System

Abstract

A system is provided.SOLUTION: A system, comprising: means for receiving a post content inputted by a user; means for sending the post content to a server; means for analyzing and classifying the post content received by the server using a natural language processing technology; means for summarizing the analyzed and classified post content; means for identifying misinformation; means for realizing multi-language support; means for storing the analyzed and classified information in a database; means for notifying a concerned party of the stored information; and means for the concerned party to browse the stored information.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Existing disaster information sharing systems face challenges such as difficulty in sharing information in real time, lack of multilingual support, and difficulty in centralizing and quickly searching information. This increases the risk of delays in requests for assistance and safety confirmation during disasters, ultimately delaying rescue efforts. Existing systems are limited to landline phones and specific websites, which limits access and can make it difficult to obtain information. [Means for solving the problem]

[0005] The present invention provides a means for receiving user-entered posts and sending them to a server. The server then analyzes and classifies the received posts using natural language processing technology and summarizes the analyzed and classified posts. It also includes a means for identifying misinformation and providing multilingual support. Furthermore, it provides a means for storing the analyzed and classified information in a database and notifying relevant parties as needed. Stakeholders can easily view the stored information. Additionally, the system includes a means for utilizing location information to collect posts by region and efficiently distribute requests for assistance to relevant parties. This provides a disaster information sharing platform accessible from smartphones, PCs, and landlines, enabling real-time information sharing and rapid assistance.

[0006] "User" refers to an individual or organization that uses the system to post or view disaster information.

[0007] "Posted content" refers to messages such as safety information and support requests that users input in the event of a disaster.

[0008] The "receiving means" is a mechanism by which the server receives the posted content that the user inputs and sends via the terminal.

[0009] "Natural language processing technology" is a technology that analyzes the text of posted content, understands its meaning, and automatically classifies and summarizes it.

[0010] "Analysis" is the process of using natural language processing techniques to understand the content of a post and semantically evaluate that content.

[0011] "Classification" is the process of dividing the analyzed posts into categories such as safety information and requests for assistance.

[0012] "Summarizing" is the process of summarizing a post in a concise form, with the goal of providing the necessary information in a few sentences.

[0013] "Misinformation identification means" is a mechanism for detecting and eliminating false or incorrect information contained in posted content.

[0014] "Multilingual support means" refers to the technology and functionality that can understand, analyze, summarize, and classify content posted in different languages.

[0015] A "database" is a system that stores analyzed and classified posted content and manages it so that it can be searched and referenced later.

[0016] "Notification means" refers to a system for efficiently conveying important information and requests for assistance to relevant parties.

[0017] A "stakeholder" is any individual, group, or institution that needs to receive information and act on that information during a disaster.

[0018] The "viewing means" is a mechanism for displaying saved posted content in a format that is easy for users and related parties to check.

[0019] "Location information" is geographical information that indicates where a user or posted content originated.

[0020] A "request for assistance" is a post that includes a request or demand for help or assistance in the event of a disaster.

[0021] The "disaster information sharing platform" refers to the entire system for collecting, analyzing, and sharing information such as safety confirmation and requests for assistance during a disaster. [Brief explanation of the drawings]

[0022] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3]FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0023] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0024] First, the terms used in the following description will be explained.

[0025] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0026] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0027] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0028] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0029] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0030] [First embodiment]

[0031] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0032] 1, a 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.

[0033] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0034] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0035] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the 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.

[0036] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0037] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0038] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0039] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0040] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0041] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0042] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0043] The present invention is a disaster information sharing platform that enables rapid and efficient information sharing in the event of a disaster.

[0044] Platform Overview

[0045] Users use their devices (smartphones, PCs, landlines, etc.) to input and send information about the safety of their loved ones and requests for assistance during a disaster. The server then receives the posted content and analyzes and classifies the data using natural language processing technology. This analyzed information is summarized and stored in a database, utilizing functions to identify misinformation and support multiple languages. The stored information is notified to relevant parties as necessary. In addition, relevant parties and other users can easily view the stored information.

[0046] System operation procedures

[0047] 1. User Submissions

[0048] First, after a disaster occurs, a user accesses the disaster information sharing platform using their own device. For example, they enter "I'm safe" as information about their safety. They also enter "Please help me, my house has collapsed" as a request for assistance. Once they have finished entering the information, they click the send button to send the information.

[0049] 2. Data transmission by the terminal

[0050] The device receives the user's post and sends it to the server in a format that includes the input content, timestamp, user ID, location information, etc. The device sends the data to the server using an HTTP POST request.

[0051] 3. Data reception and analysis by the server

[0052] When the server receives the posted data, it first decodes the data and then begins analyzing and classifying the content of the post using natural language processing technology. For example, if a post says "Help me, my house has collapsed," the natural language processing engine will analyze it, output the summary "Calling for help due to collapsed house," and classify the category as "Request for assistance."

[0053] 4. Data Summarization and Misinformation Identification

[0054] The server then summarises the analysed and categorized data to identify inaccurate or false information. If it is determined to be false, it will ask the user to reconfirm or restrict the display. Multilingual support is also included, and posts made in different languages ​​will be analysed and summarised appropriately.

[0055] 5. Saving to database and notification

[0056] The analyzed, classified, and summarized data is stored in a database. After storage, assistance requests and other information are sent to relevant organizations as needed. For example, API notifications can be sent to emergency services to encourage a rapid response.

[0057] 6. Information viewing by stakeholders and users

[0058] The system allows users and other stakeholders to easily view the saved information using their devices. For example, when checking information in the "Request for Help" category, relevant posts are retrieved from the database through a search query. If necessary, location information can be used to display safety information and requests for help by region.

[0059] Specific examples

[0060] Let's consider a specific scenario: suppose a disaster occurs and user A posts "Help me, my house has collapsed!" in their home. In this case:

[0061] 1. User A's actions:

[0062] User A enters a post on their smartphone and clicks the send button.

[0063] 2. Device behavior:

[0064] The device sends the post content to the server.

[0065] 3. Server Operation:

[0066] The server receives the posts, analyzes them using a natural language processing engine, and summarizes and categorizes them.

[0067] 4. Analysis and storage:

[0068] The analyzed data is stored in a database with a summary of "Help needed due to collapsed house" and a category of "Request for assistance."

[0069] 5. Notice:

[0070] Emergency services will be notified if necessary.

[0071] 6. Viewing Information:

[0072] Other users and related organizations can easily view the stored information and quickly begin rescue operations.

[0073] The above is a specific embodiment for carrying out the present invention, which enables quick and reliable information sharing and support activities in the event of a disaster.

[0074] The processing flow will be explained below.

[0075] Step 1:

[0076] User enters post

[0077] A user accesses the disaster information sharing platform using a smartphone or PC, enters a message such as "Help me, my house has collapsed" in the input field, and clicks the send button.

[0078] Step 2:

[0079] The device sends the post to the server

[0080] The device detects user input and generates a data packet containing the post content, a timestamp, the user ID, and location information, which is then sent to the server as an HTTP POST request.

[0081] Step 3:

[0082] The server receives the posted data

[0083] The server waits for POST requests at the specified endpoint. Upon receiving the request, the server decodes the data and prepares the post for analysis.

[0084] Step 4:

[0085] The server analyzes the posted data

[0086] The server uses a natural language processing engine to analyze the content of the post. Specifically, it analyzes the message "Help me, my house has collapsed" to extract meaning and generate a summary, such as "My house has collapsed and I am asking for help."

[0087] Step 5:

[0088] The server categorizes the submitted data.

[0089] The server uses a natural language processing engine to categorize the posts, and the analyzed messages are categorized as "requests for assistance."

[0090] Step 6:

[0091] Server identifies misinformation

[0092] The server uses a misinformation identification algorithm to check whether the post contains misinformation. If it is determined to be misinformation, the server will either ask the user to reconfirm or will not display the post.

[0093] Step 7:

[0094] The server converts the data into multiple languages.

[0095] If posts are made in different languages, the server uses a multilingual engine to translate, parse and categorize the posts.

[0096] Step 8:

[0097] The server stores the parsed data in a database

[0098] The server stores the analyzed, categorized, and summarized data in a database, along with the post timestamp, user ID, and location information.

[0099] Step 9:

[0100] The server sends notifications to interested parties

[0101] The server notifies the relevant authorities of the request for assistance, for example by using an emergency service API to send the request for assistance and promptly respond.

[0102] Step 10:

[0103] The server provides the data to the interested parties and other users.

[0104] When a related person or another user accesses the system, the server retrieves the relevant information from the database and provides it to the user or the related person, allowing the related person to immediately check the necessary information and take action.

[0105] Example 1

[0106] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0107] In the event of a disaster, there is a need to collect and share information quickly and efficiently, eliminate misinformation, and provide accurate information. However, existing systems have problems with delays in information collection and analysis, a high risk of misinformation spreading, and insufficient multilingual support, which hinders the ability of relevant parties and disaster response organizations to respond quickly.

[0108] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0109] In this invention, the server includes means for receiving posted content entered by a user, means for analyzing and classifying the posted content received by the communication device using natural language processing technology, means for summarizing the analyzed and classified posted content, means for detecting misinformation, means for realizing multilingual support, means for storing the analyzed and classified information in a database, means for notifying relevant parties of the stored information, means for relevant parties to view the stored information, and means for notifying relevant organizations of support request information in a timely manner. This enables quick and accurate information sharing and quick response by relevant parties in the event of a disaster.

[0110] A "user" is someone who accesses the disaster information sharing platform and posts information such as safety information and requests for assistance.

[0111] A "communication device" is a device that has the function of sending the content posted by a user to a server. This includes smartphones and PCs.

[0112] "Server" means a central computing system that analyzes, categorizes, stores, and, if necessary, notifies interested parties of received posts.

[0113] "Natural language processing technology" is a computer science technology that analyzes posted content and understands its meaning and category.

[0114] "Analysis" refers to the act of examining received posts in detail and processing the information to make it easier to understand.

[0115] "Classification" refers to dividing the analyzed post content into specific categories.

[0116] "Summary" refers to the compilation of analyzed and classified information in a concise form.

[0117] "Misinformation" refers to information that is inaccurate or false.

[0118] "Detection" refers to finding specific information (in this case, false information).

[0119] "Multilingual support" refers to the ability to properly parse and summarize information posted in different languages.

[0120] A "database" is a system that permanently stores analyzed and classified information.

[0121] "Storage" refers to the permanent recording of analyzed and classified information in a database.

[0122] "Notification" is the act of communicating stored information to relevant parties.

[0123] "Stakeholders" refers to all individuals and organizations involved in disaster response.

[0124] "Assistance request information" refers to information that includes information requiring help in the event of a disaster.

[0125] "Relevant organizations" refers to organizations and facilities engaged in emergency response and relief operations.

[0126] The present invention provides a disaster information sharing platform for realizing rapid and efficient information sharing during disasters. Various hardware and software are required to implement the present invention. The specific configuration and operation procedures are described below.

[0127] 1. User Submissions

[0128] A user accesses the disaster information sharing platform using their own device (smartphone, PC, landline, etc.). For example, the user starts the browser on their smartphone and logs in to the disaster information sharing platform. They enter "Help me, my house has collapsed" in the form and tap the send button.

[0129] 2. Data transmission by the terminal

[0130] The device generates data including the post content entered by the user, a timestamp, a user ID, and location information. This data is sent to the server using an HTTP POST request. Specifically, the smartphone application generates the entered data in JSON format and sends the HTTP POST request in the background.

[0131] 3. Data reception and decoding by the server

[0132] The server decodes the received data and converts it into an internal format for parsing. Decoding typically involves JSON deserialization. For example, the Java Gson library can be used to perform the decoding. The received post, "Help, my house has collapsed," is then handled by the server in the appropriate object format.

[0133] 4. Data analysis and classification using natural language processing

[0134] The server uses a natural language processing engine (e.g., Google Cloud Natural Language API) to analyze the received post. Through this analysis, the text "Help me, my house has collapsed" is converted into a summary "My house has collapsed, so I'm asking for help" and classified into the "Request for assistance" category. The analysis results in a summary and a category.

[0135] 5. Data Summarization and Misinformation Identification

[0136] The server then summarises the analysed and categorized data. This process identifies inaccurate or misleading information. If misinformation is identified, the server will either ask the user for further confirmation or restrict its display. Fact-checking algorithms and heuristic rules are used to identify misinformation.

[0137] 6. Saving to the database

[0138] The server stores the summarized and categorized data in a persistent database, typically via SQL queries or a NoSQL-enabled storage system, and includes information such as post summaries, categories, original post content, and timestamps.

[0139] 7. Sending required notifications

[0140] The server will then use the stored information to send notifications to relevant organizations and users as needed. For example, for information in the "assistance request" category, a notification can be sent to emergency services via an API to facilitate a prompt response. One possible API to use is the Twilio API.

[0141] 8. Viewing of Information by Interested Parties and Other Users

[0142] Stakeholders and other users can view the information stored in the database from their own devices. For example, they can access the disaster information sharing platform from a smartphone or PC browser and use the filtering function to search for and view information in the "request for assistance" category.

[0143] By using this specific operating procedure, the disaster information sharing platform of the present invention enables rapid and accurate information sharing in the event of a disaster, enabling relevant parties to respond quickly.

[0144] Example prompts to input to the generative AI model

[0145] "Please explain the disaster information sharing platform that enables fast and efficient information sharing in the event of a disaster. Please provide specific examples of how users input information and how the server analyzes, stores, and notifies users."

[0146] The above is a specific embodiment for carrying out the present invention, which enables quick and reliable information sharing and support activities in the event of a disaster.

[0147] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0148] Step 1: User input

[0149] Users access the disaster information sharing platform from devices such as smartphones or PCs and input information about their safety and requests for assistance. For example, they might type "Help me, my house has collapsed" and click the send button. The input information includes the content of the post as text data, a timestamp, user ID, and location information.

[0150] Step 2: Send data by device

[0151] The device generates the post content and related information (timestamp, user ID, location information) entered by the user in JSON format and sends it to the server using an HTTP POST request. The entered data is converted into a JSON object and sent to the server.

[0152] Step 3: Server receives and decodes data

[0153] When the server receives an HTTP request, it decodes the JSON data and converts it to an internal format. It converts each field of the JSON data received as input to the appropriate data type and stores it as a data object on the server. The output is a data object that can be parsed.

[0154] Step 4: Data analysis and classification using natural language processing

[0155] The server uses natural language processing technology (e.g., Google Cloud Natural Language API) to analyze the decoded post content. The analyzed data is summarized and classified into categories. For example, analyzing the post content "Help me, my house has collapsed" outputs the summary "Calling for help due to collapsed house" and the category "Request for assistance."

[0156] Step 5: Summarize data and identify misinformation

[0157] The server summarizes the analyzed and classified data and checks whether it contains any misinformation. Specifically, it verifies the data using fact-checking algorithms and heuristic rules. If it is determined to be misinformation, it sends a notification to the user requesting further confirmation. The server outputs the summarized data and the results of the misinformation identification.

[0158] Step 6: Saving to the Database

[0159] The server stores the summary and identified data in a persistent database (e.g., an SQL database), including the post summary, category, original post content, timestamp, user ID, and location information.

[0160] Step 7: Sending required notifications

[0161] The server will then send notifications to relevant authorities and other users as needed based on the stored information. For example, for information in the "assistance request" category, it will notify emergency services via API, facilitating rapid rescue operations. The output of sending a notification is a message confirming that the notification was successful.

[0162] Step 8: Information Viewed by Interested Parties and Other Users

[0163] Stakeholders and other users access the disaster information sharing platform from their own devices to search and view stored information. They use search queries to filter information in the "assistance request" category, displaying safety information and assistance request information by region. The output of the data viewed by users is the filtered specific assistance request information.

[0164] (Application example 1)

[0165] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0166] In times of disaster, rapid and accurate information sharing is required, but conventional systems face challenges in collecting, classifying, summarizing, and supporting multiple languages, identifying misinformation, and quickly notifying relevant parties. Another issue is the inability to effectively utilize real-time notification and location information sharing functions. This creates the risk of delays in providing necessary assistance and the spread of incorrect information.

[0167] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0168] In this invention, the server includes means for receiving posts entered by users, means for transmitting the posts to the server, means for analyzing and classifying the posts received by the server using natural language processing technology, means for summarizing the analyzed and classified posts, means for identifying misinformation, means for realizing multilingual support, means for storing the analyzed and classified information in a database, means for notifying relevant parties of the stored information, means for relevant parties to view the stored information, means for providing real-time notifications, and means for sharing user location information. This enables rapid and accurate sharing of information in the event of a disaster and efficient requesting of assistance from relevant parties.

[0169] "User" refers to an individual or organization that posts and views information using the disaster information sharing platform.

[0170] "Posted content" refers to information such as safety information and requests for assistance that users input to the system in the event of a disaster.

[0171] "Server" means a central computer system that receives, analyzes, categorizes, summarizes, stores, and notifies users of the posted content.

[0172] "Natural language processing technology" is a computer technology used to analyze text data and understand, classify, and summarize its content.

[0173] "Summarizing" is a process of summarizing the posted content concisely and to the most important points only.

[0174] "Misinformation" refers to posts that contain information that is different from the actual facts or that contain inaccurate content.

[0175] "Multilingual support" refers to the ability to properly analyze, classify, and summarize posts entered in different languages.

[0176] A "database" is a data storage system for efficiently storing and managing analyzed and classified information.

[0177] "Stakeholders" are individuals and organizations that receive information necessary for sharing information and providing support during disasters.

[0178] "Real-time notification" is a function that instantly notifies relevant parties and other users the moment disaster information is posted.

[0179] "User location information" is geographic data that allows a user to identify their current location.

[0180] The present invention provides a "disaster information sharing platform" that enables rapid and efficient information sharing during disasters. This platform is realized by a system including the following means:

[0181] First, users post disaster information using their smartphones, general-purpose computers (PCs), or fixed communication devices. The posted content includes information on the safety of users and requests for assistance. The information entered by the user is sent from the device to the server, and the data containing the posted content is then sent to the server along with a timestamp and user ID.

[0182] The server analyzes and classifies the received posts using natural language processing (NLP). As a result of this analysis, the posts are classified into appropriate categories. For example, a post such as "Help me, my house has collapsed" would be classified as a "Request for assistance." The server also has multilingual capabilities, allowing it to properly analyze information posted in multiple languages.

[0183] The analyzed and classified posts are then summarized. The summarization process extracts the important points of the posts and summarizes them into the minimum necessary information. The system also includes a function to identify misinformation, and if inaccurate information is posted, the system will ask the user to reconfirm it or restrict its display. This ensures reliable information sharing.

[0184] The summarized data is stored in a database, and at the same time, real-time notifications are sent to relevant parties. These notifications are intended to quickly convey requests for assistance and important safety information to relevant parties. In addition, relevant parties and other users can easily view the saved information on their devices. Furthermore, by utilizing user location information, it becomes possible to efficiently share information and request assistance by region.

[0185] The software used to realize this system includes the requests library for HTTP communication, the geopy library for handling location information, and the langid library for analyzing natural language. Also, on the server side, a system runs to process and analyze the received data and store it in a database.

[0186] As a concrete example, consider the case of an earthquake disaster. When User A posts "Help me, my house has collapsed!" to an app on his or her smartphone, this information is immediately sent to the server. The server then analyzes the post, summarizes and categorizes it, and stores it in a database as a "request for assistance." At the same time, real-time notifications are sent to emergency services and relevant organizations, allowing for prompt assistance efforts to begin.

[0187] Example of an input prompt for a generative AI model:

[0188] "We are developing a security information sharing application for use in emergencies. Users can post information about their safety and requests for assistance to the app on their smartphones, and the information needs to be quickly shared with relevant organizations and other users. Please provide specific code examples."

[0189] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0190] Step 1:

[0191] Users post disaster information using their devices (smartphones, PCs, fixed communication devices, etc.). Specifically, users open the application, enter information about the safety of others and requests for assistance, and click the send button. The entered post content is then saved on the device.

[0192] Input: Disaster information entered by the user into the application

[0193] Output: Post content data saved on the device

[0194] Step 2:

[0195] The device sends the content of the post received from the user to the server. The device generates a payload including the content of the post, a timestamp, the user ID, and location information, and sends it to the server as an HTTP POST request.

[0196] Input: User post content, timestamp, user ID, location information

[0197] Output: HTTP POST request to the server

[0198] Step 3:

[0199] The server receives the HTTP POST request and decodes the post data. The decoded data is then analyzed using natural language processing (NLP) to understand the content of the post. At the same time, multilingual support ensures that posts in different languages ​​are properly processed.

[0200] Input: Post content data received as an HTTP POST request

[0201] Output: Analyzed and categorized post content data

[0202] Step 4:

[0203] The server summarizes the analyzed and classified posts. During the summarization process, key points are extracted from the posts and the input data is summarized in a concise format. For example, a post saying "Help me, my house has collapsed" is summarized as "My house has collapsed, so I'm asking for help."

[0204] Input: Analyzed and categorized post content data

[0205] Output: Summary post data

[0206] Step 5:

[0207] The server uses the summarized data to identify misinformation, determines the accuracy of the posted content based on certain criteria, and if it contains inaccurate information, asks the user to reconfirm it or restricts its display.

[0208] Input: Summary post content data

[0209] Output: Data with misinformation corrected or limited

[0210] Step 6:

[0211] The server stores the summarized and misinformation-identified data in a database for later use in searches and notifications.

[0212] Input: Data with misinformation corrected or restricted

[0213] Output: Data stored in the database

[0214] Step 7:

[0215] Based on the stored data, the server sends real-time notifications to relevant parties, including requests for assistance and important safety information, which are delivered immediately to the relevant parties' devices.

[0216] Input: Data stored in a database

[0217] Output: Real-time notifications sent to the relevant parties' devices

[0218] Step 8:

[0219] The saved information can be viewed by relevant parties and other users via their devices (smartphones, PCs, fixed communication devices, etc.) By utilizing location information, requests for assistance and safety information for each region can also be displayed.

[0220] Input: Data stored in a database

[0221] Output: Information displayed on the devices of stakeholders and other users

[0222] Through these steps, we will achieve rapid and reliable sharing of information in the event of a disaster. HTTP communication, natural language processing, and location information services play important roles in building this system.

[0223] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0224] The present invention is a disaster information sharing platform that enables rapid and efficient information sharing during disasters, and in particular is a system that combines an emotion engine that recognizes the user's emotions.

[0225] Platform Overview

[0226] This platform utilizes users' devices (smartphones, PCs, landlines, etc.) to input and send information about the safety of users and requests for assistance during disasters. The server receives the posted content and analyzes and classifies the data using natural language processing technology and an emotion engine. The analyzed information is summarized, organized using functions to identify misinformation and support multiple languages, and stored in a database. The stored information is notified to relevant parties as necessary. In addition, relevant parties and other users can easily view the stored information.

[0227] System operation procedures

[0228] 1. User Submissions

[0229] After a disaster occurs, users access the disaster information sharing platform using their own devices. They enter information about their safety and a request for assistance, and click the send button. For example, they might enter, "Help me! My house has collapsed!"

[0230] 2. Data transmission by the terminal

[0231] The device receives the user's post and generates a data packet containing the message, timestamp, user ID, location information, and emotion data generated by the emotion engine. This data packet is then sent to the server as an HTTP POST request.

[0232] 3. Data reception and analysis by the server

[0233] The server waits for POST requests at the specified endpoint, decodes the data, and prepares the post for analysis.

[0234] 4. Server analysis and classification

[0235] The server uses a natural language processing engine to analyze the post. For example, it analyzes the message "Help me, my house has collapsed!" and generates a summary. In this case, the summary is "My house has collapsed, I'm asking for help." The analyzed message is then classified into a category called "Request for assistance."

[0236] 5. Emotion Recognition by Emotion Engine

[0237] The server uses an emotion engine to recognize the user's emotion from the content of the post. For example, the emotion contained in the post may be recognized as "urgency" or "fear." This emotion data is used for further analysis and classification.

[0238] 6. Misinformation Identification and Summarization

[0239] The server uses a misinformation identification algorithm to check for inaccuracies or misinformation. The summary might be "urgent help needed due to house collapse."

[0240] 7. Data Retention and Notification

[0241] The server then stores the analyzed, classified, and summarized data in a database. This includes the post's timestamp, user ID, location information, and emotion data. If necessary, the server notifies relevant organizations of requests for assistance. For example, the server can use an emergency service API to send information about a request for assistance and prompt a response.

[0242] 8. Viewing Information

[0243] The system allows users and other stakeholders to easily view the saved information on their devices. For example, when checking information in the "Request for Help" category, relevant posts are retrieved from the database through a search query. If necessary, location information can be used to display safety information and requests for help by region.

[0244] Specific examples

[0245] Let us explain this system using a specific scenario. For example, if a disaster occurs and user A posts "Help me, my house has collapsed!" at home:

[0246] 1. User input:

[0247] User A types "Help me, my house has collapsed!" into his smartphone and clicks the send button.

[0248] 2. Device behavior:

[0249] The device analyzes the input data using an emotion engine and identifies the emotion as "fear." It then sends a data packet containing the post data to the server.

[0250] 3. Server reception and analysis:

[0251] The server receives the data, analyzes the post using natural language processing technology, and summarizes it as "Help wanted after house collapse." The emotion engine provides the emotion data "fear."

[0252] 4. Data Retention and Notification:

[0253] The analyzed data is stored in a database and notified to relevant agencies as a "request for assistance."

[0254] 5. Viewing Information:

[0255] Other users and related organizations can view the assistance request information and provide prompt assistance.

[0256] The above is a specific embodiment for carrying out the present invention. This system enables quick and accurate information sharing and support activities in the event of a disaster.

[0257] The processing flow will be explained below.

[0258] Step 1:

[0259] User enters post

[0260] A user accesses the disaster information sharing platform using a smartphone or PC, enters a message such as "Help me, my house has collapsed!" into the input field, and clicks the send button.

[0261] Step 2:

[0262] The device sends the post to the server

[0263] The device detects user input and generates a data packet containing the post content, timestamp, user ID, location information, and emotion data generated by the emotion engine. This data packet is then sent to the server as an HTTP POST request.

[0264] Step 3:

[0265] The server receives the posted data

[0266] The server waits for POST requests at the specified endpoint. The server receives the request, decodes it, and prepares the post for analysis.

[0267] Step 4:

[0268] The server analyzes the posted data

[0269] The server uses a natural language processing engine to analyze the content of the post. For example, the message "Help me, my house has collapsed!" can be analyzed and summarized as "Help needed due to collapse of house."

[0270] Step 5:

[0271] The server categorizes the submitted data.

[0272] The server uses a natural language processing engine to categorize the post into categories such as "safety information" or "request for assistance." In this example, it is classified as a "request for assistance."

[0273] Step 6:

[0274] The server uses an emotion engine to recognize emotions.

[0275] The server uses an emotion engine to analyze the content of the post and identify the user's emotion, which in this case is recognized as "fear" or "urgency."

[0276] Step 7:

[0277] Server identifies misinformation

[0278] The server uses a misinformation identification algorithm to verify the authenticity of the posted content. If it is determined to be misinformation, the server will ask the user to confirm or restrict the posting.

[0279] Step 8:

[0280] The server stores it in the database

[0281] The server stores the analyzed, categorized, and summarized data in a database, including the post content, timestamp, user ID, location information, and emotion data.

[0282] Step 9:

[0283] The server notifies the relevant parties

[0284] The server notifies the relevant organizations of the assistance request information, for example, by using an emergency service API to send the assistance request information including emotion data.

[0285] Step 10:

[0286] The server provides the information to interested parties and other users.

[0287] When a related person or another user accesses the system, the server retrieves the relevant information from the database and provides it to the user or the related person, allowing the related person to immediately check the necessary information and take action.

[0288] Step 11:

[0289] User and stakeholder information viewing

[0290] Users and related parties can access the database and view the stored information, for example, information in the "request for assistance" category and emotional data, and take appropriate action.

[0291] The above are the processing steps of a specific embodiment of a disaster information sharing platform that combines an emotion engine. This system is expected to enable faster and more accurate information sharing and support activities during disasters.

[0292] Example 2

[0293] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0294] In modern times, when a disaster occurs, there is a need for rapid and accurate information sharing, but current systems lack the accuracy of information and the ability to recognize emotions, leading to the spread of misinformation and the inability to provide appropriate support promptly.In addition, there are issues with insufficient multilingual support and use of location information, making it difficult to efficiently distribute requests for support by region.

[0295] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes an analysis means using an emotion engine that recognizes the user's emotion, a means for identifying false information, and a means for realizing multilingual support. This enables the rapid and accurate sharing of disaster information, the priority processing of information based on emotion, the elimination of false information, multilingual support, and the utilization of location information.

[0296] "User" refers to a person or organization that uses the system to input disaster information or request assistance.

[0297] "Posted content" refers to disaster-related information and requests for assistance that users enter and send to the system.

[0298] An "emotion engine" is a technology or software that recognizes and analyzes emotions from user posts.

[0299] "Natural language processing technology" refers to technology that enables computers to understand, analyze, and generate natural language.

[0300] "Means for identifying misinformation" refers to methods or algorithms for identifying whether posted content is misinformation.

[0301] "Multilingual Method" means any technology or method for displaying, parsing, or sharing Posting Content in multiple languages.

[0302] A "database" is a data repository for storing analyzed and classified information.

[0303] "Interested Person" means a person or institution that can view the stored information and take action if necessary.

[0304] "Location information" refers to information that indicates the geographical location when a user enters a post.

[0305] A "request for assistance" is information provided by a user when a disaster occurs, indicating that assistance is required.

[0306] A "server" is a computer system that receives information sent by users and analyzes, classifies, stores, and notifies them.

[0307] "Terminal" means the device used by a User to access the System and input and submit Posting Content.

[0308] The present invention is a disaster information sharing platform that enables rapid and efficient information sharing during disasters, and in particular, a system that combines an emotion engine that recognizes the user's emotions. Specific embodiments for carrying out the invention are described below.

[0309] Platform Overview

[0310] This platform is a system that utilizes users' devices (smartphones, personal computers, fixed communication devices, etc.) to input and send information on the safety of users and requests for assistance during disasters. The server receives the user's posts and analyzes and classifies the data using natural language processing technology and an emotion engine. The analyzed information is summarized, organized using functions to identify misinformation and support multiple languages, and stored in a database. The stored information is notified to relevant parties as necessary. In addition, relevant parties and other users can easily view the stored information.

[0311] System operation procedures

[0312] 1. User Submissions

[0313] After a disaster occurs, users access the disaster information sharing platform using their own devices. They enter information about their safety and a request for assistance, and click the send button. For example, they might enter, "Help me! My house has collapsed!"

[0314] 2. Data transmission by the terminal

[0315] The device receives the user's post and generates a data packet containing the message, timestamp, user ID, location information, and emotion data generated by the emotion engine. This data packet is then sent to the server as an HTTP POST request.

[0316] 3. Data reception and analysis by the server

[0317] The server waits for POST requests at the specified endpoint, and upon receiving the request, it decodes the data and prepares the post for analysis.

[0318] Technology used

[0319] The natural language processing technology used by the server for data analysis includes functions such as text summarization, classification, and keyword extraction. The emotion engine analyzes user emotions (e.g., urgency, fear, etc.) from posted content and uses this information to prioritize and select information to notify relevant parties. The misinformation identification algorithm detects whether posted content contains inaccurate or false information and removes it as necessary.

[0320] The multilingual feature automatically translates posts into multiple languages, ensuring appropriate information for stakeholders who speak different languages.

[0321] Stored Data and Notifications

[0322] The data analyzed, classified, and summarized by the server is stored in a database. This data includes post timestamps, user IDs, location information, and emotional data. Urgent assistance requests are notified from the server to relevant organizations via API, facilitating a prompt response. In addition, relevant parties and other users can easily view the stored information via their devices and provide assistance as needed.

[0323] Specific examples

[0324] For example, if a disaster occurs and user A posts "Help, my house has collapsed!" at home, the system will operate as follows:

[0325] 1. User input:

[0326] User A types "Help me, my house has collapsed!" into his smartphone and clicks the send button.

[0327] 2. Device behavior:

[0328] The device analyzes the input data with an emotion engine, identifies the emotion as "fear," and then sends this data packet to the server.

[0329] 3. Server reception and analysis:

[0330] The server receives the data, analyzes the post using natural language processing technology, and summarizes it as "Help wanted after house collapse." The emotion engine provides the emotion data "fear."

[0331] 4. Data Retention and Notification:

[0332] The analyzed data is stored in a database and notified to relevant agencies as a "request for assistance."

[0333] 5. Viewing Information:

[0334] Other users and related organizations can view the information about requests for assistance and provide prompt relief. For example, when checking information in the "Request for Assistance" category, relevant posts are retrieved from the database through a search query. If necessary, safety information and assistance requests by region can also be displayed based on location information.

[0335] Prompt Sentence Examples

[0336] An example prompt for a generative AI model might look like this:

[0337] "Please explain in detail how users will input safety information and requests for assistance from home in the event of a disaster and how the information will be processed."

[0338] The above is a specific embodiment for carrying out the present invention. This system enables quick and accurate information sharing and support activities in the event of a disaster.

[0339] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0340] Step 1: User Submission

[0341] Specific actions

[0342] Users access the disaster information sharing platform and use their own devices (smartphones, personal computers, fixed communication devices, etc.) to input information about the safety of others and requests for assistance.

[0343] input

[0344] A user types "Help me, my house is collapsing!" into a text box.

[0345] output

[0346] The user's input is sent to the terminal, and the next process is carried out.

[0347] Step 2: Send data by device

[0348] Specific actions

[0349] The device receives the user's post, analyzes the message with an emotion engine, generates emotion data, and then generates a data packet containing the message, timestamp, user ID, location information, and emotion data.

[0350] input

[0351] User posts and metadata acquired by the device (timestamp, user ID, location information).

[0352] output

[0353] A data packet sent to a server as an HTTP POST request.

[0354] Step 3: Server receives data

[0355] Specific actions

[0356] The server waits for POST requests at the specified endpoint and decodes the data packets after receiving the request.

[0357] input

[0358] The HTTP POST request sent from the device.

[0359] output

[0360] Decoded user post content, timestamp, user ID, location, and sentiment data.

[0361] Step 4: Data analysis and classification by the server

[0362] Specific actions

[0363] The server uses natural language processing technology to analyze and summarize the content of posts, and also categorizes the posts into appropriate categories.

[0364] input

[0365] Decoded post content and metadata.

[0366] output

[0367] A summarized message and a classified category (e.g., a summary of "seeking help due to collapse of house" and a category of "request for assistance").

[0368] Step 5: Emotion Recognition with the Emotion Engine

[0369] Specific actions

[0370] The server uses an emotion engine to recognize the user's emotion from the posted content and generates emotion data.

[0371] input

[0372] Abridged post content.

[0373] output

[0374] Emotional data (e.g., "fear" or "urgency").

[0375] Step 6: Misinformation Identification and Summarization

[0376] Specific actions

[0377] The server uses a misinformation-detection algorithm to check the submission for inaccuracies and misinformation. The summary is finalized.

[0378] input

[0379] Summarized post content and sentiment data.

[0380] output

[0381] Misinformation identified and finalized summary (e.g., "urgent assistance needed due to house collapse").

[0382] Step 7: Data storage and notification

[0383] Specific actions

[0384] The server stores the analyzed, classified, and summarized data in a database and notifies relevant organizations as necessary.

[0385] input

[0386] Finalized summary, timestamp, user ID, location, and emotion data.

[0387] output

[0388] Data stored in databases and notifications to relevant authorities (e.g., sending assistance requests using emergency services APIs).

[0389] Step 8: Information visible to stakeholders and other users

[0390] Specific actions

[0391] Search and view stored information through devices used by related parties or other users.

[0392] input

[0393] Request information from a stored database.

[0394] output

[0395] The search results will show relevant posts (e.g., posts in the "Request for Help" category). If necessary, location-based information will also be displayed for each region.

[0396] (Application example 2)

[0397] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0398] Conventional disaster information sharing systems have difficulty in properly recognizing and analyzing the emotions in the information entered by users, and furthermore, have problems in that they do not adequately guarantee safe information sharing during disasters. This can lead to delays in responding to requests for assistance quickly and appropriately, making it difficult to provide efficient assistance during disasters.

[0399] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving posted content entered by a user, means for transmitting the posted content to the server, means for analyzing and classifying the posted content received by the server using natural language processing technology, means for summarizing the analyzed and classified posted content, means for identifying misinformation, means for realizing multilingual support, means for storing the analyzed and classified information in a database, means for notifying relevant parties of the stored information, means for relevant parties to view the stored information, means for recognizing emotion data related to the posted content entered by a user and using the emotion data for analysis and classification, and means for securely protecting information through end-to-end encryption in the event of a disaster. This enables rapid and accurate information sharing and safe assistance requests in the event of a disaster.

[0400] "Posted content" is data including disaster information and requests for assistance entered by the user.

[0401] A "server" is a computer system that receives, analyzes, classifies, and stores content posted by users, and notifies relevant parties.

[0402] "Natural language processing technology" is a technology for analyzing the content of posts and understanding their meaning and intent.

[0403] A "summary" is a short summary of the main points of the analyzed and classified post.

[0404] "Misinformation identification" is the process of identifying inaccurate or false information in posts.

[0405] "Multilingual support" is a function that understands and responds to posts in multiple languages.

[0406] "Emotion data" is emotional information recognized from the content posted by a user.

[0407] "End-to-end encryption" is a technology that encrypts communication data from the sender to the recipient, protecting it from access by third parties.

[0408] "Location information" is geographical data that indicates the region from which the posted content was sent.

[0409] A "database" is a storage system in which analyzed and classified information is stored.

[0410] "Notification" is an action to inform relevant parties of stored information.

[0411] "Stakeholders" are individuals, organizations, or institutions involved in providing assistance or sharing information during a disaster.

[0412] This invention is a disaster information sharing system that realizes fast and efficient information sharing during disasters, and in particular, it is combined with an emotion engine that recognizes the user's emotions. This system utilizes the user's device (smartphone, PC, landline phone, etc.) to efficiently process safety information and assistance requests entered by the user during a disaster.

[0413] System configuration

[0414] This system consists of the following main elements:

[0415] 1. Terminal

[0416] This refers to the devices that users use to input content, including smartphones, PCs, and landlines. Smartphones, in particular, use GPS to obtain user location information.

[0417] 2. Server

[0418] The server analyzes and classifies the received posts, and stores and manages the necessary data. Specifically, it has the following functions:

[0419] Natural language processing technology: Provides technology that analyzes post content and extracts and classifies key points.

[0420] Emotion engine: Recognizes emotions from user input and uses that information for analysis and classification.

[0421] End-to-end encryption: A security technology that prevents third parties from intercepting data as it is transmitted.

[0422] Misinformation Identification: The platform has algorithms that check the accuracy of posts and filter out misinformation.

[0423] Multilingual support: Process and parse posts entered in multiple languages.

[0424] Database: A storage system that stores analyzed and classified information and notifies relevant parties as needed.

[0425] Notification system: A system to notify relevant parties of stored information and promptly provide necessary assistance.

[0426] Program processing

[0427] When a user types and submits a post from their device, the following process occurs:

[0428] Data acquisition: The device receives disaster information and assistance requests entered by the user. For example, a message like "Help me, my house has collapsed!"

[0429] Timestamp generation: The server obtains the current time and adds it to the posted data, ensuring that the information is up to date.

[0430] Emotion Recognition: The emotion engine analyzes the content of posts and extracts emotional data, such as "fear."

[0431] Summary generation: Using natural language processing techniques, we provide a concise summary of the post, such as "Help needed after house collapse."

[0432] Data storage: The analyzed information is stored in a database, including timestamps, user IDs, location information, and emotion data.

[0433] Notification: Notify relevant parties of the stored data, allowing for a prompt response.

[0434] Adding specific examples

[0435] For example, if a user types "There is no water in the evacuation shelter, please help!" during a disaster, the system will act as follows:

[0436] 1. User input: The user types "There is no water at the evacuation center, please help!" into their smartphone and sends it.

[0437] 2. Device behavior: The emotion engine identifies the event as an "urgent" event. The device adds location information and a timestamp to the posted data and sends it to the server.

[0438] 3. Server reception and analysis: The server receives the data and uses natural language processing technology to generate a summary of "shelters, water shortages." The emotion data "urgent" is added.

[0439] 4. Data storage and notification: The analyzed data will be stored in a database and notified to the relevant authorities.

[0440] Prompt Sentence Examples

[0441] "Please enter information for a quick request for assistance during a disaster: 'There is a food shortage at the evacuation center, please help!'"

[0442] This allows users to input information appropriately, and serves as a reference for those involved to carry out support activities quickly.

[0443] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0444] Step 1:

[0445] The device receives the post entered by the user. The user enters disaster information or a request for assistance on their smartphone and clicks the send button. This input includes location information. For example, a message might read, "Help me, my house has collapsed!"

[0446] Input: Messages typed by the user into their smartphone

[0447] Output: Input data received by the device (message and location information)

[0448] Specific behavior:

[0449] The user accesses the smartphone app, types a message, and sends it.

[0450] Step 2:

[0451] The device analyzes the content of the post using an emotion engine to extract emotional data, recognizing emotions such as "fear" from the content of the post and adding the emotional data to the data packet.

[0452] Input: Input data received by the device (messages and location information)

[0453] Output: A data packet with emotion data added by the emotion engine (e.g., fear).

[0454] Specific behavior:

[0455] The emotion engine analyzes the content of the post and extracts emotions using natural language processing technology.

[0456] Step 3:

[0457] The device sends a data packet to the server as an HTTP POST request, which includes the message, a timestamp, a user ID, location information, and emotion data.

[0458] Input: Data packet with attached emotion data (e.g., message, timestamp, user ID, location, emotion data)

[0459] Output: Data packet received by the server

[0460] Specific behavior:

[0461] The device constructs a data packet and sends it to the server as an HTTP POST request.

[0462] Step 4:

[0463] The server decodes the received data packets and analyzes the content of the post. It uses natural language processing technology to extract the main points of the post and generate a summary. For example, it generates a summary such as "Help wanted after house collapse."

[0464] Input: Data packet received by the server

[0465] Output: Parsed summary message

[0466] Specific behavior:

[0467] The server uses natural language processing technology to analyze the post and extract the main points.

[0468] Step 5:

[0469] The server uses a misinformation identification algorithm to check whether the post contains misinformation and decides whether to keep it as accurate information or remove it as misinformation.

[0470] Input: Parsed summary message

[0471] Output: Summary message with misinformation removed

[0472] Specific behavior:

[0473] The server applies a misinformation identification algorithm to verify the accuracy of the input data.

[0474] Step 6:

[0475] The server stores the analyzed and categorized information in a database, including posted messages, timestamps, user IDs, location information, and emotional data.

[0476] Input: Summary message with misinformation removed

[0477] Output: Data stored in the database

[0478] Specific behavior:

[0479] The server stores the analyzed and classified information in a database.

[0480] Step 7:

[0481] The server then notifies the relevant parties of the stored information, for example, emergency services and assistance agencies via an API.

[0482] Input: Data stored in the database

[0483] Output: Notification message to interested parties

[0484] Specific behavior:

[0485] The server will notify the relevant parties and arrange for prompt assistance.

[0486] Step 8:

[0487] Stakeholders can view the saved information. Stakeholders can view saved posts and summaries through a dedicated interface.

[0488] Input: Data in the database

[0489] Output: Information viewed by interested parties

[0490] Specific behavior:

[0491] Stakeholders can view the information through a browser or dedicated application.

[0492] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0493] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0494] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0495] [Second embodiment]

[0496] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0497] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0498] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0499] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0500] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0501] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0502] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0503] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0504] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0505] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0506] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0507] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0508] The present invention is a disaster information sharing platform that enables rapid and efficient information sharing in the event of a disaster.

[0509] Platform Overview

[0510] Users use their devices (smartphones, PCs, landlines, etc.) to input and send information about the safety of their loved ones and requests for assistance during a disaster. The server then receives the posted content and analyzes and classifies the data using natural language processing technology. This analyzed information is summarized and stored in a database, utilizing functions to identify misinformation and support multiple languages. The stored information is notified to relevant parties as necessary. In addition, relevant parties and other users can easily view the stored information.

[0511] System operation procedures

[0512] 1. User Submissions

[0513] First, after a disaster occurs, a user accesses the disaster information sharing platform using their own device. For example, they enter "I'm safe" as information about their safety. They also enter "Please help me, my house has collapsed" as a request for assistance. Once they have finished entering the information, they click the send button to send the information.

[0514] 2. Data transmission by the terminal

[0515] The device receives the user's post and sends it to the server in a format that includes the input content, timestamp, user ID, location information, etc. The device sends the data to the server using an HTTP POST request.

[0516] 3. Data reception and analysis by the server

[0517] When the server receives the posted data, it first decodes the data and then begins analyzing and classifying the content of the post using natural language processing technology. For example, if a post says "Help me, my house has collapsed," the natural language processing engine will analyze it, output the summary "Calling for help due to collapsed house," and classify the category as "Request for assistance."

[0518] 4. Data Summarization and Misinformation Identification

[0519] The server then summarises the analysed and categorized data to identify inaccurate or false information. If it is determined to be false, it will ask the user to reconfirm or restrict the display. Multilingual support is also included, and posts made in different languages ​​will be analysed and summarised appropriately.

[0520] 5. Saving to database and notification

[0521] The analyzed, classified, and summarized data is stored in a database. After storage, assistance requests and other information are sent to relevant organizations as needed. For example, API notifications can be sent to emergency services to encourage a rapid response.

[0522] 6. Information viewing by stakeholders and users

[0523] The system allows users and other stakeholders to easily view the saved information using their devices. For example, when checking information in the "Request for Help" category, relevant posts are retrieved from the database through a search query. If necessary, location information can be used to display safety information and requests for help by region.

[0524] Specific examples

[0525] Let's consider a specific scenario: suppose a disaster occurs and user A posts "Help me, my house has collapsed!" in their home. In this case:

[0526] 1. User A's actions:

[0527] User A enters a post on their smartphone and clicks the send button.

[0528] 2. Device behavior:

[0529] The device sends the post content to the server.

[0530] 3. Server Operation:

[0531] The server receives the posts, analyzes them using a natural language processing engine, and summarizes and categorizes them.

[0532] 4. Analysis and storage:

[0533] The analyzed data is stored in a database with a summary of "Help needed due to collapsed house" and a category of "Request for assistance."

[0534] 5. Notice:

[0535] Emergency services will be notified if necessary.

[0536] 6. Viewing Information:

[0537] Other users and related organizations can easily view the stored information and quickly begin rescue operations.

[0538] The above is a specific embodiment for carrying out the present invention, which enables quick and reliable information sharing and support activities in the event of a disaster.

[0539] The processing flow will be explained below.

[0540] Step 1:

[0541] User enters post

[0542] A user accesses the disaster information sharing platform using a smartphone or PC, enters a message such as "Help me, my house has collapsed" in the input field, and clicks the send button.

[0543] Step 2:

[0544] The device sends the post to the server

[0545] The device detects user input and generates a data packet containing the post content, a timestamp, the user ID, and location information, which is then sent to the server as an HTTP POST request.

[0546] Step 3:

[0547] The server receives the posted data

[0548] The server waits for POST requests at the specified endpoint. Upon receiving the request, the server decodes the data and prepares the post for analysis.

[0549] Step 4:

[0550] The server analyzes the posted data

[0551] The server uses a natural language processing engine to analyze the content of the post. Specifically, it analyzes the message "Help me, my house has collapsed" to extract meaning and generate a summary, such as "My house has collapsed and I am asking for help."

[0552] Step 5:

[0553] The server categorizes the submitted data.

[0554] The server uses a natural language processing engine to categorize the posts, and the analyzed messages are categorized as "requests for assistance."

[0555] Step 6:

[0556] Server identifies misinformation

[0557] The server uses a misinformation identification algorithm to check whether the post contains misinformation. If it is determined to be misinformation, the server will either ask the user to reconfirm or will not display the post.

[0558] Step 7:

[0559] The server converts the data into multiple languages.

[0560] If posts are made in different languages, the server uses a multilingual engine to translate, parse and categorize the posts.

[0561] Step 8:

[0562] The server stores the parsed data in a database

[0563] The server stores the analyzed, categorized, and summarized data in a database, along with the post timestamp, user ID, and location information.

[0564] Step 9:

[0565] The server sends notifications to interested parties

[0566] The server notifies the relevant authorities of the request for assistance, for example by using an emergency service API to send the request for assistance and promptly respond.

[0567] Step 10:

[0568] The server provides the data to the interested parties and other users.

[0569] When a related person or another user accesses the system, the server retrieves the relevant information from the database and provides it to the user or the related person, allowing the related person to immediately check the necessary information and take action.

[0570] Example 1

[0571] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0572] In the event of a disaster, there is a need to collect and share information quickly and efficiently, eliminate misinformation, and provide accurate information. However, existing systems have problems with delays in information collection and analysis, a high risk of misinformation spreading, and insufficient multilingual support, which hinders the ability of relevant parties and disaster response organizations to respond quickly.

[0573] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0574] In this invention, the server includes means for receiving posted content entered by a user, means for analyzing and classifying the posted content received by the communication device using natural language processing technology, means for summarizing the analyzed and classified posted content, means for detecting misinformation, means for realizing multilingual support, means for storing the analyzed and classified information in a database, means for notifying relevant parties of the stored information, means for relevant parties to view the stored information, and means for notifying relevant organizations of support request information in a timely manner. This enables quick and accurate information sharing and quick response by relevant parties in the event of a disaster.

[0575] A "user" is someone who accesses the disaster information sharing platform and posts information such as safety information and requests for assistance.

[0576] A "communication device" is a device that has the function of sending the content posted by a user to a server. This includes smartphones and PCs.

[0577] "Server" means a central computing system that analyzes, categorizes, stores, and, if necessary, notifies interested parties of received posts.

[0578] "Natural language processing technology" is a computer science technology that analyzes posted content and understands its meaning and category.

[0579] "Analysis" refers to the act of examining received posts in detail and processing the information to make it easier to understand.

[0580] "Classification" refers to dividing the analyzed post content into specific categories.

[0581] "Summary" refers to the compilation of analyzed and classified information in a concise form.

[0582] "Misinformation" refers to information that is inaccurate or false.

[0583] "Detection" refers to finding specific information (in this case, false information).

[0584] "Multilingual support" refers to the ability to properly parse and summarize information posted in different languages.

[0585] A "database" is a system that permanently stores analyzed and classified information.

[0586] "Storage" refers to the permanent recording of analyzed and classified information in a database.

[0587] "Notification" is the act of communicating stored information to relevant parties.

[0588] "Stakeholders" refers to all individuals and organizations involved in disaster response.

[0589] "Assistance request information" refers to information that includes information requiring help in the event of a disaster.

[0590] "Relevant organizations" refers to organizations and facilities engaged in emergency response and relief operations.

[0591] The present invention provides a disaster information sharing platform for realizing rapid and efficient information sharing during disasters. Various hardware and software are required to implement the present invention. The specific configuration and operation procedures are described below.

[0592] 1. User Submissions

[0593] A user accesses the disaster information sharing platform using their own device (smartphone, PC, landline, etc.). For example, the user starts the browser on their smartphone and logs in to the disaster information sharing platform. They enter "Help me, my house has collapsed" in the form and tap the send button.

[0594] 2. Data transmission by the terminal

[0595] The device generates data including the post content entered by the user, a timestamp, a user ID, and location information. This data is sent to the server using an HTTP POST request. Specifically, the smartphone application generates the entered data in JSON format and sends the HTTP POST request in the background.

[0596] 3. Data reception and decoding by the server

[0597] The server decodes the received data and converts it into an internal format for parsing. Decoding typically involves JSON deserialization. For example, the Java Gson library can be used to perform the decoding. The received post, "Help, my house has collapsed," is then handled by the server in the appropriate object format.

[0598] 4. Data analysis and classification using natural language processing

[0599] The server uses a natural language processing engine (e.g., Google Cloud Natural Language API) to analyze the received post. Through this analysis, the text "Help me, my house has collapsed" is converted into a summary "My house has collapsed, so I'm asking for help" and classified into the "Request for assistance" category. The analysis results in a summary and a category.

[0600] 5. Data Summarization and Misinformation Identification

[0601] The server then summarises the analysed and categorized data. This process identifies inaccurate or misleading information. If misinformation is identified, the server will either ask the user for further confirmation or restrict its display. Fact-checking algorithms and heuristic rules are used to identify misinformation.

[0602] 6. Saving to the database

[0603] The server stores the summarized and categorized data in a persistent database, typically via SQL queries or a NoSQL-enabled storage system, and includes information such as post summaries, categories, original post content, and timestamps.

[0604] 7. Sending required notifications

[0605] The server will then use the stored information to send notifications to relevant organizations and users as needed. For example, for information in the "assistance request" category, a notification can be sent to emergency services via an API to facilitate a prompt response. One possible API to use is the Twilio API.

[0606] 8. Viewing of Information by Interested Parties and Other Users

[0607] Stakeholders and other users can view the information stored in the database from their own devices. For example, they can access the disaster information sharing platform from a smartphone or PC browser and use the filtering function to search for and view information in the "request for assistance" category.

[0608] By using this specific operating procedure, the disaster information sharing platform of the present invention enables rapid and accurate information sharing in the event of a disaster, enabling relevant parties to respond quickly.

[0609] Example prompts to input to the generative AI model

[0610] "Please explain the disaster information sharing platform that enables fast and efficient information sharing in the event of a disaster. Please provide specific examples of how users input information and how the server analyzes, stores, and notifies users."

[0611] The above is a specific embodiment for carrying out the present invention, which enables quick and reliable information sharing and support activities in the event of a disaster.

[0612] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0613] Step 1: User input

[0614] Users access the disaster information sharing platform from devices such as smartphones or PCs and input information about their safety and requests for assistance. For example, they might type "Help me, my house has collapsed" and click the send button. The input information includes the content of the post as text data, a timestamp, user ID, and location information.

[0615] Step 2: Send data by device

[0616] The device generates the post content and related information (timestamp, user ID, location information) entered by the user in JSON format and sends it to the server using an HTTP POST request. The entered data is converted into a JSON object and sent to the server.

[0617] Step 3: Server receives and decodes data

[0618] When the server receives an HTTP request, it decodes the JSON data and converts it to an internal format. It converts each field of the JSON data received as input to the appropriate data type and stores it as a data object on the server. The output is a data object that can be parsed.

[0619] Step 4: Data analysis and classification using natural language processing

[0620] The server uses natural language processing technology (e.g., Google Cloud Natural Language API) to analyze the decoded post content. The analyzed data is summarized and classified into categories. For example, analyzing the post content "Help me, my house has collapsed" outputs the summary "Calling for help due to collapsed house" and the category "Request for assistance."

[0621] Step 5: Summarize data and identify misinformation

[0622] The server summarizes the analyzed and classified data and checks whether it contains any misinformation. Specifically, it verifies the data using fact-checking algorithms and heuristic rules. If it is determined to be misinformation, it sends a notification to the user requesting further confirmation. The server outputs the summarized data and the results of the misinformation identification.

[0623] Step 6: Saving to the Database

[0624] The server stores the summary and identified data in a persistent database (e.g., an SQL database), including the post summary, category, original post content, timestamp, user ID, and location information.

[0625] Step 7: Sending required notifications

[0626] The server will then send notifications to relevant authorities and other users as needed based on the stored information. For example, for information in the "assistance request" category, it will notify emergency services via API, facilitating rapid rescue operations. The output of sending a notification is a message confirming that the notification was successful.

[0627] Step 8: Information Viewed by Interested Parties and Other Users

[0628] Stakeholders and other users access the disaster information sharing platform from their own devices to search and view stored information. They use search queries to filter information in the "assistance request" category, displaying safety information and assistance request information by region. The output of the data viewed by users is the filtered specific assistance request information.

[0629] (Application example 1)

[0630] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0631] In times of disaster, rapid and accurate information sharing is required, but conventional systems face challenges in collecting, classifying, summarizing, and supporting multiple languages, identifying misinformation, and quickly notifying relevant parties. Another issue is the inability to effectively utilize real-time notification and location information sharing functions. This creates the risk of delays in providing necessary assistance and the spread of incorrect information.

[0632] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0633] In this invention, the server includes means for receiving posts entered by users, means for transmitting the posts to the server, means for analyzing and classifying the posts received by the server using natural language processing technology, means for summarizing the analyzed and classified posts, means for identifying misinformation, means for realizing multilingual support, means for storing the analyzed and classified information in a database, means for notifying relevant parties of the stored information, means for relevant parties to view the stored information, means for providing real-time notifications, and means for sharing user location information. This enables rapid and accurate sharing of information in the event of a disaster and efficient requesting of assistance from relevant parties.

[0634] "User" refers to an individual or organization that posts and views information using the disaster information sharing platform.

[0635] "Posted content" refers to information such as safety information and requests for assistance that users input to the system in the event of a disaster.

[0636] "Server" means a central computer system that receives, analyzes, categorizes, summarizes, stores, and notifies users of the posted content.

[0637] "Natural language processing technology" is a computer technology used to analyze text data and understand, classify, and summarize its content.

[0638] "Summarizing" is a process of summarizing the posted content concisely and to the most important points only.

[0639] "Misinformation" refers to posts that contain information that is different from the actual facts or that contain inaccurate content.

[0640] "Multilingual support" refers to the ability to properly analyze, classify, and summarize posts entered in different languages.

[0641] A "database" is a data storage system for efficiently storing and managing analyzed and classified information.

[0642] "Stakeholders" are individuals and organizations that receive information necessary for sharing information and providing support during disasters.

[0643] "Real-time notification" is a function that instantly notifies relevant parties and other users the moment disaster information is posted.

[0644] "User location information" is geographic data that allows a user to identify their current location.

[0645] The present invention provides a "disaster information sharing platform" that enables rapid and efficient information sharing during disasters. This platform is realized by a system including the following means:

[0646] First, users post disaster information using their smartphones, general-purpose computers (PCs), or fixed communication devices. The posted content includes information on the safety of users and requests for assistance. The information entered by the user is sent from the device to the server, and the data containing the posted content is then sent to the server along with a timestamp and user ID.

[0647] The server analyzes and classifies the received posts using natural language processing (NLP). As a result of this analysis, the posts are classified into appropriate categories. For example, a post such as "Help me, my house has collapsed" would be classified as a "Request for assistance." The server also has multilingual capabilities, allowing it to properly analyze information posted in multiple languages.

[0648] The analyzed and classified posts are then summarized. The summarization process extracts the important points of the posts and summarizes them into the minimum necessary information. The system also includes a function to identify misinformation, and if inaccurate information is posted, the system will ask the user to reconfirm it or restrict its display. This ensures reliable information sharing.

[0649] The summarized data is stored in a database, and at the same time, real-time notifications are sent to relevant parties. These notifications are intended to quickly convey requests for assistance and important safety information to relevant parties. In addition, relevant parties and other users can easily view the saved information on their devices. Furthermore, by utilizing user location information, it becomes possible to efficiently share information and request assistance by region.

[0650] The software used to realize this system includes the requests library for HTTP communication, the geopy library for handling location information, and the langid library for analyzing natural language. Also, on the server side, a system runs to process and analyze the received data and store it in a database.

[0651] As a concrete example, consider the case of an earthquake disaster. When User A posts "Help me, my house has collapsed!" to an app on his or her smartphone, this information is immediately sent to the server. The server then analyzes the post, summarizes and categorizes it, and stores it in a database as a "request for assistance." At the same time, real-time notifications are sent to emergency services and relevant organizations, allowing for prompt assistance efforts to begin.

[0652] Example of an input prompt for a generative AI model:

[0653] "We are developing a security information sharing application for use in emergencies. Users can post information about their safety and requests for assistance to the app on their smartphones, and the information needs to be quickly shared with relevant organizations and other users. Please provide specific code examples."

[0654] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0655] Step 1:

[0656] Users post disaster information using their devices (smartphones, PCs, fixed communication devices, etc.). Specifically, users open the application, enter information about the safety of others and requests for assistance, and click the send button. The entered post content is then saved on the device.

[0657] Input: Disaster information entered by the user into the application

[0658] Output: Post content data saved on the device

[0659] Step 2:

[0660] The device sends the content of the post received from the user to the server. The device generates a payload including the content of the post, a timestamp, the user ID, and location information, and sends it to the server as an HTTP POST request.

[0661] Input: User post content, timestamp, user ID, location information

[0662] Output: HTTP POST request to the server

[0663] Step 3:

[0664] The server receives the HTTP POST request and decodes the post data. The decoded data is then analyzed using natural language processing (NLP) to understand the content of the post. At the same time, multilingual support ensures that posts in different languages ​​are properly processed.

[0665] Input: Post content data received as an HTTP POST request

[0666] Output: Analyzed and categorized post content data

[0667] Step 4:

[0668] The server summarizes the analyzed and classified posts. During the summarization process, key points are extracted from the posts and the input data is summarized in a concise format. For example, a post saying "Help me, my house has collapsed" is summarized as "My house has collapsed, so I'm asking for help."

[0669] Input: Analyzed and categorized post content data

[0670] Output: Summary post data

[0671] Step 5:

[0672] The server uses the summarized data to identify misinformation, determines the accuracy of the posted content based on certain criteria, and if it contains inaccurate information, asks the user to reconfirm it or restricts its display.

[0673] Input: Summary post content data

[0674] Output: Data with misinformation corrected or limited

[0675] Step 6:

[0676] The server stores the summarized and misinformation-identified data in a database for later use in searches and notifications.

[0677] Input: Data with misinformation corrected or restricted

[0678] Output: Data stored in the database

[0679] Step 7:

[0680] Based on the stored data, the server sends real-time notifications to relevant parties, including requests for assistance and important safety information, which are delivered immediately to the relevant parties' devices.

[0681] Input: Data stored in a database

[0682] Output: Real-time notifications sent to the relevant parties' devices

[0683] Step 8:

[0684] The saved information can be viewed by relevant parties and other users via their devices (smartphones, PCs, fixed communication devices, etc.) By utilizing location information, requests for assistance and safety information for each region can also be displayed.

[0685] Input: Data stored in a database

[0686] Output: Information displayed on the devices of stakeholders and other users

[0687] Through these steps, we will achieve rapid and reliable sharing of information in the event of a disaster. HTTP communication, natural language processing, and location information services play important roles in building this system.

[0688] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0689] The present invention is a disaster information sharing platform that enables rapid and efficient information sharing during disasters, and in particular is a system that combines an emotion engine that recognizes the user's emotions.

[0690] Platform Overview

[0691] This platform utilizes users' devices (smartphones, PCs, landlines, etc.) to input and send information about the safety of users and requests for assistance during disasters. The server receives the posted content and analyzes and classifies the data using natural language processing technology and an emotion engine. The analyzed information is summarized, organized using functions to identify misinformation and support multiple languages, and stored in a database. The stored information is notified to relevant parties as necessary. In addition, relevant parties and other users can easily view the stored information.

[0692] System operation procedures

[0693] 1. User Submissions

[0694] After a disaster occurs, users access the disaster information sharing platform using their own devices. They enter information about their safety and a request for assistance, and click the send button. For example, they might enter, "Help me! My house has collapsed!"

[0695] 2. Data transmission by the terminal

[0696] The device receives the user's post and generates a data packet containing the message, timestamp, user ID, location information, and emotion data generated by the emotion engine. This data packet is then sent to the server as an HTTP POST request.

[0697] 3. Data reception and analysis by the server

[0698] The server waits for POST requests at the specified endpoint, decodes the data, and prepares the post for analysis.

[0699] 4. Server analysis and classification

[0700] The server uses a natural language processing engine to analyze the post. For example, it analyzes the message "Help me, my house has collapsed!" and generates a summary. In this case, the summary is "My house has collapsed, I'm asking for help." The analyzed message is then classified into a category called "Request for assistance."

[0701] 5. Emotion Recognition by Emotion Engine

[0702] The server uses an emotion engine to recognize the user's emotion from the content of the post. For example, the emotion contained in the post may be recognized as "urgency" or "fear." This emotion data is used for further analysis and classification.

[0703] 6. Misinformation Identification and Summarization

[0704] The server uses a misinformation identification algorithm to check for inaccuracies or misinformation. The summary might be "urgent help needed due to house collapse."

[0705] 7. Data Retention and Notification

[0706] The server then stores the analyzed, classified, and summarized data in a database. This includes the post's timestamp, user ID, location information, and emotion data. If necessary, the server notifies relevant organizations of requests for assistance. For example, the server can use an emergency service API to send information about a request for assistance and prompt a response.

[0707] 8. Viewing Information

[0708] The system allows users and other stakeholders to easily view the saved information on their devices. For example, when checking information in the "Request for Help" category, relevant posts are retrieved from the database through a search query. If necessary, location information can be used to display safety information and requests for help by region.

[0709] Specific examples

[0710] Let us explain this system using a specific scenario. For example, if a disaster occurs and user A posts "Help me, my house has collapsed!" at home:

[0711] 1. User input:

[0712] User A types "Help me, my house has collapsed!" into his smartphone and clicks the send button.

[0713] 2. Device behavior:

[0714] The device analyzes the input data using an emotion engine and identifies the emotion as "fear." It then sends a data packet containing the post data to the server.

[0715] 3. Server reception and analysis:

[0716] The server receives the data, analyzes the post using natural language processing technology, and summarizes it as "Help wanted after house collapse." The emotion engine provides the emotion data "fear."

[0717] 4. Data Retention and Notification:

[0718] The analyzed data is stored in a database and notified to relevant agencies as a "request for assistance."

[0719] 5. Viewing Information:

[0720] Other users and related organizations can view the assistance request information and provide prompt assistance.

[0721] The above is a specific embodiment for carrying out the present invention. This system enables quick and accurate information sharing and support activities in the event of a disaster.

[0722] The processing flow will be explained below.

[0723] Step 1:

[0724] User enters post

[0725] A user accesses the disaster information sharing platform using a smartphone or PC, enters a message such as "Help me, my house has collapsed!" into the input field, and clicks the send button.

[0726] Step 2:

[0727] The device sends the post to the server

[0728] The device detects user input and generates a data packet containing the post content, timestamp, user ID, location information, and emotion data generated by the emotion engine. This data packet is then sent to the server as an HTTP POST request.

[0729] Step 3:

[0730] The server receives the posted data

[0731] The server waits for POST requests at the specified endpoint. The server receives the request, decodes it, and prepares the post for analysis.

[0732] Step 4:

[0733] The server analyzes the posted data

[0734] The server uses a natural language processing engine to analyze the content of the post. For example, the message "Help me, my house has collapsed!" can be analyzed and summarized as "Help needed due to collapse of house."

[0735] Step 5:

[0736] The server categorizes the submitted data.

[0737] The server uses a natural language processing engine to categorize the post into categories such as "safety information" or "request for assistance." In this example, it is classified as a "request for assistance."

[0738] Step 6:

[0739] The server uses an emotion engine to recognize emotions.

[0740] The server uses an emotion engine to analyze the content of the post and identify the user's emotion, which in this case is recognized as "fear" or "urgency."

[0741] Step 7:

[0742] Server identifies misinformation

[0743] The server uses a misinformation identification algorithm to verify the authenticity of the posted content. If it is determined to be misinformation, the server will ask the user to confirm or restrict the posting.

[0744] Step 8:

[0745] The server stores it in the database

[0746] The server stores the analyzed, categorized, and summarized data in a database, including the post content, timestamp, user ID, location information, and emotion data.

[0747] Step 9:

[0748] The server notifies the relevant parties

[0749] The server notifies the relevant organizations of the assistance request information, for example, by using an emergency service API to send the assistance request information including emotion data.

[0750] Step 10:

[0751] The server provides the information to interested parties and other users.

[0752] When a related person or another user accesses the system, the server retrieves the relevant information from the database and provides it to the user or the related person, allowing the related person to immediately check the necessary information and take action.

[0753] Step 11:

[0754] User and stakeholder information viewing

[0755] Users and related parties can access the database and view the stored information, for example, information in the "request for assistance" category and emotional data, and take appropriate action.

[0756] The above are the processing steps of a specific embodiment of a disaster information sharing platform that combines an emotion engine. This system is expected to enable faster and more accurate information sharing and support activities during disasters.

[0757] Example 2

[0758] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0759] In modern times, when a disaster occurs, there is a need for rapid and accurate information sharing, but current systems lack the accuracy of information and the ability to recognize emotions, leading to the spread of misinformation and the inability to provide appropriate support promptly.In addition, there are issues with insufficient multilingual support and use of location information, making it difficult to efficiently distribute requests for support by region.

[0760] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes an analysis means using an emotion engine that recognizes the user's emotion, a means for identifying false information, and a means for realizing multilingual support. This enables the rapid and accurate sharing of disaster information, the priority processing of information based on emotion, the elimination of false information, multilingual support, and the utilization of location information.

[0761] "User" refers to a person or organization that uses the system to input disaster information or request assistance.

[0762] "Posted content" refers to disaster-related information and requests for assistance that users enter and send to the system.

[0763] An "emotion engine" is a technology or software that recognizes and analyzes emotions from user posts.

[0764] "Natural language processing technology" refers to technology that enables computers to understand, analyze, and generate natural language.

[0765] "Means for identifying misinformation" refers to methods or algorithms for identifying whether posted content is misinformation.

[0766] "Multilingual Method" means any technology or method for displaying, parsing, or sharing Posting Content in multiple languages.

[0767] A "database" is a data repository for storing analyzed and classified information.

[0768] "Interested Person" means a person or institution that can view the stored information and take action if necessary.

[0769] "Location information" refers to information that indicates the geographical location when a user enters a post.

[0770] A "request for assistance" is information provided by a user when a disaster occurs, indicating that assistance is required.

[0771] A "server" is a computer system that receives information sent by users and analyzes, classifies, stores, and notifies them.

[0772] "Terminal" means the device used by a User to access the System and input and submit Posting Content.

[0773] The present invention is a disaster information sharing platform that enables rapid and efficient information sharing during disasters, and in particular, a system that combines an emotion engine that recognizes the user's emotions. Specific embodiments for carrying out the invention are described below.

[0774] Platform Overview

[0775] This platform is a system that utilizes users' devices (smartphones, personal computers, fixed communication devices, etc.) to input and send information on the safety of users and requests for assistance during disasters. The server receives the user's posts and analyzes and classifies the data using natural language processing technology and an emotion engine. The analyzed information is summarized, organized using functions to identify misinformation and support multiple languages, and stored in a database. The stored information is notified to relevant parties as necessary. In addition, relevant parties and other users can easily view the stored information.

[0776] System operation procedures

[0777] 1. User Submissions

[0778] After a disaster occurs, users access the disaster information sharing platform using their own devices. They enter information about their safety and a request for assistance, and click the send button. For example, they might enter, "Help me! My house has collapsed!"

[0779] 2. Data transmission by the terminal

[0780] The device receives the user's post and generates a data packet containing the message, timestamp, user ID, location information, and emotion data generated by the emotion engine. This data packet is then sent to the server as an HTTP POST request.

[0781] 3. Data reception and analysis by the server

[0782] The server waits for POST requests at the specified endpoint, and upon receiving the request, it decodes the data and prepares the post for analysis.

[0783] Technology used

[0784] The natural language processing technology used by the server for data analysis includes functions such as text summarization, classification, and keyword extraction. The emotion engine analyzes user emotions (e.g., urgency, fear, etc.) from posted content and uses this information to prioritize and select information to notify relevant parties. The misinformation identification algorithm detects whether posted content contains inaccurate or false information and removes it as necessary.

[0785] The multilingual feature automatically translates posts into multiple languages, ensuring appropriate information for stakeholders who speak different languages.

[0786] Stored Data and Notifications

[0787] The data analyzed, classified, and summarized by the server is stored in a database. This data includes post timestamps, user IDs, location information, and emotional data. Urgent assistance requests are notified from the server to relevant organizations via API, facilitating a prompt response. In addition, relevant parties and other users can easily view the stored information via their devices and provide assistance as needed.

[0788] Specific examples

[0789] For example, if a disaster occurs and user A posts "Help, my house has collapsed!" at home, the system will operate as follows:

[0790] 1. User input:

[0791] User A types "Help me, my house has collapsed!" into his smartphone and clicks the send button.

[0792] 2. Device behavior:

[0793] The device analyzes the input data with an emotion engine, identifies the emotion as "fear," and then sends this data packet to the server.

[0794] 3. Server reception and analysis:

[0795] The server receives the data, analyzes the post using natural language processing technology, and summarizes it as "Help wanted after house collapse." The emotion engine provides the emotion data "fear."

[0796] 4. Data Retention and Notification:

[0797] The analyzed data is stored in a database and notified to relevant agencies as a "request for assistance."

[0798] 5. Viewing Information:

[0799] Other users and related organizations can view the information about requests for assistance and provide prompt relief. For example, when checking information in the "Request for Assistance" category, relevant posts are retrieved from the database through a search query. If necessary, safety information and assistance requests by region can also be displayed based on location information.

[0800] Prompt Sentence Examples

[0801] An example prompt for a generative AI model might look like this:

[0802] "Please explain in detail how users will input safety information and requests for assistance from home in the event of a disaster and how the information will be processed."

[0803] The above is a specific embodiment for carrying out the present invention. This system enables quick and accurate information sharing and support activities in the event of a disaster.

[0804] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0805] Step 1: User Submission

[0806] Specific actions

[0807] Users access the disaster information sharing platform and use their own devices (smartphones, personal computers, fixed communication devices, etc.) to input information about the safety of others and requests for assistance.

[0808] input

[0809] A user types "Help me, my house is collapsing!" into a text box.

[0810] output

[0811] The user's input is sent to the terminal, and the next process is carried out.

[0812] Step 2: Send data by device

[0813] Specific actions

[0814] The device receives the user's post, analyzes the message with an emotion engine, generates emotion data, and then generates a data packet containing the message, timestamp, user ID, location information, and emotion data.

[0815] input

[0816] User posts and metadata acquired by the device (timestamp, user ID, location information).

[0817] output

[0818] A data packet sent to a server as an HTTP POST request.

[0819] Step 3: Server receives data

[0820] Specific actions

[0821] The server waits for POST requests at the specified endpoint and decodes the data packets after receiving the request.

[0822] input

[0823] The HTTP POST request sent from the device.

[0824] output

[0825] Decoded user post content, timestamp, user ID, location, and sentiment data.

[0826] Step 4: Data analysis and classification by the server

[0827] Specific actions

[0828] The server uses natural language processing technology to analyze and summarize the content of posts, and also categorizes the posts into appropriate categories.

[0829] input

[0830] Decoded post content and metadata.

[0831] output

[0832] A summarized message and a classified category (e.g., a summary of "seeking help due to collapse of house" and a category of "request for assistance").

[0833] Step 5: Emotion Recognition with the Emotion Engine

[0834] Specific actions

[0835] The server uses an emotion engine to recognize the user's emotion from the posted content and generates emotion data.

[0836] input

[0837] Abridged post content.

[0838] output

[0839] Emotional data (e.g., "fear" or "urgency").

[0840] Step 6: Misinformation Identification and Summarization

[0841] Specific actions

[0842] The server uses a misinformation-detection algorithm to check the submission for inaccuracies and misinformation. The summary is finalized.

[0843] input

[0844] Summarized post content and sentiment data.

[0845] output

[0846] Misinformation identified and finalized summary (e.g., "urgent assistance needed due to house collapse").

[0847] Step 7: Data storage and notification

[0848] Specific actions

[0849] The server stores the analyzed, classified, and summarized data in a database and notifies relevant organizations as necessary.

[0850] input

[0851] Finalized summary, timestamp, user ID, location, and emotion data.

[0852] output

[0853] Data stored in databases and notifications to relevant authorities (e.g., sending assistance requests using emergency services APIs).

[0854] Step 8: Information visible to stakeholders and other users

[0855] Specific actions

[0856] Search and view stored information through devices used by related parties or other users.

[0857] input

[0858] Request information from a stored database.

[0859] output

[0860] The search results will show relevant posts (e.g., posts in the "Request for Help" category). If necessary, location-based information will also be displayed for each region.

[0861] (Application example 2)

[0862] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0863] Conventional disaster information sharing systems have difficulty in properly recognizing and analyzing the emotions in the information entered by users, and furthermore, have problems in that they do not adequately guarantee safe information sharing during disasters. This can lead to delays in responding to requests for assistance quickly and appropriately, making it difficult to provide efficient assistance during disasters.

[0864] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving posted content entered by a user, means for transmitting the posted content to the server, means for analyzing and classifying the posted content received by the server using natural language processing technology, means for summarizing the analyzed and classified posted content, means for identifying misinformation, means for realizing multilingual support, means for storing the analyzed and classified information in a database, means for notifying relevant parties of the stored information, means for relevant parties to view the stored information, means for recognizing emotion data related to the posted content entered by a user and using the emotion data for analysis and classification, and means for securely protecting information through end-to-end encryption in the event of a disaster. This enables rapid and accurate information sharing and safe assistance requests in the event of a disaster.

[0865] "Posted content" is data including disaster information and requests for assistance entered by the user.

[0866] A "server" is a computer system that receives, analyzes, classifies, and stores content posted by users, and notifies relevant parties.

[0867] "Natural language processing technology" is a technology for analyzing the content of posts and understanding their meaning and intent.

[0868] A "summary" is a short summary of the main points of the analyzed and classified post.

[0869] "Misinformation identification" is the process of identifying inaccurate or false information in posts.

[0870] "Multilingual support" is a function that understands and responds to posts in multiple languages.

[0871] "Emotion data" is emotional information recognized from the content posted by a user.

[0872] "End-to-end encryption" is a technology that encrypts communication data from the sender to the recipient, protecting it from access by third parties.

[0873] "Location information" is geographical data that indicates the region from which the posted content was sent.

[0874] A "database" is a storage system in which analyzed and classified information is stored.

[0875] "Notification" is an action to inform relevant parties of stored information.

[0876] "Stakeholders" are individuals, organizations, or institutions involved in providing assistance or sharing information during a disaster.

[0877] This invention is a disaster information sharing system that realizes fast and efficient information sharing during disasters, and in particular, it is combined with an emotion engine that recognizes the user's emotions. This system utilizes the user's device (smartphone, PC, landline phone, etc.) to efficiently process safety information and assistance requests entered by the user during a disaster.

[0878] System configuration

[0879] This system consists of the following main elements:

[0880] 1. Terminal

[0881] This refers to the devices that users use to input content, including smartphones, PCs, and landlines. Smartphones, in particular, use GPS to obtain user location information.

[0882] 2. Server

[0883] The server analyzes and classifies the received posts, and stores and manages the necessary data. Specifically, it has the following functions:

[0884] Natural language processing technology: Provides technology that analyzes post content and extracts and classifies key points.

[0885] Emotion engine: Recognizes emotions from user input and uses that information for analysis and classification.

[0886] End-to-end encryption: A security technology that prevents third parties from intercepting data as it is transmitted.

[0887] Misinformation Identification: The platform has algorithms that check the accuracy of posts and filter out misinformation.

[0888] Multilingual support: Process and parse posts entered in multiple languages.

[0889] Database: A storage system that stores analyzed and classified information and notifies relevant parties as needed.

[0890] Notification system: A system to notify relevant parties of stored information and promptly provide necessary assistance.

[0891] Program processing

[0892] When a user types and submits a post from their device, the following process occurs:

[0893] Data acquisition: The device receives disaster information and assistance requests entered by the user. For example, a message like "Help me, my house has collapsed!"

[0894] Timestamp generation: The server obtains the current time and adds it to the posted data, ensuring that the information is up to date.

[0895] Emotion Recognition: The emotion engine analyzes the content of posts and extracts emotional data, such as "fear."

[0896] Summary generation: Using natural language processing techniques, we provide a concise summary of the post, such as "Help needed after house collapse."

[0897] Data storage: The analyzed information is stored in a database, including timestamps, user IDs, location information, and emotion data.

[0898] Notification: Notify relevant parties of the stored data, allowing for a prompt response.

[0899] Adding specific examples

[0900] For example, if a user types "There is no water in the evacuation shelter, please help!" during a disaster, the system will act as follows:

[0901] 1. User input: The user types "There is no water at the evacuation center, please help!" into their smartphone and sends it.

[0902] 2. Device behavior: The emotion engine identifies the event as an "urgent" event. The device adds location information and a timestamp to the posted data and sends it to the server.

[0903] 3. Server reception and analysis: The server receives the data and uses natural language processing technology to generate a summary of "shelters, water shortages." The emotion data "urgent" is added.

[0904] 4. Data storage and notification: The analyzed data will be stored in a database and notified to the relevant authorities.

[0905] Prompt Sentence Examples

[0906] "Please enter information for a quick request for assistance during a disaster: 'There is a food shortage at the evacuation center, please help!'"

[0907] This allows users to input information appropriately, and serves as a reference for those involved to carry out support activities quickly.

[0908] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0909] Step 1:

[0910] The device receives the post entered by the user. The user enters disaster information or a request for assistance on their smartphone and clicks the send button. This input includes location information. For example, a message might read, "Help me, my house has collapsed!"

[0911] Input: Messages typed by the user into their smartphone

[0912] Output: Input data received by the device (message and location information)

[0913] Specific behavior:

[0914] The user accesses the smartphone app, types a message, and sends it.

[0915] Step 2:

[0916] The device analyzes the content of the post using an emotion engine to extract emotional data, recognizing emotions such as "fear" from the content of the post and adding the emotional data to the data packet.

[0917] Input: Input data received by the device (messages and location information)

[0918] Output: A data packet with emotion data added by the emotion engine (e.g., fear).

[0919] Specific behavior:

[0920] The emotion engine analyzes the content of the post and extracts emotions using natural language processing technology.

[0921] Step 3:

[0922] The device sends a data packet to the server as an HTTP POST request, which includes the message, a timestamp, a user ID, location information, and emotion data.

[0923] Input: Data packet with attached emotion data (e.g., message, timestamp, user ID, location, emotion data)

[0924] Output: Data packet received by the server

[0925] Specific behavior:

[0926] The device constructs a data packet and sends it to the server as an HTTP POST request.

[0927] Step 4:

[0928] The server decodes the received data packets and analyzes the content of the post. It uses natural language processing technology to extract the main points of the post and generate a summary. For example, it generates a summary such as "Help wanted after house collapse."

[0929] Input: Data packet received by the server

[0930] Output: Parsed summary message

[0931] Specific behavior:

[0932] The server uses natural language processing technology to analyze the post and extract the main points.

[0933] Step 5:

[0934] The server uses a misinformation identification algorithm to check whether the post contains misinformation and decides whether to keep it as accurate information or remove it as misinformation.

[0935] Input: Parsed summary message

[0936] Output: Summary message with misinformation removed

[0937] Specific behavior:

[0938] The server applies a misinformation identification algorithm to verify the accuracy of the input data.

[0939] Step 6:

[0940] The server stores the analyzed and categorized information in a database, including posted messages, timestamps, user IDs, location information, and emotional data.

[0941] Input: Summary message with misinformation removed

[0942] Output: Data stored in the database

[0943] Specific behavior:

[0944] The server stores the analyzed and classified information in a database.

[0945] Step 7:

[0946] The server then notifies the relevant parties of the stored information, for example, emergency services and assistance agencies via an API.

[0947] Input: Data stored in the database

[0948] Output: Notification message to interested parties

[0949] Specific behavior:

[0950] The server will notify the relevant parties and arrange for prompt assistance.

[0951] Step 8:

[0952] Stakeholders can view the saved information. Stakeholders can view saved posts and summaries through a dedicated interface.

[0953] Input: Data in the database

[0954] Output: Information viewed by interested parties

[0955] Specific behavior:

[0956] Stakeholders can view the information through a browser or dedicated application.

[0957] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0958] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0959] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0960] [Third embodiment]

[0961] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0962] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0963] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0964] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0965] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0966] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0967] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0968] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0969] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0970] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0971] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0972] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0973] The present invention is a disaster information sharing platform that enables rapid and efficient information sharing in the event of a disaster.

[0974] Platform Overview

[0975] Users use their devices (smartphones, PCs, landlines, etc.) to input and send information about the safety of their loved ones and requests for assistance during a disaster. The server then receives the posted content and analyzes and classifies the data using natural language processing technology. This analyzed information is summarized and stored in a database, utilizing functions to identify misinformation and support multiple languages. The stored information is notified to relevant parties as necessary. In addition, relevant parties and other users can easily view the stored information.

[0976] System operation procedures

[0977] 1. User Submissions

[0978] First, after a disaster occurs, a user accesses the disaster information sharing platform using their own device. For example, they enter "I'm safe" as information about their safety. They also enter "Please help me, my house has collapsed" as a request for assistance. Once they have finished entering the information, they click the send button to send the information.

[0979] 2. Data transmission by the terminal

[0980] The device receives the user's post and sends it to the server in a format that includes the input content, timestamp, user ID, location information, etc. The device sends the data to the server using an HTTP POST request.

[0981] 3. Data reception and analysis by the server

[0982] When the server receives the posted data, it first decodes the data and then begins analyzing and classifying the content of the post using natural language processing technology. For example, if a post says "Help me, my house has collapsed," the natural language processing engine will analyze it, output the summary "Calling for help due to collapsed house," and classify the category as "Request for assistance."

[0983] 4. Data Summarization and Misinformation Identification

[0984] The server then summarises the analysed and categorized data to identify inaccurate or false information. If it is determined to be false, it will ask the user to reconfirm or restrict the display. Multilingual support is also included, and posts made in different languages ​​will be analysed and summarised appropriately.

[0985] 5. Saving to database and notification

[0986] The analyzed, classified, and summarized data is stored in a database. After storage, assistance requests and other information are sent to relevant organizations as needed. For example, API notifications can be sent to emergency services to encourage a rapid response.

[0987] 6. Information viewing by stakeholders and users

[0988] The system allows users and other stakeholders to easily view the saved information using their devices. For example, when checking information in the "Request for Help" category, relevant posts are retrieved from the database through a search query. If necessary, location information can be used to display safety information and requests for help by region.

[0989] Specific examples

[0990] Let's consider a specific scenario: suppose a disaster occurs and user A posts "Help me, my house has collapsed!" in their home. In this case:

[0991] 1. User A's actions:

[0992] User A enters a post on their smartphone and clicks the send button.

[0993] 2. Device behavior:

[0994] The device sends the post content to the server.

[0995] 3. Server Operation:

[0996] The server receives the posts, analyzes them using a natural language processing engine, and summarizes and categorizes them.

[0997] 4. Analysis and storage:

[0998] The analyzed data is stored in a database with a summary of "Help needed due to collapsed house" and a category of "Request for assistance."

[0999] 5. Notice:

[1000] Emergency services will be notified if necessary.

[1001] 6. Viewing Information:

[1002] Other users and related organizations can easily view the stored information and quickly begin rescue operations.

[1003] The above is a specific embodiment for carrying out the present invention, which enables quick and reliable information sharing and support activities in the event of a disaster.

[1004] The processing flow will be explained below.

[1005] Step 1:

[1006] User enters post

[1007] A user accesses the disaster information sharing platform using a smartphone or PC, enters a message such as "Help me, my house has collapsed" in the input field, and clicks the send button.

[1008] Step 2:

[1009] The device sends the post to the server

[1010] The device detects user input and generates a data packet containing the post content, a timestamp, the user ID, and location information, which is then sent to the server as an HTTP POST request.

[1011] Step 3:

[1012] The server receives the posted data

[1013] The server waits for POST requests at the specified endpoint. Upon receiving the request, the server decodes the data and prepares the post for analysis.

[1014] Step 4:

[1015] The server analyzes the posted data

[1016] The server uses a natural language processing engine to analyze the content of the post. Specifically, it analyzes the message "Help me, my house has collapsed" to extract meaning and generate a summary, such as "My house has collapsed and I am asking for help."

[1017] Step 5:

[1018] The server categorizes the submitted data.

[1019] The server uses a natural language processing engine to categorize the posts, and the analyzed messages are categorized as "requests for assistance."

[1020] Step 6:

[1021] Server identifies misinformation

[1022] The server uses a misinformation identification algorithm to check whether the post contains misinformation. If it is determined to be misinformation, the server will either ask the user to reconfirm or will not display the post.

[1023] Step 7:

[1024] The server converts the data into multiple languages.

[1025] If posts are made in different languages, the server uses a multilingual engine to translate, parse and categorize the posts.

[1026] Step 8:

[1027] The server stores the parsed data in a database

[1028] The server stores the analyzed, categorized, and summarized data in a database, along with the post timestamp, user ID, and location information.

[1029] Step 9:

[1030] The server sends notifications to interested parties

[1031] The server notifies the relevant authorities of the request for assistance, for example by using an emergency service API to send the request for assistance and promptly respond.

[1032] Step 10:

[1033] The server provides the data to the interested parties and other users.

[1034] When a related person or another user accesses the system, the server retrieves the relevant information from the database and provides it to the user or the related person, allowing the related person to immediately check the necessary information and take action.

[1035] Example 1

[1036] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1037] In the event of a disaster, there is a need to collect and share information quickly and efficiently, eliminate misinformation, and provide accurate information. However, existing systems have problems with delays in information collection and analysis, a high risk of misinformation spreading, and insufficient multilingual support, which hinders the ability of relevant parties and disaster response organizations to respond quickly.

[1038] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1039] In this invention, the server includes means for receiving posted content entered by a user, means for analyzing and classifying the posted content received by the communication device using natural language processing technology, means for summarizing the analyzed and classified posted content, means for detecting misinformation, means for realizing multilingual support, means for storing the analyzed and classified information in a database, means for notifying relevant parties of the stored information, means for relevant parties to view the stored information, and means for notifying relevant organizations of support request information in a timely manner. This enables quick and accurate information sharing and quick response by relevant parties in the event of a disaster.

[1040] A "user" is someone who accesses the disaster information sharing platform and posts information such as safety information and requests for assistance.

[1041] A "communication device" is a device that has the function of sending the content posted by a user to a server. This includes smartphones and PCs.

[1042] "Server" means a central computing system that analyzes, categorizes, stores, and, if necessary, notifies interested parties of received posts.

[1043] "Natural language processing technology" is a computer science technology that analyzes posted content and understands its meaning and category.

[1044] "Analysis" refers to the act of examining received posts in detail and processing the information to make it easier to understand.

[1045] "Classification" refers to dividing the analyzed post content into specific categories.

[1046] "Summary" refers to the compilation of analyzed and classified information in a concise form.

[1047] "Misinformation" refers to information that is inaccurate or false.

[1048] "Detection" refers to finding specific information (in this case, false information).

[1049] "Multilingual support" refers to the ability to properly parse and summarize information posted in different languages.

[1050] A "database" is a system that permanently stores analyzed and classified information.

[1051] "Storage" refers to the permanent recording of analyzed and classified information in a database.

[1052] "Notification" is the act of communicating stored information to relevant parties.

[1053] "Stakeholders" refers to all individuals and organizations involved in disaster response.

[1054] "Assistance request information" refers to information that includes information requiring help in the event of a disaster.

[1055] "Relevant organizations" refers to organizations and facilities engaged in emergency response and relief operations.

[1056] The present invention provides a disaster information sharing platform for realizing rapid and efficient information sharing during disasters. Various hardware and software are required to implement the present invention. The specific configuration and operation procedures are described below.

[1057] 1. User Submissions

[1058] A user accesses the disaster information sharing platform using their own device (smartphone, PC, landline, etc.). For example, the user starts the browser on their smartphone and logs in to the disaster information sharing platform. They enter "Help me, my house has collapsed" in the form and tap the send button.

[1059] 2. Data transmission by the terminal

[1060] The device generates data including the post content entered by the user, a timestamp, a user ID, and location information. This data is sent to the server using an HTTP POST request. Specifically, the smartphone application generates the entered data in JSON format and sends the HTTP POST request in the background.

[1061] 3. Data reception and decoding by the server

[1062] The server decodes the received data and converts it into an internal format for parsing. Decoding typically involves JSON deserialization. For example, the Java Gson library can be used to perform the decoding. The received post, "Help, my house has collapsed," is then handled by the server in the appropriate object format.

[1063] 4. Data analysis and classification using natural language processing

[1064] The server uses a natural language processing engine (e.g., Google Cloud Natural Language API) to analyze the received post. Through this analysis, the text "Help me, my house has collapsed" is converted into a summary "My house has collapsed, so I'm asking for help" and classified into the "Request for assistance" category. The analysis results in a summary and a category.

[1065] 5. Data Summarization and Misinformation Identification

[1066] The server then summarises the analysed and categorized data. This process identifies inaccurate or misleading information. If misinformation is identified, the server will either ask the user for further confirmation or restrict its display. Fact-checking algorithms and heuristic rules are used to identify misinformation.

[1067] 6. Saving to the database

[1068] The server stores the summarized and categorized data in a persistent database, typically via SQL queries or a NoSQL-enabled storage system, and includes information such as post summaries, categories, original post content, and timestamps.

[1069] 7. Sending required notifications

[1070] The server will then use the stored information to send notifications to relevant organizations and users as needed. For example, for information in the "assistance request" category, a notification can be sent to emergency services via an API to facilitate a prompt response. One possible API to use is the Twilio API.

[1071] 8. Viewing of Information by Interested Parties and Other Users

[1072] Stakeholders and other users can view the information stored in the database from their own devices. For example, they can access the disaster information sharing platform from a smartphone or PC browser and use the filtering function to search for and view information in the "request for assistance" category.

[1073] By using this specific operating procedure, the disaster information sharing platform of the present invention enables rapid and accurate information sharing in the event of a disaster, enabling relevant parties to respond quickly.

[1074] Example prompts to input to the generative AI model

[1075] "Please explain the disaster information sharing platform that enables fast and efficient information sharing in the event of a disaster. Please provide specific examples of how users input information and how the server analyzes, stores, and notifies users."

[1076] The above is a specific embodiment for carrying out the present invention, which enables quick and reliable information sharing and support activities in the event of a disaster.

[1077] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1078] Step 1: User input

[1079] Users access the disaster information sharing platform from devices such as smartphones or PCs and input information about their safety and requests for assistance. For example, they might type "Help me, my house has collapsed" and click the send button. The input information includes the content of the post as text data, a timestamp, user ID, and location information.

[1080] Step 2: Send data by device

[1081] The device generates the post content and related information (timestamp, user ID, location information) entered by the user in JSON format and sends it to the server using an HTTP POST request. The entered data is converted into a JSON object and sent to the server.

[1082] Step 3: Server receives and decodes data

[1083] When the server receives an HTTP request, it decodes the JSON data and converts it to an internal format. It converts each field of the JSON data received as input to the appropriate data type and stores it as a data object on the server. The output is a data object that can be parsed.

[1084] Step 4: Data analysis and classification using natural language processing

[1085] The server uses natural language processing technology (e.g., Google Cloud Natural Language API) to analyze the decoded post content. The analyzed data is summarized and classified into categories. For example, analyzing the post content "Help me, my house has collapsed" outputs the summary "Calling for help due to collapsed house" and the category "Request for assistance."

[1086] Step 5: Summarize data and identify misinformation

[1087] The server summarizes the analyzed and classified data and checks whether it contains any misinformation. Specifically, it verifies the data using fact-checking algorithms and heuristic rules. If it is determined to be misinformation, it sends a notification to the user requesting further confirmation. The server outputs the summarized data and the results of the misinformation identification.

[1088] Step 6: Saving to the Database

[1089] The server stores the summary and identified data in a persistent database (e.g., an SQL database), including the post summary, category, original post content, timestamp, user ID, and location information.

[1090] Step 7: Sending required notifications

[1091] The server will then send notifications to relevant authorities and other users as needed based on the stored information. For example, for information in the "assistance request" category, it will notify emergency services via API, facilitating rapid rescue operations. The output of sending a notification is a message confirming that the notification was successful.

[1092] Step 8: Information Viewed by Interested Parties and Other Users

[1093] Stakeholders and other users access the disaster information sharing platform from their own devices to search and view stored information. They use search queries to filter information in the "assistance request" category, displaying safety information and assistance request information by region. The output of the data viewed by users is the filtered specific assistance request information.

[1094] (Application example 1)

[1095] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1096] In times of disaster, rapid and accurate information sharing is required, but conventional systems face challenges in collecting, classifying, summarizing, and supporting multiple languages, identifying misinformation, and quickly notifying relevant parties. Another issue is the inability to effectively utilize real-time notification and location information sharing functions. This creates the risk of delays in providing necessary assistance and the spread of incorrect information.

[1097] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1098] In this invention, the server includes means for receiving posts entered by users, means for transmitting the posts to the server, means for analyzing and classifying the posts received by the server using natural language processing technology, means for summarizing the analyzed and classified posts, means for identifying misinformation, means for realizing multilingual support, means for storing the analyzed and classified information in a database, means for notifying relevant parties of the stored information, means for relevant parties to view the stored information, means for providing real-time notifications, and means for sharing user location information. This enables rapid and accurate sharing of information in the event of a disaster and efficient requesting of assistance from relevant parties.

[1099] "User" refers to an individual or organization that posts and views information using the disaster information sharing platform.

[1100] "Posted content" refers to information such as safety information and requests for assistance that users input to the system in the event of a disaster.

[1101] "Server" means a central computer system that receives, analyzes, categorizes, summarizes, stores, and notifies users of the posted content.

[1102] "Natural language processing technology" is a computer technology used to analyze text data and understand, classify, and summarize its content.

[1103] "Summarizing" is a process of summarizing the posted content concisely and to the most important points only.

[1104] "Misinformation" refers to posts that contain information that is different from the actual facts or that contain inaccurate content.

[1105] "Multilingual support" refers to the ability to properly analyze, classify, and summarize posts entered in different languages.

[1106] A "database" is a data storage system for efficiently storing and managing analyzed and classified information.

[1107] "Stakeholders" are individuals and organizations that receive information necessary for sharing information and providing support during disasters.

[1108] "Real-time notification" is a function that instantly notifies relevant parties and other users the moment disaster information is posted.

[1109] "User location information" is geographic data that allows a user to identify their current location.

[1110] The present invention provides a "disaster information sharing platform" that enables rapid and efficient information sharing during disasters. This platform is realized by a system including the following means:

[1111] First, users post disaster information using their smartphones, general-purpose computers (PCs), or fixed communication devices. The posted content includes information on the safety of users and requests for assistance. The information entered by the user is sent from the device to the server, and the data containing the posted content is then sent to the server along with a timestamp and user ID.

[1112] The server analyzes and classifies the received posts using natural language processing (NLP). As a result of this analysis, the posts are classified into appropriate categories. For example, a post such as "Help me, my house has collapsed" would be classified as a "Request for assistance." The server also has multilingual capabilities, allowing it to properly analyze information posted in multiple languages.

[1113] The analyzed and classified posts are then summarized. The summarization process extracts the important points of the posts and summarizes them into the minimum necessary information. The system also includes a function to identify misinformation, and if inaccurate information is posted, the system will ask the user to reconfirm it or restrict its display. This ensures reliable information sharing.

[1114] The summarized data is stored in a database, and at the same time, real-time notifications are sent to relevant parties. These notifications are intended to quickly convey requests for assistance and important safety information to relevant parties. In addition, relevant parties and other users can easily view the saved information on their devices. Furthermore, by utilizing user location information, it becomes possible to efficiently share information and request assistance by region.

[1115] The software used to realize this system includes the requests library for HTTP communication, the geopy library for handling location information, and the langid library for analyzing natural language. Also, on the server side, a system runs to process and analyze the received data and store it in a database.

[1116] As a concrete example, consider the case of an earthquake disaster. When User A posts "Help me, my house has collapsed!" to an app on his or her smartphone, this information is immediately sent to the server. The server then analyzes the post, summarizes and categorizes it, and stores it in a database as a "request for assistance." At the same time, real-time notifications are sent to emergency services and relevant organizations, allowing for prompt assistance efforts to begin.

[1117] Example of an input prompt for a generative AI model:

[1118] "We are developing a security information sharing application for use in emergencies. Users can post information about their safety and requests for assistance to the app on their smartphones, and the information needs to be quickly shared with relevant organizations and other users. Please provide specific code examples."

[1119] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1120] Step 1:

[1121] Users post disaster information using their devices (smartphones, PCs, fixed communication devices, etc.). Specifically, users open the application, enter information about the safety of others and requests for assistance, and click the send button. The entered post content is then saved on the device.

[1122] Input: Disaster information entered by the user into the application

[1123] Output: Post content data saved on the device

[1124] Step 2:

[1125] The device sends the content of the post received from the user to the server. The device generates a payload including the content of the post, a timestamp, the user ID, and location information, and sends it to the server as an HTTP POST request.

[1126] Input: User post content, timestamp, user ID, location information

[1127] Output: HTTP POST request to the server

[1128] Step 3:

[1129] The server receives the HTTP POST request and decodes the post data. The decoded data is then analyzed using natural language processing (NLP) to understand the content of the post. At the same time, multilingual support ensures that posts in different languages ​​are properly processed.

[1130] Input: Post content data received as an HTTP POST request

[1131] Output: Analyzed and categorized post content data

[1132] Step 4:

[1133] The server summarizes the analyzed and classified posts. During the summarization process, key points are extracted from the posts and the input data is summarized in a concise format. For example, a post saying "Help me, my house has collapsed" is summarized as "My house has collapsed, so I'm asking for help."

[1134] Input: Analyzed and categorized post content data

[1135] Output: Summary post data

[1136] Step 5:

[1137] The server uses the summarized data to identify misinformation, determines the accuracy of the posted content based on certain criteria, and if it contains inaccurate information, asks the user to reconfirm it or restricts its display.

[1138] Input: Summary post content data

[1139] Output: Data with misinformation corrected or limited

[1140] Step 6:

[1141] The server stores the summarized and misinformation-identified data in a database for later use in searches and notifications.

[1142] Input: Data with misinformation corrected or restricted

[1143] Output: Data stored in the database

[1144] Step 7:

[1145] Based on the stored data, the server sends real-time notifications to relevant parties, including requests for assistance and important safety information, which are delivered immediately to the relevant parties' devices.

[1146] Input: Data stored in a database

[1147] Output: Real-time notifications sent to the relevant parties' devices

[1148] Step 8:

[1149] The saved information can be viewed by relevant parties and other users via their devices (smartphones, PCs, fixed communication devices, etc.) By utilizing location information, requests for assistance and safety information for each region can also be displayed.

[1150] Input: Data stored in a database

[1151] Output: Information displayed on the devices of stakeholders and other users

[1152] Through these steps, we will achieve rapid and reliable sharing of information in the event of a disaster. HTTP communication, natural language processing, and location information services play important roles in building this system.

[1153] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1154] The present invention is a disaster information sharing platform that enables rapid and efficient information sharing during disasters, and in particular is a system that combines an emotion engine that recognizes the user's emotions.

[1155] Platform Overview

[1156] This platform utilizes users' devices (smartphones, PCs, landlines, etc.) to input and send information about the safety of users and requests for assistance during disasters. The server receives the posted content and analyzes and classifies the data using natural language processing technology and an emotion engine. The analyzed information is summarized, organized using functions to identify misinformation and support multiple languages, and stored in a database. The stored information is notified to relevant parties as necessary. In addition, relevant parties and other users can easily view the stored information.

[1157] System operation procedures

[1158] 1. User Submissions

[1159] After a disaster occurs, users access the disaster information sharing platform using their own devices. They enter information about their safety and a request for assistance, and click the send button. For example, they might enter, "Help me! My house has collapsed!"

[1160] 2. Data transmission by the terminal

[1161] The device receives the user's post and generates a data packet containing the message, timestamp, user ID, location information, and emotion data generated by the emotion engine. This data packet is then sent to the server as an HTTP POST request.

[1162] 3. Data reception and analysis by the server

[1163] The server waits for POST requests at the specified endpoint, decodes the data, and prepares the post for analysis.

[1164] 4. Server analysis and classification

[1165] The server uses a natural language processing engine to analyze the post. For example, it analyzes the message "Help me, my house has collapsed!" and generates a summary. In this case, the summary is "My house has collapsed, I'm asking for help." The analyzed message is then classified into a category called "Request for assistance."

[1166] 5. Emotion Recognition by Emotion Engine

[1167] The server uses an emotion engine to recognize the user's emotion from the content of the post. For example, the emotion contained in the post may be recognized as "urgency" or "fear." This emotion data is used for further analysis and classification.

[1168] 6. Misinformation Identification and Summarization

[1169] The server uses a misinformation identification algorithm to check for inaccuracies or misinformation. The summary might be "urgent help needed due to house collapse."

[1170] 7. Data Retention and Notification

[1171] The server then stores the analyzed, classified, and summarized data in a database. This includes the post's timestamp, user ID, location information, and emotion data. If necessary, the server notifies relevant organizations of requests for assistance. For example, the server can use an emergency service API to send information about a request for assistance and prompt a response.

[1172] 8. Viewing Information

[1173] The system allows users and other stakeholders to easily view the saved information on their devices. For example, when checking information in the "Request for Help" category, relevant posts are retrieved from the database through a search query. If necessary, location information can be used to display safety information and requests for help by region.

[1174] Specific examples

[1175] Let us explain this system using a specific scenario. For example, if a disaster occurs and user A posts "Help me, my house has collapsed!" at home:

[1176] 1. User input:

[1177] User A types "Help me, my house has collapsed!" into his smartphone and clicks the send button.

[1178] 2. Device behavior:

[1179] The device analyzes the input data using an emotion engine and identifies the emotion as "fear." It then sends a data packet containing the post data to the server.

[1180] 3. Server reception and analysis:

[1181] The server receives the data, analyzes the post using natural language processing technology, and summarizes it as "Help wanted after house collapse." The emotion engine provides the emotion data "fear."

[1182] 4. Data Retention and Notification:

[1183] The analyzed data is stored in a database and notified to relevant agencies as a "request for assistance."

[1184] 5. Viewing Information:

[1185] Other users and related organizations can view the assistance request information and provide prompt assistance.

[1186] The above is a specific embodiment for carrying out the present invention. This system enables quick and accurate information sharing and support activities in the event of a disaster.

[1187] The processing flow will be explained below.

[1188] Step 1:

[1189] User enters post

[1190] A user accesses the disaster information sharing platform using a smartphone or PC, enters a message such as "Help me, my house has collapsed!" into the input field, and clicks the send button.

[1191] Step 2:

[1192] The device sends the post to the server

[1193] The device detects user input and generates a data packet containing the post content, timestamp, user ID, location information, and emotion data generated by the emotion engine. This data packet is then sent to the server as an HTTP POST request.

[1194] Step 3:

[1195] The server receives the posted data

[1196] The server waits for POST requests at the specified endpoint. The server receives the request, decodes it, and prepares the post for analysis.

[1197] Step 4:

[1198] The server analyzes the posted data

[1199] The server uses a natural language processing engine to analyze the content of the post. For example, the message "Help me, my house has collapsed!" can be analyzed and summarized as "Help needed due to collapse of house."

[1200] Step 5:

[1201] The server categorizes the submitted data.

[1202] The server uses a natural language processing engine to categorize the post into categories such as "safety information" or "request for assistance." In this example, it is classified as a "request for assistance."

[1203] Step 6:

[1204] The server uses an emotion engine to recognize emotions.

[1205] The server uses an emotion engine to analyze the content of the post and identify the user's emotion, which in this case is recognized as "fear" or "urgency."

[1206] Step 7:

[1207] Server identifies misinformation

[1208] The server uses a misinformation identification algorithm to verify the authenticity of the posted content. If it is determined to be misinformation, the server will ask the user to confirm or restrict the posting.

[1209] Step 8:

[1210] The server stores it in the database

[1211] The server stores the analyzed, categorized, and summarized data in a database, including the post content, timestamp, user ID, location information, and emotion data.

[1212] Step 9:

[1213] The server notifies the relevant parties

[1214] The server notifies the relevant organizations of the assistance request information, for example, by using an emergency service API to send the assistance request information including emotion data.

[1215] Step 10:

[1216] The server provides the information to interested parties and other users.

[1217] When a related person or another user accesses the system, the server retrieves the relevant information from the database and provides it to the user or the related person, allowing the related person to immediately check the necessary information and take action.

[1218] Step 11:

[1219] User and stakeholder information viewing

[1220] Users and related parties can access the database and view the stored information, for example, information in the "request for assistance" category and emotional data, and take appropriate action.

[1221] The above are the processing steps of a specific embodiment of a disaster information sharing platform that combines an emotion engine. This system is expected to enable faster and more accurate information sharing and support activities during disasters.

[1222] Example 2

[1223] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1224] In modern times, when a disaster occurs, there is a need for rapid and accurate information sharing, but current systems lack the accuracy of information and the ability to recognize emotions, leading to the spread of misinformation and the inability to provide appropriate support promptly.In addition, there are issues with insufficient multilingual support and use of location information, making it difficult to efficiently distribute requests for support by region.

[1225] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes an analysis means using an emotion engine that recognizes the user's emotion, a means for identifying false information, and a means for realizing multilingual support. This enables the rapid and accurate sharing of disaster information, the priority processing of information based on emotion, the elimination of false information, multilingual support, and the utilization of location information.

[1226] "User" refers to a person or organization that uses the system to input disaster information or request assistance.

[1227] "Posted content" refers to disaster-related information and requests for assistance that users enter and send to the system.

[1228] An "emotion engine" is a technology or software that recognizes and analyzes emotions from user posts.

[1229] "Natural language processing technology" refers to technology that enables computers to understand, analyze, and generate natural language.

[1230] "Means for identifying misinformation" refers to methods or algorithms for identifying whether posted content is misinformation.

[1231] "Multilingual Method" means any technology or method for displaying, parsing, or sharing Posting Content in multiple languages.

[1232] A "database" is a data repository for storing analyzed and classified information.

[1233] "Interested Person" means a person or institution that can view the stored information and take action if necessary.

[1234] "Location information" refers to information that indicates the geographical location when a user enters a post.

[1235] A "request for assistance" is information provided by a user when a disaster occurs, indicating that assistance is required.

[1236] A "server" is a computer system that receives information sent by users and analyzes, classifies, stores, and notifies them.

[1237] "Terminal" means the device used by a User to access the System and input and submit Posting Content.

[1238] The present invention is a disaster information sharing platform that enables rapid and efficient information sharing during disasters, and in particular, a system that combines an emotion engine that recognizes the user's emotions. Specific embodiments for carrying out the invention are described below.

[1239] Platform Overview

[1240] This platform is a system that utilizes users' devices (smartphones, personal computers, fixed communication devices, etc.) to input and send information on the safety of users and requests for assistance during disasters. The server receives the user's posts and analyzes and classifies the data using natural language processing technology and an emotion engine. The analyzed information is summarized, organized using functions to identify misinformation and support multiple languages, and stored in a database. The stored information is notified to relevant parties as necessary. In addition, relevant parties and other users can easily view the stored information.

[1241] System operation procedures

[1242] 1. User Submissions

[1243] After a disaster occurs, users access the disaster information sharing platform using their own devices. They enter information about their safety and a request for assistance, and click the send button. For example, they might enter, "Help me! My house has collapsed!"

[1244] 2. Data transmission by the terminal

[1245] The device receives the user's post and generates a data packet containing the message, timestamp, user ID, location information, and emotion data generated by the emotion engine. This data packet is then sent to the server as an HTTP POST request.

[1246] 3. Data reception and analysis by the server

[1247] The server waits for POST requests at the specified endpoint, and upon receiving the request, it decodes the data and prepares the post for analysis.

[1248] Technology used

[1249] The natural language processing technology used by the server for data analysis includes functions such as text summarization, classification, and keyword extraction. The emotion engine analyzes user emotions (e.g., urgency, fear, etc.) from posted content and uses this information to prioritize and select information to notify relevant parties. The misinformation identification algorithm detects whether posted content contains inaccurate or false information and removes it as necessary.

[1250] The multilingual feature automatically translates posts into multiple languages, ensuring appropriate information for stakeholders who speak different languages.

[1251] Stored Data and Notifications

[1252] The data analyzed, classified, and summarized by the server is stored in a database. This data includes post timestamps, user IDs, location information, and emotional data. Urgent assistance requests are notified from the server to relevant organizations via API, facilitating a prompt response. In addition, relevant parties and other users can easily view the stored information via their devices and provide assistance as needed.

[1253] Specific examples

[1254] For example, if a disaster occurs and user A posts "Help, my house has collapsed!" at home, the system will operate as follows:

[1255] 1. User input:

[1256] User A types "Help me, my house has collapsed!" into his smartphone and clicks the send button.

[1257] 2. Device behavior:

[1258] The device analyzes the input data with an emotion engine, identifies the emotion as "fear," and then sends this data packet to the server.

[1259] 3. Server reception and analysis:

[1260] The server receives the data, analyzes the post using natural language processing technology, and summarizes it as "Help wanted after house collapse." The emotion engine provides the emotion data "fear."

[1261] 4. Data Retention and Notification:

[1262] The analyzed data is stored in a database and notified to relevant agencies as a "request for assistance."

[1263] 5. Viewing Information:

[1264] Other users and related organizations can view the information about requests for assistance and provide prompt relief. For example, when checking information in the "Request for Assistance" category, relevant posts are retrieved from the database through a search query. If necessary, safety information and assistance requests by region can also be displayed based on location information.

[1265] Prompt Sentence Examples

[1266] An example prompt for a generative AI model might look like this:

[1267] "Please explain in detail how users will input safety information and requests for assistance from home in the event of a disaster and how the information will be processed."

[1268] The above is a specific embodiment for carrying out the present invention. This system enables quick and accurate information sharing and support activities in the event of a disaster.

[1269] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1270] Step 1: User Submission

[1271] Specific actions

[1272] Users access the disaster information sharing platform and use their own devices (smartphones, personal computers, fixed communication devices, etc.) to input information about the safety of others and requests for assistance.

[1273] input

[1274] A user types "Help me, my house is collapsing!" into a text box.

[1275] output

[1276] The user's input is sent to the terminal, and the next process is carried out.

[1277] Step 2: Send data by device

[1278] Specific actions

[1279] The device receives the user's post, analyzes the message with an emotion engine, generates emotion data, and then generates a data packet containing the message, timestamp, user ID, location information, and emotion data.

[1280] input

[1281] User posts and metadata acquired by the device (timestamp, user ID, location information).

[1282] output

[1283] A data packet sent to a server as an HTTP POST request.

[1284] Step 3: Server receives data

[1285] Specific actions

[1286] The server waits for POST requests at the specified endpoint and decodes the data packets after receiving the request.

[1287] input

[1288] The HTTP POST request sent from the device.

[1289] output

[1290] Decoded user post content, timestamp, user ID, location, and sentiment data.

[1291] Step 4: Data analysis and classification by the server

[1292] Specific actions

[1293] The server uses natural language processing technology to analyze and summarize the content of posts, and also categorizes the posts into appropriate categories.

[1294] input

[1295] Decoded post content and metadata.

[1296] output

[1297] A summarized message and a classified category (e.g., a summary of "seeking help due to collapse of house" and a category of "request for assistance").

[1298] Step 5: Emotion Recognition with the Emotion Engine

[1299] Specific actions

[1300] The server uses an emotion engine to recognize the user's emotion from the posted content and generates emotion data.

[1301] input

[1302] Abridged post content.

[1303] output

[1304] Emotional data (e.g., "fear" or "urgency").

[1305] Step 6: Misinformation Identification and Summarization

[1306] Specific actions

[1307] The server uses a misinformation-detection algorithm to check the submission for inaccuracies and misinformation. The summary is finalized.

[1308] input

[1309] Summarized post content and sentiment data.

[1310] output

[1311] Misinformation identified and finalized summary (e.g., "urgent assistance needed due to house collapse").

[1312] Step 7: Data storage and notification

[1313] Specific actions

[1314] The server stores the analyzed, classified, and summarized data in a database and notifies relevant organizations as necessary.

[1315] input

[1316] Finalized summary, timestamp, user ID, location, and emotion data.

[1317] output

[1318] Data stored in databases and notifications to relevant authorities (e.g., sending assistance requests using emergency services APIs).

[1319] Step 8: Information visible to stakeholders and other users

[1320] Specific actions

[1321] Search and view stored information through devices used by related parties or other users.

[1322] input

[1323] Request information from a stored database.

[1324] output

[1325] The search results will show relevant posts (e.g., posts in the "Request for Help" category). If necessary, location-based information will also be displayed for each region.

[1326] (Application example 2)

[1327] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1328] Conventional disaster information sharing systems have difficulty in properly recognizing and analyzing the emotions in the information entered by users, and furthermore, have problems in that they do not adequately guarantee safe information sharing during disasters. This can lead to delays in responding to requests for assistance quickly and appropriately, making it difficult to provide efficient assistance during disasters.

[1329] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving posted content entered by a user, means for transmitting the posted content to the server, means for analyzing and classifying the posted content received by the server using natural language processing technology, means for summarizing the analyzed and classified posted content, means for identifying misinformation, means for realizing multilingual support, means for storing the analyzed and classified information in a database, means for notifying relevant parties of the stored information, means for relevant parties to view the stored information, means for recognizing emotion data related to the posted content entered by a user and using the emotion data for analysis and classification, and means for securely protecting information through end-to-end encryption in the event of a disaster. This enables rapid and accurate information sharing and safe assistance requests in the event of a disaster.

[1330] "Posted content" is data including disaster information and requests for assistance entered by the user.

[1331] A "server" is a computer system that receives, analyzes, classifies, and stores content posted by users, and notifies relevant parties.

[1332] "Natural language processing technology" is a technology for analyzing the content of posts and understanding their meaning and intent.

[1333] A "summary" is a short summary of the main points of the analyzed and classified post.

[1334] "Misinformation identification" is the process of identifying inaccurate or false information in posts.

[1335] "Multilingual support" is a function that understands and responds to posts in multiple languages.

[1336] "Emotion data" is emotional information recognized from the content posted by a user.

[1337] "End-to-end encryption" is a technology that encrypts communication data from the sender to the recipient, protecting it from access by third parties.

[1338] "Location information" is geographical data that indicates the region from which the posted content was sent.

[1339] A "database" is a storage system in which analyzed and classified information is stored.

[1340] "Notification" is an action to inform relevant parties of stored information.

[1341] "Stakeholders" are individuals, organizations, or institutions involved in providing assistance or sharing information during a disaster.

[1342] This invention is a disaster information sharing system that realizes fast and efficient information sharing during disasters, and in particular, it is combined with an emotion engine that recognizes the user's emotions. This system utilizes the user's device (smartphone, PC, landline phone, etc.) to efficiently process safety information and assistance requests entered by the user during a disaster.

[1343] System configuration

[1344] This system consists of the following main elements:

[1345] 1. Terminal

[1346] This refers to the devices that users use to input content, including smartphones, PCs, and landlines. Smartphones, in particular, use GPS to obtain user location information.

[1347] 2. Server

[1348] The server analyzes and classifies the received posts, and stores and manages the necessary data. Specifically, it has the following functions:

[1349] Natural language processing technology: Provides technology that analyzes post content and extracts and classifies key points.

[1350] Emotion engine: Recognizes emotions from user input and uses that information for analysis and classification.

[1351] End-to-end encryption: A security technology that prevents third parties from intercepting data as it is transmitted.

[1352] Misinformation Identification: The platform has algorithms that check the accuracy of posts and filter out misinformation.

[1353] Multilingual support: Process and parse posts entered in multiple languages.

[1354] Database: A storage system that stores analyzed and classified information and notifies relevant parties as needed.

[1355] Notification system: A system to notify relevant parties of stored information and promptly provide necessary assistance.

[1356] Program processing

[1357] When a user types and submits a post from their device, the following process occurs:

[1358] Data acquisition: The device receives disaster information and assistance requests entered by the user. For example, a message like "Help me, my house has collapsed!"

[1359] Timestamp generation: The server obtains the current time and adds it to the posted data, ensuring that the information is up to date.

[1360] Emotion Recognition: The emotion engine analyzes the content of posts and extracts emotional data, such as "fear."

[1361] Summary generation: Using natural language processing techniques, we provide a concise summary of the post, such as "Help needed after house collapse."

[1362] Data storage: The analyzed information is stored in a database, including timestamps, user IDs, location information, and emotion data.

[1363] Notification: Notify relevant parties of the stored data, allowing for a prompt response.

[1364] Adding specific examples

[1365] For example, if a user types "There is no water in the evacuation shelter, please help!" during a disaster, the system will act as follows:

[1366] 1. User input: The user types "There is no water at the evacuation center, please help!" into their smartphone and sends it.

[1367] 2. Device behavior: The emotion engine identifies the event as an "urgent" event. The device adds location information and a timestamp to the posted data and sends it to the server.

[1368] 3. Server reception and analysis: The server receives the data and uses natural language processing technology to generate a summary of "shelters, water shortages." The emotion data "urgent" is added.

[1369] 4. Data storage and notification: The analyzed data will be stored in a database and notified to the relevant authorities.

[1370] Prompt Sentence Examples

[1371] "Please enter information for a quick request for assistance during a disaster: 'There is a food shortage at the evacuation center, please help!'"

[1372] This allows users to input information appropriately, and serves as a reference for those involved to carry out support activities quickly.

[1373] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1374] Step 1:

[1375] The device receives the post entered by the user. The user enters disaster information or a request for assistance on their smartphone and clicks the send button. This input includes location information. For example, a message might read, "Help me, my house has collapsed!"

[1376] Input: Messages typed by the user into their smartphone

[1377] Output: Input data received by the device (message and location information)

[1378] Specific behavior:

[1379] The user accesses the smartphone app, types a message, and sends it.

[1380] Step 2:

[1381] The device analyzes the content of the post using an emotion engine to extract emotional data, recognizing emotions such as "fear" from the content of the post and adding the emotional data to the data packet.

[1382] Input: Input data received by the device (messages and location information)

[1383] Output: A data packet with emotion data added by the emotion engine (e.g., fear).

[1384] Specific behavior:

[1385] The emotion engine analyzes the content of the post and extracts emotions using natural language processing technology.

[1386] Step 3:

[1387] The device sends a data packet to the server as an HTTP POST request, which includes the message, a timestamp, a user ID, location information, and emotion data.

[1388] Input: Data packet with attached emotion data (e.g., message, timestamp, user ID, location, emotion data)

[1389] Output: Data packet received by the server

[1390] Specific behavior:

[1391] The device constructs a data packet and sends it to the server as an HTTP POST request.

[1392] Step 4:

[1393] The server decodes the received data packets and analyzes the content of the post. It uses natural language processing technology to extract the main points of the post and generate a summary. For example, it generates a summary such as "Help wanted after house collapse."

[1394] Input: Data packet received by the server

[1395] Output: Parsed summary message

[1396] Specific behavior:

[1397] The server uses natural language processing technology to analyze the post and extract the main points.

[1398] Step 5:

[1399] The server uses a misinformation identification algorithm to check whether the post contains misinformation and decides whether to keep it as accurate information or remove it as misinformation.

[1400] Input: Parsed summary message

[1401] Output: Summary message with misinformation removed

[1402] Specific behavior:

[1403] The server applies a misinformation identification algorithm to verify the accuracy of the input data.

[1404] Step 6:

[1405] The server stores the analyzed and categorized information in a database, including posted messages, timestamps, user IDs, location information, and emotional data.

[1406] Input: Summary message with misinformation removed

[1407] Output: Data stored in the database

[1408] Specific behavior:

[1409] The server stores the analyzed and classified information in a database.

[1410] Step 7:

[1411] The server then notifies the relevant parties of the stored information, for example, emergency services and assistance agencies via an API.

[1412] Input: Data stored in the database

[1413] Output: Notification message to interested parties

[1414] Specific behavior:

[1415] The server will notify the relevant parties and arrange for prompt assistance.

[1416] Step 8:

[1417] Stakeholders can view the saved information. Stakeholders can view saved posts and summaries through a dedicated interface.

[1418] Input: Data in the database

[1419] Output: Information viewed by interested parties

[1420] Specific behavior:

[1421] Stakeholders can view the information through a browser or dedicated application.

[1422] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1423] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1424] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1425] [Fourth embodiment]

[1426] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1427] 7, a 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.

[1428] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1429] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1430] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1431] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1432] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1433] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1434] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1435] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1436] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1437] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1438] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1439] The present invention is a disaster information sharing platform that enables rapid and efficient information sharing in the event of a disaster.

[1440] Platform Overview

[1441] Users use their devices (smartphones, PCs, landlines, etc.) to input and send information about the safety of their loved ones and requests for assistance during a disaster. The server then receives the posted content and analyzes and classifies the data using natural language processing technology. This analyzed information is summarized and stored in a database, utilizing functions to identify misinformation and support multiple languages. The stored information is notified to relevant parties as necessary. In addition, relevant parties and other users can easily view the stored information.

[1442] System operation procedures

[1443] 1. User Submissions

[1444] First, after a disaster occurs, a user accesses the disaster information sharing platform using their own device. For example, they enter "I'm safe" as information about their safety. They also enter "Please help me, my house has collapsed" as a request for assistance. Once they have finished entering the information, they click the send button to send the information.

[1445] 2. Data transmission by the terminal

[1446] The device receives the user's post and sends it to the server in a format that includes the input content, timestamp, user ID, location information, etc. The device sends the data to the server using an HTTP POST request.

[1447] 3. Data reception and analysis by the server

[1448] When the server receives the posted data, it first decodes the data and then begins analyzing and classifying the content of the post using natural language processing technology. For example, if a post says "Help me, my house has collapsed," the natural language processing engine will analyze it, output the summary "Calling for help due to collapsed house," and classify the category as "Request for assistance."

[1449] 4. Data Summarization and Misinformation Identification

[1450] The server then summarises the analysed and categorized data to identify inaccurate or false information. If it is determined to be false, it will ask the user to reconfirm or restrict the display. Multilingual support is also included, and posts made in different languages ​​will be analysed and summarised appropriately.

[1451] 5. Saving to database and notification

[1452] The analyzed, classified, and summarized data is stored in a database. After storage, assistance requests and other information are sent to relevant organizations as needed. For example, API notifications can be sent to emergency services to encourage a rapid response.

[1453] 6. Information viewing by stakeholders and users

[1454] The system allows users and other stakeholders to easily view the saved information using their devices. For example, when checking information in the "Request for Help" category, relevant posts are retrieved from the database through a search query. If necessary, location information can be used to display safety information and requests for help by region.

[1455] Specific examples

[1456] Let's consider a specific scenario: suppose a disaster occurs and user A posts "Help me, my house has collapsed!" in their home. In this case:

[1457] 1. User A's actions:

[1458] User A enters a post on their smartphone and clicks the send button.

[1459] 2. Device behavior:

[1460] The device sends the post content to the server.

[1461] 3. Server Operation:

[1462] The server receives the posts, analyzes them using a natural language processing engine, and summarizes and categorizes them.

[1463] 4. Analysis and storage:

[1464] The analyzed data is stored in a database with a summary of "Help needed due to collapsed house" and a category of "Request for assistance."

[1465] 5. Notice:

[1466] Emergency services will be notified if necessary.

[1467] 6. Viewing Information:

[1468] Other users and related organizations can easily view the stored information and quickly begin rescue operations.

[1469] The above is a specific embodiment for carrying out the present invention, which enables quick and reliable information sharing and support activities in the event of a disaster.

[1470] The processing flow will be explained below.

[1471] Step 1:

[1472] User enters post

[1473] A user accesses the disaster information sharing platform using a smartphone or PC, enters a message such as "Help me, my house has collapsed" in the input field, and clicks the send button.

[1474] Step 2:

[1475] The device sends the post to the server

[1476] The device detects user input and generates a data packet containing the post content, a timestamp, the user ID, and location information, which is then sent to the server as an HTTP POST request.

[1477] Step 3:

[1478] The server receives the posted data

[1479] The server waits for POST requests at the specified endpoint. Upon receiving the request, the server decodes the data and prepares the post for analysis.

[1480] Step 4:

[1481] The server analyzes the posted data

[1482] The server uses a natural language processing engine to analyze the content of the post. Specifically, it analyzes the message "Help me, my house has collapsed" to extract meaning and generate a summary, such as "My house has collapsed and I am asking for help."

[1483] Step 5:

[1484] The server categorizes the submitted data.

[1485] The server uses a natural language processing engine to categorize the posts, and the analyzed messages are categorized as "requests for assistance."

[1486] Step 6:

[1487] Server identifies misinformation

[1488] The server uses a misinformation identification algorithm to check whether the post contains misinformation. If it is determined to be misinformation, the server will either ask the user to reconfirm or will not display the post.

[1489] Step 7:

[1490] The server converts the data into multiple languages.

[1491] If posts are made in different languages, the server uses a multilingual engine to translate, parse and categorize the posts.

[1492] Step 8:

[1493] The server stores the parsed data in a database

[1494] The server stores the analyzed, categorized, and summarized data in a database, along with the post timestamp, user ID, and location information.

[1495] Step 9:

[1496] The server sends notifications to interested parties

[1497] The server notifies the relevant authorities of the request for assistance, for example by using an emergency service API to send the request for assistance and promptly respond.

[1498] Step 10:

[1499] The server provides the data to the interested parties and other users.

[1500] When a related person or another user accesses the system, the server retrieves the relevant information from the database and provides it to the user or the related person, allowing the related person to immediately check the necessary information and take action.

[1501] Example 1

[1502] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1503] In the event of a disaster, there is a need to collect and share information quickly and efficiently, eliminate misinformation, and provide accurate information. However, existing systems have problems with delays in information collection and analysis, a high risk of misinformation spreading, and insufficient multilingual support, which hinders the ability of relevant parties and disaster response organizations to respond quickly.

[1504] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1505] In this invention, the server includes means for receiving posted content entered by a user, means for analyzing and classifying the posted content received by the communication device using natural language processing technology, means for summarizing the analyzed and classified posted content, means for detecting misinformation, means for realizing multilingual support, means for storing the analyzed and classified information in a database, means for notifying relevant parties of the stored information, means for relevant parties to view the stored information, and means for notifying relevant organizations of support request information in a timely manner. This enables quick and accurate information sharing and quick response by relevant parties in the event of a disaster.

[1506] A "user" is someone who accesses the disaster information sharing platform and posts information such as safety information and requests for assistance.

[1507] A "communication device" is a device that has the function of sending the content posted by a user to a server. This includes smartphones and PCs.

[1508] "Server" means a central computing system that analyzes, categorizes, stores, and, if necessary, notifies interested parties of received posts.

[1509] "Natural language processing technology" is a computer science technology that analyzes posted content and understands its meaning and category.

[1510] "Analysis" refers to the act of examining received posts in detail and processing the information to make it easier to understand.

[1511] "Classification" refers to dividing the analyzed post content into specific categories.

[1512] "Summary" refers to the compilation of analyzed and classified information in a concise form.

[1513] "Misinformation" refers to information that is inaccurate or false.

[1514] "Detection" refers to finding specific information (in this case, false information).

[1515] "Multilingual support" refers to the ability to properly parse and summarize information posted in different languages.

[1516] A "database" is a system that permanently stores analyzed and classified information.

[1517] "Storage" refers to the permanent recording of analyzed and classified information in a database.

[1518] "Notification" is the act of communicating stored information to relevant parties.

[1519] "Stakeholders" refers to all individuals and organizations involved in disaster response.

[1520] "Assistance request information" refers to information that includes information requiring help in the event of a disaster.

[1521] "Relevant organizations" refers to organizations and facilities engaged in emergency response and relief operations.

[1522] The present invention provides a disaster information sharing platform for realizing rapid and efficient information sharing during disasters. Various hardware and software are required to implement the present invention. The specific configuration and operation procedures are described below.

[1523] 1. User Submissions

[1524] A user accesses the disaster information sharing platform using their own device (smartphone, PC, landline, etc.). For example, the user starts the browser on their smartphone and logs in to the disaster information sharing platform. They enter "Help me, my house has collapsed" in the form and tap the send button.

[1525] 2. Data transmission by the terminal

[1526] The device generates data including the post content entered by the user, a timestamp, a user ID, and location information. This data is sent to the server using an HTTP POST request. Specifically, the smartphone application generates the entered data in JSON format and sends the HTTP POST request in the background.

[1527] 3. Data reception and decoding by the server

[1528] The server decodes the received data and converts it into an internal format for parsing. Decoding typically involves JSON deserialization. For example, the Java Gson library can be used to perform the decoding. The received post, "Help, my house has collapsed," is then handled by the server in the appropriate object format.

[1529] 4. Data analysis and classification using natural language processing

[1530] The server uses a natural language processing engine (e.g., Google Cloud Natural Language API) to analyze the received post. Through this analysis, the text "Help me, my house has collapsed" is converted into a summary "My house has collapsed, so I'm asking for help" and classified into the "Request for assistance" category. The analysis results in a summary and a category.

[1531] 5. Data Summarization and Misinformation Identification

[1532] The server then summarises the analysed and categorized data. This process identifies inaccurate or misleading information. If misinformation is identified, the server will either ask the user for further confirmation or restrict its display. Fact-checking algorithms and heuristic rules are used to identify misinformation.

[1533] 6. Saving to the database

[1534] The server stores the summarized and categorized data in a persistent database, typically via SQL queries or a NoSQL-enabled storage system, and includes information such as post summaries, categories, original post content, and timestamps.

[1535] 7. Sending required notifications

[1536] The server will then use the stored information to send notifications to relevant organizations and users as needed. For example, for information in the "assistance request" category, a notification can be sent to emergency services via an API to facilitate a prompt response. One possible API to use is the Twilio API.

[1537] 8. Viewing of Information by Interested Parties and Other Users

[1538] Stakeholders and other users can view the information stored in the database from their own devices. For example, they can access the disaster information sharing platform from a smartphone or PC browser and use the filtering function to search for and view information in the "request for assistance" category.

[1539] By using this specific operating procedure, the disaster information sharing platform of the present invention enables rapid and accurate information sharing in the event of a disaster, enabling relevant parties to respond quickly.

[1540] Example prompts to input to the generative AI model

[1541] "Please explain the disaster information sharing platform that enables fast and efficient information sharing in the event of a disaster. Please provide specific examples of how users input information and how the server analyzes, stores, and notifies users."

[1542] The above is a specific embodiment for carrying out the present invention, which enables quick and reliable information sharing and support activities in the event of a disaster.

[1543] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1544] Step 1: User input

[1545] Users access the disaster information sharing platform from devices such as smartphones or PCs and input information about their safety and requests for assistance. For example, they might type "Help me, my house has collapsed" and click the send button. The input information includes the content of the post as text data, a timestamp, user ID, and location information.

[1546] Step 2: Send data by device

[1547] The device generates the post content and related information (timestamp, user ID, location information) entered by the user in JSON format and sends it to the server using an HTTP POST request. The entered data is converted into a JSON object and sent to the server.

[1548] Step 3: Server receives and decodes data

[1549] When the server receives an HTTP request, it decodes the JSON data and converts it to an internal format. It converts each field of the JSON data received as input to the appropriate data type and stores it as a data object on the server. The output is a data object that can be parsed.

[1550] Step 4: Data analysis and classification using natural language processing

[1551] The server uses natural language processing technology (e.g., Google Cloud Natural Language API) to analyze the decoded post content. The analyzed data is summarized and classified into categories. For example, analyzing the post content "Help me, my house has collapsed" outputs the summary "Calling for help due to collapsed house" and the category "Request for assistance."

[1552] Step 5: Summarize data and identify misinformation

[1553] The server summarizes the analyzed and classified data and checks whether it contains any misinformation. Specifically, it verifies the data using fact-checking algorithms and heuristic rules. If it is determined to be misinformation, it sends a notification to the user requesting further confirmation. The server outputs the summarized data and the results of the misinformation identification.

[1554] Step 6: Saving to the Database

[1555] The server stores the summary and identified data in a persistent database (e.g., an SQL database), including the post summary, category, original post content, timestamp, user ID, and location information.

[1556] Step 7: Sending required notifications

[1557] The server will then send notifications to relevant authorities and other users as needed based on the stored information. For example, for information in the "assistance request" category, it will notify emergency services via API, facilitating rapid rescue operations. The output of sending a notification is a message confirming that the notification was successful.

[1558] Step 8: Information Viewed by Interested Parties and Other Users

[1559] Stakeholders and other users access the disaster information sharing platform from their own devices to search and view stored information. They use search queries to filter information in the "assistance request" category, displaying safety information and assistance request information by region. The output of the data viewed by users is the filtered specific assistance request information.

[1560] (Application example 1)

[1561] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1562] In times of disaster, rapid and accurate information sharing is required, but conventional systems face challenges in collecting, classifying, summarizing, and supporting multiple languages, identifying misinformation, and quickly notifying relevant parties. Another issue is the inability to effectively utilize real-time notification and location information sharing functions. This creates the risk of delays in providing necessary assistance and the spread of incorrect information.

[1563] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1564] In this invention, the server includes means for receiving posts entered by users, means for transmitting the posts to the server, means for analyzing and classifying the posts received by the server using natural language processing technology, means for summarizing the analyzed and classified posts, means for identifying misinformation, means for realizing multilingual support, means for storing the analyzed and classified information in a database, means for notifying relevant parties of the stored information, means for relevant parties to view the stored information, means for providing real-time notifications, and means for sharing user location information. This enables rapid and accurate sharing of information in the event of a disaster and efficient requesting of assistance from relevant parties.

[1565] "User" refers to an individual or organization that posts and views information using the disaster information sharing platform.

[1566] "Posted content" refers to information such as safety information and requests for assistance that users input to the system in the event of a disaster.

[1567] "Server" means a central computer system that receives, analyzes, categorizes, summarizes, stores, and notifies users of the posted content.

[1568] "Natural language processing technology" is a computer technology used to analyze text data and understand, classify, and summarize its content.

[1569] "Summarizing" is a process of summarizing the posted content concisely and to the most important points only.

[1570] "Misinformation" refers to posts that contain information that is different from the actual facts or that contain inaccurate content.

[1571] "Multilingual support" refers to the ability to properly analyze, classify, and summarize posts entered in different languages.

[1572] A "database" is a data storage system for efficiently storing and managing analyzed and classified information.

[1573] "Stakeholders" are individuals and organizations that receive information necessary for sharing information and providing support during disasters.

[1574] "Real-time notification" is a function that instantly notifies relevant parties and other users the moment disaster information is posted.

[1575] "User location information" is geographic data that allows a user to identify their current location.

[1576] The present invention provides a "disaster information sharing platform" that enables rapid and efficient information sharing during disasters. This platform is realized by a system including the following means:

[1577] First, users post disaster information using their smartphones, general-purpose computers (PCs), or fixed communication devices. The posted content includes information on the safety of users and requests for assistance. The information entered by the user is sent from the device to the server, and the data containing the posted content is then sent to the server along with a timestamp and user ID.

[1578] The server analyzes and classifies the received posts using natural language processing (NLP). As a result of this analysis, the posts are classified into appropriate categories. For example, a post such as "Help me, my house has collapsed" would be classified as a "Request for assistance." The server also has multilingual capabilities, allowing it to properly analyze information posted in multiple languages.

[1579] The analyzed and classified posts are then summarized. The summarization process extracts the important points of the posts and summarizes them into the minimum necessary information. The system also includes a function to identify misinformation, and if inaccurate information is posted, the system will ask the user to reconfirm it or restrict its display. This ensures reliable information sharing.

[1580] The summarized data is stored in a database, and at the same time, real-time notifications are sent to relevant parties. These notifications are intended to quickly convey requests for assistance and important safety information to relevant parties. In addition, relevant parties and other users can easily view the saved information on their devices. Furthermore, by utilizing user location information, it becomes possible to efficiently share information and request assistance by region.

[1581] The software used to realize this system includes the requests library for HTTP communication, the geopy library for handling location information, and the langid library for analyzing natural language. Also, on the server side, a system runs to process and analyze the received data and store it in a database.

[1582] As a concrete example, consider the case of an earthquake disaster. When User A posts "Help me, my house has collapsed!" to an app on his or her smartphone, this information is immediately sent to the server. The server then analyzes the post, summarizes and categorizes it, and stores it in a database as a "request for assistance." At the same time, real-time notifications are sent to emergency services and relevant organizations, allowing for prompt assistance efforts to begin.

[1583] Example of an input prompt for a generative AI model:

[1584] "We are developing a security information sharing application for use in emergencies. Users can post information about their safety and requests for assistance to the app on their smartphones, and the information needs to be quickly shared with relevant organizations and other users. Please provide specific code examples."

[1585] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1586] Step 1:

[1587] Users post disaster information using their devices (smartphones, PCs, fixed communication devices, etc.). Specifically, users open the application, enter information about the safety of others and requests for assistance, and click the send button. The entered post content is then saved on the device.

[1588] Input: Disaster information entered by the user into the application

[1589] Output: Post content data saved on the device

[1590] Step 2:

[1591] The device sends the content of the post received from the user to the server. The device generates a payload including the content of the post, a timestamp, the user ID, and location information, and sends it to the server as an HTTP POST request.

[1592] Input: User post content, timestamp, user ID, location information

[1593] Output: HTTP POST request to the server

[1594] Step 3:

[1595] The server receives the HTTP POST request and decodes the post data. The decoded data is then analyzed using natural language processing (NLP) to understand the content of the post. At the same time, multilingual support ensures that posts in different languages ​​are properly processed.

[1596] Input: Post content data received as an HTTP POST request

[1597] Output: Analyzed and categorized post content data

[1598] Step 4:

[1599] The server summarizes the analyzed and classified posts. During the summarization process, key points are extracted from the posts and the input data is summarized in a concise format. For example, a post saying "Help me, my house has collapsed" is summarized as "My house has collapsed, so I'm asking for help."

[1600] Input: Analyzed and categorized post content data

[1601] Output: Summary post data

[1602] Step 5:

[1603] The server uses the summarized data to identify misinformation, determines the accuracy of the posted content based on certain criteria, and if it contains inaccurate information, asks the user to reconfirm it or restricts its display.

[1604] Input: Summary post content data

[1605] Output: Data with misinformation corrected or limited

[1606] Step 6:

[1607] The server stores the summarized and misinformation-identified data in a database for later use in searches and notifications.

[1608] Input: Data with misinformation corrected or restricted

[1609] Output: Data stored in the database

[1610] Step 7:

[1611] Based on the stored data, the server sends real-time notifications to relevant parties, including requests for assistance and important safety information, which are delivered immediately to the relevant parties' devices.

[1612] Input: Data stored in a database

[1613] Output: Real-time notifications sent to the relevant parties' devices

[1614] Step 8:

[1615] The saved information can be viewed by relevant parties and other users via their devices (smartphones, PCs, fixed communication devices, etc.) By utilizing location information, requests for assistance and safety information for each region can also be displayed.

[1616] Input: Data stored in a database

[1617] Output: Information displayed on the devices of stakeholders and other users

[1618] Through these steps, we will achieve rapid and reliable sharing of information in the event of a disaster. HTTP communication, natural language processing, and location information services play important roles in building this system.

[1619] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1620] The present invention is a disaster information sharing platform that enables rapid and efficient information sharing during disasters, and in particular is a system that combines an emotion engine that recognizes the user's emotions.

[1621] Platform Overview

[1622] This platform utilizes users' devices (smartphones, PCs, landlines, etc.) to input and send information about the safety of users and requests for assistance during disasters. The server receives the posted content and analyzes and classifies the data using natural language processing technology and an emotion engine. The analyzed information is summarized, organized using functions to identify misinformation and support multiple languages, and stored in a database. The stored information is notified to relevant parties as necessary. In addition, relevant parties and other users can easily view the stored information.

[1623] System operation procedures

[1624] 1. User Submissions

[1625] After a disaster occurs, users access the disaster information sharing platform using their own devices. They enter information about their safety and a request for assistance, and click the send button. For example, they might enter, "Help me! My house has collapsed!"

[1626] 2. Data transmission by the terminal

[1627] The device receives the user's post and generates a data packet containing the message, timestamp, user ID, location information, and emotion data generated by the emotion engine. This data packet is then sent to the server as an HTTP POST request.

[1628] 3. Data reception and analysis by the server

[1629] The server waits for POST requests at the specified endpoint, decodes the data, and prepares the post for analysis.

[1630] 4. Server analysis and classification

[1631] The server uses a natural language processing engine to analyze the post. For example, it analyzes the message "Help me, my house has collapsed!" and generates a summary. In this case, the summary is "My house has collapsed, I'm asking for help." The analyzed message is then classified into a category called "Request for assistance."

[1632] 5. Emotion Recognition by Emotion Engine

[1633] The server uses an emotion engine to recognize the user's emotion from the content of the post. For example, the emotion contained in the post may be recognized as "urgency" or "fear." This emotion data is used for further analysis and classification.

[1634] 6. Misinformation Identification and Summarization

[1635] The server uses a misinformation identification algorithm to check for inaccuracies or misinformation. The summary might be "urgent help needed due to house collapse."

[1636] 7. Data Retention and Notification

[1637] The server then stores the analyzed, classified, and summarized data in a database. This includes the post's timestamp, user ID, location information, and emotion data. If necessary, the server notifies relevant organizations of requests for assistance. For example, the server can use an emergency service API to send information about a request for assistance and prompt a response.

[1638] 8. Viewing Information

[1639] The system allows users and other stakeholders to easily view the saved information on their devices. For example, when checking information in the "Request for Help" category, relevant posts are retrieved from the database through a search query. If necessary, location information can be used to display safety information and requests for help by region.

[1640] Specific examples

[1641] Let us explain this system using a specific scenario. For example, if a disaster occurs and user A posts "Help me, my house has collapsed!" at home:

[1642] 1. User input:

[1643] User A types "Help me, my house has collapsed!" into his smartphone and clicks the send button.

[1644] 2. Device behavior:

[1645] The device analyzes the input data using an emotion engine and identifies the emotion as "fear." It then sends a data packet containing the post data to the server.

[1646] 3. Server reception and analysis:

[1647] The server receives the data, analyzes the post using natural language processing technology, and summarizes it as "Help wanted after house collapse." The emotion engine provides the emotion data "fear."

[1648] 4. Data Retention and Notification:

[1649] The analyzed data is stored in a database and notified to relevant agencies as a "request for assistance."

[1650] 5. Viewing Information:

[1651] Other users and related organizations can view the assistance request information and provide prompt assistance.

[1652] The above is a specific embodiment for carrying out the present invention. This system enables quick and accurate information sharing and support activities in the event of a disaster.

[1653] The processing flow will be explained below.

[1654] Step 1:

[1655] User enters post

[1656] A user accesses the disaster information sharing platform using a smartphone or PC, enters a message such as "Help me, my house has collapsed!" into the input field, and clicks the send button.

[1657] Step 2:

[1658] The device sends the post to the server

[1659] The device detects user input and generates a data packet containing the post content, timestamp, user ID, location information, and emotion data generated by the emotion engine. This data packet is then sent to the server as an HTTP POST request.

[1660] Step 3:

[1661] The server receives the posted data

[1662] The server waits for POST requests at the specified endpoint. The server receives the request, decodes it, and prepares the post for analysis.

[1663] Step 4:

[1664] The server analyzes the posted data

[1665] The server uses a natural language processing engine to analyze the content of the post. For example, the message "Help me, my house has collapsed!" can be analyzed and summarized as "Help needed due to collapse of house."

[1666] Step 5:

[1667] The server categorizes the submitted data.

[1668] The server uses a natural language processing engine to categorize the post into categories such as "safety information" or "request for assistance." In this example, it is classified as a "request for assistance."

[1669] Step 6:

[1670] The server uses an emotion engine to recognize emotions.

[1671] The server uses an emotion engine to analyze the content of the post and identify the user's emotion, which in this case is recognized as "fear" or "urgency."

[1672] Step 7:

[1673] Server identifies misinformation

[1674] The server uses a misinformation identification algorithm to verify the authenticity of the posted content. If it is determined to be misinformation, the server will ask the user to confirm or restrict the posting.

[1675] Step 8:

[1676] The server stores it in the database

[1677] The server stores the analyzed, categorized, and summarized data in a database, including the post content, timestamp, user ID, location information, and emotion data.

[1678] Step 9:

[1679] The server notifies the relevant parties

[1680] The server notifies the relevant organizations of the assistance request information, for example, by using an emergency service API to send the assistance request information including emotion data.

[1681] Step 10:

[1682] The server provides the information to interested parties and other users.

[1683] When a related person or another user accesses the system, the server retrieves the relevant information from the database and provides it to the user or the related person, allowing the related person to immediately check the necessary information and take action.

[1684] Step 11:

[1685] User and stakeholder information viewing

[1686] Users and related parties can access the database and view the stored information, for example, information in the "request for assistance" category and emotional data, and take appropriate action.

[1687] The above are the processing steps of a specific embodiment of a disaster information sharing platform that combines an emotion engine. This system is expected to enable faster and more accurate information sharing and support activities during disasters.

[1688] Example 2

[1689] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1690] In modern times, when a disaster occurs, there is a need for rapid and accurate information sharing, but current systems lack the accuracy of information and the ability to recognize emotions, leading to the spread of misinformation and the inability to provide appropriate support promptly.In addition, there are issues with insufficient multilingual support and use of location information, making it difficult to efficiently distribute requests for support by region.

[1691] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes an analysis means using an emotion engine that recognizes the user's emotion, a means for identifying false information, and a means for realizing multilingual support. This enables the rapid and accurate sharing of disaster information, the priority processing of information based on emotion, the elimination of false information, multilingual support, and the utilization of location information.

[1692] "User" refers to a person or organization that uses the system to input disaster information or request assistance.

[1693] "Posted content" refers to disaster-related information and requests for assistance that users enter and send to the system.

[1694] An "emotion engine" is a technology or software that recognizes and analyzes emotions from user posts.

[1695] "Natural language processing technology" refers to technology that enables computers to understand, analyze, and generate natural language.

[1696] "Means for identifying misinformation" refers to methods or algorithms for identifying whether posted content is misinformation.

[1697] "Multilingual Method" means any technology or method for displaying, parsing, or sharing Posting Content in multiple languages.

[1698] A "database" is a data repository for storing analyzed and classified information.

[1699] "Interested Person" means a person or institution that can view the stored information and take action if necessary.

[1700] "Location information" refers to information that indicates the geographical location when a user enters a post.

[1701] A "request for assistance" is information provided by a user when a disaster occurs, indicating that assistance is required.

[1702] A "server" is a computer system that receives information sent by users and analyzes, classifies, stores, and notifies them.

[1703] "Terminal" means the device used by a User to access the System and input and submit Posting Content.

[1704] The present invention is a disaster information sharing platform that enables rapid and efficient information sharing during disasters, and in particular, a system that combines an emotion engine that recognizes the user's emotions. Specific embodiments for carrying out the invention are described below.

[1705] Platform Overview

[1706] This platform is a system that utilizes users' devices (smartphones, personal computers, fixed communication devices, etc.) to input and send information on the safety of users and requests for assistance during disasters. The server receives the user's posts and analyzes and classifies the data using natural language processing technology and an emotion engine. The analyzed information is summarized, organized using functions to identify misinformation and support multiple languages, and stored in a database. The stored information is notified to relevant parties as necessary. In addition, relevant parties and other users can easily view the stored information.

[1707] System operation procedures

[1708] 1. User Submissions

[1709] After a disaster occurs, users access the disaster information sharing platform using their own devices. They enter information about their safety and a request for assistance, and click the send button. For example, they might enter, "Help me! My house has collapsed!"

[1710] 2. Data transmission by the terminal

[1711] The device receives the user's post and generates a data packet containing the message, timestamp, user ID, location information, and emotion data generated by the emotion engine. This data packet is then sent to the server as an HTTP POST request.

[1712] 3. Data reception and analysis by the server

[1713] The server waits for POST requests at the specified endpoint, and upon receiving the request, it decodes the data and prepares the post for analysis.

[1714] Technology used

[1715] The natural language processing technology used by the server for data analysis includes functions such as text summarization, classification, and keyword extraction. The emotion engine analyzes user emotions (e.g., urgency, fear, etc.) from posted content and uses this information to prioritize and select information to notify relevant parties. The misinformation identification algorithm detects whether posted content contains inaccurate or false information and removes it as necessary.

[1716] The multilingual feature automatically translates posts into multiple languages, ensuring appropriate information for stakeholders who speak different languages.

[1717] Stored Data and Notifications

[1718] The data analyzed, classified, and summarized by the server is stored in a database. This data includes post timestamps, user IDs, location information, and emotional data. Urgent assistance requests are notified from the server to relevant organizations via API, facilitating a prompt response. In addition, relevant parties and other users can easily view the stored information via their devices and provide assistance as needed.

[1719] Specific examples

[1720] For example, if a disaster occurs and user A posts "Help, my house has collapsed!" at home, the system will operate as follows:

[1721] 1. User input:

[1722] User A types "Help me, my house has collapsed!" into his smartphone and clicks the send button.

[1723] 2. Device behavior:

[1724] The device analyzes the input data with an emotion engine, identifies the emotion as "fear," and then sends this data packet to the server.

[1725] 3. Server reception and analysis:

[1726] The server receives the data, analyzes the post using natural language processing technology, and summarizes it as "Help wanted after house collapse." The emotion engine provides the emotion data "fear."

[1727] 4. Data Retention and Notification:

[1728] The analyzed data is stored in a database and notified to relevant agencies as a "request for assistance."

[1729] 5. Viewing Information:

[1730] Other users and related organizations can view the information about requests for assistance and provide prompt relief. For example, when checking information in the "Request for Assistance" category, relevant posts are retrieved from the database through a search query. If necessary, safety information and assistance requests by region can also be displayed based on location information.

[1731] Prompt Sentence Examples

[1732] An example prompt for a generative AI model might look like this:

[1733] "Please explain in detail how users will input safety information and requests for assistance from home in the event of a disaster and how the information will be processed."

[1734] The above is a specific embodiment for carrying out the present invention. This system enables quick and accurate information sharing and support activities in the event of a disaster.

[1735] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1736] Step 1: User Submission

[1737] Specific actions

[1738] Users access the disaster information sharing platform and use their own devices (smartphones, personal computers, fixed communication devices, etc.) to input information about the safety of others and requests for assistance.

[1739] input

[1740] A user types "Help me, my house is collapsing!" into a text box.

[1741] output

[1742] The user's input is sent to the terminal, and the next process is carried out.

[1743] Step 2: Send data by device

[1744] Specific actions

[1745] The device receives the user's post, analyzes the message with an emotion engine, generates emotion data, and then generates a data packet containing the message, timestamp, user ID, location information, and emotion data.

[1746] input

[1747] User posts and metadata acquired by the device (timestamp, user ID, location information).

[1748] output

[1749] A data packet sent to a server as an HTTP POST request.

[1750] Step 3: Server receives data

[1751] Specific actions

[1752] The server waits for POST requests at the specified endpoint and decodes the data packets after receiving the request.

[1753] input

[1754] The HTTP POST request sent from the device.

[1755] output

[1756] Decoded user post content, timestamp, user ID, location, and sentiment data.

[1757] Step 4: Data analysis and classification by the server

[1758] Specific actions

[1759] The server uses natural language processing technology to analyze and summarize the content of posts, and also categorizes the posts into appropriate categories.

[1760] input

[1761] Decoded post content and metadata.

[1762] output

[1763] A summarized message and a classified category (e.g., a summary of "seeking help due to collapse of house" and a category of "request for assistance").

[1764] Step 5: Emotion Recognition with the Emotion Engine

[1765] Specific actions

[1766] The server uses an emotion engine to recognize the user's emotion from the posted content and generates emotion data.

[1767] input

[1768] Abridged post content.

[1769] output

[1770] Emotional data (e.g., "fear" or "urgency").

[1771] Step 6: Misinformation Identification and Summarization

[1772] Specific actions

[1773] The server uses a misinformation-detection algorithm to check the submission for inaccuracies and misinformation. The summary is finalized.

[1774] input

[1775] Summarized post content and sentiment data.

[1776] output

[1777] Misinformation identified and finalized summary (e.g., "urgent assistance needed due to house collapse").

[1778] Step 7: Data storage and notification

[1779] Specific actions

[1780] The server stores the analyzed, classified, and summarized data in a database and notifies relevant organizations as necessary.

[1781] input

[1782] Finalized summary, timestamp, user ID, location, and emotion data.

[1783] output

[1784] Data stored in databases and notifications to relevant authorities (e.g., sending assistance requests using emergency services APIs).

[1785] Step 8: Information visible to stakeholders and other users

[1786] Specific actions

[1787] Search and view stored information through devices used by related parties or other users.

[1788] input

[1789] Request information from a stored database.

[1790] output

[1791] The search results will show relevant posts (e.g., posts in the "Request for Help" category). If necessary, location-based information will also be displayed for each region.

[1792] (Application example 2)

[1793] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1794] Conventional disaster information sharing systems have difficulty in properly recognizing and analyzing the emotions in the information entered by users, and furthermore, have problems in that they do not adequately guarantee safe information sharing during disasters. This can lead to delays in responding to requests for assistance quickly and appropriately, making it difficult to provide efficient assistance during disasters.

[1795] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving posted content entered by a user, means for transmitting the posted content to the server, means for analyzing and classifying the posted content received by the server using natural language processing technology, means for summarizing the analyzed and classified posted content, means for identifying misinformation, means for realizing multilingual support, means for storing the analyzed and classified information in a database, means for notifying relevant parties of the stored information, means for relevant parties to view the stored information, means for recognizing emotion data related to the posted content entered by a user and using the emotion data for analysis and classification, and means for securely protecting information through end-to-end encryption in the event of a disaster. This enables rapid and accurate information sharing and safe assistance requests in the event of a disaster.

[1796] "Posted content" is data including disaster information and requests for assistance entered by the user.

[1797] A "server" is a computer system that receives, analyzes, classifies, and stores content posted by users, and notifies relevant parties.

[1798] "Natural language processing technology" is a technology for analyzing the content of posts and understanding their meaning and intent.

[1799] A "summary" is a short summary of the main points of the analyzed and classified post.

[1800] "Misinformation identification" is the process of identifying inaccurate or false information in posts.

[1801] "Multilingual support" is a function that understands and responds to posts in multiple languages.

[1802] "Emotion data" is emotional information recognized from the content posted by a user.

[1803] "End-to-end encryption" is a technology that encrypts communication data from the sender to the recipient, protecting it from access by third parties.

[1804] "Location information" is geographical data that indicates the region from which the posted content was sent.

[1805] A "database" is a storage system in which analyzed and classified information is stored.

[1806] "Notification" is an action to inform relevant parties of stored information.

[1807] "Stakeholders" are individuals, organizations, or institutions involved in providing assistance or sharing information during a disaster.

[1808] This invention is a disaster information sharing system that realizes fast and efficient information sharing during disasters, and in particular, it is combined with an emotion engine that recognizes the user's emotions. This system utilizes the user's device (smartphone, PC, landline phone, etc.) to efficiently process safety information and assistance requests entered by the user during a disaster.

[1809] System configuration

[1810] This system consists of the following main elements:

[1811] 1. Terminal

[1812] This refers to the devices that users use to input content, including smartphones, PCs, and landlines. Smartphones, in particular, use GPS to obtain user location information.

[1813] 2. Server

[1814] The server analyzes and classifies the received posts, and stores and manages the necessary data. Specifically, it has the following functions:

[1815] Natural language processing technology: Provides technology that analyzes post content and extracts and classifies key points.

[1816] Emotion engine: Recognizes emotions from user input and uses that information for analysis and classification.

[1817] End-to-end encryption: A security technology that prevents third parties from intercepting data as it is transmitted.

[1818] Misinformation Identification: The platform has algorithms that check the accuracy of posts and filter out misinformation.

[1819] Multilingual support: Process and parse posts entered in multiple languages.

[1820] Database: A storage system that stores analyzed and classified information and notifies relevant parties as needed.

[1821] Notification system: A system to notify relevant parties of stored information and promptly provide necessary assistance.

[1822] Program processing

[1823] When a user types and submits a post from their device, the following process occurs:

[1824] Data acquisition: The device receives disaster information and assistance requests entered by the user. For example, a message like "Help me, my house has collapsed!"

[1825] Timestamp generation: The server obtains the current time and adds it to the posted data, ensuring that the information is up to date.

[1826] Emotion Recognition: The emotion engine analyzes the content of posts and extracts emotional data, such as "fear."

[1827] Summary generation: Using natural language processing techniques, we provide a concise summary of the post, such as "Help needed after house collapse."

[1828] Data storage: The analyzed information is stored in a database, including timestamps, user IDs, location information, and emotion data.

[1829] Notification: Notify relevant parties of the stored data, allowing for a prompt response.

[1830] Adding specific examples

[1831] For example, if a user types "There is no water in the evacuation shelter, please help!" during a disaster, the system will act as follows:

[1832] 1. User input: The user types "There is no water at the evacuation center, please help!" into their smartphone and sends it.

[1833] 2. Device behavior: The emotion engine identifies the event as an "urgent" event. The device adds location information and a timestamp to the posted data and sends it to the server.

[1834] 3. Server reception and analysis: The server receives the data and uses natural language processing technology to generate a summary of "shelters, water shortages." The emotion data "urgent" is added.

[1835] 4. Data storage and notification: The analyzed data will be stored in a database and notified to the relevant authorities.

[1836] Prompt Sentence Examples

[1837] "Please enter information for a quick request for assistance during a disaster: 'There is a food shortage at the evacuation center, please help!'"

[1838] This allows users to input information appropriately, and serves as a reference for those involved to carry out support activities quickly.

[1839] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1840] Step 1:

[1841] The device receives the post entered by the user. The user enters disaster information or a request for assistance on their smartphone and clicks the send button. This input includes location information. For example, a message might read, "Help me, my house has collapsed!"

[1842] Input: Messages typed by the user into their smartphone

[1843] Output: Input data received by the device (message and location information)

[1844] Specific behavior:

[1845] The user accesses the smartphone app, types a message, and sends it.

[1846] Step 2:

[1847] The device analyzes the content of the post using an emotion engine to extract emotional data, recognizing emotions such as "fear" from the content of the post and adding the emotional data to the data packet.

[1848] Input: Input data received by the device (messages and location information)

[1849] Output: A data packet with emotion data added by the emotion engine (e.g., fear).

[1850] Specific behavior:

[1851] The emotion engine analyzes the content of the post and extracts emotions using natural language processing technology.

[1852] Step 3:

[1853] The device sends a data packet to the server as an HTTP POST request, which includes the message, a timestamp, a user ID, location information, and emotion data.

[1854] Input: Data packet with attached emotion data (e.g., message, timestamp, user ID, location, emotion data)

[1855] Output: Data packet received by the server

[1856] Specific behavior:

[1857] The device constructs a data packet and sends it to the server as an HTTP POST request.

[1858] Step 4:

[1859] The server decodes the received data packets and analyzes the content of the post. It uses natural language processing technology to extract the main points of the post and generate a summary. For example, it generates a summary such as "Help wanted after house collapse."

[1860] Input: Data packet received by the server

[1861] Output: Parsed summary message

[1862] Specific behavior:

[1863] The server uses natural language processing technology to analyze the post and extract the main points.

[1864] Step 5:

[1865] The server uses a misinformation identification algorithm to check whether the post contains misinformation and decides whether to keep it as accurate information or remove it as misinformation.

[1866] Input: Parsed summary message

[1867] Output: Summary message with misinformation removed

[1868] Specific behavior:

[1869] The server applies a misinformation identification algorithm to verify the accuracy of the input data.

[1870] Step 6:

[1871] The server stores the analyzed and categorized information in a database, including posted messages, timestamps, user IDs, location information, and emotional data.

[1872] Input: Summary message with misinformation removed

[1873] Output: Data stored in the database

[1874] Specific behavior:

[1875] The server stores the analyzed and classified information in a database.

[1876] Step 7:

[1877] The server then notifies the relevant parties of the stored information, for example, emergency services and assistance agencies via an API.

[1878] Input: Data stored in the database

[1879] Output: Notification message to interested parties

[1880] Specific behavior:

[1881] The server will notify the relevant parties and arrange for prompt assistance.

[1882] Step 8:

[1883] Stakeholders can view the saved information. Stakeholders can view saved posts and summaries through a dedicated interface.

[1884] Input: Data in the database

[1885] Output: Information viewed by interested parties

[1886] Specific behavior:

[1887] Stakeholders can view the information through a browser or dedicated application.

[1888] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1889] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1890] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1891] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1892] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1893] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1894] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1895] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1896] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1897] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1898] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1899] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1900] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1901] 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.

[1902] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1903] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1904] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific processing may be a single processor.

[1905] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1906] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1907] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1908] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1909] The following is further disclosed regarding the above embodiment.

[1910] (Claim 1)

[1911] A means for receiving user-entered submissions;

[1912] a means for transmitting the posted content to a server;

[1913] A means for analyzing and classifying the posted content received by the server using natural language processing technology;

[1914] A means of summarizing the analyzed and categorized posts;

[1915] a means of identifying misinformation;

[1916] A means to achieve multilingual support,

[1917] A means for storing the analyzed and classified information in a database;

[1918] a means of notifying interested parties of the stored information;

[1919] A means for interested parties to view the stored information; and

[1920] A system including:

[1921] (Claim 2)

[1922] The system of claim 1, further comprising means for utilizing location information to collect posts by region and efficiently distribute requests for assistance to relevant parties.

[1923] (Claim 3)

[1924] 10. The system of claim 1, accessible from a smartphone, a PC, and a landline phone.

[1925] "Example 1"

[1926] (Claim 1)

[1927] A means for receiving user-entered submissions;

[1928] means for transmitting the posted content to a communication device;

[1929] A means for analyzing and classifying the posted content received by the communication device using natural language processing technology;

[1930] A means of summarizing the analyzed and categorized posts;

[1931] a means for detecting misinformation;

[1932] A means to achieve multilingual support,

[1933] A means for storing the analyzed and classified information in a database;

[1934] a means of notifying interested parties of the stored information;

[1935] A means for interested parties to view the stored information; and

[1936] A means of notifying relevant agencies of assistance requests in a timely manner;

[1937] A system including:

[1938] (Claim 2)

[1939] The system of claim 1, further comprising means for utilizing location information to collect posts by region and efficiently distribute requests for assistance to relevant parties.

[1940] (Claim 3)

[1941] 10. The system of claim 1, accessible from an information terminal device.

[1942] "Application Example 1"

[1943] (Claim 1)

[1944] A means for receiving user-entered submissions;

[1945] a means for transmitting the posted content to a server;

[1946] A means for analyzing and classifying the posted content received by the server using natural language processing technology;

[1947] A means of summarizing the analyzed and categorized posts;

[1948] a means of identifying misinformation;

[1949] A means to achieve multilingual support,

[1950] A means for storing the analyzed and classified information in a database;

[1951] a means of notifying interested parties of the stored information;

[1952] A means for interested parties to view the stored information; and

[1953] a means for providing real-time notifications;

[1954] a means for sharing user location information;

[1955] A system including:

[1956] (Claim 2)

[1957] The system of claim 1, further comprising means for utilizing location information to collect posts by region and efficiently distribute requests for assistance to relevant parties.

[1958] (Claim 3)

[1959] 10. The system of claim 1, accessible from a smartphone, a general-purpose computer, and a fixed communication device.

[1960] "Example 2: Combining Emotion Engines"

[1961] (Claim 1)

[1962] A means for receiving user-entered submissions;

[1963] a means for transmitting the posted content to a server;

[1964] A means for analyzing and classifying the posted content received by the server using natural language processing technology;

[1965] A means of summarizing the analyzed and categorized posts;

[1966] a means of identifying misinformation;

[1967] an analyzing means using an emotion engine that recognizes the user's emotion;

[1968] A means to achieve multilingual support,

[1969] A means for storing the analyzed and classified information in a database;

[1970] a means of notifying interested parties of the stored information;

[1971] A means for interested parties to view the stored information; and

[1972] A system including:

[1973] (Claim 2)

[1974] The system of claim 1, further comprising means for utilizing location information to collect posts by region and efficiently distribute requests for assistance to relevant parties.

[1975] (Claim 3)

[1976] 10. The system of claim 1, accessible from a smartphone, a personal computer, and a fixed communication device.

[1977] "Application example 2 when combining emotion engines"

[1978] (Claim 1)

[1979] A means for receiving user-entered submissions;

[1980] a means for transmitting the posted content to a server;

[1981] A means for analyzing and classifying the posted content received by the server using natural language processing technology;

[1982] A means of summarizing the analyzed and categorized posts;

[1983] a means of identifying misinformation;

[1984] A means to achieve multilingual support,

[1985] A means for storing the analyzed and classified information in a database;

[1986] a means of notifying interested parties of the stored information;

[1987] A means for interested parties to view the stored information; and

[1988] A means for recognizing emotional data related to the content of posts entered by users and utilizing the emotional data for analysis and classification;

[1989] A system that includes a means to secure information through end-to-end encryption in the event of a disaster.

[1990] (Claim 2)

[1991] The system of claim 1, further comprising means for utilizing location information to collect posts by region and efficiently distribute requests for assistance to relevant parties.

[1992] (Claim 3)

[1993] 10. The system of claim 1, accessible from a smart device. [Explanation of symbols]

[1994] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for receiving user-entered submissions; a means for transmitting the posted content to a server; A means for analyzing and classifying the posted content received by the server using natural language processing technology; A means of summarizing the analyzed and classified posts, a means of identifying misinformation; A means to achieve multilingual support, A means for storing the analyzed and classified information in a database; a means of notifying interested parties of the stored information; A means for interested parties to view the stored information; and A system including:

2. The system according to claim 1, further comprising means for collecting posted content by region using location information and efficiently distributing requests for assistance to relevant parties.

3. The system of claim 1 , accessible from a smartphone, a PC, and a landline phone.

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