system

Abstract

We provide the system. [Solution] A device that enables the input and transmission of information even under conditions where communication infrastructure is limited, using wireless communication means and having means capable of compressing and transmitting information even in offline mode, A means including a server capable of updating disaster information in real time and saving it to a database, A method using AI to respond to user requests for assistance, equipped with an automated message system for checking on the safety of users, and prioritizing and distributing information, A means of providing multilingual translation and mental support using generative models, A communication system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the event of a disaster, it is difficult to quickly and efficiently share information between disaster victims and supporters even when the communication infrastructure is restricted. Furthermore, there is a need to eliminate the barriers of various languages and mental unrest in the disaster area. By solving these problems, it is required that disaster victims and their relatives can safely exchange information and receive necessary support in a timely manner.

Means for Solving the Problems

[0005] This invention provides a device using wireless communication means to enable information input and transmission even in offline environments. This makes it possible to compress and efficiently transmit information even when the communication infrastructure is unstable. Furthermore, the server updates disaster information in real time and stores it in a database, facilitating the management of diverse information. In addition, it is equipped with an AI-powered automatic safety confirmation message function that processes information in response to user requests for assistance. Moreover, by providing mental support using a multilingual translation function and generative model, it overcomes language barriers and realizes emotional care for disaster victims. This provides a system that allows disaster victims and their families to quickly access necessary information and gain a sense of security.

[0006] "Communication infrastructure" refers to the hardware and software foundation that enables information transmission, and includes telephone lines, wireless connections, and internet connections.

[0007] "Offline mode" is a function that allows a system or device to operate without being connected to the internet or a broad network, eliminating normal network dependency and allowing it to operate independently.

[0008] "Wireless communication means" refers to technologies that use radio waves to send and receive data, and includes Wi-Fi and mobile phone networks.

[0009] "AI" is an abbreviation for artificial intelligence, which refers to technology in which computer systems imitate or reproduce human intellectual functions, including automated decision-making and analysis.

[0010] A "database" is a system for systematically managing, storing, searching, and updating large amounts of information.

[0011] "Multilingual translation functionality" is a technology that translates text and audio between different languages, allowing users to access information in languages ​​other than their native tongue.

[0012] A "generative model" is a machine learning model that learns patterns from data and generates new data.

[0013] "Mental support" refers to activities or functions that provide assistance to reduce stress and anxiety and maintain or improve mental health.

[0014] A "request for assistance" refers to a request or inquiry made by a user to obtain specific goods or services. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12]It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0018] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), and the like.

[0019] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0023] [First Embodiment]

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

[0025] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0032] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0035] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0036] This invention is a communication system for efficiently sharing information and confirming the safety of individuals during disasters, enabling a rapid response even under conditions of unstable communication infrastructure. The system's basic components consist of a user-operated terminal, a server for managing information, and wireless communication means to support communication between the two.

[0037] User actions

[0038] Users can input and submit personal safety information and assistance needs through their devices. Furthermore, users can receive information tailored to their language settings using the system's multilingual translation function. Information to help alleviate mental stress during disasters is also provided through this system.

[0039] Device functions

[0040] Even when a regular internet connection is unavailable, the device can compress and send / receive information via SMS, a wireless communication method. Data entered by the user is temporarily stored within the device before being sent to the server. The transmission method is automatically adjusted according to the network conditions.

[0041] Server Functions

[0042] The server receives information sent from multiple devices and stores it in a database. Data is updated in real time, and AI is used to determine the priority of information and optimize notifications to the relevant users. In addition, the server analyzes real-time data collected from disaster areas and distributes information such as the locations of evacuation centers and the supply status of necessary supplies to devices.

[0043] Specific example

[0044] For example, in the event of a major earthquake, if a user reports to their device that they are "evacuating and safe," that information is immediately sent to the server. The server analyzes this information and notifies family members and relevant parties that "the user's safety has been confirmed." Also, if the user requests food, the AI ​​automatically notifies the nearest aid provider and ensures that a response is taken.

[0045] The system's program, configured in this way, aims to provide safety and security during disasters by facilitating the smooth flow of information and responding immediately to the needs of those affected.

[0046] The following describes the processing flow.

[0047] Step 1:

[0048] Users input their status and necessary support information on their device. This information can be entered using a multiple-choice format or free-text format, and can include photos and location information.

[0049] Step 2:

[0050] The device temporarily stores the entered information and checks the network status. If a Wi-Fi connection is available, it compresses the data and sends it to the server; if the connection is unstable or unavailable, it automatically sends the information via SMS.

[0051] Step 3:

[0052] The server receives information sent from the terminal and registers it in the database. The information is tagged and classified into categories such as safety status and requests for assistance.

[0053] Step 4:

[0054] The server uses AI to analyze incoming information and set priorities. For example, requests for assistance from dangerous areas are processed with high priority.

[0055] Step 5:

[0056] The server takes necessary actions based on the priority of the information. Specifically, it sends information to available support personnel and sends notifications to relevant users.

[0057] Step 6:

[0058] Users can receive notifications from the server on their devices and check the progress of their support and other important information. Furthermore, a multilingual translation function ensures that necessary information is displayed in the user's language.

[0059] Step 7:

[0060] The server updates the database at regular intervals and automatically sends safety confirmation messages to unverified users. This enables continuous status monitoring.

[0061] (Example 1)

[0062] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0063] This invention aims to solve the problem of difficulty in quickly and accurately transmitting information about the safety and support needs of disaster victims and providing necessary support when communication infrastructure is unstable during a disaster. In particular, it aims to provide a method for smoothly sharing information while overcoming communication barriers between different languages ​​and realizing appropriate and rapid support for disaster victims.

[0064] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0065] In this invention, the server includes means for transmitting information even in situations where the communication environment is unstable, means for updating disaster cases in real time and saving them on a recording medium, and means for using a generative model to respond to support requests from users. This makes it possible to quickly and accurately share information even in the event of a disaster and to efficiently provide necessary support to disaster victims.

[0066] "Communication environment" is a concept that refers to the state of the infrastructure and network used to send and receive information.

[0067] "Wireless communication methods" refer to methods of transmitting information without requiring a physical connection, and include Bluetooth, Wi-Fi, and SMS.

[0068] "Offline mode" refers to a system function that allows operation and processing even when a network connection is unavailable.

[0069] "Disaster cases" refer to events or situations affected by disasters, and specifically include damage caused by earthquakes and floods.

[0070] A "recording medium" refers to a physical or digital means of storing information in a way that allows it to be referenced later.

[0071] A "processing device" refers to an electronic device or system that has the ability to receive, process, and store information.

[0072] "User" refers to an individual or organization that operates this system, and is the entity that inputs or receives information through a terminal.

[0073] A "generative model" refers to an algorithm or software that has the ability to generate new information based on existing data.

[0074] A "request for assistance" refers to a request for necessary support or assistance, and is information that a user sends through the system.

[0075] The "safety confirmation function" refers to a system function that shares and confirms information about an individual's safety and health status.

[0076] "Multilingual translation" refers to the process of converting information between different languages ​​into a form that is mutually understandable.

[0077] "Optimizing information notifications" refers to the process of delivering information in the most efficient and useful format for users.

[0078] This invention provides a system that enables rapid and accurate information transmission even in situations where communication environments are unstable during disasters. The user's terminal uses wireless communication to compress and transmit information even when network connectivity is unstable. Furthermore, information can be stored and transmitted later, even when offline.

[0079] The server updates disaster reports received from multiple terminals in real time and stores them on a recording medium. The received information is analyzed using a generative AI model to determine the priority of the information. As a result, information of the highest importance is notified to other users and support organizations in an optimized format, starting with the most important information.

[0080] Specifically, for example, after a major earthquake, if a user enters and sends a message such as "evacuating, safe" from a shelter, that information is immediately sent to the server. Based on this information, the server uses a generative AI model to prioritize and quickly notifies family and relevant parties that "the user is safe." Furthermore, if the user requests food assistance, the server forwards that information to the helpers through the generative AI model, and appropriate action is taken immediately.

[0081] This system also incorporates a multilingual translation function for psychological support during disasters, and uses a generative AI model to provide users with necessary emotional support information. An example of a prompt message is: "Please write a program that explains the safety confirmation process during an earthquake. Please describe in detail the flow from the user's report to the server's notification."

[0082] This enables smooth information sharing even during disasters, allowing for prompt and appropriate support to be provided to those affected.

[0083] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0084] Step 1:

[0085] Users operate a terminal to input information about their safety and assistance needs during a disaster. This input information is in text format and includes specific situational descriptions such as "evacuating, safe" or "food assistance needed." The terminal temporarily stores this information in memory. To process the input data, formatting and removal of unnecessary characters are performed.

[0086] Step 2:

[0087] The device evaluates the network status and selects the optimal communication method. Normally, an internet connection is used, but if unavailable, an alternative method such as SMS is chosen. After determining the appropriate communication method, the device compresses the data and prepares it for transmission. During this process, the data size is reduced through a compression algorithm, improving transmission efficiency.

[0088] Step 3:

[0089] The server receives the data sent from the terminal. The server stores the information in a database and prepares it for future processing. The received data includes the user's identification information and input content. Here, the data is standardized and converted into a format ready for the next processing step.

[0090] Step 4:

[0091] The server uses a generative AI model to analyze incoming data. Prioritization analysis assesses the urgency and importance of the information. This model classifies information based on past data and context, identifying high-priority information. Based on the results of this process, important information is placed in a separate queue.

[0092] Step 5:

[0093] The server sends notifications to relevant users and support organizations based on high-priority information. The notification content is in a message format optimized by a generative AI model. Summary messages such as "The user is safe" are sent to the family. The server also notifies relevant parties of the need for assistance. Data output here includes real-time message transmission.

[0094] (Application Example 1)

[0095] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0096] Even when communication infrastructure is restricted during a disaster, the challenge lies in efficiently collecting and disseminating information on individuals' safety status and necessary support, as well as providing optimal evacuation routes. This challenge could make rapid and accurate information sharing impossible, potentially making it difficult to ensure the safety of disaster victims.

[0097] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0098] This invention includes a server that uses wireless communication means to compress and transmit information even in offline mode, means to collect information on safety conditions and necessary support during a disaster and share it among users using location information, and means that analyze location information based on disaster information, provide the optimal evacuation route, and notify users of the progress. This enables effective information sharing and safety assurance even during a disaster, without relying on communication infrastructure.

[0099] "A situation where communication infrastructure is limited" refers to a state in which normal communication networks are not functioning, or are not functioning adequately, due to disasters, communication failures, etc.

[0100] "Wireless communication" refers to technologies that transmit and receive data using radio waves without requiring cables or wired connections.

[0101] "Offline mode" is a mode that allows a device to perform limited functions and operations even in environments where an internet connection is unavailable.

[0102] "Means of compressing and transmitting information" refers to methods that provide technology to improve communication time and bandwidth efficiency by converting large amounts of information into smaller amounts.

[0103] "Safety status" is an indicator that shows the extent to which the risks faced by an individual or group during a disaster have been reduced.

[0104] "Information on necessary support" refers to data regarding the supplies, assistance, or services that disaster victims need during a disaster.

[0105] "Location information" refers to data that represents the geographical location of a specific person or object.

[0106] "Disaster information" refers to data concerning the impact of a disaster, the extent of damage, and the situation and trends of those affected.

[0107] An "optimal evacuation route" is a path calculated based on location information to enable safe and rapid evacuation during a disaster.

[0108] "Means characterized by notifying the progress" refers to a technology or method for informing a user of the progress or changes in a procedure or situation.

[0109] In an embodiment of this invention, a smartphone, a server, and wireless communication means are used as the main components. The smartphone is equipped with an interface that receives information on safety status and necessary assistance entered by the user, and is capable of transmitting data including location information to the server via wireless communication means.

[0110] The server is developed using Python and provides an API using Flask. Information compression incorporates algorithms that enable efficient data transmission. The server analyzes information priority and optimal evacuation routes using a generative AI model based on TENSORFLOW®. Received information is stored in a PostgreSQL database, and a common translation API is used for multilingual translation. Based on the user's location during a disaster and real-time disaster information, the server calculates the safest and fastest evacuation route and notifies the user's device of its progress.

[0111] Users can submit situation reports and assistance requests in multiple languages ​​through the application and move according to evacuation routes provided by the server. The system utilizes AI to prioritize information and automatically deliver necessary assistance.

[0112] For example, in the event of a flood, if a user enters "Start Evacuation" in the app, their smartphone sends that information to the server. The server analyzes the received information, calculates a safe evacuation route based on the user's current location and the progress of the flood, and notifies the user.

[0113] An example of a prompt statement utilizing a generative AI model can be written as follows:

[0114] "The system has detected that the user is in a flood-affected area. Based on the user's current location, it will suggest a safe evacuation route and provide instructions to the user."

[0115] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0116] Step 1:

[0117] Users input safety information and necessary assistance details using a smartphone application. This input includes location information. The device compresses this data and transmits it to a server via wireless communication. The input information is compressed in JSON format and reaches the server via a base station.

[0118] Step 2:

[0119] The server deserializes the received JSON data and parses each field. This process identifies the user's location and assistance request, and stores this information in the database. The parsed data is stored in the corresponding table within the PostgreSQL database.

[0120] Step 3:

[0121] The server launches a generative AI model utilizing TensorFlow, prioritizing and analyzing the most important data from the stored data. This analysis generates optimal evacuation routes and information that should be prioritized for notification. The AI ​​model performs calculations based on past data patterns and the current situation to obtain the optimal output.

[0122] Step 4:

[0123] The server uses a translation API to convert the generated information into multiple languages ​​according to the user's language settings. The converted information is then prepared for transmission to the user. The translated text is added to the transmission queue based on its corresponding language code.

[0124] Step 5:

[0125] The server sends the optimal evacuation route and emergency notifications to the user's device, along with information translated into multiple languages. The user's device displays the received data as a dialog box or notification, prompting the user to take appropriate action. Notifications are delivered to the device as push notifications, providing important instructions to ensure the user's safety.

[0126] This sequence of events enables a swift and appropriate response even during disasters.

[0127] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0128] This invention is a communication system designed to efficiently share information, confirm the safety of individuals, and provide psychological support during disasters, and incorporates an emotion engine. The system includes a user-operated terminal, a server for processing and managing information, and an emotion engine responsible for recognizing and analyzing emotions.

[0129] User actions

[0130] Users can input safety information and requests for assistance via their devices. This information can be entered as text, images, and audio. In addition, the user's daily interactions are analyzed by an emotion engine to help infer their emotional state.

[0131] Device functions

[0132] The terminal receives input information from the user and provides that data to the emotion engine. The emotion engine identifies the user's emotions based on the diverse data and sends the results to the server. This creates a foundation for providing more appropriate messages and support information.

[0133] Server Functions

[0134] The server stores and analyzes information received from terminals and the emotion engine. It registers the information in the database in real time and customizes safety confirmation messages and support information based on the emotional state of disaster victims. For users in specific emotional states, it provides emotional support by sending special safety confirmation and emotional support messages.

[0135] Functions of the Emotion Engine

[0136] The emotion engine analyzes user input to detect sentiment and behavioral patterns in text, and the server adjusts its response based on the results. This process enables support that can address the user's long-term emotional needs.

[0137] Specific example

[0138] For example, if a user enters a text message expressing their fear and anxiety immediately after a disaster, the emotion engine analyzes the text and recognizes that the user is currently experiencing intense anxiety. The server then uses this information to automatically generate and send a message to the user to provide immediate reassurance. If necessary, it can also guide the user towards seeking mental health support from a professional.

[0139] This invention is a system that enables individualized responses according to the emotional state of disaster victims and provides rapid and appropriate support according to the circumstances at the time.

[0140] The following describes the processing flow.

[0141] Step 1:

[0142] Users enter text messages into their devices containing information about their safety, requests for assistance, or their feelings. The entered information can also include images and audio, allowing the emotion engine to gather diverse data.

[0143] Step 2:

[0144] The device sends information entered by the user to the emotion engine. The emotion engine processes this data and analyzes the user's emotional state. The analysis results include, for example, stress levels and feelings of security.

[0145] Step 3:

[0146] The emotion engine sends the analysis results to the server. The server uses this information to compare it with existing data in the database and determines how safety confirmation and support information should be applied.

[0147] Step 4:

[0148] The server generates customized safety confirmation messages and necessary support information based on the user's emotional state. If the emotion engine indicates anxiety or tension, the server generates a special message to provide reassurance.

[0149] Step 5:

[0150] The server sends generated messages and support information to the terminal. The terminal notifies the user of the received information. This allows the user to learn about kind messages tailored to their emotional state and specific support options.

[0151] Step 6:

[0152] Users can take further action based on the information they receive. For example, if they need counseling from a professional, they can accept the guidance.

[0153] Step 7:

[0154] The server periodically re-evaluates the user's emotional state and provides up-to-date information and support as needed. This operation ensures continuous support.

[0155] (Example 2)

[0156] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0157] During disasters, communication infrastructure is often limited, making it difficult to quickly input and transmit information. At the same time, there is a need for immediate and accurate responses to emotional support and requests for assistance from disaster victims. In such situations, a communication system is needed that enables effective and rapid information sharing and provides appropriate support.

[0158] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0159] In this invention, the server includes means for enabling communication devices to input and transmit information even in disaster environments, means for compressing information using wireless communication and enabling transmission even offline, and a data server for updating and storing information in real time. This makes it possible to quickly share support requests and safety confirmation information from users, and to provide emotional support tailored to their emotional state, even in situations where communication infrastructure is limited.

[0160] A "communication device" is a device equipped with the function of inputting and transmitting information in a disaster environment.

[0161] Wireless communication is a technology that uses radio waves to transmit information without cables.

[0162] A "data server" is a computer system used to store information and to update and manage that data in real time.

[0163] "Artificial intelligence" is a computer-generated system that has the ability to automatically notify users of their requests for assistance and to prioritize information.

[0164] A "generative model" refers to an algorithm that analyzes input data and generates output corresponding to the emotional state.

[0165] "Mental support" refers to support activities that take into account the emotional state of users during disasters and provide a sense of security and stability.

[0166] The embodiment of this invention primarily utilizes a user-operated terminal, a server for processing and managing information, and an emotion engine for recognizing and analyzing emotions. Each element functions as follows:

[0167] Users use a device to input information about their safety during a disaster and requests for assistance. This input can be in various formats, including text, images, and audio, and also includes the user's everyday interactions. The device sends this information to an emotion engine, which then uses specific software to perform sentiment analysis.

[0168] The emotion engine uses a generative AI model to analyze the emotional state of text data. Based on the results of this analysis, the server is configured to generate and quickly send an appropriate response to the user.

[0169] The server records data received from terminals via multiple communication media in real time into a database. This makes it possible to understand each user's situation and emotional state and provide specific messages and support accordingly.

[0170] To give a concrete example, a user experiences intense anxiety immediately after a disaster and inputs this emotion as a text message into their device. The emotion engine detects this "anxiety," and the server automatically generates a reassuring message and sends it to the user. Furthermore, if necessary, it can also guide the user to mental health care methods provided by experts.

[0171] A concrete example of a prompt message would be: "Analyze the user's anxiety level from the text they enter. Based on the results, generate a message that provides reassurance."

[0172] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0173] Step 1:

[0174] Users input safety information and assistance requests via their devices. Input methods include text, images, and audio. The entered information is converted to a digital format and saved to the device's standby data storage. Specifically, the user launches the smartphone app, follows the instructions to enter information, and presses the "Send" button. The input includes descriptions of the disaster situation and the user's own condition.

[0175] Step 2:

[0176] The device sends user input information to the emotion engine. Here, the input data is encoded through a specific software protocol and sent to the emotion engine's API endpoint. Specifically, the device converts the input data into a given format and transfers it to the emotion engine via a secure connection. The input provided to the engine includes text and audio data.

[0177] Step 3:

[0178] The emotion engine analyzes incoming data using a generative AI model. The analysis process identifies emotional patterns from text data and classifies emotional states based on these patterns. Specifically, a text analysis algorithm is activated to extract the emotional tone of words and phrases and classify them into emotional categories such as "anxiety" or "relief." The identified emotional states are recorded as numerical values ​​or tags as output.

[0179] Step 4:

[0180] The server receives emotional information sent from the emotion engine and stores it in a database. Based on the analyzed emotional state, the server automatically generates customized response messages according to the individual user's needs. Specifically, the server executes database queries, selects a message template appropriate for the corresponding emotional state, and inserts information as needed. The output is the customized message sent to the user.

[0181] Step 5:

[0182] The server sends the generated response message to the user's device. A secure and rapid data transmission method is used for this process. Specifically, the server pushes the message to the device via the network. The input is the message generated by the server, and the output is displayed on the user's device.

[0183] Step 6:

[0184] Users check messages received on their devices to obtain necessary instructions and reassurance. Specific actions include tapping a notification to open the app and reading the displayed message. The output consists of emotional support and instructions regarding necessary next steps.

[0185] (Application Example 2)

[0186] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0187] In the event of a disaster, it is crucial to quickly confirm the safety of victims and provide them with psychological support. However, traditional methods face challenges in providing appropriate assistance due to limitations in communication infrastructure and insufficient sentiment analysis technology. Furthermore, multilingual support and rapid information transfer to experts are not easily achieved. In such situations, victims may not receive timely and appropriate information and support, potentially increasing their anxiety and stress.

[0188] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0189] In this invention, the server includes emotion recognition means for collecting and analyzing emotional information from users, automatic response generation means for generating and transmitting customized support information based on the analyzed emotional information, means for providing expert information to users who need mental support, and cross-platform data transmission and reception means for multiple platforms. This makes it possible to provide rapid and appropriate support according to the emotional state of disaster victims and to alleviate their anxiety.

[0190] "Emotion recognition means" refers to technology that analyzes emotional information collected from users to identify the user's emotional state.

[0191] An "automatic response generation method" is a technology that automatically generates and sends appropriate support information and messages to the user based on analyzed emotional information.

[0192] "Means of providing expert information to users who need emotional support" refers to a process of providing appropriate expert information based on the results of sentiment analysis, according to the user's condition.

[0193] "Cross-platform data transmission and reception methods" refer to technologies that enable the smooth transmission and reception of data between multiple different devices, such as smartphones and tablets.

[0194] The system necessary to implement this invention includes an emotion recognition means, an automatic response generation means, a means for providing expert information to users who require emotional support, and a cross-platform data transmission and reception means.

[0195] First, users input safety and emotional information using devices such as smartphones and tablets. This information can be collected in text, image, or audio format and reflects the user's actual emotional state. The device sends this data to emotion recognition software, such as Google Cloud Natural Language API. The emotion recognition software analyzes this data to identify the user's emotional state. For example, if a user inputs "I am very anxious," that text will be analyzed as indicating a high level of anxiety.

[0196] The analyzed emotional information is sent to a server. The server operates a backend system based on Node.js and Express and stores the data in Amazon DynamoDB. Based on this information, the server uses an automated response generation mechanism to generate appropriate support information and messages. For example, if a user is showing strong anxiety, it can generate a message guiding them to relaxation techniques to alleviate anxiety and send it to the user's device via Firebase Cloud Messaging.

[0197] Furthermore, if a user requiring mental health support is identified, the server will provide information on appropriate professionals. This information includes a list of local professionals who can schedule psychological counseling appointments.

[0198] To ensure cross-platform compatibility, the system uses React Native to seamlessly send and receive data between different devices. This allows users to access the system anytime, anywhere, on their preferred device.

[0199] For example, in response to input such as "I'm so scared of earthquakes I can't sleep," the system automatically sends a message including "breathing exercises to alleviate anxiety" and "contact information for a local counselor."

[0200] An example of a prompt for a generative AI model is: "Generate a message aimed at providing emotional support to a user after a disaster. Input: I'm scared of the earthquake and can't sleep." This aims to generate specific methods and messages to alleviate the user's anxiety.

[0201] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0202] Step 1:

[0203] The user uses a device to input safety information and emotional information. The input data is in the form of text, images, and audio, and these reflect the user's emotional state. The device transmits this information to an emotion recognition system.

[0204] Step 2:

[0205] The emotion recognition system receives input data and performs emotion analysis using the Google Cloud Natural Language API. It extracts emotional characteristics from the input data to identify the user's emotional state. This classifies the text into emotional categories such as "anxiety" or "reassurance." The analysis results are then transferred to the server.

[0206] Step 3:

[0207] Based on the received sentiment analysis results, the server generates appropriate support information and messages using an automated response generation system. A backend using Node.js and Express determines the content of the message to be generated and proceeds to the next processing stage. The generated message is obtained as output.

[0208] Step 4:

[0209] The generated message is sent to the user's device via Firebase Cloud Messaging. The server prepares the message and sends it to the user's device in real time, ensuring that the user receives personalized support information.

[0210] Step 5:

[0211] If the server determines that a user requires mental health support, it will take steps to provide the user with information on suitable professionals. It will retrieve a list of appropriate professionals from its database and prepare them for the user. This information will be sent to the user, enabling them to access support.

[0212] Step 6:

[0213] User interactions such as clicks trigger cross-platform data transmission using React Native. This step provides a seamless experience across devices, making it easier for users to take action.

[0214] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0215] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0216] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0217] [Second Embodiment]

[0218] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0219] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0220] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0221] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0222] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0223] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0224] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0225] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0226] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0227] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0228] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0229] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0230] This invention is a communication system for efficiently sharing information and confirming the safety of individuals during disasters, enabling a rapid response even under conditions of unstable communication infrastructure. The system's basic components consist of a user-operated terminal, a server for managing information, and wireless communication means to support communication between the two.

[0231] User actions

[0232] Users can input and submit personal safety information and assistance needs through their devices. Furthermore, users can receive information tailored to their language settings using the system's multilingual translation function. Information to help alleviate mental stress during disasters is also provided through this system.

[0233] Device functions

[0234] Even when a regular internet connection is unavailable, the device can compress and send / receive information via SMS, a wireless communication method. Data entered by the user is temporarily stored within the device before being sent to the server. The transmission method is automatically adjusted according to the network conditions.

[0235] Server Functions

[0236] The server receives information sent from multiple devices and stores it in a database. Data is updated in real time, and AI is used to determine the priority of information and optimize notifications to the relevant users. In addition, the server analyzes real-time data collected from disaster areas and distributes information such as the locations of evacuation centers and the supply status of necessary supplies to devices.

[0237] Specific example

[0238] For example, in the event of a major earthquake, if a user reports to their device that they are "evacuating and safe," that information is immediately sent to the server. The server analyzes this information and notifies family members and relevant parties that "the user's safety has been confirmed." Also, if the user requests food, the AI ​​automatically notifies the nearest aid provider and ensures that a response is taken.

[0239] The system's program, configured in this way, aims to provide safety and security during disasters by facilitating the smooth flow of information and responding immediately to the needs of those affected.

[0240] The following describes the processing flow.

[0241] Step 1:

[0242] Users input their status and necessary support information on their device. This information can be entered using a multiple-choice format or free-text format, and can include photos and location information.

[0243] Step 2:

[0244] The device temporarily stores the entered information and checks the network status. If a Wi-Fi connection is available, it compresses the data and sends it to the server; if the connection is unstable or unavailable, it automatically sends the information via SMS.

[0245] Step 3:

[0246] The server receives information sent from the terminal and registers it in the database. The information is tagged and classified into categories such as safety status and requests for assistance.

[0247] Step 4:

[0248] The server uses AI to analyze incoming information and set priorities. For example, requests for assistance from dangerous areas are processed with high priority.

[0249] Step 5:

[0250] The server takes necessary actions based on the priority of the information. Specifically, it sends information to available support personnel and sends notifications to relevant users.

[0251] Step 6:

[0252] Users can receive notifications from the server on their devices and check the progress of their support and other important information. Furthermore, a multilingual translation function ensures that necessary information is displayed in the user's language.

[0253] Step 7:

[0254] The server updates the database at regular intervals and automatically sends safety confirmation messages to unverified users. This enables continuous status monitoring.

[0255] (Example 1)

[0256] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0257] This invention aims to solve the problem of difficulty in quickly and accurately transmitting information about the safety and support needs of disaster victims and providing necessary support when communication infrastructure is unstable during a disaster. In particular, it aims to provide a method for smoothly sharing information while overcoming communication barriers between different languages ​​and realizing appropriate and rapid support for disaster victims.

[0258] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0259] In this invention, the server includes means for transmitting information even in situations where the communication environment is unstable, means for updating disaster cases in real time and saving them on a recording medium, and means for using a generative model to respond to support requests from users. This makes it possible to quickly and accurately share information even in the event of a disaster and to efficiently provide necessary support to disaster victims.

[0260] "Communication environment" is a concept that refers to the state of the infrastructure and network used to send and receive information.

[0261] "Wireless communication methods" refer to methods of transmitting information without requiring a physical connection, and include Bluetooth, Wi-Fi, and SMS.

[0262] "Offline mode" refers to a system function that allows operation and processing even when a network connection is unavailable.

[0263] "Disaster cases" refer to events or situations affected by disasters, and specifically include damage caused by earthquakes and floods.

[0264] A "recording medium" refers to a physical or digital means of storing information in a way that allows it to be referenced later.

[0265] A "processing device" refers to an electronic device or system that has the ability to receive, process, and store information.

[0266] "User" refers to an individual or organization that operates this system, and is the entity that inputs or receives information through a terminal.

[0267] A "generative model" refers to an algorithm or software that has the ability to generate new information based on existing data.

[0268] A "request for assistance" refers to a request for necessary support or assistance, and is information that a user sends through the system.

[0269] The "safety confirmation function" refers to a system function that shares and confirms information about an individual's safety and health status.

[0270] "Multilingual translation" refers to the process of converting information between different languages ​​into a form that is mutually understandable.

[0271] "Optimizing information notifications" refers to the process of delivering information in the most efficient and useful format for users.

[0272] This invention provides a system that enables rapid and accurate information transmission even in situations where communication environments are unstable during disasters. The user's terminal uses wireless communication to compress and transmit information even when network connectivity is unstable. Furthermore, information can be stored and transmitted later, even when offline.

[0273] The server updates disaster reports received from multiple terminals in real time and stores them on a recording medium. The received information is analyzed using a generative AI model to determine the priority of the information. As a result, information of the highest importance is notified to other users and support organizations in an optimized format, starting with the most important information.

[0274] Specifically, for example, after a major earthquake, if a user enters and sends a message such as "evacuating, safe" from a shelter, that information is immediately sent to the server. Based on this information, the server uses a generative AI model to prioritize and quickly notifies family and relevant parties that "the user is safe." Furthermore, if the user requests food assistance, the server forwards that information to the helpers through the generative AI model, and appropriate action is taken immediately.

[0275] This system also incorporates a multilingual translation function for psychological support during disasters, and uses a generative AI model to provide users with necessary emotional support information. An example of a prompt message is: "Please write a program that explains the safety confirmation process during an earthquake. Please describe in detail the flow from the user's report to the server's notification."

[0276] This enables smooth information sharing even during disasters, allowing for prompt and appropriate support to be provided to those affected.

[0277] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0278] Step 1:

[0279] The user operates the terminal to input safety information and support needs during disasters. This input information is in text format and includes specific situation descriptions such as "sheltering, safe" or "food support needed". The terminal temporarily stores this information in memory. To process the input data, formatting and elimination of unnecessary characters are performed.

[0280] Step 2:

[0281] The terminal evaluates the network status and selects the optimal communication means. Usually, an Internet connection is used, but if it is unavailable, alternative means such as SMS are selected. After determining the appropriate communication means, the terminal compresses the data and prepares for transmission. At this time, the amount of data is reduced through the compression algorithm, improving the transmission efficiency.

[0282] Step 3:

[0283] The server receives the data transmitted from the terminal. The server stores the information in the database and prepares for future processing. The received data includes the user's identification information and input content. Here, data homogenization is performed and converted into a format prepared for the next process.

[0284] Step 4:

[0285] The server analyzes the received data using a generated AI model. Through priority analysis, the urgency and importance of the information are evaluated. This model classifies based on past data and situations and identifies information with high priority. Based on the results obtained from this process, important information is placed in a separate queue.

[0286] Step 5:

[0287] The server sends notifications to relevant users and support organizations based on high-priority information. The notification content is in a message format optimized by a generative AI model. Summary messages such as "The user is safe" are sent to the family. The server also notifies relevant parties of the need for assistance. Data output here includes real-time message transmission.

[0288] (Application Example 1)

[0289] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0290] Even when communication infrastructure is restricted during a disaster, the challenge lies in efficiently collecting and disseminating information on individuals' safety status and necessary support, as well as providing optimal evacuation routes. This challenge could make rapid and accurate information sharing impossible, potentially making it difficult to ensure the safety of disaster victims.

[0291] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0292] This invention includes a server that uses wireless communication means to compress and transmit information even in offline mode, means to collect information on safety conditions and necessary support during a disaster and share it among users using location information, and means that analyze location information based on disaster information, provide the optimal evacuation route, and notify users of the progress. This enables effective information sharing and safety assurance even during a disaster, without relying on communication infrastructure.

[0293] "A situation where communication infrastructure is limited" refers to a state in which normal communication networks are not functioning, or are not functioning adequately, due to disasters, communication failures, etc.

[0294] "Wireless communication" refers to technologies that transmit and receive data using radio waves without requiring cables or wired connections.

[0295] "Offline mode" is a mode that allows a device to perform limited functions and operations even in environments where an internet connection is unavailable.

[0296] "Means of compressing and transmitting information" refers to methods that provide technology to improve communication time and bandwidth efficiency by converting large amounts of information into smaller amounts.

[0297] "Safety status" is an indicator that shows the extent to which the risks faced by an individual or group during a disaster have been reduced.

[0298] "Information on necessary support" refers to data regarding the supplies, assistance, or services that disaster victims need during a disaster.

[0299] "Location information" refers to data that represents the geographical location of a specific person or object.

[0300] "Disaster information" refers to data concerning the impact of a disaster, the extent of damage, and the situation and trends of those affected.

[0301] An "optimal evacuation route" is a path calculated based on location information to enable safe and rapid evacuation during a disaster.

[0302] "Means characterized by notifying the progress" refers to a technology or method for informing a user of the progress or changes in a procedure or situation.

[0303] In an embodiment of this invention, a smartphone, a server, and wireless communication means are used as the main components. The smartphone is equipped with an interface that receives information on safety status and necessary assistance entered by the user, and is capable of transmitting data including location information to the server via wireless communication means.

[0304] The server is developed using Python and provides an API using Flask. An algorithm that enables efficient data transmission is incorporated for information compression. The server analyzes the priority of information and the optimal evacuation route using a generative AI model based on TensorFlow. The received information is stored in a PostgreSQL database, and a common translation API is utilized for multilingual translation. The server calculates the safest and quickest evacuation route based on the user's location and real-time disaster information during a disaster and notifies the user's terminal of its progress.

[0305] Users can report situations and request assistance in multiple languages through the application and move according to the evacuation route presented by the server. The system can utilize AI to prioritize information and automatically distribute necessary assistance information.

[0306] As a specific example, when a flood occurs and a user inputs "Start evacuation" on the application, the smartphone sends this information to the server. The server analyzes the received information, calculates a safe evacuation route based on the user's current location and the progress of the flood, and notifies the user.

[0307] Examples of prompt texts using the generative AI model can be described as follows.

[0308] "It has been detected that the user is in a flood area. Based on the current location information, present a safe evacuation route and give instructions to the user."

[0309] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0310] Step 1:

[0311] Users input safety information and necessary assistance details using a smartphone application. This input includes location information. The device compresses this data and transmits it to a server via wireless communication. The input information is compressed in JSON format and reaches the server via a base station.

[0312] Step 2:

[0313] The server deserializes the received JSON data and parses each field. This process identifies the user's location and assistance request, and stores this information in the database. The parsed data is stored in the corresponding table within the PostgreSQL database.

[0314] Step 3:

[0315] The server launches a generative AI model utilizing TensorFlow, prioritizing and analyzing the most important data from the stored data. This analysis generates optimal evacuation routes and information that should be prioritized for notification. The AI ​​model performs calculations based on past data patterns and the current situation to obtain the optimal output.

[0316] Step 4:

[0317] The server uses a translation API to convert the generated information into multiple languages ​​according to the user's language settings. The converted information is then prepared for transmission to the user. The translated text is added to the transmission queue based on its corresponding language code.

[0318] Step 5:

[0319] The server sends the optimal evacuation route and emergency notifications to the user's device, along with information translated into multiple languages. The user's device displays the received data as a dialog box or notification, prompting the user to take appropriate action. Notifications are delivered to the device as push notifications, providing important instructions to ensure the user's safety.

[0320] This sequence of events enables a swift and appropriate response even during disasters.

[0321] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0322] This invention is a communication system designed to efficiently share information, confirm the safety of individuals, and provide psychological support during disasters, and incorporates an emotion engine. The system includes a user-operated terminal, a server for processing and managing information, and an emotion engine responsible for recognizing and analyzing emotions.

[0323] User actions

[0324] Users can input safety information and requests for assistance via their devices. This information can be entered as text, images, and audio. In addition, the user's daily interactions are analyzed by an emotion engine to help infer their emotional state.

[0325] Device functions

[0326] The terminal receives input information from the user and provides that data to the emotion engine. The emotion engine identifies the user's emotions based on the diverse data and sends the results to the server. This creates a foundation for providing more appropriate messages and support information.

[0327] Server Functions

[0328] The server stores and analyzes information received from terminals and the emotion engine. It registers the information in the database in real time and customizes safety confirmation messages and support information based on the emotional state of disaster victims. For users in specific emotional states, it provides emotional support by sending special safety confirmation and emotional support messages.

[0329] Functions of the Emotion Engine

[0330] The emotion engine analyzes user input to detect sentiment and behavioral patterns in text, and the server adjusts its response based on the results. This process enables support that can address the user's long-term emotional needs.

[0331] Specific example

[0332] For example, if a user enters a text message expressing their fear and anxiety immediately after a disaster, the emotion engine analyzes the text and recognizes that the user is currently experiencing intense anxiety. The server then uses this information to automatically generate and send a message to the user to provide immediate reassurance. If necessary, it can also guide the user towards seeking mental health support from a professional.

[0333] This invention is a system that enables individualized responses according to the emotional state of disaster victims and provides rapid and appropriate support according to the circumstances at the time.

[0334] The following describes the processing flow.

[0335] Step 1:

[0336] Users enter text messages into their devices containing information about their safety, requests for assistance, or their feelings. The entered information can also include images and audio, allowing the emotion engine to gather diverse data.

[0337] Step 2:

[0338] The device sends information entered by the user to the emotion engine. The emotion engine processes this data and analyzes the user's emotional state. The analysis results include, for example, stress levels and feelings of security.

[0339] Step 3:

[0340] The emotion engine sends the analysis results to the server. The server uses this information to compare it with existing data in the database and determines how safety confirmation and support information should be applied.

[0341] Step 4:

[0342] The server generates customized safety confirmation messages and necessary support information based on the user's emotional state. If the emotion engine indicates anxiety or tension, the server generates a special message to provide reassurance.

[0343] Step 5:

[0344] The server sends generated messages and support information to the terminal. The terminal notifies the user of the received information. This allows the user to learn about kind messages tailored to their emotional state and specific support options.

[0345] Step 6:

[0346] Users can take further action based on the information they receive. For example, if they need counseling from a professional, they can accept the guidance.

[0347] Step 7:

[0348] The server periodically re-evaluates the user's emotional state and provides up-to-date information and support as needed. This operation ensures continuous support.

[0349] (Example 2)

[0350] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0351] During disasters, communication infrastructure is often limited, making it difficult to quickly input and transmit information. At the same time, there is a need for immediate and accurate responses to emotional support and requests for assistance from disaster victims. In such situations, a communication system is needed that enables effective and rapid information sharing and provides appropriate support.

[0352] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0353] In this invention, the server includes means for enabling communication devices to input and transmit information even in disaster environments, means for compressing information using wireless communication and enabling transmission even offline, and a data server for updating and storing information in real time. This makes it possible to quickly share support requests and safety confirmation information from users, and to provide emotional support tailored to their emotional state, even in situations where communication infrastructure is limited.

[0354] A "communication device" is a device equipped with the function of inputting and transmitting information in a disaster environment.

[0355] Wireless communication is a technology that uses radio waves to transmit information without cables.

[0356] A "data server" is a computer system used to store information and to update and manage that data in real time.

[0357] "Artificial intelligence" is a computer-generated system that has the ability to automatically notify users of their requests for assistance and to prioritize information.

[0358] A "generative model" refers to an algorithm that analyzes input data and generates output corresponding to the emotional state.

[0359] "Mental support" refers to support activities that take into account the emotional state of users during disasters and provide a sense of security and stability.

[0360] The embodiment of this invention primarily utilizes a user-operated terminal, a server for processing and managing information, and an emotion engine for recognizing and analyzing emotions. Each element functions as follows:

[0361] Users use a device to input information about their safety during a disaster and requests for assistance. This input can be in various formats, including text, images, and audio, and also includes the user's everyday interactions. The device sends this information to an emotion engine, which then uses specific software to perform sentiment analysis.

[0362] The emotion engine uses a generative AI model to analyze the emotional state of text data. Based on the results of this analysis, the server is configured to generate and quickly send an appropriate response to the user.

[0363] The server records data received from terminals via multiple communication media in real time into a database. This makes it possible to understand each user's situation and emotional state and provide specific messages and support accordingly.

[0364] To give a concrete example, a user experiences intense anxiety immediately after a disaster and inputs this emotion as a text message into their device. The emotion engine detects this "anxiety," and the server automatically generates a reassuring message and sends it to the user. Furthermore, if necessary, it can also guide the user to mental health care methods provided by experts.

[0365] A concrete example of a prompt message would be: "Analyze the user's anxiety level from the text they enter. Based on the results, generate a message that provides reassurance."

[0366] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0367] Step 1:

[0368] Users input safety information and assistance requests via their devices. Input methods include text, images, and audio. The entered information is converted to a digital format and saved to the device's standby data storage. Specifically, the user launches the smartphone app, follows the instructions to enter information, and presses the "Send" button. The input includes descriptions of the disaster situation and the user's own condition.

[0369] Step 2:

[0370] The device sends user input information to the emotion engine. Here, the input data is encoded through a specific software protocol and sent to the emotion engine's API endpoint. Specifically, the device converts the input data into a given format and transfers it to the emotion engine via a secure connection. The input provided to the engine includes text and audio data.

[0371] Step 3:

[0372] The emotion engine analyzes incoming data using a generative AI model. The analysis process identifies emotional patterns from text data and classifies emotional states based on these patterns. Specifically, a text analysis algorithm is activated to extract the emotional tone of words and phrases and classify them into emotional categories such as "anxiety" or "relief." The identified emotional states are recorded as numerical values ​​or tags as output.

[0373] Step 4:

[0374] The server receives emotional information sent from the emotion engine and stores it in a database. Based on the analyzed emotional state, the server automatically generates customized response messages according to the individual user's needs. Specifically, the server executes database queries, selects a message template appropriate for the corresponding emotional state, and inserts information as needed. The output is the customized message sent to the user.

[0375] Step 5:

[0376] The server sends the generated response message to the user's device. A secure and rapid data transmission method is used for this process. Specifically, the server pushes the message to the device via the network. The input is the message generated by the server, and the output is displayed on the user's device.

[0377] Step 6:

[0378] Users check messages received on their devices to obtain necessary instructions and reassurance. Specific actions include tapping a notification to open the app and reading the displayed message. The output consists of emotional support and instructions regarding necessary next steps.

[0379] (Application Example 2)

[0380] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0381] In the event of a disaster, it is crucial to quickly confirm the safety of victims and provide them with psychological support. However, traditional methods face challenges in providing appropriate assistance due to limitations in communication infrastructure and insufficient sentiment analysis technology. Furthermore, multilingual support and rapid information transfer to experts are not easily achieved. In such situations, victims may not receive timely and appropriate information and support, potentially increasing their anxiety and stress.

[0382] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0383] In this invention, the server includes emotion recognition means for collecting and analyzing emotional information from users, automatic response generation means for generating and transmitting customized support information based on the analyzed emotional information, means for providing expert information to users who need mental support, and cross-platform data transmission and reception means for multiple platforms. This makes it possible to provide rapid and appropriate support according to the emotional state of disaster victims and to alleviate their anxiety.

[0384] "Emotion recognition means" refers to technology that analyzes emotional information collected from users to identify the user's emotional state.

[0385] An "automatic response generation method" is a technology that automatically generates and sends appropriate support information and messages to the user based on analyzed emotional information.

[0386] "Means of providing expert information to users who need emotional support" refers to a process of providing appropriate expert information based on the results of sentiment analysis, according to the user's condition.

[0387] "Cross-platform data transmission and reception methods" refer to technologies that enable the smooth transmission and reception of data between multiple different devices, such as smartphones and tablets.

[0388] The system necessary to implement this invention includes an emotion recognition means, an automatic response generation means, a means for providing expert information to users who require emotional support, and a cross-platform data transmission and reception means.

[0389] First, users input safety and emotional information using devices such as smartphones and tablets. This information can be collected in text, image, or audio format and reflects the user's actual emotional state. The device sends this data to emotion recognition software, such as the Google Cloud Natural Language API. The emotion recognition software analyzes this data to identify the user's emotional state. For example, if a user inputs "I am very anxious," that text will be analyzed as indicating a high level of anxiety.

[0390] The analyzed emotional information is sent to a server. The server operates a backend system based on Node.js and Express and stores the data in Amazon DynamoDB. Based on this information, the server uses an automated response generation mechanism to generate appropriate support information and messages. For example, if a user is showing strong anxiety, it can generate a message guiding them to relaxation techniques to alleviate anxiety and send it to the user's device via Firebase Cloud Messaging.

[0391] Furthermore, if a user requiring mental health support is identified, the server will provide information on appropriate professionals. This information includes a list of local professionals who can schedule psychological counseling appointments.

[0392] To ensure cross-platform compatibility, the system uses React Native to seamlessly send and receive data between different devices. This allows users to access the system anytime, anywhere, on their preferred device.

[0393] For example, in response to input such as "I'm so scared of earthquakes I can't sleep," the system automatically sends a message including "breathing exercises to alleviate anxiety" and "contact information for a local counselor."

[0394] An example of a prompt for a generative AI model is: "Generate a message aimed at providing emotional support to a user after a disaster. Input: I'm scared of the earthquake and can't sleep." This aims to generate specific methods and messages to alleviate the user's anxiety.

[0395] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0396] Step 1:

[0397] The user uses a device to input safety information and emotional information. The input data is in the form of text, images, and audio, and these reflect the user's emotional state. The device transmits this information to an emotion recognition system.

[0398] Step 2:

[0399] The emotion recognition system receives input data and performs emotion analysis using the Google Cloud Natural Language API. It extracts emotional characteristics from the input data to identify the user's emotional state. This classifies the text into emotional categories such as "anxiety" or "reassurance." The analysis results are then transferred to the server.

[0400] Step 3:

[0401] Based on the received sentiment analysis results, the server generates appropriate support information and messages using an automated response generation system. A backend using Node.js and Express determines the content of the message to be generated and proceeds to the next processing stage. The generated message is obtained as output.

[0402] Step 4:

[0403] The generated message is sent to the user's device via Firebase Cloud Messaging. The server prepares the message and sends it to the user's device in real time, ensuring that the user receives personalized support information.

[0404] Step 5:

[0405] If the server determines that a user requires mental health support, it will take steps to provide the user with information on suitable professionals. It will retrieve a list of appropriate professionals from its database and prepare them for the user. This information will be sent to the user, enabling them to access support.

[0406] Step 6:

[0407] User interactions such as clicks trigger cross-platform data transmission using React Native. This step provides a seamless experience across devices, making it easier for users to take action.

[0408] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0409] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0410] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0411] [Third Embodiment]

[0412] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0413] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0414] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0415] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0416] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0417] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0418] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0419] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0420] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0421] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0422] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0423] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0424] This invention is a communication system for efficiently sharing information and confirming the safety of individuals during disasters, enabling a rapid response even under conditions of unstable communication infrastructure. The system's basic components consist of a user-operated terminal, a server for managing information, and wireless communication means to support communication between the two.

[0425] User actions

[0426] Users can input and submit personal safety information and assistance needs through their devices. Furthermore, users can receive information tailored to their language settings using the system's multilingual translation function. Information to help alleviate mental stress during disasters is also provided through this system.

[0427] Device functions

[0428] Even when a regular internet connection is unavailable, the device can compress and send / receive information via SMS, a wireless communication method. Data entered by the user is temporarily stored within the device before being sent to the server. The transmission method is automatically adjusted according to the network conditions.

[0429] Server Functions

[0430] The server receives information sent from multiple devices and stores it in a database. Data is updated in real time, and AI is used to determine the priority of information and optimize notifications to the relevant users. In addition, the server analyzes real-time data collected from disaster areas and distributes information such as the locations of evacuation centers and the supply status of necessary supplies to devices.

[0431] Specific example

[0432] For example, in the event of a major earthquake, if a user reports to their device that they are "evacuating and safe," that information is immediately sent to the server. The server analyzes this information and notifies family members and relevant parties that "the user's safety has been confirmed." Also, if the user requests food, the AI ​​automatically notifies the nearest aid provider and ensures that a response is taken.

[0433] The system's program, configured in this way, aims to provide safety and security during disasters by facilitating the smooth flow of information and responding immediately to the needs of those affected.

[0434] The following describes the processing flow.

[0435] Step 1:

[0436] Users input their status and necessary support information on their device. This information can be entered using a multiple-choice format or free-text format, and can include photos and location information.

[0437] Step 2:

[0438] The device temporarily stores the entered information and checks the network status. If a Wi-Fi connection is available, it compresses the data and sends it to the server; if the connection is unstable or unavailable, it automatically sends the information via SMS.

[0439] Step 3:

[0440] The server receives information sent from the terminal and registers it in the database. The information is tagged and classified into categories such as safety status and requests for assistance.

[0441] Step 4:

[0442] The server uses AI to analyze incoming information and set priorities. For example, requests for assistance from dangerous areas are processed with high priority.

[0443] Step 5:

[0444] The server takes necessary actions based on the priority of the information. Specifically, it sends information to available support personnel and sends notifications to relevant users.

[0445] Step 6:

[0446] Users can receive notifications from the server on their devices and check the progress of their support and other important information. Furthermore, a multilingual translation function ensures that necessary information is displayed in the user's language.

[0447] Step 7:

[0448] The server updates the database at regular intervals and automatically sends safety confirmation messages to unverified users. This enables continuous status monitoring.

[0449] (Example 1)

[0450] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0451] This invention aims to solve the problem of difficulty in quickly and accurately transmitting information about the safety and support needs of disaster victims and providing necessary support when communication infrastructure is unstable during a disaster. In particular, it aims to provide a method for smoothly sharing information while overcoming communication barriers between different languages ​​and realizing appropriate and rapid support for disaster victims.

[0452] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0453] In this invention, the server includes means for transmitting information even in situations where the communication environment is unstable, means for updating disaster cases in real time and saving them on a recording medium, and means for using a generative model to respond to support requests from users. This makes it possible to quickly and accurately share information even in the event of a disaster and to efficiently provide necessary support to disaster victims.

[0454] "Communication environment" is a concept that refers to the state of the infrastructure and network used to send and receive information.

[0455] "Wireless communication methods" refer to methods of transmitting information without requiring a physical connection, and include Bluetooth, Wi-Fi, and SMS.

[0456] "Offline mode" refers to a system function that allows operation and processing even when a network connection is unavailable.

[0457] "Disaster cases" refer to events or situations affected by disasters, and specifically include damage caused by earthquakes and floods.

[0458] A "recording medium" refers to a physical or digital means of storing information in a way that allows it to be referenced later.

[0459] A "processing device" refers to an electronic device or system that has the ability to receive, process, and store information.

[0460] "User" refers to an individual or organization that operates this system, and is the entity that inputs or receives information through a terminal.

[0461] A "generative model" refers to an algorithm or software that has the ability to generate new information based on existing data.

[0462] A "request for assistance" refers to a request for necessary support or assistance, and is information that a user sends through the system.

[0463] The "safety confirmation function" refers to a system function that shares and confirms information about an individual's safety and health status.

[0464] "Multilingual translation" refers to the process of converting information between different languages ​​into a form that is mutually understandable.

[0465] "Optimizing information notifications" refers to the process of delivering information in the most efficient and useful format for users.

[0466] This invention provides a system that enables rapid and accurate information transmission even in situations where communication environments are unstable during disasters. The user's terminal uses wireless communication to compress and transmit information even when network connectivity is unstable. Furthermore, information can be stored and transmitted later, even when offline.

[0467] The server updates disaster reports received from multiple terminals in real time and stores them on a recording medium. The received information is analyzed using a generative AI model to determine the priority of the information. As a result, information of the highest importance is notified to other users and support organizations in an optimized format, starting with the most important information.

[0468] Specifically, for example, after a major earthquake, if a user enters and sends a message such as "evacuating, safe" from a shelter, that information is immediately sent to the server. Based on this information, the server uses a generative AI model to prioritize and quickly notifies family and relevant parties that "the user is safe." Furthermore, if the user requests food assistance, the server forwards that information to the helpers through the generative AI model, and appropriate action is taken immediately.

[0469] This system also incorporates a multilingual translation function for psychological support during disasters, and uses a generative AI model to provide users with necessary emotional support information. An example of a prompt message is: "Please write a program that explains the safety confirmation process during an earthquake. Please describe in detail the flow from the user's report to the server's notification."

[0470] This enables smooth information sharing even during disasters, allowing for prompt and appropriate support to be provided to those affected.

[0471] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0472] Step 1:

[0473] Users operate a terminal to input information about their safety and assistance needs during a disaster. This input information is in text format and includes specific situational descriptions such as "evacuating, safe" or "food assistance needed." The terminal temporarily stores this information in memory. To process the input data, formatting and removal of unnecessary characters are performed.

[0474] Step 2:

[0475] The device evaluates the network status and selects the optimal communication method. Normally, an internet connection is used, but if unavailable, an alternative method such as SMS is chosen. After determining the appropriate communication method, the device compresses the data and prepares it for transmission. During this process, the data size is reduced through a compression algorithm, improving transmission efficiency.

[0476] Step 3:

[0477] The server receives the data sent from the terminal. The server stores the information in a database and prepares it for future processing. The received data includes the user's identification information and input content. Here, the data is standardized and converted into a format ready for the next processing step.

[0478] Step 4:

[0479] The server uses a generative AI model to analyze incoming data. Prioritization analysis assesses the urgency and importance of the information. This model classifies information based on past data and context, identifying high-priority information. Based on the results of this process, important information is placed in a separate queue.

[0480] Step 5:

[0481] The server sends notifications to relevant users and support organizations based on high-priority information. The notification content is in a message format optimized by a generative AI model. Summary messages such as "The user is safe" are sent to the family. The server also notifies relevant parties of the need for assistance. Data output here includes real-time message transmission.

[0482] (Application Example 1)

[0483] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0484] Even when communication infrastructure is restricted during a disaster, the challenge lies in efficiently collecting and disseminating information on individuals' safety status and necessary support, as well as providing optimal evacuation routes. This challenge could make rapid and accurate information sharing impossible, potentially making it difficult to ensure the safety of disaster victims.

[0485] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0486] This invention includes a server that uses wireless communication means to compress and transmit information even in offline mode, means to collect information on safety conditions and necessary support during a disaster and share it among users using location information, and means that analyze location information based on disaster information, provide the optimal evacuation route, and notify users of the progress. This enables effective information sharing and safety assurance even during a disaster, without relying on communication infrastructure.

[0487] "A situation where communication infrastructure is limited" refers to a state in which normal communication networks are not functioning, or are not functioning adequately, due to disasters, communication failures, etc.

[0488] "Wireless communication" refers to technologies that transmit and receive data using radio waves without requiring cables or wired connections.

[0489] "Offline mode" is a mode that allows a device to perform limited functions and operations even in environments where an internet connection is unavailable.

[0490] "Means of compressing and transmitting information" refers to methods that provide technology to improve communication time and bandwidth efficiency by converting large amounts of information into smaller amounts.

[0491] "Safety status" is an indicator that shows the extent to which the risks faced by an individual or group during a disaster have been reduced.

[0492] "Information on necessary support" refers to data regarding the supplies, assistance, or services that disaster victims need during a disaster.

[0493] "Location information" refers to data that represents the geographical location of a specific person or object.

[0494] "Disaster information" refers to data concerning the impact of a disaster, the extent of damage, and the situation and trends of those affected.

[0495] An "optimal evacuation route" is a path calculated based on location information to enable safe and rapid evacuation during a disaster.

[0496] "Means characterized by notifying the progress" refers to a technology or method for informing a user of the progress or changes in a procedure or situation.

[0497] In an embodiment of this invention, a smartphone, a server, and wireless communication means are used as the main components. The smartphone is equipped with an interface that receives information on safety status and necessary assistance entered by the user, and is capable of transmitting data including location information to the server via wireless communication means.

[0498] The server is developed using Python and provides an API using Flask. Information compression incorporates algorithms that enable efficient data transmission. The server analyzes information priority and optimal evacuation routes using a generative AI model based on TensorFlow. Received information is stored in a PostgreSQL database, and a common translation API is used for multilingual translation. Based on the user's location during a disaster and real-time disaster information, the server calculates the safest and fastest evacuation route and notifies the user's device of its progress.

[0499] Users can submit situation reports and assistance requests in multiple languages ​​through the application and move according to evacuation routes provided by the server. The system utilizes AI to prioritize information and automatically deliver necessary assistance.

[0500] For example, in the event of a flood, if a user enters "Start Evacuation" in the app, their smartphone sends that information to the server. The server analyzes the received information, calculates a safe evacuation route based on the user's current location and the progress of the flood, and notifies the user.

[0501] An example of a prompt statement utilizing a generative AI model can be written as follows:

[0502] "The system has detected that the user is in a flood-affected area. Based on the user's current location, it will suggest a safe evacuation route and provide instructions to the user."

[0503] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0504] Step 1:

[0505] Users input safety information and necessary assistance details using a smartphone application. This input includes location information. The device compresses this data and transmits it to a server via wireless communication. The input information is compressed in JSON format and reaches the server via a base station.

[0506] Step 2:

[0507] The server deserializes the received JSON data and parses each field. This process identifies the user's location and assistance request, and stores this information in the database. The parsed data is stored in the corresponding table within the PostgreSQL database.

[0508] Step 3:

[0509] The server launches a generative AI model utilizing TensorFlow, prioritizing and analyzing the most important data from the stored data. This analysis generates optimal evacuation routes and information that should be prioritized for notification. The AI ​​model performs calculations based on past data patterns and the current situation to obtain the optimal output.

[0510] Step 4:

[0511] The server uses a translation API to convert the generated information into multiple languages ​​according to the user's language settings. The converted information is then prepared for transmission to the user. The translated text is added to the transmission queue based on its corresponding language code.

[0512] Step 5:

[0513] The server sends the optimal evacuation route and emergency notifications to the user's device, along with information translated into multiple languages. The user's device displays the received data as a dialog box or notification, prompting the user to take appropriate action. Notifications are delivered to the device as push notifications, providing important instructions to ensure the user's safety.

[0514] This sequence of events enables a swift and appropriate response even during disasters.

[0515] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0516] This invention is a communication system designed to efficiently share information, confirm the safety of individuals, and provide psychological support during disasters, and incorporates an emotion engine. The system includes a user-operated terminal, a server for processing and managing information, and an emotion engine responsible for recognizing and analyzing emotions.

[0517] User actions

[0518] Users can input safety information and requests for assistance via their devices. This information can be entered as text, images, and audio. In addition, the user's daily interactions are analyzed by an emotion engine to help infer their emotional state.

[0519] Device functions

[0520] The terminal receives input information from the user and provides that data to the emotion engine. The emotion engine identifies the user's emotions based on the diverse data and sends the results to the server. This creates a foundation for providing more appropriate messages and support information.

[0521] Server Functions

[0522] The server stores and analyzes information received from terminals and the emotion engine. It registers the information in the database in real time and customizes safety confirmation messages and support information based on the emotional state of disaster victims. For users in specific emotional states, it provides emotional support by sending special safety confirmation and emotional support messages.

[0523] Functions of the Emotion Engine

[0524] The emotion engine analyzes user input to detect sentiment and behavioral patterns in text, and the server adjusts its response based on the results. This process enables support that can address the user's long-term emotional needs.

[0525] Specific example

[0526] For example, if a user enters a text message expressing their fear and anxiety immediately after a disaster, the emotion engine analyzes the text and recognizes that the user is currently experiencing intense anxiety. The server then uses this information to automatically generate and send a message to the user to provide immediate reassurance. If necessary, it can also guide the user towards seeking mental health support from a professional.

[0527] This invention is a system that enables individualized responses according to the emotional state of disaster victims and provides rapid and appropriate support according to the circumstances at the time.

[0528] The following describes the processing flow.

[0529] Step 1:

[0530] Users enter text messages into their devices containing information about their safety, requests for assistance, or their feelings. The entered information can also include images and audio, allowing the emotion engine to gather diverse data.

[0531] Step 2:

[0532] The device sends information entered by the user to the emotion engine. The emotion engine processes this data and analyzes the user's emotional state. The analysis results include, for example, stress levels and feelings of security.

[0533] Step 3:

[0534] The emotion engine sends the analysis results to the server. The server uses this information to compare it with existing data in the database and determines how safety confirmation and support information should be applied.

[0535] Step 4:

[0536] The server generates customized safety confirmation messages and necessary support information based on the user's emotional state. If the emotion engine indicates anxiety or tension, the server generates a special message to provide reassurance.

[0537] Step 5:

[0538] The server sends generated messages and support information to the terminal. The terminal notifies the user of the received information. This allows the user to learn about kind messages tailored to their emotional state and specific support options.

[0539] Step 6:

[0540] Users can take further action based on the information they receive. For example, if they need counseling from a professional, they can accept the guidance.

[0541] Step 7:

[0542] The server periodically re-evaluates the user's emotional state and provides up-to-date information and support as needed. This operation ensures continuous support.

[0543] (Example 2)

[0544] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0545] During disasters, communication infrastructure is often limited, making it difficult to quickly input and transmit information. At the same time, there is a need for immediate and accurate responses to emotional support and requests for assistance from disaster victims. In such situations, a communication system is needed that enables effective and rapid information sharing and provides appropriate support.

[0546] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0547] In this invention, the server includes means for enabling communication devices to input and transmit information even in disaster environments, means for compressing information using wireless communication and enabling transmission even offline, and a data server for updating and storing information in real time. This makes it possible to quickly share support requests and safety confirmation information from users, and to provide emotional support tailored to their emotional state, even in situations where communication infrastructure is limited.

[0548] A "communication device" is a device equipped with the function of inputting and transmitting information in a disaster environment.

[0549] Wireless communication is a technology that uses radio waves to transmit information without cables.

[0550] A "data server" is a computer system used to store information and to update and manage that data in real time.

[0551] "Artificial intelligence" is a computer-generated system that has the ability to automatically notify users of their requests for assistance and to prioritize information.

[0552] A "generative model" refers to an algorithm that analyzes input data and generates output corresponding to the emotional state.

[0553] "Mental support" refers to support activities that take into account the emotional state of users during disasters and provide a sense of security and stability.

[0554] The embodiment of this invention primarily utilizes a user-operated terminal, a server for processing and managing information, and an emotion engine for recognizing and analyzing emotions. Each element functions as follows:

[0555] Users use a device to input information about their safety during a disaster and requests for assistance. This input can be in various formats, including text, images, and audio, and also includes the user's everyday interactions. The device sends this information to an emotion engine, which then uses specific software to perform sentiment analysis.

[0556] The emotion engine uses a generative AI model to analyze the emotional state of text data. Based on the results of this analysis, the server is configured to generate and quickly send an appropriate response to the user.

[0557] The server records data received from terminals via multiple communication media in real time into a database. This makes it possible to understand each user's situation and emotional state and provide specific messages and support accordingly.

[0558] To give a concrete example, a user experiences intense anxiety immediately after a disaster and inputs this emotion as a text message into their device. The emotion engine detects this "anxiety," and the server automatically generates a reassuring message and sends it to the user. Furthermore, if necessary, it can also guide the user to mental health care methods provided by experts.

[0559] A concrete example of a prompt message would be: "Analyze the user's anxiety level from the text they enter. Based on the results, generate a message that provides reassurance."

[0560] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0561] Step 1:

[0562] Users input safety information and assistance requests via their devices. Input methods include text, images, and audio. The entered information is converted to a digital format and saved to the device's standby data storage. Specifically, the user launches the smartphone app, follows the instructions to enter information, and presses the "Send" button. The input includes descriptions of the disaster situation and the user's own condition.

[0563] Step 2:

[0564] The device sends user input information to the emotion engine. Here, the input data is encoded through a specific software protocol and sent to the emotion engine's API endpoint. Specifically, the device converts the input data into a given format and transfers it to the emotion engine via a secure connection. The input provided to the engine includes text and audio data.

[0565] Step 3:

[0566] The emotion engine analyzes incoming data using a generative AI model. The analysis process identifies emotional patterns from text data and classifies emotional states based on these patterns. Specifically, a text analysis algorithm is activated to extract the emotional tone of words and phrases and classify them into emotional categories such as "anxiety" or "relief." The identified emotional states are recorded as numerical values ​​or tags as output.

[0567] Step 4:

[0568] The server receives emotional information sent from the emotion engine and stores it in a database. Based on the analyzed emotional state, the server automatically generates customized response messages according to the individual user's needs. Specifically, the server executes database queries, selects a message template appropriate for the corresponding emotional state, and inserts information as needed. The output is the customized message sent to the user.

[0569] Step 5:

[0570] The server sends the generated response message to the user's device. A secure and rapid data transmission method is used for this process. Specifically, the server pushes the message to the device via the network. The input is the message generated by the server, and the output is displayed on the user's device.

[0571] Step 6:

[0572] Users check messages received on their devices to obtain necessary instructions and reassurance. Specific actions include tapping a notification to open the app and reading the displayed message. The output consists of emotional support and instructions regarding necessary next steps.

[0573] (Application Example 2)

[0574] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0575] In the event of a disaster, it is crucial to quickly confirm the safety of victims and provide them with psychological support. However, traditional methods face challenges in providing appropriate assistance due to limitations in communication infrastructure and insufficient sentiment analysis technology. Furthermore, multilingual support and rapid information transfer to experts are not easily achieved. In such situations, victims may not receive timely and appropriate information and support, potentially increasing their anxiety and stress.

[0576] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0577] In this invention, the server includes emotion recognition means for collecting and analyzing emotional information from users, automatic response generation means for generating and transmitting customized support information based on the analyzed emotional information, means for providing expert information to users who need mental support, and cross-platform data transmission and reception means for multiple platforms. This makes it possible to provide rapid and appropriate support according to the emotional state of disaster victims and to alleviate their anxiety.

[0578] "Emotion recognition means" refers to technology that analyzes emotional information collected from users to identify the user's emotional state.

[0579] An "automatic response generation method" is a technology that automatically generates and sends appropriate support information and messages to the user based on analyzed emotional information.

[0580] "Means of providing expert information to users who need emotional support" refers to a process of providing appropriate expert information based on the results of sentiment analysis, according to the user's condition.

[0581] "Cross-platform data transmission and reception methods" refer to technologies that enable the smooth transmission and reception of data between multiple different devices, such as smartphones and tablets.

[0582] The system necessary to implement this invention includes an emotion recognition means, an automatic response generation means, a means for providing expert information to users who require emotional support, and a cross-platform data transmission and reception means.

[0583] First, users input safety and emotional information using devices such as smartphones and tablets. This information can be collected in text, image, or audio format and reflects the user's actual emotional state. The device sends this data to emotion recognition software, such as the Google Cloud Natural Language API. The emotion recognition software analyzes this data to identify the user's emotional state. For example, if a user inputs "I am very anxious," that text will be analyzed as indicating a high level of anxiety.

[0584] The analyzed emotional information is sent to a server. The server operates a backend system based on Node.js and Express and stores the data in Amazon DynamoDB. Based on this information, the server uses an automated response generation mechanism to generate appropriate support information and messages. For example, if a user is showing strong anxiety, it can generate a message guiding them to relaxation techniques to alleviate anxiety and send it to the user's device via Firebase Cloud Messaging.

[0585] Furthermore, if a user requiring mental health support is identified, the server will provide information on appropriate professionals. This information includes a list of local professionals who can schedule psychological counseling appointments.

[0586] To ensure cross-platform compatibility, the system uses React Native to seamlessly send and receive data between different devices. This allows users to access the system anytime, anywhere, on their preferred device.

[0587] For example, in response to input such as "I'm so scared of earthquakes I can't sleep," the system automatically sends a message including "breathing exercises to alleviate anxiety" and "contact information for a local counselor."

[0588] An example of a prompt for a generative AI model is: "Generate a message aimed at providing emotional support to a user after a disaster. Input: I'm scared of the earthquake and can't sleep." This aims to generate specific methods and messages to alleviate the user's anxiety.

[0589] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0590] Step 1:

[0591] The user uses a device to input safety information and emotional information. The input data is in the form of text, images, and audio, and these reflect the user's emotional state. The device transmits this information to an emotion recognition system.

[0592] Step 2:

[0593] The emotion recognition system receives input data and performs emotion analysis using the Google Cloud Natural Language API. It extracts emotional characteristics from the input data to identify the user's emotional state. This classifies the text into emotional categories such as "anxiety" or "reassurance." The analysis results are then transferred to the server.

[0594] Step 3:

[0595] Based on the received sentiment analysis results, the server generates appropriate support information and messages using an automated response generation system. A backend using Node.js and Express determines the content of the message to be generated and proceeds to the next processing stage. The generated message is obtained as output.

[0596] Step 4:

[0597] The generated message is sent to the user's device via Firebase Cloud Messaging. The server prepares the message and sends it to the user's device in real time, ensuring that the user receives personalized support information.

[0598] Step 5:

[0599] If the server determines that a user requires mental health support, it will take steps to provide the user with information on suitable professionals. It will retrieve a list of appropriate professionals from its database and prepare them for the user. This information will be sent to the user, enabling them to access support.

[0600] Step 6:

[0601] User interactions such as clicks trigger cross-platform data transmission using React Native. This step provides a seamless experience across devices, making it easier for users to take action.

[0602] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0603] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0604] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0605] [Fourth Embodiment]

[0606] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0607] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0608] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0609] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0610] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0611] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0612] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0613] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0614] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0615] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0616] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0617] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0618] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0619] This invention is a communication system for efficiently sharing information and confirming the safety of individuals during disasters, enabling a rapid response even under conditions of unstable communication infrastructure. The system's basic components consist of a user-operated terminal, a server for managing information, and wireless communication means to support communication between the two.

[0620] User actions

[0621] Users can input and submit personal safety information and assistance needs through their devices. Furthermore, users can receive information tailored to their language settings using the system's multilingual translation function. Information to help alleviate mental stress during disasters is also provided through this system.

[0622] Device functions

[0623] Even when a regular internet connection is unavailable, the device can compress and send / receive information via SMS, a wireless communication method. Data entered by the user is temporarily stored within the device before being sent to the server. The transmission method is automatically adjusted according to the network conditions.

[0624] Server Functions

[0625] The server receives information sent from multiple devices and stores it in a database. Data is updated in real time, and AI is used to determine the priority of information and optimize notifications to the relevant users. In addition, the server analyzes real-time data collected from disaster areas and distributes information such as the locations of evacuation centers and the supply status of necessary supplies to devices.

[0626] Specific example

[0627] For example, in the event of a major earthquake, if a user reports to their device that they are "evacuating and safe," that information is immediately sent to the server. The server analyzes this information and notifies family members and relevant parties that "the user's safety has been confirmed." Also, if the user requests food, the AI ​​automatically notifies the nearest aid provider and ensures that a response is taken.

[0628] The system's program, configured in this way, aims to provide safety and security during disasters by facilitating the smooth flow of information and responding immediately to the needs of those affected.

[0629] The following describes the processing flow.

[0630] Step 1:

[0631] Users input their status and necessary support information on their device. This information can be entered using a multiple-choice format or free-text format, and can include photos and location information.

[0632] Step 2:

[0633] The device temporarily stores the entered information and checks the network status. If a Wi-Fi connection is available, it compresses the data and sends it to the server; if the connection is unstable or unavailable, it automatically sends the information via SMS.

[0634] Step 3:

[0635] The server receives information sent from the terminal and registers it in the database. The information is tagged and classified into categories such as safety status and requests for assistance.

[0636] Step 4:

[0637] The server uses AI to analyze incoming information and set priorities. For example, requests for assistance from dangerous areas are processed with high priority.

[0638] Step 5:

[0639] The server takes necessary actions based on the priority of the information. Specifically, it sends information to available support personnel and sends notifications to relevant users.

[0640] Step 6:

[0641] Users can receive notifications from the server on their devices and check the progress of their support and other important information. Furthermore, a multilingual translation function ensures that necessary information is displayed in the user's language.

[0642] Step 7:

[0643] The server updates the database at regular intervals and automatically sends safety confirmation messages to unverified users. This enables continuous status monitoring.

[0644] (Example 1)

[0645] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0646] This invention aims to solve the problem of difficulty in quickly and accurately transmitting information about the safety and support needs of disaster victims and providing necessary support when communication infrastructure is unstable during a disaster. In particular, it aims to provide a method for smoothly sharing information while overcoming communication barriers between different languages ​​and realizing appropriate and rapid support for disaster victims.

[0647] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0648] In this invention, the server includes means for transmitting information even in situations where the communication environment is unstable, means for updating disaster cases in real time and saving them on a recording medium, and means for using a generative model to respond to support requests from users. This makes it possible to quickly and accurately share information even in the event of a disaster and to efficiently provide necessary support to disaster victims.

[0649] "Communication environment" is a concept that refers to the state of the infrastructure and network used to send and receive information.

[0650] "Wireless communication methods" refer to methods of transmitting information without requiring a physical connection, and include Bluetooth, Wi-Fi, and SMS.

[0651] "Offline mode" refers to a system function that allows operation and processing even when a network connection is unavailable.

[0652] "Disaster cases" refer to events or situations affected by disasters, and specifically include damage caused by earthquakes and floods.

[0653] A "recording medium" refers to a physical or digital means of storing information in a way that allows it to be referenced later.

[0654] A "processing device" refers to an electronic device or system that has the ability to receive, process, and store information.

[0655] "User" refers to an individual or organization that operates this system, and is the entity that inputs or receives information through a terminal.

[0656] A "generative model" refers to an algorithm or software that has the ability to generate new information based on existing data.

[0657] A "request for assistance" refers to a request for necessary support or assistance, and is information that a user sends through the system.

[0658] The "safety confirmation function" refers to a system function that shares and confirms information about an individual's safety and health status.

[0659] "Multilingual translation" refers to the process of converting information between different languages ​​into a form that is mutually understandable.

[0660] "Optimizing information notifications" refers to the process of delivering information in the most efficient and useful format for users.

[0661] This invention provides a system that enables rapid and accurate information transmission even in situations where communication environments are unstable during disasters. The user's terminal uses wireless communication to compress and transmit information even when network connectivity is unstable. Furthermore, information can be stored and transmitted later, even when offline.

[0662] The server updates disaster reports received from multiple terminals in real time and stores them on a recording medium. The received information is analyzed using a generative AI model to determine the priority of the information. As a result, information of the highest importance is notified to other users and support organizations in an optimized format, starting with the most important information.

[0663] Specifically, for example, after a major earthquake, if a user enters and sends a message such as "evacuating, safe" from a shelter, that information is immediately sent to the server. Based on this information, the server uses a generative AI model to prioritize and quickly notifies family and relevant parties that "the user is safe." Furthermore, if the user requests food assistance, the server forwards that information to the helpers through the generative AI model, and appropriate action is taken immediately.

[0664] This system also incorporates a multilingual translation function for psychological support during disasters, and uses a generative AI model to provide users with necessary emotional support information. An example of a prompt message is: "Please write a program that explains the safety confirmation process during an earthquake. Please describe in detail the flow from the user's report to the server's notification."

[0665] This enables smooth information sharing even during disasters, allowing for prompt and appropriate support to be provided to those affected.

[0666] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0667] Step 1:

[0668] Users operate a terminal to input information about their safety and assistance needs during a disaster. This input information is in text format and includes specific situational descriptions such as "evacuating, safe" or "food assistance needed." The terminal temporarily stores this information in memory. To process the input data, formatting and removal of unnecessary characters are performed.

[0669] Step 2:

[0670] The device evaluates the network status and selects the optimal communication method. Normally, an internet connection is used, but if unavailable, an alternative method such as SMS is chosen. After determining the appropriate communication method, the device compresses the data and prepares it for transmission. During this process, the data size is reduced through a compression algorithm, improving transmission efficiency.

[0671] Step 3:

[0672] The server receives the data sent from the terminal. The server stores the information in a database and prepares it for future processing. The received data includes the user's identification information and input content. Here, the data is standardized and converted into a format ready for the next processing step.

[0673] Step 4:

[0674] The server uses a generative AI model to analyze incoming data. Prioritization analysis assesses the urgency and importance of the information. This model classifies information based on past data and context, identifying high-priority information. Based on the results of this process, important information is placed in a separate queue.

[0675] Step 5:

[0676] The server sends notifications to relevant users and support organizations based on high-priority information. The notification content is in a message format optimized by a generative AI model. Summary messages such as "The user is safe" are sent to the family. The server also notifies relevant parties of the need for assistance. Data output here includes real-time message transmission.

[0677] (Application Example 1)

[0678] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0679] Even when communication infrastructure is restricted during a disaster, the challenge lies in efficiently collecting and disseminating information on individuals' safety status and necessary support, as well as providing optimal evacuation routes. This challenge could make rapid and accurate information sharing impossible, potentially making it difficult to ensure the safety of disaster victims.

[0680] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0681] This invention includes a server that uses wireless communication means to compress and transmit information even in offline mode, means to collect information on safety conditions and necessary support during a disaster and share it among users using location information, and means that analyze location information based on disaster information, provide the optimal evacuation route, and notify users of the progress. This enables effective information sharing and safety assurance even during a disaster, without relying on communication infrastructure.

[0682] "A situation where communication infrastructure is limited" refers to a state in which normal communication networks are not functioning, or are not functioning adequately, due to disasters, communication failures, etc.

[0683] "Wireless communication" refers to technologies that transmit and receive data using radio waves without requiring cables or wired connections.

[0684] "Offline mode" is a mode that allows a device to perform limited functions and operations even in environments where an internet connection is unavailable.

[0685] "Means of compressing and transmitting information" refers to methods that provide technology to improve communication time and bandwidth efficiency by converting large amounts of information into smaller amounts.

[0686] "Safety status" is an indicator that shows the extent to which the risks faced by an individual or group during a disaster have been reduced.

[0687] "Information on necessary support" refers to data regarding the supplies, assistance, or services that disaster victims need during a disaster.

[0688] "Location information" refers to data that represents the geographical location of a specific person or object.

[0689] "Disaster information" refers to data concerning the impact of a disaster, the extent of damage, and the situation and trends of those affected.

[0690] An "optimal evacuation route" is a path calculated based on location information to enable safe and rapid evacuation during a disaster.

[0691] "Means characterized by notifying the progress" refers to a technology or method for informing a user of the progress or changes in a procedure or situation.

[0692] In an embodiment of this invention, a smartphone, a server, and wireless communication means are used as the main components. The smartphone is equipped with an interface that receives information on safety status and necessary assistance entered by the user, and is capable of transmitting data including location information to the server via wireless communication means.

[0693] The server is developed using Python and provides an API using Flask. Information compression incorporates algorithms that enable efficient data transmission. The server analyzes information priority and optimal evacuation routes using a generative AI model based on TensorFlow. Received information is stored in a PostgreSQL database, and a common translation API is used for multilingual translation. Based on the user's location during a disaster and real-time disaster information, the server calculates the safest and fastest evacuation route and notifies the user's device of its progress.

[0694] Users can submit situation reports and assistance requests in multiple languages ​​through the application and move according to evacuation routes provided by the server. The system utilizes AI to prioritize information and automatically deliver necessary assistance.

[0695] For example, in the event of a flood, if a user enters "Start Evacuation" in the app, their smartphone sends that information to the server. The server analyzes the received information, calculates a safe evacuation route based on the user's current location and the progress of the flood, and notifies the user.

[0696] An example of a prompt statement utilizing a generative AI model can be written as follows:

[0697] "The system has detected that the user is in a flood-affected area. Based on the user's current location, it will suggest a safe evacuation route and provide instructions to the user."

[0698] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0699] Step 1:

[0700] Users input safety information and necessary assistance details using a smartphone application. This input includes location information. The device compresses this data and transmits it to a server via wireless communication. The input information is compressed in JSON format and reaches the server via a base station.

[0701] Step 2:

[0702] The server deserializes the received JSON data and parses each field. This process identifies the user's location and assistance request, and stores this information in the database. The parsed data is stored in the corresponding table within the PostgreSQL database.

[0703] Step 3:

[0704] The server launches a generative AI model utilizing TensorFlow, prioritizing and analyzing the most important data from the stored data. This analysis generates optimal evacuation routes and information that should be prioritized for notification. The AI ​​model performs calculations based on past data patterns and the current situation to obtain the optimal output.

[0705] Step 4:

[0706] The server uses a translation API to convert the generated information into multiple languages ​​according to the user's language settings. The converted information is then prepared for transmission to the user. The translated text is added to the transmission queue based on its corresponding language code.

[0707] Step 5:

[0708] The server sends the optimal evacuation route and emergency notifications to the user's device, along with information translated into multiple languages. The user's device displays the received data as a dialog box or notification, prompting the user to take appropriate action. Notifications are delivered to the device as push notifications, providing important instructions to ensure the user's safety.

[0709] This sequence of events enables a swift and appropriate response even during disasters.

[0710] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0711] This invention is a communication system designed to efficiently share information, confirm the safety of individuals, and provide psychological support during disasters, and incorporates an emotion engine. The system includes a user-operated terminal, a server for processing and managing information, and an emotion engine responsible for recognizing and analyzing emotions.

[0712] User actions

[0713] Users can input safety information and requests for assistance via their devices. This information can be entered as text, images, and audio. In addition, the user's daily interactions are analyzed by an emotion engine to help infer their emotional state.

[0714] Device functions

[0715] The terminal receives input information from the user and provides that data to the emotion engine. The emotion engine identifies the user's emotions based on the diverse data and sends the results to the server. This creates a foundation for providing more appropriate messages and support information.

[0716] Server Functions

[0717] The server stores and analyzes information received from terminals and the emotion engine. It registers the information in the database in real time and customizes safety confirmation messages and support information based on the emotional state of disaster victims. For users in specific emotional states, it provides emotional support by sending special safety confirmation and emotional support messages.

[0718] Functions of the Emotion Engine

[0719] The emotion engine analyzes user input to detect sentiment and behavioral patterns in text, and the server adjusts its response based on the results. This process enables support that can address the user's long-term emotional needs.

[0720] Specific example

[0721] For example, if a user enters a text message expressing their fear and anxiety immediately after a disaster, the emotion engine analyzes the text and recognizes that the user is currently experiencing intense anxiety. The server then uses this information to automatically generate and send a message to the user to provide immediate reassurance. If necessary, it can also guide the user towards seeking mental health support from a professional.

[0722] This invention is a system that enables individualized responses according to the emotional state of disaster victims and provides rapid and appropriate support according to the circumstances at the time.

[0723] The following describes the processing flow.

[0724] Step 1:

[0725] Users enter text messages into their devices containing information about their safety, requests for assistance, or their feelings. The entered information can also include images and audio, allowing the emotion engine to gather diverse data.

[0726] Step 2:

[0727] The device sends information entered by the user to the emotion engine. The emotion engine processes this data and analyzes the user's emotional state. The analysis results include, for example, stress levels and feelings of security.

[0728] Step 3:

[0729] The emotion engine sends the analysis results to the server. The server uses this information to compare it with existing data in the database and determines how safety confirmation and support information should be applied.

[0730] Step 4:

[0731] The server generates customized safety confirmation messages and necessary support information based on the user's emotional state. If the emotion engine indicates anxiety or tension, the server generates a special message to provide reassurance.

[0732] Step 5:

[0733] The server sends generated messages and support information to the terminal. The terminal notifies the user of the received information. This allows the user to learn about kind messages tailored to their emotional state and specific support options.

[0734] Step 6:

[0735] Users can take further action based on the information they receive. For example, if they need counseling from a professional, they can accept the guidance.

[0736] Step 7:

[0737] The server periodically re-evaluates the user's emotional state and provides up-to-date information and support as needed. This operation ensures continuous support.

[0738] (Example 2)

[0739] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0740] During disasters, communication infrastructure is often limited, making it difficult to quickly input and transmit information. At the same time, there is a need for immediate and accurate responses to emotional support and requests for assistance from disaster victims. In such situations, a communication system is needed that enables effective and rapid information sharing and provides appropriate support.

[0741] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0742] In this invention, the server includes means for enabling communication devices to input and transmit information even in disaster environments, means for compressing information using wireless communication and enabling transmission even offline, and a data server for updating and storing information in real time. This makes it possible to quickly share support requests and safety confirmation information from users, and to provide emotional support tailored to their emotional state, even in situations where communication infrastructure is limited.

[0743] A "communication device" is a device equipped with the function of inputting and transmitting information in a disaster environment.

[0744] Wireless communication is a technology that uses radio waves to transmit information without cables.

[0745] A "data server" is a computer system used to store information and to update and manage that data in real time.

[0746] "Artificial intelligence" is a computer-generated system that has the ability to automatically notify users of their requests for assistance and to prioritize information.

[0747] A "generative model" refers to an algorithm that analyzes input data and generates output corresponding to the emotional state.

[0748] "Mental support" refers to support activities that take into account the emotional state of users during disasters and provide a sense of security and stability.

[0749] The embodiment of this invention primarily utilizes a user-operated terminal, a server for processing and managing information, and an emotion engine for recognizing and analyzing emotions. Each element functions as follows:

[0750] Users use a device to input information about their safety during a disaster and requests for assistance. This input can be in various formats, including text, images, and audio, and also includes the user's everyday interactions. The device sends this information to an emotion engine, which then uses specific software to perform sentiment analysis.

[0751] The emotion engine uses a generative AI model to analyze the emotional state of text data. Based on the results of this analysis, the server is configured to generate and quickly send an appropriate response to the user.

[0752] The server records data received from terminals via multiple communication media in real time into a database. This makes it possible to understand each user's situation and emotional state and provide specific messages and support accordingly.

[0753] To give a concrete example, a user experiences intense anxiety immediately after a disaster and inputs this emotion as a text message into their device. The emotion engine detects this "anxiety," and the server automatically generates a reassuring message and sends it to the user. Furthermore, if necessary, it can also guide the user to mental health care methods provided by experts.

[0754] A concrete example of a prompt message would be: "Analyze the user's anxiety level from the text they enter. Based on the results, generate a message that provides reassurance."

[0755] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0756] Step 1:

[0757] Users input safety information and assistance requests via their devices. Input methods include text, images, and audio. The entered information is converted to a digital format and saved to the device's standby data storage. Specifically, the user launches the smartphone app, follows the instructions to enter information, and presses the "Send" button. The input includes descriptions of the disaster situation and the user's own condition.

[0758] Step 2:

[0759] The device sends user input information to the emotion engine. Here, the input data is encoded through a specific software protocol and sent to the emotion engine's API endpoint. Specifically, the device converts the input data into a given format and transfers it to the emotion engine via a secure connection. The input provided to the engine includes text and audio data.

[0760] Step 3:

[0761] The emotion engine analyzes incoming data using a generative AI model. The analysis process identifies emotional patterns from text data and classifies emotional states based on these patterns. Specifically, a text analysis algorithm is activated to extract the emotional tone of words and phrases and classify them into emotional categories such as "anxiety" or "relief." The identified emotional states are recorded as numerical values ​​or tags as output.

[0762] Step 4:

[0763] The server receives emotional information sent from the emotion engine and stores it in a database. Based on the analyzed emotional state, the server automatically generates customized response messages according to the individual user's needs. Specifically, the server executes database queries, selects a message template appropriate for the corresponding emotional state, and inserts information as needed. The output is the customized message sent to the user.

[0764] Step 5:

[0765] The server sends the generated response message to the user's device. A secure and rapid data transmission method is used for this process. Specifically, the server pushes the message to the device via the network. The input is the message generated by the server, and the output is displayed on the user's device.

[0766] Step 6:

[0767] Users check messages received on their devices to obtain necessary instructions and reassurance. Specific actions include tapping a notification to open the app and reading the displayed message. The output consists of emotional support and instructions regarding necessary next steps.

[0768] (Application Example 2)

[0769] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0770] In the event of a disaster, it is crucial to quickly confirm the safety of victims and provide them with psychological support. However, traditional methods face challenges in providing appropriate assistance due to limitations in communication infrastructure and insufficient sentiment analysis technology. Furthermore, multilingual support and rapid information transfer to experts are not easily achieved. In such situations, victims may not receive timely and appropriate information and support, potentially increasing their anxiety and stress.

[0771] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0772] In this invention, the server includes emotion recognition means for collecting and analyzing emotional information from users, automatic response generation means for generating and transmitting customized support information based on the analyzed emotional information, means for providing expert information to users who need mental support, and cross-platform data transmission and reception means for multiple platforms. This makes it possible to provide rapid and appropriate support according to the emotional state of disaster victims and to alleviate their anxiety.

[0773] "Emotion recognition means" refers to technology that analyzes emotional information collected from users to identify the user's emotional state.

[0774] An "automatic response generation method" is a technology that automatically generates and sends appropriate support information and messages to the user based on analyzed emotional information.

[0775] "Means of providing expert information to users who need emotional support" refers to a process of providing appropriate expert information based on the results of sentiment analysis, according to the user's condition.

[0776] "Cross-platform data transmission and reception methods" refer to technologies that enable the smooth transmission and reception of data between multiple different devices, such as smartphones and tablets.

[0777] The system necessary to implement this invention includes an emotion recognition means, an automatic response generation means, a means for providing expert information to users who require emotional support, and a cross-platform data transmission and reception means.

[0778] First, users input safety and emotional information using devices such as smartphones and tablets. This information can be collected in text, image, or audio format and reflects the user's actual emotional state. The device sends this data to emotion recognition software, such as the Google Cloud Natural Language API. The emotion recognition software analyzes this data to identify the user's emotional state. For example, if a user inputs "I am very anxious," that text will be analyzed as indicating a high level of anxiety.

[0779] The analyzed emotional information is sent to a server. The server operates a backend system based on Node.js and Express and stores the data in Amazon DynamoDB. Based on this information, the server uses an automated response generation mechanism to generate appropriate support information and messages. For example, if a user is showing strong anxiety, it can generate a message guiding them to relaxation techniques to alleviate anxiety and send it to the user's device via Firebase Cloud Messaging.

[0780] Furthermore, if a user requiring mental health support is identified, the server will provide information on appropriate professionals. This information includes a list of local professionals who can schedule psychological counseling appointments.

[0781] To ensure cross-platform compatibility, the system uses React Native to seamlessly send and receive data between different devices. This allows users to access the system anytime, anywhere, on their preferred device.

[0782] For example, in response to input such as "I'm so scared of earthquakes I can't sleep," the system automatically sends a message including "breathing exercises to alleviate anxiety" and "contact information for a local counselor."

[0783] An example of a prompt for a generative AI model is: "Generate a message aimed at providing emotional support to a user after a disaster. Input: I'm scared of the earthquake and can't sleep." This aims to generate specific methods and messages to alleviate the user's anxiety.

[0784] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0785] Step 1:

[0786] The user uses a device to input safety information and emotional information. The input data is in the form of text, images, and audio, and these reflect the user's emotional state. The device transmits this information to an emotion recognition system.

[0787] Step 2:

[0788] The emotion recognition system receives input data and performs emotion analysis using the Google Cloud Natural Language API. It extracts emotional characteristics from the input data to identify the user's emotional state. This classifies the text into emotional categories such as "anxiety" or "reassurance." The analysis results are then transferred to the server.

[0789] Step 3:

[0790] Based on the received sentiment analysis results, the server generates appropriate support information and messages using an automated response generation system. A backend using Node.js and Express determines the content of the message to be generated and proceeds to the next processing stage. The generated message is obtained as output.

[0791] Step 4:

[0792] The generated message is sent to the user's device via Firebase Cloud Messaging. The server prepares the message and sends it to the user's device in real time, ensuring that the user receives personalized support information.

[0793] Step 5:

[0794] If the server determines that a user requires mental health support, it will take steps to provide the user with information on suitable professionals. It will retrieve a list of appropriate professionals from its database and prepare them for the user. This information will be sent to the user, enabling them to access support.

[0795] Step 6:

[0796] User interactions such as clicks trigger cross-platform data transmission using React Native. This step provides a seamless experience across devices, making it easier for users to take action.

[0797] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0798] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0799] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0800] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0801] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0802] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0803] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0804] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0805] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0806] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0807] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0808] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0809] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0810] 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.

[0811] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0812] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0813] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0814] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0815] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0816] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0817] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0818] The following is further disclosed regarding the embodiments described above.

[0819] (Claim 1)

[0820] A device that enables the input and transmission of information even under conditions where communication infrastructure is limited, using wireless communication means and having means capable of compressing and transmitting information even in offline mode,

[0821] A means including a server capable of updating disaster information in real time and saving it to a database,

[0822] A method using AI to respond to user requests for assistance, equipped with an automated message system for checking on the safety of users, and prioritizing and distributing information,

[0823] A means of providing multilingual translation and mental support using generative models,

[0824] A communication system that includes this.

[0825] (Claim 2)

[0826] The system according to claim 1, which has a function to transmit information entered by disaster victims to a server via multiple communication media, the server stores the received information in a database and notifies other users of that information.

[0827] (Claim 3)

[0828] The system according to claim 1, characterized in that it has a means of analyzing support requests, quickly transferring information to appropriate supporters or organizations, and notifying the user of the progress.

[0829] "Example 1"

[0830] (Claim 1)

[0831] A device that enables information input and transmission even in unstable communication environments, using wireless communication means and capable of compressing and transmitting information even in offline mode,

[0832] A means including a processing device capable of updating disaster cases in real time and saving them to a recording medium,

[0833] A method that responds to support requests from users, has an automated message function for confirming safety, and uses a generative model to prioritize and distribute information,

[0834] A means of providing multilingual translation and mental support using generative models,

[0835] A means of saving user input to the terminal and selecting and transmitting the appropriate communication method according to the network conditions,

[0836] A means of receiving multiple pieces of information and optimizing notifications based on priority,

[0837] A system that includes this.

[0838] (Claim 2)

[0839] The system according to claim 1, which has a function to transmit information entered by disaster victims to a processing device via multiple communication media, the processing device stores the received information on a recording medium, and notifies other users of that information.

[0840] (Claim 3)

[0841] The system according to claim 1, characterized by having a means to analyze support requests, quickly transfer information to appropriate supporters or groups, and notify users of the progress.

[0842] "Application Example 1"

[0843] (Claim 1)

[0844] A device that enables the input and transmission of information even under conditions where communication infrastructure is limited, using wireless communication means and having means capable of compressing and transmitting information even in offline mode,

[0845] A means to collect information on safety conditions and necessary support during a disaster, and to share this information among users using location information,

[0846] A means including a server capable of updating disaster information in real time and saving it to a database,

[0847] A method using artificial intelligence to respond to user requests for assistance, equipped with an automated message system for checking on the safety of users, and prioritizing and distributing information,

[0848] A means of providing multilingual translation and mental support using generative models,

[0849] A system that includes this.

[0850] (Claim 2)

[0851] The system according to claim 1, which has a function to transmit information entered by disaster victims to a server via multiple communication media, the server stores the received information in a database and notifies other users of that information.

[0852] (Claim 3)

[0853] The system according to claim 1, characterized in that it has a function to analyze location information based on disaster information and provide the optimal evacuation route, and notifies the user of the progress of the evacuation.

[0854] "Example 2 of combining an emotion engine"

[0855] (Claim 1)

[0856] A means to enable communication devices to input and transmit information even in a disaster environment,

[0857] A means of compressing information using wireless communication so that it can be transmitted even offline,

[0858] A means including a data server that updates and stores information in real time,

[0859] A method using artificial intelligence that has an automatic notification function in response to user requests for assistance and prioritizes information,

[0860] A means of providing mental support tailored to emotional states using a generative model,

[0861] A system that includes this.

[0862] (Claim 2)

[0863] The system according to claim 1, which transmits information entered by a user to a data server via multiple communication terminals and notifies other users of the received information.

[0864] (Claim 3)

[0865] The system according to claim 1, which analyzes support requests, quickly forwards information to appropriate supporters or organizations, and notifies the user of the progress.

[0866] "Application example 2 when combining with an emotional engine"

[0867] (Claim 1)

[0868] A means of communication that efficiently implements information sharing and psychological support during disasters,

[0869] A means of emotion recognition that collects and analyzes emotional information from users,

[0870] An automated response generation means that generates and transmits customized support information based on analyzed emotional information,

[0871] A means of providing expert information to users who need mental health support,

[0872] A data transmission and reception method that supports cross-platform compatibility across multiple platforms,

[0873] A system that includes this.

[0874] (Claim 2)

[0875] The system according to claim 1, wherein the emotion recognition means analyzes emotional information in real time and automatically generates and sends a message related to the user's emotional state.

[0876] (Claim 3)

[0877] The system according to claim 1, characterized in that it promptly transfers information to appropriate supporters or organizations based on the user's emotional information and notifies the user of the progress. [Explanation of Symbols]

[0878] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A device that enables the input and transmission of information even under conditions where communication infrastructure is limited, using wireless communication means and having means capable of compressing and transmitting information even in offline mode, A means including a server capable of updating disaster information in real time and saving it to a database, A method using AI to respond to user requests for assistance, equipped with an automated message system for checking on the safety of users, and prioritizing and distributing information, A means of providing multilingual translation and mental support using generative models, A communication system that includes this.

2. The system according to claim 1, which has a function to transmit information entered by disaster victims to a server via multiple communication media, the server stores the received information in a database and notifies other users of that information.

3. The system according to claim 1, characterized in that it has a means of analyzing support requests, quickly transferring information to appropriate supporters or organizations, and notifying the user of the progress.

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