for collecting information from multiple sources,

[0812] A sentiment analysis engine means for analyzing collected information and evaluating the user's psychological state,

[0813] A programming means for visualizing the analysis results in real time using a map information processing system,

[0814] A communication module means for sending customized warning notifications based on the user's psychological state,

[0815] A display interface means for users to manipulate and obtain information,

[0816] A system that includes this.

[0817] (Claim 2)

[0818] A generation AI model for generating personalized evacuation information based on analyzed information, according to claim 1.

[0819] (Claim 3)

[0820] A message generation program for providing psychological support according to the emotional state of a user, as described in claim 1.

[0821] "Application example 2 when combining with an emotional engine"

[0822] (Claim 1)

[0823] A digital program means for collecting information from multiple sources,

[0824] A natural language processing engine and sentiment analysis engine means for analyzing collected information and determining the extent of damage and the emotional state of users,

[0825] A display program means for visualizing analysis results in real time using a geographic information system and providing an interface that reflects emotional states,

[0826] A communication module means for sending warning notifications and emotionally stabilizing information to users,

[0827] An interface means for users to manipulate a map to obtain information and receive interactive messages that respond to their emotions,

[0828] A system that includes this.

[0829] (Claim 2)

[0830] The system according to claim 1, which includes an analysis module for evaluating the reliability of information and filtering out low-reliability information.

[0831] (Claim 3)

[0832] A navigation program for providing a sense of security in an emergency by suggesting an evacuation route based on the user's location information and emotional state, according to claim 1. [Explanation of Symbols]

[0833] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A computer-readable programming means for collecting information from multiple sources, A natural language processing engine means for analyzing collected information and determining the extent of damage, A programming method for visualizing analysis results in real time using a geographic information system, A communication module means for sending warning notifications to users, A user interface means for users to manipulate a map and obtain information, A system that includes this.

2. The system according to claim 1, an evaluation module for evaluating the reliability of information and filtering out low-reliability information.

3. A navigation program for suggesting evacuation routes based on the user's location information in an emergency, as described in claim 1.

Images