system

Abstract

We provide the system. [Solution] A data collection method for collecting weather information, seismic activity information, and social media information, A data preprocessing means for converting and integrating collected information into a common format, An analytical means for analyzing integrated data and generating a relief plan, Information provision means for displaying the generated relief plan through a user interface, A system that includes this.

Description

Technical Field

[0004] , , ,

[0005] , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the event of a disaster, it is required to collect highly reliable information in real time and quickly generate an effective relief plan that minimizes damage. Currently, data from various information sources must be manually collected and integrated, which poses a risk of analysis delays and errors. For this reason, there is a problem that information is insufficient and appropriate responses are difficult. In addition, information integration is complex, and there is a possibility of making incorrect judgments. It is an object of the present invention to provide a system that can solve such problems and respond to disasters quickly and accurately.

Means for Solving the Problems

[0005] This invention automatically collects weather information, seismic activity information, and social media information, and performs rapid preprocessing by converting this data into an integrated format. Furthermore, it includes dedicated analytical means for analyzing the integrated dataset and utilizes the capabilities of generative AI to generate effective relief plans during disasters. These plans are provided in real time through a user interface to support users in taking quick and accurate actions. These means make it possible to streamline information provision and decision-making during disasters and prevent the escalation of damage.

[0006] "Data collection means" refers to devices or methods that automatically acquire necessary data from various information sources, including weather information, seismic activity information, and social media information.

[0007] "Data preprocessing means" refers to processes or systems that convert collected raw data into a common format and optimize it for analysis.

[0008] "Analysis methods" refer to techniques and algorithms used to analyze information using integrated datasets and derive results tailored to specific purposes.

[0009] "Information provision means" refers to interfaces or media that communicate analysis results and generated relief plans to users visually or in other ways to support decision-making.

[0010] A "relief plan" is a plan that outlines specific actions and measures to minimize damage during a disaster and to ensure safe and effective evacuation and support.

[0011] A "disaster" is an event that involves large-scale damage and disruption caused by natural phenomena or other factors.

[0012] A "user interface" refers to the design and technical elements that function as points of contact for exchanging data and instructions between a system and a user.

[0013] "Generative AI" is an artificial intelligence technology designed to assist in data analysis, pattern discovery, and the automation of specific tasks. [Brief explanation of the drawing]

[0014] [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] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] 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.

[0018] 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.

[0019] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0020] 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).

[0021] 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."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] This system provides a series of processes for collecting data in real time from diverse sources and generating optimal relief plans during disasters. First, the server accesses external data sources via the internet to collect weather information, seismic activity information, social media posts, and more. Since this information is often provided in different formats, the server preprocesses this data to convert it into a common format. This integrates different datasets, making them usable in the next analysis step.

[0036] Next, the server analyzes the integrated dataset to determine the most needed actions and predictions at specific locations. This process utilizes generative AI to design optimal response strategies based on historical data and real-time conditions. The generated relief plan includes comprehensive elements such as selecting evacuation routes, recommending safe shelters, and prioritizing the supply of necessary materials.

[0037] The analysis results and generated relief plan are sent to the terminal and presented to the user through a user interface. This interface is designed to be intuitive and help users quickly understand the situation and make decisions. For example, in the event of an earthquake, the safest route to a shelter and the shelter's capacity are displayed, allowing the user to choose their course of action based on that information.

[0038] This system will significantly simplify appropriate responses during disasters, from the individual level to the municipal level, and will support efforts to minimize damage. For example, if heavy rain is expected in a certain area, the server will identify high-risk areas based on weather data and send necessary evacuation orders to terminals. Users can then evacuate quickly based on this information. This will improve the speed and accuracy of disaster response and reduce actual damage.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The server connects to multiple external APIs to collect weather information, seismic activity information, and social media posts. The acquired data is collected in different formats and types, but the server temporarily stores them in individual databases.

[0042] Step 2:

[0043] The server performs preprocessing to convert the collected data into a unified format. This process involves synchronizing data with different timestamps and performing cross-referencing based on location information, preparing the data for later analysis.

[0044] Step 3:

[0045] The server inputs the pre-processed data into the generating AI and begins the analysis. The generating AI extracts patterns from different information sources and assesses the disaster risk in a specific area.

[0046] Step 4:

[0047] Based on the analysis results, the server generates a relief plan to provide to the user. This plan includes recommended shelters, evacuation routes, identification of safe locations, and priority for supplying materials.

[0048] Step 5:

[0049] The server sends the generated relief plan to the terminal.

[0050] Step 6:

[0051] The terminal displays received relief plans to the user via a user interface. The user interface is intuitive and easy to use, designed to allow users to easily check information in map and list formats.

[0052] Step 7:

[0053] Based on the information displayed on their device, users can select the optimal evacuation course and begin responding quickly. This allows them to take actions that mitigate the risks associated with anticipated disasters.

[0054] (Example 1)

[0055] 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."

[0056] Developing rapid and effective response measures during a disaster requires the collection and analysis of vast amounts of information, demanding both accuracy and execution capabilities. Traditional methods have struggled to integrate diverse data from real-time updated sources and efficiently formulate optimal action plans. Furthermore, there is a need to provide this information to users immediately.

[0057] 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.

[0058] In this invention, the server includes means for collecting information, means for converting and integrating the collected information into a common format, and means for analyzing the integrated information and generating an optimal process plan using an artificial intelligence model. This makes it possible to collect and analyze information in real time and quickly provide the optimal relief plan through a user interface.

[0059] "Means of collecting information" refers to devices or programs for obtaining necessary data from various external sources.

[0060] "Means for converting and integrating collected information into a common format" refers to a device or program for centralizing information provided in different formats and converting and integrating it into a unified data format.

[0061] "Means for analyzing integrated information and generating an optimal process plan using an artificial intelligence model" refers to a device or program that analyzes integrated information using advanced computing techniques and formulates an appropriate plan using machine learning models or artificial intelligence.

[0062] "Means provided through communication devices" refers to devices or programs that include interfaces and communication protocols for communicating the generated plan to the user.

[0063] This invention implements a system in which a server provides optimal response measures during a disaster. The server first accesses external information sources and collects diverse information, including weather data, earthquake information, and posts from social media. This includes data acquisition using APIs over the internet. Cloud-based data services and web scraping techniques may also be used.

[0064] Next, the server converts and integrates the collected information into a unified data format. In this process, data format conversion software is used to standardize the data into formats such as JSON or XML. Data cleaning techniques are also used to handle missing data and eliminate duplicate data.

[0065] The server then moves on to analyzing the integrated dataset. This analysis uses a generative AI model, employing machine learning algorithms and data mining techniques to predict future actions based on historical and real-time data. At this stage, the generative AI model is prompted to "assess disaster risk in a specific area and recommend necessary countermeasures," which generates a detailed relief plan.

[0066] The generated relief plan is sent to the terminal, which the user receives through the interface. For example, when heavy rain is expected, the server can identify high-risk areas from real-time weather data and send information on safe evacuation routes and shelters to the terminal. Based on this information, the user can quickly begin evacuation and minimize the risks associated with the disaster.

[0067] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0068] Step 1:

[0069] The server accesses external data sources and collects information. It uses authentication credentials and query parameters from weather data APIs, earthquake information APIs, and social media APIs as input. It sends API requests to retrieve information, and the retrieved data is output. This data is received in raw data format (e.g., JSON, XML).

[0070] Step 2:

[0071] The server converts the collected information into a common format and integrates the data. It uses raw data received as input. This information is processed by format conversion software, performing data cleaning such as filling in missing data and removing duplicate data. The output is a converted and integrated unified data format.

[0072] Step 3:

[0073] The server analyzes the integrated data. The input is data converted into a common format. Using a generative AI model, it performs data mining and machine learning analysis with the prompt "Assess disaster risk in a specific area and recommend necessary countermeasures." The output is predictive data and suggestions that are incorporated into a relief plan.

[0074] Step 4:

[0075] The server sends the generated relief plan to the terminal. The input is the relief plan obtained through analysis. This plan is sent to the terminal via a communication protocol. The output is a relief plan in a format that can be confirmed as received by the terminal.

[0076] Step 5:

[0077] The terminal displays the received relief plan on its user interface. The input is the relief plan sent from the server. The terminal uses a GUI to visually display the information, making it easy for the user to understand. The output is the displayed information in a state that the user can verify.

[0078] Step 6:

[0079] The user acts based on the presented relief plan. The input is the plan displayed on the terminal. The user makes specific decisions regarding evacuation routes and movement to evacuation sites, and then takes action. The output is the implementation of appropriate response actions.

[0080] (Application Example 1)

[0081] 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."

[0082] A challenge exists in the event of a disaster: a lack of information to make quick and accurate decisions regarding evacuation. Furthermore, warnings and suggestions for evacuation routes tailored to the risks faced by individuals are insufficient. Therefore, it is necessary to develop a system that supports rapid decision-making to minimize damage.

[0083] 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.

[0084] In this invention, the server includes information gathering means, data preprocessing means, analysis means, information provision means, suggestion means, and warning means. This enables the provision of information for rapid evacuation actions during disasters, as well as real-time suggestions and warnings regarding evacuation routes and safe directions.

[0085] "Information gathering means" refers to devices or functions for collecting necessary information from various data sources, such as weather information, seismic activity information, and social media information.

[0086] "Data preprocessing means" refers to a device or processing function for converting collected information into a common format and integrating it in a consistent manner.

[0087] "Analysis means" refers to a device or algorithm with the processing capability to create an optimal relief plan based on integrated data.

[0088] "Information provision means" refers to a device or function that displays and communicates generated relief plans and other important information to users via a user interface.

[0089] "Suggested means" refers to a device or function for providing users with a rapid evacuation route or recommendation.

[0090] A "warning device" is a device or alert function that appropriately warns users of potential disasters or dangers that may occur in real time.

[0091] To implement this invention, the following system configuration must be considered. The server obtains necessary information from various data sources on the internet using information gathering means that collect weather information, seismic activity information, and social media information. Here, cloud-based services such as AWS® Lambda can be used to collect data in real time and process the data efficiently.

[0092] Next, data preprocessing is used to convert the collected information into a common format and integrate it in a consistent manner. At this stage, dedicated algorithms for data transformation and cleaning are applied.

[0093] The integrated data is analyzed using an analysis tool powered by Amazon SageMaker. This allows a generated AI model to create a relief plan based on the integrated data. This analysis considers past disaster patterns and the current situation. The generated relief plan is then transmitted to the user's terminal via an information delivery system.

[0094] On the device, the relief plan is presented through an intuitive user interface built with React Native. This UI allows users to quickly obtain necessary information and provides suggestions for evacuation routes and safe evacuation directions. It also includes a warning system that provides real-time alerts for situations where risk is increasing.

[0095] For example, if heavy rainfall is predicted in a specific area, the system will identify high-risk areas based on weather data for that area and present users with safe evacuation routes. Users can then quickly begin evacuating based on the indicated routes. This will improve the speed and accuracy of disaster response and help minimize damage.

[0096] Specific examples of prompt statements in generative AI models are as follows:

[0097] "Analyze the following disaster scenario and generate the optimal evacuation routes for disaster victims. This area is at high risk of flooding due to heavy rainfall. Please consider historical data and real-time rainfall."

[0098] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0099] Step 1:

[0100] The server activates information gathering mechanisms to acquire weather information, seismic activity information, and social media information from the internet. It uses diverse data sources as input and collects this data in an unstructured, raw format. The output is a dataset containing all the necessary information in its raw, unprocessed state. Specifically, it acquires data from weather stations, earthquake observation systems, and social media platforms via APIs.

[0101] Step 2:

[0102] The server uses data preprocessing to convert the raw data collected in Step 1 into a common format and integrate it. The input is the raw data from Step 1, and the output is an integrated dataset with a unified format. Specifically, it performs data cleaning, noise reduction, and format standardization to maintain consistency across different data sources.

[0103] Step 3:

[0104] The server uses an integrated dataset as an analysis tool and generates a relief plan using a generative AI model. The input is the integrated dataset from step 2, and the output is a dataset of evacuation routes and safety guidelines as the relief plan. Specifically, prompts are fed into the AI ​​model, and real-time countermeasures are designed while comparing past disaster patterns with the current situation.

[0105] Step 4:

[0106] The server sends the generated relief plan to the user's terminal via an information delivery method. The input is the relief plan dataset from step 3, and the output is the information displayed to the user. Specifically, the relief plan is packaged in an appropriate data format and sent to the mobile device via the internet.

[0107] Step 5:

[0108] The terminal uses suggestion and warning mechanisms to provide evacuation suggestions and warnings through the user interface. The input is the relief plan displayed in Step 4, and the output is the instructions and alert information received by the user. Specifically, an application built with React Native presents information in an easy-to-understand format for the user and provides real-time warnings as needed.

[0109] 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.

[0110] This system is an advanced platform for generating effective relief plans during disasters, providing more personalized responses by taking into account the user's emotional state. The system includes a series of processes: data collection from diverse sources, data preprocessing, analysis, relief plan generation, and information delivery.

[0111] First, the server collects weather information, seismic activity information, and social media information via the internet. Because this information is provided in various formats, the server preprocesses the data to convert it into a common format. The unified data is then input into a generating AI for analysis. This analysis includes identifying the extent and urgency of the disaster's impact.

[0112] In particular, this invention evaluates the user's mental state and stress level by analyzing emotional data obtainable from the user (e.g., social media posts and emotion estimation through speech recognition). The emotion engine uses this data to individually adjust the method and timing of information delivery based on the user's emotional state. For example, if the user is feeling stressed, the emotion engine will make the information presentation easier to understand or adjust the amount of information provided.

[0113] The relief plan and emotionally-driven response measures generated in this way are displayed to the user through their device. The user interface is designed to intuitively present geographical information and emotion-based guidelines. For example, for users whose anxiety levels are high during a disaster, the emotion engine highlights points requiring special attention and promotes appropriate action.

[0114] In this way, by combining emotion recognition with a conventional data-driven approach, this system enables the minimization of human casualties and the provision of rapid and accurate disaster response. This combination provides users facing disasters with a sense of security and supports optimal decision-making.

[0115] The following describes the processing flow.

[0116] Step 1:

[0117] The server automatically retrieves data via APIs to collect weather information, seismic activity information, and social media information from external data sources. This allows for the collection of a wide range of information in real time, enabling early detection of signs and the spread of disasters.

[0118] Step 2:

[0119] The server converts the collected data into a common format and stores it in an integrated database. This preprocessing involves organizing the data based on timestamps and geographical information, preparing it for analysis.

[0120] Step 3:

[0121] The server performs generative AI analysis using the integrated dataset. This analysis procedure identifies the scope of the disaster's impact and assesses the level of risk people face.

[0122] Step 4:

[0123] The server generates a relief plan based on the analysis results. During this process, the optimal evacuation routes, shelters, and supply priorities are determined according to the type and scale of the disaster.

[0124] Step 5:

[0125] The server uses an emotion engine to assess the user's emotional state. It analyzes social media posts and audio data to identify the user's stress level and emotional state.

[0126] Step 6:

[0127] The server adjusts the content and presentation of the relief plan based on the user's emotional state. For example, it prioritizes displaying reassuring information to users who are feeling anxious.

[0128] Step 7:

[0129] The device presents the generated relief plan and emotion-based response strategies to the user via a user interface. This information is provided in visual maps and concise list formats to facilitate quick access and understanding.

[0130] Step 8:

[0131] Users select the optimal course of action for safe evacuation based on the information presented on their device. By following the provided guidance, they can minimize risks during a disaster.

[0132] (Example 2)

[0133] 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".

[0134] In times of disaster, it is crucial to quickly and appropriately assess the situation and provide individualized relief plans based on that assessment. Conventional systems have limitations in collecting and analyzing disaster-related data, and have struggled to provide responses tailored to users' emotional states. This could potentially lead to the provision of information that is not necessarily optimal for disaster victims.

[0135] 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.

[0136] In this invention, the server includes data collection means for collecting environmental information, data preprocessing means for converting and integrating the collected data into a unified format, analysis means for inputting the integrated data into a computational model and generating impact analysis and policies, emotion analysis means for analyzing the user's emotional state and adaptively adjusting the method and timing of providing information, and information provision means for providing the generated policies and personalized information through a human-machine interface. This enables the rapid and accurate provision of relief plans during disasters and responses tailored to the emotional state of individual users.

[0137] "Environmental information" refers to data obtained from various sources, including weather information, seismic activity information, and social media, and is fundamental data for understanding the impact and situation of disasters.

[0138] "Data collection methods" refer to techniques and technologies for automatically acquiring necessary data from the internet and various information sources.

[0139] "Data preprocessing means" refers to operations or processes for formally unifying collected data and preparing it for analysis.

[0140] "Analytical means" refers to methods and models used to perform analysis based on integrated data, in accordance with a specific purpose, and derive results.

[0141] "Emotional analysis means" refers to a technology or method that analyzes a user's emotional state and adjusts the way and timing of information provision based on that analysis.

[0142] A "human-machine interface" is an interface that allows users to interact with machine systems, enabling them to intuitively present information and perform operations.

[0143] This system is an advanced platform for providing rapid and personalized relief plans during disasters. Specifically, the server collects environmental information, converts the data into a unified format, and then performs analysis using a computational model. Based on the analysis results, it enables the provision of information that takes into account the user's emotional state.

[0144] The server collects environmental information from various sources via the internet. It can obtain weather and earthquake data using APIs. The collected data is preprocessed using libraries such as Python's Pandas library and converted into a unified format. This process prepares the dataset necessary for analysis.

[0145] The standardized data is fed into a generative AI model for analysis. Here, deep learning frameworks such as TENSORFLOW® and PyTorch are used to analyze the impact of the disaster. Based on the results of this analysis, a relief plan is generated and sent to the user's terminal.

[0146] Furthermore, as a sentiment analysis technique, the server collects user social media data and voice data, and uses the NLP library Hugging Face to analyze the user's emotional state. The sentiment information obtained through this analysis is used to flexibly adjust the method and timing of information delivery.

[0147] The user interface displays the relief plan and related information generated through the terminal. If the user is feeling anxious, this interface highlights actions that require particular attention and indicates appropriate evacuation locations and routes. An example of a prompt for providing this information is the instruction given to the model: "Create an appropriate relief plan based on the current disaster situation and provide advice that is sensitive to the user's feelings."

[0148] In this way, the system combines information gathering and analysis with information provision based on the user's emotional state, enabling rapid and accurate disaster response.

[0149] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0150] Step 1:

[0151] The server collects environmental information via the internet. Specifically, it obtains weather data, seismic activity information, and social media posts via APIs. The input at this stage is raw data provided by each information source, which is stored in a local database. The output is a collection of the raw data obtained from each information source.

[0152] Step 2:

[0153] The server performs data preprocessing to convert the collected data into a unified format. Specifically, it uses the Python Pandas library to clean the data, handle missing data, and unify the format. The input is the raw data acquired in step 1, and the output is an integrated dataset suitable for analysis.

[0154] Step 3:

[0155] The server performs analysis by inputting pre-processed data into a generating AI model. Specifically, it uses TensorFlow to analyze the data and evaluate the scope and urgency of the disaster's impact. The input for this step is an integrated dataset, and the output is the disaster impact analysis results.

[0156] Step 4:

[0157] The server creates a relief plan based on the analysis results. The generated plan includes evacuation advisories and safe travel routes. During this process, prompts are given to the generating AI model to construct a specific plan. The output is an individualized relief plan.

[0158] Step 5:

[0159] The server collects and analyzes user emotional data. It analyzes social media posts and audio data using Hugging Face's NLP tools to assess the user's stress level and emotional state. The input is emotional data, and the output is the user's emotional analysis results.

[0160] Step 6:

[0161] The device provides information using the generated relief plan and sentiment analysis results. Specifically, it shows the user appropriate evacuation actions along with map information. The output consists of visualized information and guidelines.

[0162] Step 7:

[0163] The user takes appropriate action based on the information displayed on the device. Specific actions include moving to an evacuation center and preparing emergency supplies. The input for this step is information provided by the device.

[0164] (Application Example 2)

[0165] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0166] During disasters, amidst a flood of information, obtaining an optimal relief plan tailored to individual circumstances and emotional states is a challenging task. Furthermore, even with sufficient information, understanding it and taking appropriate action requires information that considers the user's mental state. Conventional systems have struggled to consider these factors, preventing personalized and effective disaster response.

[0167] 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.

[0168] In this invention, the server includes data collection means for collecting weather information, seismic activity information, and communication network information; data preprocessing means for converting and integrating the collected information into a common format; analysis means for analyzing the integrated data and generating a relief plan; emotion measurement means for evaluating the user's emotional state and acquiring emotional data; and personalized information provision means for adjusting the method and timing of information provision based on the user's emotional state. This enables accurate and reassuring disaster response tailored to the individual user's situation and emotions.

[0169] "Data collection means" refers to devices and processes for comprehensively collecting weather information, seismic activity information, and communication network information.

[0170] "Data preprocessing means" refers to devices and technologies for converting collected information in various formats into a unified format and integrating the information.

[0171] The "analysis means" is a function that generates a relief plan using pre-processed data and analyzes disaster response measures that are appropriate for the user's situation.

[0172] "Emotion measurement means" refers to devices or methods for evaluating a user's mental state and acquiring data related to emotions.

[0173] A "personalized information delivery method" is a system that adjusts the method and timing of information delivery according to the user's emotional state, in order to convey information appropriately.

[0174] "Information presentation means" refers to a means of directly communicating the generated relief plan to the user through a visual display device.

[0175] The system that implements this application primarily consists of a server for data collection and analysis, a terminal for measuring the user's emotional state, and a visual device for displaying the information. The server executes programs developed using programming languages ​​such as Python and JavaScript, and uses Google's TensorFlow and Facebook's PyTorch to analyze the collected weather information, seismic activity information, and communication network information data.

[0176] Specifically, the server collects data from various sources via the internet, converts it into a common format, and integrates it. The integrated data is input into a generating AI model, where it is analyzed to derive the optimal relief plan for disaster situations. Based on the analysis results, a relief plan is generated, and personalized disaster response measures are determined through information delivery methods tailored to the user's emotional state.

[0177] The emotion measurement system implemented in the device analyzes the user's facial expressions and voice in real time to acquire emotional data. Based on the user's emotional state, the method and timing of information presentation are adjusted. For example, users experiencing high stress levels are presented with information through a visually easy-to-understand interface.

[0178] By using this system, personalized action guidance can be displayed on a visual display device during a disaster, making it possible to create a safer and more secure environment for each individual user.

[0179] As a concrete example, during a flood warning in an area prone to disaster, users wearing smart glasses will intuitively see information such as, "Nearest safe evacuation location: XX Park. 10-minute walk." In this way, users are helped to take appropriate actions that align with their emotional state.

[0180] Example of a prompt:

[0181] How should information be displayed when a user is experiencing strong anxiety?

[0182] The diagram illustrates the shortest and safest evacuation route.

[0183] Based on the guide, provide concise and visually appealing information.

[0184] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0185] Step 1:

[0186] The server collects weather information, seismic activity information, and communication network information from the internet. Since the collected data is provided in different formats, the server converts it into a unified format. As a result, a standardized dataset is obtained.

[0187] Step 2:

[0188] The server uses a generative AI model to analyze the integrated data. This analysis process identifies patterns in the data and assesses the scope and urgency of the disaster's impact. As output, candidate relief plans are generated.

[0189] Step 3:

[0190] The device acquires voice and facial expression data from the user and uses this to evaluate the user's emotional state. Using an emotion analysis algorithm, it calculates stress levels and anxiety scores. The output is a metric indicating the user's emotional state.

[0191] Step 4:

[0192] The server uses the relief plan and the user's emotional state as input to personalize information delivery methods, adjusting the method and timing of information presentation. If the stress level is high, adjustments are made, such as simplifying the information presentation. The output is an improved user interface display.

[0193] Step 5:

[0194] The user confirms the final information presentation on the visual display of smart glasses. Based on the presented information, they can decide on actions to take during a disaster and take appropriate action. The output is the user's action instructions.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] [Second Embodiment]

[0199] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0200] 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.

[0201] 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).

[0202] 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.

[0203] 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.

[0204] 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).

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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".

[0211] This system provides a series of processes for collecting data in real time from diverse sources and generating optimal relief plans during disasters. First, the server accesses external data sources via the internet to collect weather information, seismic activity information, social media posts, and more. Since this information is often provided in different formats, the server preprocesses this data to convert it into a common format. This integrates different datasets, making them usable in the next analysis step.

[0212] Next, the server analyzes the integrated dataset to determine the most needed actions and predictions at specific locations. This process utilizes generative AI to design optimal response strategies based on historical data and real-time conditions. The generated relief plan includes comprehensive elements such as selecting evacuation routes, recommending safe shelters, and prioritizing the supply of necessary materials.

[0213] The analysis results and generated relief plan are sent to the terminal and presented to the user through a user interface. This interface is designed to be intuitive and help users quickly understand the situation and make decisions. For example, in the event of an earthquake, the safest route to a shelter and the shelter's capacity are displayed, allowing the user to choose their course of action based on that information.

[0214] This system will significantly simplify appropriate responses during disasters, from the individual level to the municipal level, and will support efforts to minimize damage. For example, if heavy rain is expected in a certain area, the server will identify high-risk areas based on weather data and send necessary evacuation orders to terminals. Users can then evacuate quickly based on this information. This will improve the speed and accuracy of disaster response and reduce actual damage.

[0215] The following describes the processing flow.

[0216] Step 1:

[0217] The server connects to multiple external APIs to collect weather information, seismic activity information, and social media posts. The acquired data is collected in different formats and types, but the server temporarily stores them in individual databases.

[0218] Step 2:

[0219] The server performs preprocessing to convert the collected data into a unified format. This process involves synchronizing data with different timestamps and performing cross-referencing based on location information, preparing the data for later analysis.

[0220] Step 3:

[0221] The server inputs the pre-processed data into the generating AI and begins the analysis. The generating AI extracts patterns from different information sources and assesses the disaster risk in a specific area.

[0222] Step 4:

[0223] Based on the analysis results, the server generates a relief plan to provide to the user. This plan includes recommended shelters, evacuation routes, identification of safe locations, and priority for supplying materials.

[0224] Step 5:

[0225] The server sends the generated relief plan to the terminal.

[0226] Step 6:

[0227] The terminal displays received relief plans to the user via a user interface. The user interface is intuitive and easy to use, designed to allow users to easily check information in map and list formats.

[0228] Step 7:

[0229] Based on the information displayed on their device, users can select the optimal evacuation course and begin responding quickly. This allows them to take actions that mitigate the risks associated with anticipated disasters.

[0230] (Example 1)

[0231] 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."

[0232] Developing rapid and effective response measures during a disaster requires the collection and analysis of vast amounts of information, demanding both accuracy and execution capabilities. Traditional methods have struggled to integrate diverse data from real-time updated sources and efficiently formulate optimal action plans. Furthermore, there is a need to provide this information to users immediately.

[0233] 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.

[0234] In this invention, the server includes means for collecting information, means for converting and integrating the collected information into a common format, and means for analyzing the integrated information and generating an optimal process plan using an artificial intelligence model. This makes it possible to collect and analyze information in real time and quickly provide the optimal relief plan through a user interface.

[0235] "Means of collecting information" refers to devices or programs for obtaining necessary data from various external sources.

[0236] "Means for converting and integrating collected information into a common format" refers to a device or program for centralizing information provided in different formats and converting and integrating it into a unified data format.

[0237] "Means for analyzing integrated information and generating an optimal process plan using an artificial intelligence model" refers to a device or program that analyzes integrated information using advanced computing techniques and formulates an appropriate plan using machine learning models or artificial intelligence.

[0238] "Means provided through communication devices" refers to devices or programs that include interfaces and communication protocols for communicating the generated plan to the user.

[0239] This invention implements a system in which a server provides optimal response measures during a disaster. The server first accesses external information sources and collects diverse information, including weather data, earthquake information, and posts from social media. This includes data acquisition using APIs over the internet. Cloud-based data services and web scraping techniques may also be used.

[0240] Next, the server converts and integrates the collected information into a unified data format. In this process, data format conversion software is used to standardize the data into formats such as JSON or XML. Data cleaning techniques are also used to handle missing data and eliminate duplicate data.

[0241] The server then moves on to analyzing the integrated dataset. This analysis uses a generative AI model, employing machine learning algorithms and data mining techniques to predict future actions based on historical and real-time data. At this stage, the generative AI model is prompted to "assess disaster risk in a specific area and recommend necessary countermeasures," which generates a detailed relief plan.

[0242] The generated relief plan is sent to the terminal, which the user receives through the interface. For example, when heavy rain is expected, the server can identify high-risk areas from real-time weather data and send information on safe evacuation routes and shelters to the terminal. Based on this information, the user can quickly begin evacuation and minimize the risks associated with the disaster.

[0243] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0244] Step 1:

[0245] The server accesses external data sources and collects information. It uses authentication credentials and query parameters from weather data APIs, earthquake information APIs, and social media APIs as input. It sends API requests to retrieve information, and the retrieved data is output. This data is received in raw data format (e.g., JSON, XML).

[0246] Step 2:

[0247] The server converts the collected information into a common format and integrates the data. It uses raw data received as input. This information is processed by format conversion software, performing data cleaning such as filling in missing data and removing duplicate data. The output is a converted and integrated unified data format.

[0248] Step 3:

[0249] The server analyzes the integrated data. The input is data converted into a common format. Using a generative AI model, it performs data mining and machine learning analysis with the prompt "Assess disaster risk in a specific area and recommend necessary countermeasures." The output is predictive data and suggestions that are incorporated into a relief plan.

[0250] Step 4:

[0251] The server sends the generated relief plan to the terminal. The input is the relief plan obtained through analysis. This plan is sent to the terminal via a communication protocol. The output is a relief plan in a format that can be confirmed as received by the terminal.

[0252] Step 5:

[0253] The terminal displays the received relief plan on its user interface. The input is the relief plan sent from the server. The terminal uses a GUI to visually display the information, making it easy for the user to understand. The output is the displayed information in a state that the user can verify.

[0254] Step 6:

[0255] The user acts based on the presented relief plan. The input is the plan displayed on the terminal. The user makes specific decisions regarding evacuation routes and movement to evacuation sites, and then takes action. The output is the implementation of appropriate response actions.

[0256] (Application Example 1)

[0257] 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."

[0258] A challenge exists in that there is a lack of information necessary to make quick and accurate evacuation decisions during disasters. Furthermore, warnings and suggestions for evacuation routes tailored to the risks faced by individuals are insufficient. Therefore, it is necessary to build a system that supports rapid decision-making to minimize damage.

[0259] 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.

[0260] In this invention, the server includes information gathering means, data preprocessing means, analysis means, information provision means, suggestion means, and warning means. This enables the provision of information for rapid evacuation actions during disasters, as well as real-time suggestions and warnings regarding evacuation routes and safe directions.

[0261] "Information gathering means" refers to devices or functions for collecting necessary information from various data sources, such as weather information, seismic activity information, and social media information.

[0262] "Data preprocessing means" refers to a device or processing function for converting collected information into a common format and integrating it in a consistent manner.

[0263] "Analysis means" refers to a device or algorithm with the processing capability to create an optimal relief plan based on integrated data.

[0264] "Information provision means" refers to a device or function that displays and communicates generated relief plans and other important information to users via a user interface.

[0265] "Suggested means" refers to a device or function for providing users with a rapid evacuation route or recommendation.

[0266] A "warning device" is a device or alert function that appropriately warns users of potential disasters or dangers that may occur in real time.

[0267] To implement this invention, the following system configuration must be considered. The server obtains necessary information from various data sources on the internet using information gathering means that collect weather information, seismic activity information, and social media information. Here, cloud-based services such as AWS Lambda can be used to collect data in real time and process the data efficiently.

[0268] Next, data preprocessing is used to convert the collected information into a common format and integrate it in a consistent manner. At this stage, dedicated algorithms for data transformation and cleaning are applied.

[0269] The integrated data is analyzed using an analysis tool powered by Amazon SageMaker. This allows a generated AI model to create a relief plan based on the integrated data. This analysis considers past disaster patterns and the current situation. The generated relief plan is then transmitted to the user's terminal via an information delivery system.

[0270] On the device, the relief plan is presented through an intuitive user interface built with React Native. This UI allows users to quickly obtain necessary information and provides suggestions for evacuation routes and safe evacuation directions. It also includes a warning system that provides real-time alerts for situations where risk is increasing.

[0271] For example, if heavy rainfall is predicted in a specific area, the system will identify high-risk areas based on weather data for that area and present users with safe evacuation routes. Users can then quickly begin evacuating based on the indicated routes. This will improve the speed and accuracy of disaster response and help minimize damage.

[0272] Specific examples of prompt statements in generative AI models are as follows:

[0273] "Analyze the following disaster scenario and generate the optimal evacuation routes for disaster victims. This area is at high risk of flooding due to heavy rainfall. Please consider historical data and real-time rainfall."

[0274] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0275] Step 1:

[0276] The server activates information gathering mechanisms to acquire weather information, seismic activity information, and social media information from the internet. It uses diverse data sources as input and collects this data in an unstructured, raw format. The output is a dataset containing all the necessary information in its raw, unprocessed state. Specifically, it acquires data from weather stations, earthquake observation systems, and social media platforms via APIs.

[0277] Step 2:

[0278] The server uses data preprocessing to convert the raw data collected in Step 1 into a common format and integrate it. The input is the raw data from Step 1, and the output is an integrated dataset with a unified format. Specifically, it performs data cleaning, noise reduction, and format standardization to maintain consistency across different data sources.

[0279] Step 3:

[0280] The server uses an integrated dataset as an analysis tool and generates a relief plan using a generative AI model. The input is the integrated dataset from step 2, and the output is a dataset of evacuation routes and safety guidelines as the relief plan. Specifically, prompts are fed into the AI ​​model, and real-time countermeasures are designed while comparing past disaster patterns with the current situation.

[0281] Step 4:

[0282] The server sends the generated relief plan to the user's terminal via an information delivery method. The input is the relief plan dataset from step 3, and the output is the information displayed to the user. Specifically, the relief plan is packaged in an appropriate data format and sent to the mobile device via the internet.

[0283] Step 5:

[0284] The terminal uses the proposal means and the warning means to make proposals and warnings regarding evacuation through the user interface. The input is the display relief plan in Step 4, and the output is the instructions and alert information received by the user. As a specific operation, an application constructed by React Native presents information in an easy-to-understand form for the user and issues warnings in real time as necessary.

[0285] Furthermore, an emotion engine for estimating the user's emotions may be combined. That is, the specific processing unit 290 may estimate the user's emotions using the emotion identification model 59 and perform specific processing using the user's emotions.

[0286] This system is an advanced platform for generating effective relief plans in the event of disasters, and provides more individualized responses by taking into account the user's emotional state. This system includes a series of processes such as data collection from various information sources, preprocessing of data, analysis, generation of relief plans, and information provision.

[0287] First, the server collects weather information, earthquake activity information, and social media information via the Internet. Since these pieces of information are provided in various formats, the server performs preprocessing to convert the data into a common format. The unified data is input into the generation AI and analyzed. This analysis also includes an analysis for identifying the affected area and urgency of the disaster.

[0288] In particular, in the present invention, the emotion engine analyzes emotion data that can be obtained from the user (e.g., writings on social media and emotion estimation by voice recognition) to evaluate the user's mental state and stress level. The emotion engine uses this data to individually adjust the method and timing of information provision based on the user's emotional state. For example, when the user is feeling stressed, the emotion engine makes the presentation of information easier to understand and adjusts the amount of information.

[0289] The relief plan and emotionally-driven response measures generated in this way are displayed to the user through their device. The user interface is designed to intuitively present geographical information and emotion-based guidelines. For example, for users whose anxiety levels are high during a disaster, the emotion engine highlights points requiring special attention and promotes appropriate action.

[0290] In this way, by combining emotion recognition with a conventional data-driven approach, this system enables the minimization of human casualties and the provision of rapid and accurate disaster response. This combination provides users facing disasters with a sense of security and supports optimal decision-making.

[0291] The following describes the processing flow.

[0292] Step 1:

[0293] The server automatically retrieves data via APIs to collect weather information, seismic activity information, and social media information from external data sources. This allows for the collection of a wide range of information in real time, enabling early detection of signs and the spread of disasters.

[0294] Step 2:

[0295] The server converts the collected data into a common format and stores it in an integrated database. This preprocessing involves organizing the data based on timestamps and geographical information, preparing it for analysis.

[0296] Step 3:

[0297] The server performs generative AI analysis using the integrated dataset. This analysis procedure identifies the scope of the disaster's impact and assesses the level of risk people face.

[0298] Step 4:

[0299] The server generates a relief plan based on the analysis results. In doing so, the optimal evacuation route, evacuation shelters, and the priority of material supply are determined according to the type and scale of the disaster.

[0300] Step 5:

[0301] The server uses an emotion engine to evaluate the user's emotional state. It analyzes social media posts and voice data to identify the user's stress level and emotional state.

[0302] Step 6:

[0303] The server adjusts the content and presentation method of the relief plan based on the user's emotional state. For example, for users who are feeling anxious, it is set to preferentially display information that provides reassurance.

[0304] Step 7:

[0305] The terminal presents the generated relief plan and emotion-based countermeasures to the user via the user interface. This information is provided in the form of a visual map or a concise list to assist with quick access and understanding.

[0306] Step 8:

[0307] The user selects the optimal actions for safe evacuation based on the information presented by the terminal. By following the provided guidance, the risks during the disaster can be minimized.

[0308] (Example 2)

[0309] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0310] In times of disaster, it is crucial to quickly and appropriately assess the situation and provide individualized relief plans based on that assessment. Conventional systems have limitations in collecting and analyzing disaster-related data, and have struggled to provide responses tailored to users' emotional states. This could potentially lead to the provision of information that is not necessarily optimal for disaster victims.

[0311] 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.

[0312] In this invention, the server includes data collection means for collecting environmental information, data preprocessing means for converting and integrating the collected data into a unified format, analysis means for inputting the integrated data into a computational model and generating impact analysis and policies, emotion analysis means for analyzing the user's emotional state and adaptively adjusting the method and timing of providing information, and information provision means for providing the generated policies and personalized information through a human-machine interface. This enables the rapid and accurate provision of relief plans during disasters and responses tailored to the emotional state of individual users.

[0313] "Environmental information" refers to data obtained from various sources, including weather information, seismic activity information, and social media, and is fundamental data for understanding the impact and situation of disasters.

[0314] "Data collection methods" refer to techniques and technologies for automatically acquiring necessary data from the internet and various information sources.

[0315] "Data preprocessing means" refers to operations or processes for formally unifying collected data and preparing it for analysis.

[0316] "Analytical means" refers to methods and models used to perform analysis based on integrated data, in accordance with a specific purpose, and derive results.

[0317] "Emotional analysis means" refers to a technology or method that analyzes a user's emotional state and adjusts the way and timing of information provision based on that analysis.

[0318] A "human-machine interface" is an interface that allows users to interact with machine systems, enabling them to intuitively present information and perform operations.

[0319] This system is an advanced platform for providing rapid and personalized relief plans during disasters. Specifically, the server collects environmental information, converts the data into a unified format, and then performs analysis using a computational model. Based on the analysis results, it enables the provision of information that takes into account the user's emotional state.

[0320] The server collects environmental information from various sources via the internet. It can obtain weather and earthquake data using APIs. The collected data is preprocessed using libraries such as Python's Pandas library and converted into a unified format. This process prepares the dataset necessary for analysis.

[0321] The standardized data is fed into a generative AI model for analysis. Here, deep learning frameworks such as TensorFlow and PyTorch are used to analyze the impact of the disaster. Based on the results of this analysis, a relief plan is generated and sent to the user's terminal.

[0322] Furthermore, as a sentiment analysis technique, the server collects user social media data and voice data, and uses the NLP library Hugging Face to analyze the user's emotional state. The sentiment information obtained through this analysis is used to flexibly adjust the method and timing of information delivery.

[0323] The user interface displays the relief plan and related information generated through the terminal. If the user is feeling anxious, this interface highlights actions that require particular attention and indicates appropriate evacuation locations and routes. An example of a prompt for providing this information is the instruction given to the model: "Create an appropriate relief plan based on the current disaster situation and provide advice that is sensitive to the user's feelings."

[0324] In this way, the system combines information gathering and analysis with information provision based on the user's emotional state, enabling rapid and accurate disaster response.

[0325] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0326] Step 1:

[0327] The server collects environmental information via the internet. Specifically, it obtains weather data, seismic activity information, and social media posts via APIs. The input at this stage is raw data provided by each information source, which is stored in a local database. The output is a collection of the raw data obtained from each information source.

[0328] Step 2:

[0329] The server performs data preprocessing to convert the collected data into a unified format. Specifically, it uses the Python Pandas library to clean the data, handle missing data, and unify the format. The input is the raw data acquired in step 1, and the output is an integrated dataset suitable for analysis.

[0330] Step 3:

[0331] The server performs analysis by inputting pre-processed data into a generating AI model. Specifically, it uses TensorFlow to analyze the data and evaluate the scope and urgency of the disaster's impact. The input for this step is an integrated dataset, and the output is the disaster impact analysis results.

[0332] Step 4:

[0333] The server creates a relief plan based on the analysis results. The generated plan includes evacuation advisories and safe travel routes. During this process, prompts are given to the generating AI model to construct a specific plan. The output is an individualized relief plan.

[0334] Step 5:

[0335] The server collects and analyzes user emotional data. It analyzes social media posts and audio data using Hugging Face's NLP tools to assess the user's stress level and emotional state. The input is emotional data, and the output is the user's emotional analysis results.

[0336] Step 6:

[0337] The device provides information using the generated relief plan and sentiment analysis results. Specifically, it shows the user appropriate evacuation actions along with map information. The output consists of visualized information and guidelines.

[0338] Step 7:

[0339] The user takes appropriate action based on the information displayed on the device. Specific actions include moving to an evacuation center and preparing emergency supplies. The input for this step is information provided by the device.

[0340] (Application Example 2)

[0341] 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."

[0342] During disasters, amidst a flood of information, obtaining an optimal relief plan tailored to individual circumstances and emotional states is a challenging task. Furthermore, even with sufficient information, understanding it and taking appropriate action requires information that considers the user's mental state. Conventional systems have struggled to consider these factors, preventing personalized and effective disaster response.

[0343] 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.

[0344] In this invention, the server includes data collection means for collecting weather information, seismic activity information, and communication network information; data preprocessing means for converting and integrating the collected information into a common format; analysis means for analyzing the integrated data and generating a relief plan; emotion measurement means for evaluating the user's emotional state and acquiring emotional data; and personalized information provision means for adjusting the method and timing of information provision based on the user's emotional state. This enables accurate and reassuring disaster response tailored to the individual user's situation and emotions.

[0345] "Data collection means" refers to devices and processes for comprehensively collecting weather information, seismic activity information, and communication network information.

[0346] "Data preprocessing means" refers to devices and technologies for converting collected information in various formats into a unified format and integrating the information.

[0347] The "analysis means" is a function that generates a relief plan using pre-processed data and analyzes disaster response measures that are appropriate for the user's situation.

[0348] "Emotion measurement means" refers to devices or methods for evaluating a user's mental state and acquiring data related to emotions.

[0349] A "personalized information delivery method" is a system that adjusts the method and timing of information delivery according to the user's emotional state, in order to convey information appropriately.

[0350] "Information presentation means" refers to a means of directly communicating the generated relief plan to the user through a visual display device.

[0351] The system that implements this application primarily consists of a server for data collection and analysis, a terminal for measuring the user's emotional state, and a visual device for displaying the information. The server executes programs developed using programming languages ​​such as Python and JavaScript, and uses Google's "TensorFlow" and Facebook's "PyTorch" to analyze the collected weather information, seismic activity information, and communication network information data.

[0352] Specifically, the server collects data from various sources via the internet, converts it into a common format, and integrates it. The integrated data is input into a generating AI model, where it is analyzed to derive the optimal relief plan for disaster situations. Based on the analysis results, a relief plan is generated, and personalized disaster response measures are determined through information delivery methods tailored to the user's emotional state.

[0353] The emotion measurement system implemented in the device analyzes the user's facial expressions and voice in real time to acquire emotional data. Based on the user's emotional state, the method and timing of information presentation are adjusted. For example, users experiencing high stress levels are presented with information through a visually easy-to-understand interface.

[0354] By using this system, personalized action guidance can be displayed on a visual display device during a disaster, making it possible to create a safer and more secure environment for each individual user.

[0355] As a concrete example, during a flood warning in an area prone to disaster, users wearing smart glasses will intuitively see information such as, "Nearest safe evacuation location: XX Park. 10-minute walk." In this way, users are helped to take appropriate actions that align with their emotional state.

[0356] Example of a prompt:

[0357] How should information be displayed when a user is experiencing strong anxiety?

[0358] The diagram illustrates the shortest and safest evacuation route.

[0359] Based on the guide, provide concise and visually appealing information.

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

[0361] Step 1:

[0362] The server collects weather information, seismic activity information, and communication network information from the internet. Since the collected data is provided in different formats, the server converts it into a unified format. As a result, a standardized dataset is obtained.

[0363] Step 2:

[0364] The server uses a generative AI model to analyze the integrated data. This analysis process identifies patterns in the data and assesses the scope and urgency of the disaster's impact. As output, candidate relief plans are generated.

[0365] Step 3:

[0366] The device acquires voice and facial expression data from the user and uses this to evaluate the user's emotional state. Using an emotion analysis algorithm, it calculates stress levels and anxiety scores. The output is a metric indicating the user's emotional state.

[0367] Step 4:

[0368] The server uses the relief plan and the user's emotional state as input to personalize information delivery methods, adjusting the method and timing of information presentation. If the stress level is high, adjustments are made, such as simplifying the information presentation. The output is an improved user interface display.

[0369] Step 5:

[0370] The user confirms the final information presentation on the visual display of smart glasses. Based on the presented information, they can decide on actions to take during a disaster and take appropriate action. The output is the user's action instructions.

[0371] 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.

[0372] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0373] 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.

[0374] [Third Embodiment]

[0375] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0376] 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.

[0377] 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).

[0378] 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.

[0379] 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.

[0380] 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).

[0381] 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.

[0382] 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.

[0383] 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.

[0384] 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.

[0385] 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.

[0386] 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".

[0387] This system provides a series of processes for collecting data in real time from diverse sources and generating optimal relief plans during disasters. First, the server accesses external data sources via the internet to collect weather information, seismic activity information, social media posts, and more. Since this information is often provided in different formats, the server preprocesses this data to convert it into a common format. This integrates different datasets, making them usable in the next analysis step.

[0388] Next, the server analyzes the integrated dataset to determine the most needed actions and predictions at specific locations. This process utilizes generative AI to design optimal response strategies based on historical data and real-time conditions. The generated relief plan includes comprehensive elements such as selecting evacuation routes, recommending safe shelters, and prioritizing the supply of necessary materials.

[0389] The analysis results and generated relief plan are sent to the terminal and presented to the user through a user interface. This interface is designed to be intuitive and help users quickly understand the situation and make decisions. For example, in the event of an earthquake, the safest route to a shelter and the shelter's capacity are displayed, allowing the user to choose their course of action based on that information.

[0390] This system will significantly simplify appropriate responses during disasters, from the individual level to the municipal level, and will support efforts to minimize damage. For example, if heavy rain is expected in a certain area, the server will identify high-risk areas based on weather data and send necessary evacuation orders to terminals. Users can then evacuate quickly based on this information. This will improve the speed and accuracy of disaster response and reduce actual damage.

[0391] The following describes the processing flow.

[0392] Step 1:

[0393] The server connects to multiple external APIs to collect weather information, seismic activity information, and social media posts. The acquired data is collected in different formats and types, but the server temporarily stores them in individual databases.

[0394] Step 2:

[0395] The server performs preprocessing to convert the collected data into a unified format. This process involves synchronizing data with different timestamps and performing cross-referencing based on location information, preparing the data for later analysis.

[0396] Step 3:

[0397] The server inputs the pre-processed data into the generating AI and begins the analysis. The generating AI extracts patterns from different information sources and assesses the disaster risk in a specific area.

[0398] Step 4:

[0399] Based on the analysis results, the server generates a relief plan to provide to the user. This plan includes recommended shelters, evacuation routes, identification of safe locations, and priority for supplying materials.

[0400] Step 5:

[0401] The server sends the generated relief plan to the terminal.

[0402] Step 6:

[0403] The terminal displays received relief plans to the user via a user interface. The user interface is intuitive and easy to use, designed to allow users to easily check information in map and list formats.

[0404] Step 7:

[0405] Based on the information displayed on their device, users can select the optimal evacuation course and begin responding quickly. This allows them to take actions that mitigate the risks associated with anticipated disasters.

[0406] (Example 1)

[0407] 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."

[0408] Developing rapid and effective response measures during a disaster requires the collection and analysis of vast amounts of information, demanding both accuracy and execution capabilities. Traditional methods have struggled to integrate diverse data from real-time updated sources and efficiently formulate optimal action plans. Furthermore, there is a need to provide this information to users immediately.

[0409] 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.

[0410] In this invention, the server includes means for collecting information, means for converting and integrating the collected information into a common format, and means for analyzing the integrated information and generating an optimal process plan using an artificial intelligence model. This makes it possible to collect and analyze information in real time and quickly provide the optimal relief plan through a user interface.

[0411] "Means of collecting information" refers to devices or programs for obtaining necessary data from various external sources.

[0412] "Means for converting and integrating collected information into a common format" refers to a device or program for centralizing information provided in different formats and converting and integrating it into a unified data format.

[0413] "Means for analyzing integrated information and generating an optimal process plan using an artificial intelligence model" refers to a device or program that analyzes integrated information using advanced computing techniques and formulates an appropriate plan using machine learning models or artificial intelligence.

[0414] "Means provided through communication devices" refers to devices or programs that include interfaces and communication protocols for communicating the generated plan to the user.

[0415] This invention implements a system in which a server provides optimal response measures during a disaster. The server first accesses external information sources and collects diverse information, including weather data, earthquake information, and posts from social media. This includes data acquisition using APIs over the internet. Cloud-based data services and web scraping techniques may also be used.

[0416] Next, the server converts and integrates the collected information into a unified data format. In this process, data format conversion software is used to standardize the data into formats such as JSON or XML. Data cleaning techniques are also used to handle missing data and eliminate duplicate data.

[0417] The server then moves on to analyzing the integrated dataset. This analysis uses a generative AI model, employing machine learning algorithms and data mining techniques to predict future actions based on historical and real-time data. At this stage, the generative AI model is prompted to "assess disaster risk in a specific area and recommend necessary countermeasures," which generates a detailed relief plan.

[0418] The generated relief plan is sent to the terminal, which the user receives through the interface. For example, when heavy rain is expected, the server can identify high-risk areas from real-time weather data and send information on safe evacuation routes and shelters to the terminal. Based on this information, the user can quickly begin evacuation and minimize the risks associated with the disaster.

[0419] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0420] Step 1:

[0421] The server accesses external data sources and collects information. It uses authentication credentials and query parameters from weather data APIs, earthquake information APIs, and social media APIs as input. It sends API requests to retrieve information, and the retrieved data is output. This data is received in raw data format (e.g., JSON, XML).

[0422] Step 2:

[0423] The server converts the collected information into a common format and integrates the data. It uses raw data received as input. This information is processed by format conversion software, performing data cleaning such as filling in missing data and removing duplicate data. The output is a converted and integrated unified data format.

[0424] Step 3:

[0425] The server analyzes the integrated data. The input is data converted into a common format. Using a generative AI model, it performs data mining and machine learning analysis with the prompt "Assess disaster risk in a specific area and recommend necessary countermeasures." The output is predictive data and suggestions that are incorporated into a relief plan.

[0426] Step 4:

[0427] The server sends the generated relief plan to the terminal. The input is the relief plan obtained through analysis. This plan is sent to the terminal via a communication protocol. The output is a relief plan in a format that can be confirmed as received by the terminal.

[0428] Step 5:

[0429] The terminal displays the received relief plan on its user interface. The input is the relief plan sent from the server. The terminal uses a GUI to visually display the information, making it easy for the user to understand. The output is the displayed information in a state that the user can verify.

[0430] Step 6:

[0431] The user acts based on the presented relief plan. The input is the plan displayed on the terminal. The user makes specific decisions regarding evacuation routes and movement to evacuation sites, and then takes action. The output is the implementation of appropriate response actions.

[0432] (Application Example 1)

[0433] 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."

[0434] A challenge exists in that there is a lack of information necessary to make quick and accurate evacuation decisions during disasters. Furthermore, warnings and suggestions for evacuation routes tailored to the risks faced by individuals are insufficient. Therefore, it is necessary to build a system that supports rapid decision-making to minimize damage.

[0435] 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.

[0436] In this invention, the server includes information gathering means, data preprocessing means, analysis means, information provision means, suggestion means, and warning means. This enables the provision of information for rapid evacuation actions during disasters, as well as real-time suggestions and warnings regarding evacuation routes and safe directions.

[0437] "Information gathering means" refers to devices or functions for collecting necessary information from various data sources, such as weather information, seismic activity information, and social media information.

[0438] "Data preprocessing means" refers to a device or processing function for converting collected information into a common format and integrating it in a consistent manner.

[0439] "Analysis means" refers to a device or algorithm with the processing capability to create an optimal relief plan based on integrated data.

[0440] "Information provision means" refers to a device or function that displays and communicates generated relief plans and other important information to users via a user interface.

[0441] "Suggested means" refers to a device or function for providing users with a rapid evacuation route or recommendation.

[0442] A "warning device" is a device or alert function that appropriately warns users of potential disasters or dangers that may occur in real time.

[0443] To implement this invention, the following system configuration must be considered. The server obtains necessary information from various data sources on the internet using information gathering means that collect weather information, seismic activity information, and social media information. Here, cloud-based services such as AWS Lambda can be used to collect data in real time and process the data efficiently.

[0444] Next, data preprocessing is used to convert the collected information into a common format and integrate it in a consistent manner. At this stage, dedicated algorithms for data transformation and cleaning are applied.

[0445] The integrated data is analyzed using an analysis tool powered by Amazon SageMaker. This allows a generated AI model to create a relief plan based on the integrated data. This analysis considers past disaster patterns and the current situation. The generated relief plan is then transmitted to the user's terminal via an information delivery system.

[0446] On the device, the relief plan is presented through an intuitive user interface built with React Native. This UI allows users to quickly obtain necessary information and provides suggestions for evacuation routes and safe evacuation directions. It also includes a warning system that provides real-time alerts for situations where risk is increasing.

[0447] For example, if heavy rainfall is predicted in a specific area, the system will identify high-risk areas based on weather data for that area and present users with safe evacuation routes. Users can then quickly begin evacuating based on the indicated routes. This will improve the speed and accuracy of disaster response and help minimize damage.

[0448] Specific examples of prompt statements in generative AI models are as follows:

[0449] "Analyze the following disaster scenario and generate the optimal evacuation routes for disaster victims. This area is at high risk of flooding due to heavy rainfall. Please consider historical data and real-time rainfall."

[0450] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0451] Step 1:

[0452] The server activates information gathering mechanisms to acquire weather information, seismic activity information, and social media information from the internet. It uses diverse data sources as input and collects this data in an unstructured, raw format. The output is a dataset containing all the necessary information in its raw, unprocessed state. Specifically, it acquires data from weather stations, earthquake observation systems, and social media platforms via APIs.

[0453] Step 2:

[0454] The server uses data preprocessing to convert the raw data collected in Step 1 into a common format and integrate it. The input is the raw data from Step 1, and the output is an integrated dataset with a unified format. Specifically, it performs data cleaning, noise reduction, and format standardization to maintain consistency across different data sources.

[0455] Step 3:

[0456] The server uses an integrated dataset as an analysis tool and generates a relief plan using a generative AI model. The input is the integrated dataset from step 2, and the output is a dataset of evacuation routes and safety guidelines as the relief plan. Specifically, prompts are fed into the AI ​​model, and real-time countermeasures are designed while comparing past disaster patterns with the current situation.

[0457] Step 4:

[0458] The server sends the generated relief plan to the user's terminal via an information delivery method. The input is the relief plan dataset from step 3, and the output is the information displayed to the user. Specifically, the relief plan is packaged in an appropriate data format and sent to the mobile device via the internet.

[0459] Step 5:

[0460] The terminal uses suggestion and warning mechanisms to provide evacuation suggestions and warnings through the user interface. The input is the relief plan displayed in Step 4, and the output is the instructions and alert information received by the user. Specifically, an application built with React Native presents information in an easy-to-understand format for the user and provides real-time warnings as needed.

[0461] 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.

[0462] This system is an advanced platform for generating effective relief plans during disasters, providing more personalized responses by taking into account the user's emotional state. The system includes a series of processes: data collection from diverse sources, data preprocessing, analysis, relief plan generation, and information delivery.

[0463] First, the server collects weather information, seismic activity information, and social media information via the internet. Because this information is provided in various formats, the server preprocesses the data to convert it into a common format. The unified data is then input into a generating AI for analysis. This analysis includes identifying the extent and urgency of the disaster's impact.

[0464] In particular, this invention evaluates the user's mental state and stress level by analyzing emotional data obtainable from the user (e.g., social media posts and emotion estimation through speech recognition). The emotion engine uses this data to individually adjust the method and timing of information delivery based on the user's emotional state. For example, if the user is feeling stressed, the emotion engine will make the information presentation easier to understand or adjust the amount of information provided.

[0465] The relief plan and emotionally-driven response measures generated in this way are displayed to the user through their device. The user interface is designed to intuitively present geographical information and emotion-based guidelines. For example, for users whose anxiety levels are high during a disaster, the emotion engine highlights points requiring special attention and promotes appropriate action.

[0466] In this way, by combining emotion recognition with a conventional data-driven approach, this system enables the minimization of human casualties and the provision of rapid and accurate disaster response. This combination provides users facing disasters with a sense of security and supports optimal decision-making.

[0467] The following describes the processing flow.

[0468] Step 1:

[0469] The server automatically retrieves data via APIs to collect weather information, seismic activity information, and social media information from external data sources. This allows for the collection of a wide range of information in real time, enabling early detection of signs and the spread of disasters.

[0470] Step 2:

[0471] The server converts the collected data into a common format and stores it in an integrated database. This preprocessing involves organizing the data based on timestamps and geographical information, preparing it for analysis.

[0472] Step 3:

[0473] The server performs generative AI analysis using the integrated dataset. This analysis procedure identifies the scope of the disaster's impact and assesses the level of risk people face.

[0474] Step 4:

[0475] The server generates a relief plan based on the analysis results. During this process, the optimal evacuation routes, shelters, and supply priorities are determined according to the type and scale of the disaster.

[0476] Step 5:

[0477] The server uses an emotion engine to assess the user's emotional state. It analyzes social media posts and audio data to identify the user's stress level and emotional state.

[0478] Step 6:

[0479] The server adjusts the content and presentation of the relief plan based on the user's emotional state. For example, it prioritizes displaying reassuring information to users who are feeling anxious.

[0480] Step 7:

[0481] The device presents the generated relief plan and emotion-based response strategies to the user via a user interface. This information is provided in visual maps and concise list formats to facilitate quick access and understanding.

[0482] Step 8:

[0483] Users select the optimal course of action for safe evacuation based on the information presented on their device. By following the provided guidance, they can minimize risks during a disaster.

[0484] (Example 2)

[0485] 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."

[0486] In times of disaster, it is crucial to quickly and appropriately assess the situation and provide individualized relief plans based on that assessment. Conventional systems have limitations in collecting and analyzing disaster-related data, and have struggled to provide responses tailored to users' emotional states. This could potentially lead to the provision of information that is not necessarily optimal for disaster victims.

[0487] 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.

[0488] In this invention, the server includes data collection means for collecting environmental information, data preprocessing means for converting and integrating the collected data into a unified format, analysis means for inputting the integrated data into a computational model and generating impact analysis and policies, emotion analysis means for analyzing the user's emotional state and adaptively adjusting the method and timing of providing information, and information provision means for providing the generated policies and personalized information through a human-machine interface. This enables the rapid and accurate provision of relief plans during disasters and responses tailored to the emotional state of individual users.

[0489] "Environmental information" refers to data obtained from various sources, including weather information, seismic activity information, and social media, and is fundamental data for understanding the impact and situation of disasters.

[0490] "Data collection methods" refer to techniques and technologies for automatically acquiring necessary data from the internet and various information sources.

[0491] "Data preprocessing means" refers to operations or processes for formally unifying collected data and preparing it for analysis.

[0492] "Analytical means" refers to methods and models used to perform analysis based on integrated data, in accordance with a specific purpose, and derive results.

[0493] "Emotional analysis means" refers to a technology or method that analyzes a user's emotional state and adjusts the way and timing of information provision based on that analysis.

[0494] A "human-machine interface" is an interface that allows users to interact with machine systems, enabling them to intuitively present information and perform operations.

[0495] This system is an advanced platform for providing rapid and personalized relief plans during disasters. Specifically, the server collects environmental information, converts the data into a unified format, and then performs analysis using a computational model. Based on the analysis results, it enables the provision of information that takes into account the user's emotional state.

[0496] The server collects environmental information from various sources via the internet. It can obtain weather and earthquake data using APIs. The collected data is preprocessed using libraries such as Python's Pandas library and converted into a unified format. This process prepares the dataset necessary for analysis.

[0497] The standardized data is fed into a generative AI model for analysis. Here, deep learning frameworks such as TensorFlow and PyTorch are used to analyze the impact of the disaster. Based on the results of this analysis, a relief plan is generated and sent to the user's terminal.

[0498] Furthermore, as a sentiment analysis technique, the server collects user social media data and voice data, and uses the NLP library Hugging Face to analyze the user's emotional state. The sentiment information obtained through this analysis is used to flexibly adjust the method and timing of information delivery.

[0499] The user interface displays the relief plan and related information generated through the terminal. If the user is feeling anxious, this interface highlights actions that require particular attention and indicates appropriate evacuation locations and routes. An example of a prompt for providing this information is the instruction given to the model: "Create an appropriate relief plan based on the current disaster situation and provide advice that is sensitive to the user's feelings."

[0500] In this way, the system combines information gathering and analysis with information provision based on the user's emotional state, enabling rapid and accurate disaster response.

[0501] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0502] Step 1:

[0503] The server collects environmental information via the internet. Specifically, it obtains weather data, seismic activity information, and social media posts via APIs. The input at this stage is raw data provided by each information source, which is stored in a local database. The output is a collection of the raw data obtained from each information source.

[0504] Step 2:

[0505] The server performs data preprocessing to convert the collected data into a unified format. Specifically, it uses the Python Pandas library to clean the data, handle missing data, and unify the format. The input is the raw data acquired in step 1, and the output is an integrated dataset suitable for analysis.

[0506] Step 3:

[0507] The server performs analysis by inputting pre-processed data into a generating AI model. Specifically, it uses TensorFlow to analyze the data and evaluate the scope and urgency of the disaster's impact. The input for this step is an integrated dataset, and the output is the disaster impact analysis results.

[0508] Step 4:

[0509] The server creates a relief plan based on the analysis results. The generated plan includes evacuation advisories and safe travel routes. During this process, prompts are given to the generating AI model to construct a specific plan. The output is an individualized relief plan.

[0510] Step 5:

[0511] The server collects and analyzes user emotional data. It analyzes social media posts and audio data using Hugging Face's NLP tools to assess the user's stress level and emotional state. The input is emotional data, and the output is the user's emotional analysis results.

[0512] Step 6:

[0513] The device provides information using the generated relief plan and sentiment analysis results. Specifically, it shows the user appropriate evacuation actions along with map information. The output consists of visualized information and guidelines.

[0514] Step 7:

[0515] The user takes appropriate action based on the information displayed on the device. Specific actions include moving to an evacuation center and preparing emergency supplies. The input for this step is information provided by the device.

[0516] (Application Example 2)

[0517] 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."

[0518] During disasters, amidst a flood of information, obtaining an optimal relief plan tailored to individual circumstances and emotional states is a challenging task. Furthermore, even with sufficient information, understanding it and taking appropriate action requires information that considers the user's mental state. Conventional systems have struggled to consider these factors, preventing personalized and effective disaster response.

[0519] 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.

[0520] In this invention, the server includes data collection means for collecting weather information, seismic activity information, and communication network information; data preprocessing means for converting and integrating the collected information into a common format; analysis means for analyzing the integrated data and generating a relief plan; emotion measurement means for evaluating the user's emotional state and acquiring emotional data; and personalized information provision means for adjusting the method and timing of information provision based on the user's emotional state. This enables accurate and reassuring disaster response tailored to the individual user's situation and emotions.

[0521] "Data collection means" refers to devices and processes for comprehensively collecting weather information, seismic activity information, and communication network information.

[0522] "Data preprocessing means" refers to devices and technologies for converting collected information in various formats into a unified format and integrating the information.

[0523] The "analysis means" is a function that generates a relief plan using pre-processed data and analyzes disaster response measures that are appropriate for the user's situation.

[0524] "Emotion measurement means" refers to devices or methods for evaluating a user's mental state and acquiring data related to emotions.

[0525] A "personalized information delivery method" is a system that adjusts the method and timing of information delivery according to the user's emotional state, in order to convey information appropriately.

[0526] "Information presentation means" refers to a means of directly communicating the generated relief plan to the user through a visual display device.

[0527] The system that implements this application primarily consists of a server for data collection and analysis, a terminal for measuring the user's emotional state, and a visual device for displaying the information. The server executes programs developed using programming languages ​​such as Python and JavaScript, and uses Google's "TensorFlow" and Facebook's "PyTorch" to analyze the collected weather information, seismic activity information, and communication network information data.

[0528] Specifically, the server collects data from various sources via the internet, converts it into a common format, and integrates it. The integrated data is input into a generating AI model, where it is analyzed to derive the optimal relief plan for disaster situations. Based on the analysis results, a relief plan is generated, and personalized disaster response measures are determined through information delivery methods tailored to the user's emotional state.

[0529] The emotion measurement system implemented in the device analyzes the user's facial expressions and voice in real time to acquire emotional data. Based on the user's emotional state, the method and timing of information presentation are adjusted. For example, users experiencing high stress levels are presented with information through a visually easy-to-understand interface.

[0530] By using this system, personalized action guidance can be displayed on a visual display device during a disaster, making it possible to create a safer and more secure environment for each individual user.

[0531] As a concrete example, during a flood warning in an area prone to disaster, users wearing smart glasses will intuitively see information such as, "Nearest safe evacuation location: XX Park. 10-minute walk." In this way, users are helped to take appropriate actions that align with their emotional state.

[0532] Example of a prompt:

[0533] How should information be displayed when a user is experiencing strong anxiety?

[0534] The diagram illustrates the shortest and safest evacuation route.

[0535] Based on the guide, provide concise and visually appealing information.

[0536] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0537] Step 1:

[0538] The server collects weather information, seismic activity information, and communication network information from the internet. Since the collected data is provided in different formats, the server converts it into a unified format. As a result, a standardized dataset is obtained.

[0539] Step 2:

[0540] The server uses a generative AI model to analyze the integrated data. This analysis process identifies patterns in the data and assesses the scope and urgency of the disaster's impact. As output, candidate relief plans are generated.

[0541] Step 3:

[0542] The device acquires voice and facial expression data from the user and uses this to evaluate the user's emotional state. Using an emotion analysis algorithm, it calculates stress levels and anxiety scores. The output is a metric indicating the user's emotional state.

[0543] Step 4:

[0544] The server uses the relief plan and the user's emotional state as input to personalize information delivery methods, adjusting the method and timing of information presentation. If the stress level is high, adjustments are made, such as simplifying the information presentation. The output is an improved user interface display.

[0545] Step 5:

[0546] The user confirms the final information presentation on the visual display of smart glasses. Based on the presented information, they can decide on actions to take during a disaster and take appropriate action. The output is the user's action instructions.

[0547] 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.

[0548] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0549] 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.

[0550] [Fourth Embodiment]

[0551] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0552] 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.

[0553] 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).

[0554] 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.

[0555] 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.

[0556] 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).

[0557] 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.

[0558] 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.

[0559] 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.

[0560] 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.

[0561] 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.

[0562] 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.

[0563] 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".

[0564] This system provides a series of processes for collecting data in real time from diverse sources and generating optimal relief plans during disasters. First, the server accesses external data sources via the internet to collect weather information, seismic activity information, social media posts, and more. Since this information is often provided in different formats, the server preprocesses this data to convert it into a common format. This integrates different datasets, making them usable in the next analysis step.

[0565] Next, the server analyzes the integrated dataset to determine the most needed actions and predictions at specific locations. This process utilizes generative AI to design optimal response strategies based on historical data and real-time conditions. The generated relief plan includes comprehensive elements such as selecting evacuation routes, recommending safe shelters, and prioritizing the supply of necessary materials.

[0566] The analysis results and generated relief plan are sent to the terminal and presented to the user through a user interface. This interface is designed to be intuitive and help users quickly understand the situation and make decisions. For example, in the event of an earthquake, the safest route to a shelter and the shelter's capacity are displayed, allowing the user to choose their course of action based on that information.

[0567] This system will significantly simplify appropriate responses during disasters, from the individual level to the municipal level, and will support efforts to minimize damage. For example, if heavy rain is expected in a certain area, the server will identify high-risk areas based on weather data and send necessary evacuation orders to terminals. Users can then evacuate quickly based on this information. This will improve the speed and accuracy of disaster response and reduce actual damage.

[0568] The following describes the processing flow.

[0569] Step 1:

[0570] The server connects to multiple external APIs to collect weather information, seismic activity information, and social media posts. The acquired data is collected in different formats and types, but the server temporarily stores them in individual databases.

[0571] Step 2:

[0572] The server performs preprocessing to convert the collected data into a unified format. This process involves synchronizing data with different timestamps and performing cross-referencing based on location information, preparing the data for later analysis.

[0573] Step 3:

[0574] The server inputs the pre-processed data into the generating AI and begins the analysis. The generating AI extracts patterns from different information sources and assesses the disaster risk in a specific area.

[0575] Step 4:

[0576] Based on the analysis results, the server generates a relief plan to provide to the user. This plan includes recommended shelters, evacuation routes, identification of safe locations, and priority for supplying materials.

[0577] Step 5:

[0578] The server sends the generated relief plan to the terminal.

[0579] Step 6:

[0580] The terminal displays received relief plans to the user via a user interface. The user interface is intuitive and easy to use, designed to allow users to easily check information in map and list formats.

[0581] Step 7:

[0582] Based on the information displayed on their device, users can select the optimal evacuation course and begin responding quickly. This allows them to take actions that mitigate the risks associated with anticipated disasters.

[0583] (Example 1)

[0584] 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".

[0585] Developing rapid and effective response measures during a disaster requires the collection and analysis of vast amounts of information, demanding both accuracy and execution capabilities. Traditional methods have struggled to integrate diverse data from real-time updated sources and efficiently formulate optimal action plans. Furthermore, there is a need to provide this information to users immediately.

[0586] 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.

[0587] In this invention, the server includes means for collecting information, means for converting and integrating the collected information into a common format, and means for analyzing the integrated information and generating an optimal process plan using an artificial intelligence model. This makes it possible to collect and analyze information in real time and quickly provide the optimal relief plan through a user interface.

[0588] "Means of collecting information" refers to devices or programs for obtaining necessary data from various external sources.

[0589] "Means for converting and integrating collected information into a common format" refers to a device or program for centralizing information provided in different formats and converting and integrating it into a unified data format.

[0590] "Means for analyzing integrated information and generating an optimal process plan using an artificial intelligence model" refers to a device or program that analyzes integrated information using advanced computing techniques and formulates an appropriate plan using machine learning models or artificial intelligence.

[0591] "Means provided through communication devices" refers to devices or programs that include interfaces and communication protocols for communicating the generated plan to the user.

[0592] This invention implements a system in which a server provides optimal response measures during a disaster. The server first accesses external information sources and collects diverse information, including weather data, earthquake information, and posts from social media. This includes data acquisition using APIs over the internet. Cloud-based data services and web scraping techniques may also be used.

[0593] Next, the server converts and integrates the collected information into a unified data format. In this process, data format conversion software is used to standardize the data into formats such as JSON or XML. Data cleaning techniques are also used to handle missing data and eliminate duplicate data.

[0594] The server then moves on to analyzing the integrated dataset. This analysis uses a generative AI model, employing machine learning algorithms and data mining techniques to predict future actions based on historical and real-time data. At this stage, the generative AI model is prompted to "assess disaster risk in a specific area and recommend necessary countermeasures," which generates a detailed relief plan.

[0595] The generated relief plan is sent to the terminal, which the user receives through the interface. For example, when heavy rain is expected, the server can identify high-risk areas from real-time weather data and send information on safe evacuation routes and shelters to the terminal. Based on this information, the user can quickly begin evacuation and minimize the risks associated with the disaster.

[0596] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0597] Step 1:

[0598] The server accesses external data sources and collects information. It uses authentication credentials and query parameters from weather data APIs, earthquake information APIs, and social media APIs as input. It sends API requests to retrieve information, and the retrieved data is output. This data is received in raw data format (e.g., JSON, XML).

[0599] Step 2:

[0600] The server converts the collected information into a common format and integrates the data. It uses raw data received as input. This information is processed by format conversion software, performing data cleaning such as filling in missing data and removing duplicate data. The output is a converted and integrated unified data format.

[0601] Step 3:

[0602] The server analyzes the integrated data. The input is data converted into a common format. Using a generative AI model, it performs data mining and machine learning analysis with the prompt "Assess disaster risk in a specific area and recommend necessary countermeasures." The output is predictive data and suggestions that are incorporated into a relief plan.

[0603] Step 4:

[0604] The server sends the generated relief plan to the terminal. The input is the relief plan obtained through analysis. This plan is sent to the terminal via a communication protocol. The output is a relief plan in a format that can be confirmed as received by the terminal.

[0605] Step 5:

[0606] The terminal displays the received relief plan on its user interface. The input is the relief plan sent from the server. The terminal uses a GUI to visually display the information, making it easy for the user to understand. The output is the displayed information in a state that the user can verify.

[0607] Step 6:

[0608] The user acts based on the presented relief plan. The input is the plan displayed on the terminal. The user makes specific decisions regarding evacuation routes and movement to evacuation sites, and then takes action. The output is the implementation of appropriate response actions.

[0609] (Application Example 1)

[0610] 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".

[0611] A challenge exists in that there is a lack of information necessary to make quick and accurate evacuation decisions during disasters. Furthermore, warnings and suggestions for evacuation routes tailored to the risks faced by individuals are insufficient. Therefore, it is necessary to build a system that supports rapid decision-making to minimize damage.

[0612] 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.

[0613] In this invention, the server includes information gathering means, data preprocessing means, analysis means, information provision means, suggestion means, and warning means. This enables the provision of information for rapid evacuation actions during disasters, as well as real-time suggestions and warnings regarding evacuation routes and safe directions.

[0614] "Information gathering means" refers to devices or functions for collecting necessary information from various data sources, such as weather information, seismic activity information, and social media information.

[0615] "Data preprocessing means" refers to a device or processing function for converting collected information into a common format and integrating it in a consistent manner.

[0616] "Analysis means" refers to a device or algorithm with the processing capability to create an optimal relief plan based on integrated data.

[0617] "Information provision means" refers to a device or function that displays and communicates generated relief plans and other important information to users via a user interface.

[0618] "Suggested means" refers to a device or function for providing users with a rapid evacuation route or recommendation.

[0619] A "warning device" is a device or alert function that appropriately warns users of potential disasters or dangers that may occur in real time.

[0620] To implement this invention, the following system configuration must be considered. The server obtains necessary information from various data sources on the internet using information gathering means that collect weather information, seismic activity information, and social media information. Here, cloud-based services such as AWS Lambda can be used to collect data in real time and process the data efficiently.

[0621] Next, data preprocessing is used to convert the collected information into a common format and integrate it in a consistent manner. At this stage, dedicated algorithms for data transformation and cleaning are applied.

[0622] The integrated data is analyzed using an analysis tool powered by Amazon SageMaker. This allows a generated AI model to create a relief plan based on the integrated data. This analysis considers past disaster patterns and the current situation. The generated relief plan is then transmitted to the user's terminal via an information delivery system.

[0623] On the device, the relief plan is presented through an intuitive user interface built with React Native. This UI allows users to quickly obtain necessary information and provides suggestions for evacuation routes and safe evacuation directions. It also includes a warning system that provides real-time alerts for situations where risk is increasing.

[0624] For example, if heavy rainfall is predicted in a specific area, the system will identify high-risk areas based on weather data for that area and present users with safe evacuation routes. Users can then quickly begin evacuating based on the indicated routes. This will improve the speed and accuracy of disaster response and help minimize damage.

[0625] Specific examples of prompt statements in generative AI models are as follows:

[0626] "Analyze the following disaster scenario and generate the optimal evacuation routes for disaster victims. This area is at high risk of flooding due to heavy rainfall. Please consider historical data and real-time rainfall."

[0627] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0628] Step 1:

[0629] The server activates information gathering mechanisms to acquire weather information, seismic activity information, and social media information from the internet. It uses diverse data sources as input and collects this data in an unstructured, raw format. The output is a dataset containing all the necessary information in its raw, unprocessed state. Specifically, it acquires data from weather stations, earthquake observation systems, and social media platforms via APIs.

[0630] Step 2:

[0631] The server uses data preprocessing to convert the raw data collected in Step 1 into a common format and integrate it. The input is the raw data from Step 1, and the output is an integrated dataset with a unified format. Specifically, it performs data cleaning, noise reduction, and format standardization to maintain consistency across different data sources.

[0632] Step 3:

[0633] The server uses an integrated dataset as an analysis tool and generates a relief plan using a generative AI model. The input is the integrated dataset from step 2, and the output is a dataset of evacuation routes and safety guidelines as the relief plan. Specifically, prompts are fed into the AI ​​model, and real-time countermeasures are designed while comparing past disaster patterns with the current situation.

[0634] Step 4:

[0635] The server sends the generated relief plan to the user's terminal via an information delivery method. The input is the relief plan dataset from step 3, and the output is the information displayed to the user. Specifically, the relief plan is packaged in an appropriate data format and sent to the mobile device via the internet.

[0636] Step 5:

[0637] The terminal uses suggestion and warning mechanisms to provide evacuation suggestions and warnings through the user interface. The input is the relief plan displayed in Step 4, and the output is the instructions and alert information received by the user. Specifically, an application built with React Native presents information in an easy-to-understand format for the user and provides real-time warnings as needed.

[0638] 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.

[0639] This system is an advanced platform for generating effective relief plans during disasters, providing more personalized responses by taking into account the user's emotional state. The system includes a series of processes: data collection from diverse sources, data preprocessing, analysis, relief plan generation, and information delivery.

[0640] First, the server collects weather information, seismic activity information, and social media information via the internet. Because this information is provided in various formats, the server preprocesses the data to convert it into a common format. The unified data is then input into a generating AI for analysis. This analysis includes identifying the extent and urgency of the disaster's impact.

[0641] In particular, this invention evaluates the user's mental state and stress level by analyzing emotional data obtainable from the user (e.g., social media posts and emotion estimation through speech recognition). The emotion engine uses this data to individually adjust the method and timing of information delivery based on the user's emotional state. For example, if the user is feeling stressed, the emotion engine will make the information presentation easier to understand or adjust the amount of information provided.

[0642] The relief plan and emotionally-driven response measures generated in this way are displayed to the user through their device. The user interface is designed to intuitively present geographical information and emotion-based guidelines. For example, for users whose anxiety levels are high during a disaster, the emotion engine highlights points requiring special attention and promotes appropriate action.

[0643] In this way, by combining emotion recognition with a conventional data-driven approach, this system enables the minimization of human casualties and the provision of rapid and accurate disaster response. This combination provides users facing disasters with a sense of security and supports optimal decision-making.

[0644] The following describes the processing flow.

[0645] Step 1:

[0646] The server automatically retrieves data via APIs to collect weather information, seismic activity information, and social media information from external data sources. This allows for the collection of a wide range of information in real time, enabling early detection of signs and the spread of disasters.

[0647] Step 2:

[0648] The server converts the collected data into a common format and stores it in an integrated database. This preprocessing involves organizing the data based on timestamps and geographical information, preparing it for analysis.

[0649] Step 3:

[0650] The server performs generative AI analysis using the integrated dataset. This analysis procedure identifies the scope of the disaster's impact and assesses the level of risk people face.

[0651] Step 4:

[0652] The server generates a relief plan based on the analysis results. During this process, the optimal evacuation routes, shelters, and supply priorities are determined according to the type and scale of the disaster.

[0653] Step 5:

[0654] The server uses an emotion engine to assess the user's emotional state. It analyzes social media posts and audio data to identify the user's stress level and emotional state.

[0655] Step 6:

[0656] The server adjusts the content and presentation of the relief plan based on the user's emotional state. For example, it prioritizes displaying reassuring information to users who are feeling anxious.

[0657] Step 7:

[0658] The device presents the generated relief plan and emotion-based response strategies to the user via a user interface. This information is provided in visual maps and concise list formats to facilitate quick access and understanding.

[0659] Step 8:

[0660] Users select the optimal course of action for safe evacuation based on the information presented on their device. By following the provided guidance, they can minimize risks during a disaster.

[0661] (Example 2)

[0662] 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".

[0663] In times of disaster, it is crucial to quickly and appropriately assess the situation and provide individualized relief plans based on that assessment. Conventional systems have limitations in collecting and analyzing disaster-related data, and have struggled to provide responses tailored to users' emotional states. This could potentially lead to the provision of information that is not necessarily optimal for disaster victims.

[0664] 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.

[0665] In this invention, the server includes data collection means for collecting environmental information, data preprocessing means for converting and integrating the collected data into a unified format, analysis means for inputting the integrated data into a computational model and generating impact analysis and policies, emotion analysis means for analyzing the user's emotional state and adaptively adjusting the method and timing of providing information, and information provision means for providing the generated policies and personalized information through a human-machine interface. This enables the rapid and accurate provision of relief plans during disasters and responses tailored to the emotional state of individual users.

[0666] "Environmental information" refers to data obtained from various sources, including weather information, seismic activity information, and social media, and is fundamental data for understanding the impact and situation of disasters.

[0667] "Data collection methods" refer to techniques and technologies for automatically acquiring necessary data from the internet and various information sources.

[0668] "Data preprocessing means" refers to operations or processes for formally unifying collected data and preparing it for analysis.

[0669] "Analytical means" refers to methods and models used to perform analysis based on integrated data, in accordance with a specific purpose, and derive results.

[0670] "Emotional analysis means" refers to a technology or method that analyzes a user's emotional state and adjusts the way and timing of information provision based on that analysis.

[0671] A "human-machine interface" is an interface that allows users to interact with machine systems, enabling them to intuitively present information and perform operations.

[0672] This system is an advanced platform for providing rapid and personalized relief plans during disasters. Specifically, the server collects environmental information, converts the data into a unified format, and then performs analysis using a computational model. Based on the analysis results, it enables the provision of information that takes into account the user's emotional state.

[0673] The server collects environmental information from various sources via the internet. It can obtain weather and earthquake data using APIs. The collected data is preprocessed using libraries such as Python's Pandas library and converted into a unified format. This process prepares the dataset necessary for analysis.

[0674] The standardized data is fed into a generative AI model for analysis. Here, deep learning frameworks such as TensorFlow and PyTorch are used to analyze the impact of the disaster. Based on the results of this analysis, a relief plan is generated and sent to the user's terminal.

[0675] Furthermore, as a sentiment analysis technique, the server collects user social media data and voice data, and uses the NLP library Hugging Face to analyze the user's emotional state. The sentiment information obtained through this analysis is used to flexibly adjust the method and timing of information delivery.

[0676] The user interface displays the relief plan and related information generated through the terminal. If the user is feeling anxious, this interface highlights actions that require particular attention and indicates appropriate evacuation locations and routes. An example of a prompt for providing this information is the instruction given to the model: "Create an appropriate relief plan based on the current disaster situation and provide advice that is sensitive to the user's feelings."

[0677] In this way, the system combines information gathering and analysis with information provision based on the user's emotional state, enabling rapid and accurate disaster response.

[0678] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0679] Step 1:

[0680] The server collects environmental information via the internet. Specifically, it obtains weather data, seismic activity information, and social media posts via APIs. The input at this stage is raw data provided by each information source, which is stored in a local database. The output is a collection of the raw data obtained from each information source.

[0681] Step 2:

[0682] The server performs data preprocessing to convert the collected data into a unified format. Specifically, it uses the Python Pandas library to clean the data, handle missing data, and unify the format. The input is the raw data acquired in step 1, and the output is an integrated dataset suitable for analysis.

[0683] Step 3:

[0684] The server performs analysis by inputting pre-processed data into a generating AI model. Specifically, it uses TensorFlow to analyze the data and evaluate the scope and urgency of the disaster's impact. The input for this step is an integrated dataset, and the output is the disaster impact analysis results.

[0685] Step 4:

[0686] The server creates a relief plan based on the analysis results. The generated plan includes evacuation advisories and safe travel routes. During this process, prompts are given to the generating AI model to construct a specific plan. The output is an individualized relief plan.

[0687] Step 5:

[0688] The server collects and analyzes user emotional data. It analyzes social media posts and audio data using Hugging Face's NLP tools to assess the user's stress level and emotional state. The input is emotional data, and the output is the user's emotional analysis results.

[0689] Step 6:

[0690] The device provides information using the generated relief plan and sentiment analysis results. Specifically, it shows the user appropriate evacuation actions along with map information. The output consists of visualized information and guidelines.

[0691] Step 7:

[0692] The user takes appropriate action based on the information displayed on the device. Specific actions include moving to an evacuation center and preparing emergency supplies. The input for this step is information provided by the device.

[0693] (Application Example 2)

[0694] 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".

[0695] During disasters, amidst a flood of information, obtaining an optimal relief plan tailored to individual circumstances and emotional states is a challenging task. Furthermore, even with sufficient information, understanding it and taking appropriate action requires information that considers the user's mental state. Conventional systems have struggled to consider these factors, preventing personalized and effective disaster response.

[0696] 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.

[0697] In this invention, the server includes data collection means for collecting weather information, seismic activity information, and communication network information; data preprocessing means for converting and integrating the collected information into a common format; analysis means for analyzing the integrated data and generating a relief plan; emotion measurement means for evaluating the user's emotional state and acquiring emotional data; and personalized information provision means for adjusting the method and timing of information provision based on the user's emotional state. This enables accurate and reassuring disaster response tailored to the individual user's situation and emotions.

[0698] "Data collection means" refers to devices and processes for comprehensively collecting weather information, seismic activity information, and communication network information.

[0699] "Data preprocessing means" refers to devices and technologies for converting collected information in various formats into a unified format and integrating the information.

[0700] The "analysis means" is a function that generates a relief plan using pre-processed data and analyzes disaster response measures that are appropriate for the user's situation.

[0701] "Emotion measurement means" refers to devices or methods for evaluating a user's mental state and acquiring data related to emotions.

[0702] A "personalized information delivery method" is a system that adjusts the method and timing of information delivery according to the user's emotional state, in order to convey information appropriately.

[0703] "Information presentation means" refers to a means of directly communicating the generated relief plan to the user through a visual display device.

[0704] The system that implements this application primarily consists of a server for data collection and analysis, a terminal for measuring the user's emotional state, and a visual device for displaying the information. The server executes programs developed using programming languages ​​such as Python and JavaScript, and uses Google's "TensorFlow" and Facebook's "PyTorch" to analyze the collected weather information, seismic activity information, and communication network information data.

[0705] Specifically, the server collects data from various sources via the internet, converts it into a common format, and integrates it. The integrated data is input into a generating AI model, where it is analyzed to derive the optimal relief plan for disaster situations. Based on the analysis results, a relief plan is generated, and personalized disaster response measures are determined through information delivery methods tailored to the user's emotional state.

[0706] The emotion measurement system implemented in the device analyzes the user's facial expressions and voice in real time to acquire emotional data. Based on the user's emotional state, the method and timing of information presentation are adjusted. For example, users experiencing high stress levels are presented with information through a visually easy-to-understand interface.

[0707] By using this system, personalized action guidance can be displayed on a visual display device during a disaster, making it possible to create a safer and more secure environment for each individual user.

[0708] As a concrete example, during a flood warning in an area prone to disaster, users wearing smart glasses will intuitively see information such as, "Nearest safe evacuation location: XX Park. 10-minute walk." In this way, users are helped to take appropriate actions that align with their emotional state.

[0709] Example of a prompt:

[0710] How should information be displayed when a user is experiencing strong anxiety?

[0711] The diagram illustrates the shortest and safest evacuation route.

[0712] Based on the guide, provide concise and visually appealing information.

[0713] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0714] Step 1:

[0715] The server collects weather information, seismic activity information, and communication network information from the internet. Since the collected data is provided in different formats, the server converts it into a unified format. As a result, a standardized dataset is obtained.

[0716] Step 2:

[0717] The server uses a generative AI model to analyze the integrated data. This analysis process identifies patterns in the data and assesses the scope and urgency of the disaster's impact. As output, candidate relief plans are generated.

[0718] Step 3:

[0719] The device acquires voice and facial expression data from the user and uses this to evaluate the user's emotional state. Using an emotion analysis algorithm, it calculates stress levels and anxiety scores. The output is a metric indicating the user's emotional state.

[0720] Step 4:

[0721] The server uses the relief plan and the user's emotional state as input to personalize information delivery methods, adjusting the method and timing of information presentation. If the stress level is high, adjustments are made, such as simplifying the information presentation. The output is an improved user interface display.

[0722] Step 5:

[0723] The user confirms the final information presentation on the visual display of smart glasses. Based on the presented information, they can decide on actions to take during a disaster and take appropriate action. The output is the user's action instructions.

[0724] 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.

[0725] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0726] 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.

[0727] 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.

[0728] 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.

[0729] 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.

[0730] 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.

[0731] 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.

[0732] 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."

[0733] 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.

[0734] 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.

[0735] 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.

[0736] 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.

[0737] 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.

[0738] 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.

[0739] 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.

[0740] 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.

[0741] 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.

[0742] 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.

[0743] 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.

[0744] 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.

[0745] The following is further disclosed regarding the embodiments described above.

[0746] (Claim 1)

[0747] A data collection method for collecting weather information, seismic activity information, and social media information,

[0748] A data preprocessing means for converting and integrating collected information into a common format,

[0749] An analytical means for analyzing integrated data and generating a relief plan,

[0750] Information provision means for displaying the generated relief plan through a user interface,

[0751] A system that includes this.

[0752] (Claim 2)

[0753] The system according to claim 1, further comprising analytical means for identifying the extent of the impact of disaster damage.

[0754] (Claim 3)

[0755] The system according to claim 1, further comprising a planning means for determining the priority of supplying materials.

[0756] "Example 1"

[0757] (Claim 1)

[0758] Means of collecting information,

[0759] A means of converting and integrating the collected information into a common format,

[0760] A means of analyzing integrated information and generating an optimal process plan using an artificial intelligence model,

[0761] A means of providing the generated process plan through a communication device,

[0762] A system that includes this.

[0763] (Claim 2)

[0764] The system according to claim 1, further comprising analytical means for identifying the scope of influence.

[0765] (Claim 3)

[0766] The system according to claim 1, further comprising means for determining the prioritization of a supply plan.

[0767] "Application Example 1"

[0768] (Claim 1)

[0769] Information gathering means for collecting weather information, seismic activity information and social media information,

[0770] A data preprocessing means for converting and integrating collected information into a common format,

[0771] An analytical means for analyzing integrated data and generating a relief plan,

[0772] Information provision means for displaying the generated relief plan through a user interface,

[0773] A suggestion method for providing users with rapid evacuation route suggestions,

[0774] A warning system that alerts you in real time to a safe evacuation route,

[0775] A system that includes this.

[0776] (Claim 2)

[0777] The system according to claim 1, further comprising analytical means for identifying the extent of the impact of disaster damage.

[0778] (Claim 3)

[0779] The system according to claim 1, further comprising a means for planning the prioritization of material supply.

[0780] "Example 2 of combining an emotion engine"

[0781] (Claim 1)

[0782] A data collection method for collecting environmental information,

[0783] A data preprocessing means for converting and integrating collected data into a unified format,

[0784] An analytical means that inputs data into a computational model based on integrated data to generate impact analysis and policies,

[0785] A sentiment analysis tool that analyzes the user's emotional state and adaptively adjusts the method and timing of the information provided,

[0786] Information provision means that provides generated policies and personalized information through a human-machine interface,

[0787] A system that includes this.

[0788] (Claim 2)

[0789] The system according to claim 1, further comprising analytical means for identifying the extent of the impact of disaster damage and evaluating its urgency.

[0790] (Claim 3)

[0791] The system according to claim 1, further comprising a planning means for determining the priority of logistics support.

[0792] "Application example 2 when combining with an emotional engine"

[0793] (Claim 1)

[0794] A data collection means for collecting weather information, seismic activity information, and communication network information,

[0795] A data preprocessing means for converting and integrating collected information into a common format,

[0796] An analytical means for analyzing integrated data and generating a relief plan,

[0797] An emotion measurement method that evaluates the user's emotional state and acquires emotional data,

[0798] Personalized information delivery means that adjust the method and timing of information delivery based on the user's emotional state,

[0799] Information presentation means for displaying the generated relief plan through a visual display device,

[0800] A system that includes this.

[0801] (Claim 2)

[0802] The system according to claim 1, further comprising analytical means for identifying the extent of the impact of disaster damage.

[0803] (Claim 3)

[0804] The system according to claim 1, further comprising a means for creating a plan to determine the priority of action guidance. [Explanation of symbols]

[0805] 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 data collection method for collecting weather information, seismic activity information, and social media information, A data preprocessing means for converting and integrating collected information into a common format, An analytical means for analyzing integrated data and generating a relief plan, Information provision means for displaying the generated relief plan through a user interface, A system that includes this.

2. The system according to claim 1, further comprising analytical means for identifying the extent of the impact of disaster damage.

3. The system according to claim 1, further comprising a planning means for determining the priority of supplying materials.

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