System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving hazard information; means for obtaining location information; means for analyzing a situation in a cloud; means for generating action guidance using a generative AI model; and means for providing the generated action guidance to a user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In modern society, natural disasters such as earthquakes, floods, and tsunamis occur frequently, and there is a need for prompt and appropriate evacuation instructions. However, when a disaster occurs, information becomes confusing and social infrastructure is often disrupted, making it difficult for individual users to take appropriate evacuation actions. Under such circumstances, there is a need for a system that can provide specific guidance for users to take appropriate evacuation actions. [Means for solving the problem]

[0005] The present invention provides a means for receiving disaster information, acquiring user location information, and analyzing the situation in the cloud. Furthermore, the above-mentioned problems are solved by building a system that includes a means for generating action guidance using a generative AI model and providing it to users. Specifically, the system identifies the optimal evacuation route and evacuation location based on the user's current location, and generates different action guidance based on the type of disaster and its impact, thereby supporting users in taking prompt and appropriate evacuation actions.

[0006] "Disaster information" refers to information on advance forecasts of natural disasters such as earthquakes, floods, and tsunamis, as well as information on the impact of such disasters after they occur.

[0007] "Location Information" means information that indicates the current geographic location of a user or device, obtained using GPS or other location-determining technology.

[0008] The "cloud" refers to virtual servers or data centers on a network that store, manage, and process data via the Internet.

[0009] A "generative AI model" is an algorithm or system that uses artificial intelligence techniques such as machine learning and deep learning to generate appropriate results or predictions based on input data.

[0010] "Action guidance" is information that instructs the user on specific actions to be taken in a particular situation.

[0011] "User" refers to an individual or organization that uses the disaster evacuation assistance system.

[0012] An "evacuation route" is information indicating the route the user should take to safely reach an evacuation site.

[0013] An "evacuation site" refers to a place or facility that is considered safe in the event of a disaster. [Brief explanation of the drawings]

[0014] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0015] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0017] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0018] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0020] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0022] [First embodiment]

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

[0024] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0027] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0031] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0034] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0035] The present invention provides a system that receives disaster information, acquires and analyzes user location information, and generates and provides action guidance using a generative AI model. Specific embodiments of the system are described below.

[0036] Server Processing

[0037] 1. Receiving disaster information

[0038] The server accesses various disaster information services (e.g., the Japan Meteorological Agency and earthquake research institutes) and receives information on natural disasters in real time.

[0039] The received disaster information includes parameters such as type (earthquake, flood, tsunami, etc.), location of occurrence, affected area, and predicted damage situation.

[0040] 2. Collection of user location information

[0041] The server receives the location information sent from the user's smartphone.

[0042] This allows you to determine where the user is currently located in real time.

[0043] 3. Analyze the situation

[0044] The server integrates location information and received disaster information on the cloud and performs analysis taking into account factors such as the operation status of public transport, the occupancy level of evacuation centres, and road closure status.

[0045] 4. Generating action guidance

[0046] The server generates appropriate action guidance using a generative AI model, an algorithm that predicts optimal actions based on past disaster data and current situation data.

[0047] Processing on the device (user's smartphone)

[0048] 1. Receiving Data

[0049] The terminal receives disaster information and action instructions sent from the server.

[0050] Based on the received information, specific action instructions are created for the user.

[0051] 2. Displaying Data

[0052] The device displays action guidance to the user using voice notifications, text messages, and map information.

[0053] For example, when an earthquake occurs, a notification will be displayed saying, "You are currently inside an office building. Please hide under a desk and wait until the shaking stops."

[0054] User Behavior

[0055] 1. Using the app

[0056] The user launches the Disaster Evacuation Assistant app on their smartphone and checks the notification.

[0057] 2. Follow the instructions

[0058] Users take appropriate action by following the specific guidance provided by the app.

[0059] For example, when a flood warning is issued, people will follow the notification that says, "The nearest evacuation site is XX Park. Please move quickly," and head to the designated evacuation site.

[0060] Specific examples

[0061] Example 1: When an earthquake occurs

[0062] 1. The server receives earthquake information from the Japan Meteorological Agency and analyzes the epicenter and seismic intensity.

[0063] 2. The server confirms that User A is located within an office building and generates appropriate action guidance.

[0064] 3. The device sends a notification to User A saying, "An earthquake has occurred. You are currently in an office building. Please hide under a desk and wait until the shaking stops."

[0065] 4. The user follows the instructions and hides under the desk.

[0066] Example 2: When a flood or tsunami warning is issued

[0067] 1. The server receives flood and tsunami warnings and identifies risk areas.

[0068] 2. The server verifies that User B is located within the danger zone and generates directions to a safe evacuation site.

[0069] 3. The device sends a notification to User B saying, "A tsunami warning has been issued. Your current location is dangerous. The nearest evacuation site is XX Park. Please move quickly."

[0070] 4. The user follows the instructions and heads to the designated evacuation site.

[0071] This invention can ensure the safety of users in the event of a disaster and prevent disruption to social infrastructure.

[0072] The processing flow will be explained below.

[0073] Step 1:

[0074] The server accesses various disaster information services (e.g., the Japan Meteorological Agency, earthquake research institutes) and receives real-time information on natural disasters, including the type of disaster, the location of the disaster, the extent of its impact, and predicted damage.

[0075] Step 2:

[0076] The device periodically uses the GPS sensor to obtain the user's current location, which is then sent to a server in the background in preparation for a disaster.

[0077] Step 3:

[0078] The server stores the user's location information and disaster information in a database and compares it with the disaster information.

[0079] Step 4:

[0080] The server integrates the user's location information and disaster information on the cloud, and analyzes the status of public transport, the congestion level of evacuation shelters, road closures, etc. This allows the user to grasp the specific situation they are currently in in real time.

[0081] Step 5:

[0082] The server uses a generative AI model to generate optimal guidance for each user, which predicts appropriate actions based on location based on past data and the current situation.

[0083] Step 6:

[0084] The server transmits the generated action guide to the user's terminal.

[0085] Step 7:

[0086] The device receives guidance from the server and notifies the user of the action instructions using voice, text, map information, etc. For example, when an earthquake occurs, the device displays a message saying, "You are currently in an office building. Please hide under a desk and wait until the shaking stops."

[0087] Step 8:

[0088] The user follows the notification on the device and takes the specified evacuation action. For example, in the event of a flood warning, the user will act according to instructions such as "The nearest evacuation site is XX Park. Please move quickly."

[0089] Step 9:

[0090] The server periodically updates the user's location information and new disaster information, and regenerates action guidance as necessary, allowing the user to always act based on the latest information.

[0091] By this specific processing, the present invention enables the user to take prompt and appropriate evacuation actions in the event of a disaster.

[0092] Example 1

[0093] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0094] When a natural disaster occurs, it is difficult to receive accurate disaster information in real time and generate and provide optimal action guidance based on the user's current location. There is also a need to provide users with accurate and prompt information and encourage safe evacuation. Existing systems often lack immediacy and accuracy because they receive disaster information, analyze the situation, and generate and present action guidance separately.

[0095] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0096] In this invention, the server includes means for receiving disaster information, means for acquiring location information, means for analyzing the situation in the cloud, means for generating action guidance using a generative AI model, means for providing the generated action guidance to the user, means for causing the user's terminal to receive the disaster information and action guidance, and means for displaying the received action guidance, thereby enabling the user to receive optimal action guidance in real time and take quick and safe evacuation actions.

[0097] "Means for receiving disaster information" refers to a device or system for obtaining disaster information in real time from meteorological agencies, research institutions, etc.

[0098] "Means for obtaining location information" means a device or system that uses GPS or other location measurement technology to determine a user's current location and transmits that information to a server.

[0099] "Means for analyzing situations in the cloud" refers to a device or system that uses a remote server accessible via the Internet to integrate and analyze collected data and assess the situation in real time.

[0100] "Means for generating action guidance using a generative AI model" refers to a device or system that uses artificial intelligence technology to generate optimal action guidance for a user based on past data and the current situation.

[0101] The "means for providing the generated action guide to the user" refers to a device or system for notifying the user of the generated action guide.

[0102] "Means for allowing a user's device to receive disaster information and action guidance" refers to a device or system for sending and receiving disaster information and action guidance to an electronic device such as a smartphone or tablet owned by the user.

[0103] The "means for displaying received action guidance" refers to a device or system that visually or audibly displays action guidance in the form of text, voice, map, or the like on the user's terminal.

[0104] This invention is a system that receives disaster information, acquires and analyzes user location information, and generates and provides action guidance using a generative AI model. Specific embodiments of this system are described below.

[0105] Server Processing

[0106] 1. Receiving disaster information

[0107] The server uses APIs provided by meteorological agencies and research institutions to collect the latest disaster information on earthquakes, floods, tsunamis, etc. in real time. Specifically, it obtains JSON-formatted data from various APIs and analyzes parameters such as type, location of occurrence, affected area, and predicted damage situation. This received data is stored in a database (such as an SQL database).

[0108] 2. Collection of user location information

[0109] The server collects location information (GPS data) sent from the user's smartphone in real time, which allows it to accurately identify the device's location and store it in a database.

[0110] 3. Analyze the situation

[0111] The server uses cloud services (e.g., Amazon Web Services, Microsoft® Azure®) to integrate and analyze location information and disaster information in real time. Specifically, it uses AWS® Lambda functions and Azure Functions to perform analysis taking into account the operation status of public transportation, the occupancy level of evacuation shelters, road closure status, etc. This allows it to determine whether the user's current location will be affected by the disaster.

[0112] 4. Generating action guidance

[0113] The server generates appropriate action guidance using a generative AI model (e.g., GPT-4 (registered trademark) from OpenAI (registered trademark)). For example, the prompt sentence is "Please generate the optimal action guidance for the user to safely evacuate from their current location." The generated action guidance is optimized using natural language processing and provided in an easy-to-understand format. The action guidance is generated in JSON format and sent to the user's device.

[0114] Processing on the device (user's smartphone)

[0115] 1. Receiving Data

[0116] The device receives disaster information and action instructions sent from the server via secure communication (e.g., HTTPS). This data is stored in a local database (e.g., SQLite) and prepared for immediate notification to the user.

[0117] 2. Displaying Data

[0118] The device visually displays the received guidance to the user. It displays map information along with text messages on the screen, and also provides voice notifications as needed. For example, in the event of an earthquake, the device displays evacuation locations on a map along with a message saying, "You are currently inside an office building. Please take cover under your desk and wait until the shaking stops."

[0119] User Behavior

[0120] 1. Using the app

[0121] Users launch the Disaster Evacuation Assistant app on their smartphones and check notifications. The app runs in the background, allowing them to respond immediately when a notification arrives. Users can check detailed disaster information and evacuation instructions within the app.

[0122] 2. Follow the instructions

[0123] Users take appropriate action by following the specific action instructions provided by the app. For example, when a flood warning is issued, users will follow the notification "The nearest evacuation site is XX Park. Please move quickly" and head to the designated evacuation site.

[0124] Example prompt

[0125] An example of a prompt sentence to input to a generative AI model is as follows:

[0126] "Generate optimal evacuation instructions based on the latest earthquake information and the user's location information."

[0127] "Please provide specific instructions on what to do if a flood warning is issued and the user's current location is in a risk area."

[0128] In this way, by ensuring the safety of users in the event of a disaster and providing prompt and accurate guidance on what to do, it is possible to prevent disruptions to social infrastructure.

[0129] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0130] Step 1:

[0131] Receiving disaster information

[0132] The server uses APIs provided by meteorological agencies and research institutions to collect the latest disaster information on earthquakes, floods, tsunamis, etc. in real time. Specifically, it obtains JSON-formatted data from various APIs and analyzes parameters such as type, location of occurrence, affected area, and predicted damage situation. The JSON data obtained from the API is used as input for the received data, and it is processed for storage in a database, extracting and saving the necessary items.

[0133] Step 2:

[0134] Collection of user location information

[0135] The server collects location information (GPS data) sent from the user's smartphone in real time. It inputs the GPS data sent from the user's device in JSON format, analyzes the data, and processes it to store it in a database. The analyzed location information is saved for each user ID, allowing the latest location to be checked at any time.

[0136] Step 3:

[0137] Analysis of the situation

[0138] The server uses cloud services (e.g., Amazon Web Services, Microsoft Azure) to integrate and analyze location information and disaster information in real time. Using AWS Lambda functions and Azure Functions, it performs analysis taking into account factors such as transportation operation status, evacuation shelter occupancy levels, and road closure status. Using the collected location information and disaster information as input, it determines whether specific users will be affected by the disaster. The results of this analysis are output as the necessity and priority of evacuation.

[0139] Step 4:

[0140] Generate action guide

[0141] The server generates appropriate action guidance using a generative AI model (e.g., OpenAI's GPT-4). For example, the server inputs a prompt statement such as "Please generate the optimal action guidance for the user to safely evacuate from their current location," and outputs the action guidance generated by the AI ​​model. The generated guidance is stored in a database and sent to the user's device in JSON format.

[0142] Step 5:

[0143] Receiving data

[0144] The device receives disaster information and action instructions sent from the server via secure communication (e.g., HTTPS communication). It takes the JSON-formatted action instructions sent from the server as input, stores them in a local database (e.g., SQLite), and prepares for immediate notification.

[0145] Step 6:

[0146] Viewing Data

[0147] The device visually displays the received guidance to the user. Using the guidance stored in a local database as input, the device displays map information along with text messages on the screen and, if necessary, provides audio notifications. For example, a message might read, "An earthquake has occurred. You are currently inside an office building. Please take cover under your desk and wait until the shaking stops."

[0148] Step 7:

[0149] Using the app

[0150] The user launches the Disaster Evacuation Assistant app on their smartphone and checks the notification. When the user launches the app, the latest action instructions sent from the server are displayed. User input is required, and detailed disaster information and evacuation instructions can be viewed within the app.

[0151] Step 8:

[0152] Follow the instructions

[0153] The user takes appropriate action according to the specific action guidance provided by the app. The user receives action guidance from the app as input and takes actual evacuation action based on that guidance. For example, when a flood warning is issued, the user will follow the notification "The nearest evacuation site is XX Park. Please move quickly" and head to the designated evacuation site.

[0154] (Application example 1)

[0155] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0156] In recent years, the increasing frequency and diversity of disasters has created a need for prompt and appropriate guidance. However, current systems sometimes fail to adequately ensure user safety. There is a need for technology that can obtain disaster information in real time and provide optimal guidance based on the user's current location. In addition, it is necessary to use a generative AI model to generate guidance based on prompts and provide it to users effectively using voice notifications, text messages, and map information.

[0157] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0158] In this invention, the server includes means for receiving disaster information, means for acquiring location information, means for analyzing the situation in the cloud, means for generating action guidance using a generative AI model, means for providing the generated action guidance to a user, means for identifying an optimal evacuation route and evacuation location based on the user's current location, means for inputting a prompt sentence to the generative AI model when generating the action guidance and generating optimal action guidance based on the prompt sentence, and means for providing the action guidance to the user using voice notification, text message, and map information. This enables quick and appropriate action guidance for users in the event of a disaster.

[0159] "Disaster information" refers to real-time information on natural disasters provided by the Japan Meteorological Agency, earthquake research institutes, and other organizations.

[0160] "Location information" is data indicating the current geographical coordinates obtained from a user's smartphone or the like.

[0161] The "cloud" is a distributed computing environment for managing, storing, and analyzing resources and data over the Internet.

[0162] A "generative AI model" is an artificial intelligence technology that incorporates an algorithm that generates behavioral guidance based on past data and current situational data.

[0163] "Action guidance" is information that instructs the user on the optimal action to take in the event of a disaster.

[0164] A "prompt statement" is a statement that specifies the task you want the generative AI model to perform and the requirements for the results you want it to generate.

[0165] An "evacuation route" is information about the optimal route for a user to move to a safe place.

[0166] An "evacuation site" is a designated assembly point that will ensure safety in the event of a disaster.

[0167] "Audio notification" is a method of conveying information to a user using audio.

[0168] "Text message" is a method of conveying action instructions and the like to a user using text information.

[0169] "Map information" is data for visually displaying geographical locations, routes, etc.

[0170] This system receives disaster information, acquires and analyzes the user's location information, and generates and provides action guidance using a generative AI model. This system consists of a server and a user's device.

[0171] Server Processing

[0172] The server first accesses various disaster information services and receives information on natural disasters in real time. The received disaster information includes parameters such as the type of disaster (earthquake, flood, tsunami, etc.), the location of the disaster, the extent of the impact, and the predicted damage situation. Next, it receives location information from the user's smartphone and identifies the user's current location in real time. The collected location information and the received disaster information are integrated in the cloud, and an analysis is performed taking into account the operation status of public transportation, the occupancy level of evacuation shelters, the status of road closures, etc.

[0173] The server then inputs a prompt into the generative AI model to generate appropriate guidance. This generative AI model predicts and generates optimal actions based on past disaster data and current situation data. For example, a prompt might be, "Based on the user's location information and the latest earthquake information, please generate optimal evacuation guidance for the user."

[0174] Processing on the device (user's smartphone)

[0175] The device receives disaster information and action instructions sent from the server. Based on the received information, specific action instructions are created for the user. This includes displaying action instructions to the user using voice notifications, text messages, and map information. For example, in the event of an earthquake, a notification may be displayed saying, "You are currently inside an office building. Please hide under a desk and wait until the shaking stops." Different action instructions are generated on the user's device based on the type of disaster and its impact, and are notified to the user in real time.

[0176] User Behavior

[0177] Users receive notifications from this system on their smartphones and are informed of appropriate action instructions. For example, if a flood warning is issued, the system notifies them by saying, "The nearest evacuation site is XX Park. Please move quickly." They can then head to the designated evacuation site. The guidance is provided as voice notifications, text messages, and map information, making it easy for users to understand intuitively.

[0178] Hardware and software used

[0179] This system uses a user's smartphone and a server computer. The program implementation uses the requests library for API access, the geopy library for processing geographic information, and the datetime module for timestamp processing.

[0180] With the above configuration, the present invention realizes prompt and appropriate guidance of actions to be taken by a user in the event of a disaster, thereby ensuring the safety of the user.

[0181] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0182] Step 1:

[0183] The server accesses various disaster information services and receives information on natural disasters in real time. As input, it accesses the APIs of the Japan Meteorological Agency and earthquake research institutes, and as output, it obtains data such as the type of disaster, the location of the occurrence, the extent of the impact, and the predicted damage situation. This allows the server to always have the latest disaster information.

[0184] Step 2:

[0185] The server receives current location information from the user's smartphone. The user provides location information through a smartphone app, and the server receives that location information as GPS data. The user's location data is sent as input, and the latitude and longitude data of the current location are obtained as output.

[0186] Step 3:

[0187] The server integrates the received disaster information and the user's location information on the cloud and analyzes the situation. Disaster information and the user's location information are integrated as input, and analytical data such as the distance between the disaster site and the user and the extent of the impact are obtained as output. This allows the risk level of the user's current location to be analyzed.

[0188] Step 4:

[0189] The server inputs a prompt into the generative AI model and generates optimal action guidance. For example, the prompt could be, "Please generate optimal evacuation guidance for the user based on the user's location information and the latest earthquake information." The prompt and analysis data are provided as input, and optimal action guidance is generated as output. The generative AI model runs an algorithm based on past disaster data and current situation data to predict and generate action guidance.

[0190] Step 5:

[0191] The server provides the generated action guidance to the user's smartphone. The action guidance is sent to the user as a voice notification, a text message, and map information. The generated action guidance is the input, and a notification is sent to the user's smartphone as the output. This allows the user to receive guidance to take prompt and appropriate action.

[0192] Step 6:

[0193] Users take appropriate action based on the guidance they receive through a smartphone app. For example, if a flood warning is issued, they follow the notification, "The nearest evacuation site is XX Park. Please move quickly." The user receives guidance as input and performs appropriate evacuation actions as output. The notification is provided as voice, text, and map information, making it easy for users to intuitively understand and act safely.

[0194] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0195] The present invention is a system that receives disaster information, acquires and analyzes the user's location information and emotions, and generates and provides action guidance using a generative AI model. Specific embodiments of the system are described below.

[0196] Server Processing

[0197] 1. Receiving disaster information

[0198] The server accesses various disaster information services (e.g., the Japan Meteorological Agency, earthquake research institutes) and receives real-time information on natural disasters, including the type of disaster, the location of the disaster, the extent of its impact, and predicted damage.

[0199] 2. Collection of user location information

[0200] The server receives location information sent from the user's smartphone and uses this to determine where the user is currently located.

[0201] 3. Collecting user emotion information

[0202] The server uses an emotion engine to recognize emotions from the user's facial expressions, voice tone, input text, etc. This allows it to identify the stress and anxiety the user is feeling during a disaster.

[0203] 4. Analyze the situation

[0204] The server integrates location information, disaster information, and emotional information on the cloud, and analyzes the status of public transport, the congestion of evacuation shelters, road closures, etc. Based on this, the specific situation the user is currently in can be grasped in real time.

[0205] 5. Generating action guidance

[0206] The server uses a generative AI model to generate optimal guidance for each user, which predicts appropriate actions based on location, based on past data, the current situation, and the user's emotions.

[0207] Processing on the device (user's smartphone)

[0208] 1. Receiving Data

[0209] The terminal receives disaster information, action instructions, and support messages based on emotions sent from the server.

[0210] 2. Displaying Data

[0211] The device will display guidance to the user using voice notifications, text messages, and map information, including responses based on emotions. For example, if the user is feeling anxious, the device will display a message saying, "Please stay calm. The current best evacuation route is XX."

[0212] User Behavior

[0213] 1. Using the app

[0214] The user launches the Disaster Evacuation Assistant app on their smartphone and checks the notification.

[0215] 2. Follow the instructions

[0216] The user takes appropriate action by following specific instructions provided by the app, such as "The nearest evacuation site is XX Park. Please move quickly."

[0217] Specific examples

[0218] Example 1: When an earthquake occurs

[0219] 1. The server receives earthquake information from the Japan Meteorological Agency and analyzes the epicenter and seismic intensity.

[0220] 2. The server confirms that User A is located within an office building and generates appropriate action guidance.

[0221] 3. The server determines through the emotion engine that User A is feeling anxious.

[0222] 4. The device sends a notification to User A saying, "An earthquake has occurred. You are currently inside an office building. Please hide under a desk and wait until the shaking stops. Please remain calm."

[0223] 5. The user follows the instructions and hides under the desk.

[0224] Example 2: When a flood or tsunami warning is issued

[0225] 1. The server receives flood and tsunami warnings and identifies risk areas.

[0226] 2. The server verifies that User B is located within the danger zone and generates directions to a safe evacuation site.

[0227] 3. The server, through its emotion engine, identifies that User B is confused.

[0228] 4. The device sends a notification to User B saying, "A tsunami warning has been issued. Your current location is dangerous. The nearest evacuation site is XX Park. Please move quickly. Do not panic, and act calmly."

[0229] 5. The user follows the instructions and heads to the designated evacuation site.

[0230] This invention enables users to take prompt and appropriate evacuation actions in the event of a disaster, and by taking into consideration the user's emotions, it is possible to provide support that allows users to act with greater peace of mind.

[0231] The processing flow will be explained below.

[0232] Step 1:

[0233] The server periodically sends requests to disaster information providers (e.g., the Japan Meteorological Agency, earthquake research institutes) to receive the latest disaster information. The received information includes the type of disaster (earthquake, flood, tsunami, etc.), the location of the disaster, the extent of the impact, and the predicted damage situation.

[0234] Step 2:

[0235] The device periodically uses the GPS sensor to obtain the user's current location, which is then automatically sent to the server and stored in a database.

[0236] Step 3:

[0237] The server uses an emotion engine to recognize the user's emotions in real time from the device's voice, camera, text input, etc. Emotions are evaluated based on multiple parameters, such as anxiety, stress, and calmness.

[0238] Step 4:

[0239] The server integrates location information, disaster information, and emotional information, and analyzes in real time the status of transportation within the affected area, the congestion level of evacuation centers, road closure status, and other information.

[0240] Step 5:

[0241] The server uses a generative AI model to generate optimal action guidance based on the user's current location and emotional state. The generative AI model predicts optimal actions by taking into account past disaster data, current disaster information, and the user's individual situation.

[0242] Step 6:

[0243] The server sends the generated action guide to the user's terminal, which includes specific evacuation routes, evacuation locations, and messages according to emotions.

[0244] Step 7:

[0245] The device will then provide the user with detailed instructions using voice notifications, text messages, and map information. For example, in the event of an earthquake, the device will display a message saying, "You are currently in an office building. Please take cover under a desk. Please remain calm and act accordingly."

[0246] Step 8:

[0247] The user follows the notification on the device and takes the evacuation action instructed. For example, in the case of a flood warning, the user follows the instruction "The nearest evacuation site is XX Park. Please move quickly."

[0248] Step 9:

[0249] The server continuously monitors the user's location and emotional information and regenerates new action guidance as necessary. This dynamic process helps users take appropriate actions even when the disaster situation changes.

[0250] By this specific processing, the present invention enables the user to take prompt and appropriate evacuation action in the event of a disaster, and furthermore, can provide mental care by taking into account the user's emotional state.

[0251] Example 2

[0252] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0253] When a natural disaster occurs, it is necessary to provide users with prompt and appropriate guidance. However, conventional systems identify evacuation routes using only the user's location information, and therefore do not provide personalized guidance that takes into account the user's emotional state, making it difficult to reduce the stress and anxiety users feel during a disaster. Therefore, it is necessary to provide guidance that also takes into account the user's emotional state.

[0254] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0255] In this invention, the server includes means for receiving disaster information, means for acquiring location information, means for acquiring emotion information, means for analyzing the situation in the cloud, means for generating action guidance using a generative AI model, and means for providing the generated action guidance to the user. This provides personalized action guidance based on the user's location information and emotional state, enabling the user to take optimal evacuation actions quickly and safely in the event of a disaster.

[0256] "Disaster information" refers to information about natural disasters (earthquakes, floods, tsunamis, etc.), including the type of disaster, the location of the disaster, the extent of the impact, and predicted damage.

[0257] "Location information" is information indicating the user's current location based on GPS data provided by the user's device.

[0258] "Emotional information" is information about a user's emotional state determined from the user's facial expression, voice tone, and input text, obtained using emotion recognition technology.

[0259] The "cloud" is an infrastructure that provides resources and services for storing, processing, and managing data over the Internet.

[0260] A "generative AI model" is a model that uses artificial intelligence to analyze and predict data, and automatically generates action guidance appropriate for the user.

[0261] "Action guidance" is information that instructs the user on specific actions to be taken in the event of a disaster, and includes evacuation routes, evacuation locations, messages according to the user's emotions, and the like.

[0262] "User" refers to a person who uses this system, and is an individual who should take evacuation action in the event of a disaster.

[0263] MODE FOR CARRYING OUT THE INVENTION

[0264] This invention is a system that receives disaster information, acquires and analyzes the user's location information and emotional information, and generates and provides action guidance using a generative AI model.

[0265] Server Processing

[0266] Receiving disaster information

[0267] The server sends API requests to multiple disaster information providers (e.g., the Japan Meteorological Agency, earthquake research institutes) to obtain disaster information. Disaster information includes the type of disaster, the location of the disaster, the extent of the impact, and predicted damage. The obtained information is stored in a database and updated in real time.

[0268] Collection of user location information

[0269] The server receives GPS data sent from the user's smartphone and identifies the user's current location. A dedicated app on the smartphone periodically sends location information to the server. This information is used to track the user's location in real time.

[0270] Collecting user emotion information

[0271] The server uses an emotion recognition engine (e.g., Microsoft Azure Cognitive Services, IBM Watson®) to analyze facial images, voice, and input text acquired from the user's smartphone. This allows the server to identify the user's emotional state (stress, anxiety, etc.). For example, the server analyzes the user's facial expressions through a camera and evaluates the user's excitement level from their voice.

[0272] Analysis of the situation

[0273] The server consolidates and analyzes the following data in a cloud environment:

[0274] 1. Disaster information (real-time disaster occurrence status)

[0275] 2. User location information

[0276] 3. User Emotion Information

[0277] Furthermore, the system also collects information on the operation status of public transport (e.g., Google Maps API), information on the congestion level of evacuation centres (data from government agencies), and road closure status (data from police and traffic information services) to perform comprehensive situation analysis, thereby enabling the system to grasp the specific situation of each user in real time.

[0278] Generate action guide

[0279] The server uses a generative AI model (e.g., OpenAI GPT-4) to generate action guidance based on the analysis data. The generative AI model takes into account past data, the current situation, and the user's emotions to automatically generate optimal evacuation routes, evacuation locations, first aid methods, etc. For example, it generates a message such as, "The nearest evacuation location is XX Park. Please evacuate immediately."

[0280] Processing on the device (user's smartphone)

[0281] Receiving data

[0282] The device receives disaster information, action instructions, and emotional support messages sent from the server in the form of push notifications.

[0283] Viewing Data

[0284] The terminal displays the following information to the user:

[0285] 1. Disaster Information: Breaking news on earthquakes, tsunami warnings, etc.

[0286] 2. Action Guide: Information on evacuation routes and evacuation shelters

[0287] 3. Emotional support messages: Messages of comfort and encouragement that correspond to the user's emotional state

[0288] This information is displayed in a notification bar or pop-up window, and is also accompanied by audio guidance, in an effort to reduce anxiety and confusion.

[0289] User Behavior

[0290] Using the app

[0291] The user launches the Disaster Evacuation Assistant app on their smartphone and checks the notification sent from the server. The app provides specific instructions on what actions the user should take and where to evacuate in the event of a disaster.

[0292] Follow the instructions

[0293] Users can take appropriate action based on the guidance provided by the app. For example, they can check the "route guidance to the nearest evacuation shelter" and take a safe route to the shelter.

[0294] Specific examples

[0295] Example 1: When an earthquake occurs

[0296] 1. The server receives earthquake information from the Japan Meteorological Agency and analyzes the epicenter and seismic intensity.

[0297] 2. The server confirms that User A is located within an office building and generates appropriate action guidance.

[0298] 3. The server determines through the emotion engine that User A is feeling anxious.

[0299] 4. The device sends a notification to User A saying, "An earthquake has occurred. You are currently inside an office building. Please hide under a desk and wait until the shaking stops. Please remain calm."

[0300] 5. The user follows the instructions and hides under the desk.

[0301] Example 2: When a flood or tsunami warning is issued

[0302] 1. The server receives flood and tsunami warnings and identifies risk areas.

[0303] 2. The server verifies that User B is located within the danger zone and generates directions to a safe evacuation site.

[0304] 3. The server, through its emotion engine, identifies that User B is confused.

[0305] 4. The device sends a notification to User B saying, "A tsunami warning has been issued. Your current location is dangerous. The nearest evacuation site is XX Park. Please move quickly. Do not panic, and act calmly."

[0306] 5. The user follows the instructions and heads to the designated evacuation site.

[0307] This invention makes it possible to support users so that they can act quickly and appropriately in the event of a disaster, and by providing support that takes into account their emotional state, it is possible to reduce the anxiety and stress of users.

[0308] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0309] Step 1: Receiving disaster information

[0310] The server sends an API request to the disaster information service to obtain disaster information in real time. This information includes the type of disaster, the location of the disaster, the extent of the impact, and the predicted damage situation, and is stored in a database. The API request is made as input, and disaster information data is obtained as output. This data is used in the next analysis step.

[0311] Step 2: Collect user location information

[0312] The server continuously receives GPS data from the user's smartphone. A dedicated app periodically sends location information to the server, which receives it and identifies the user's current location. The server receives the user's GPS data as input and stores the user's current location as output in a database. This location information is used for situation analysis.

[0313] Step 3: Collecting user emotion information

[0314] The server uses an emotion recognition engine to analyze facial images, voice, and input text acquired from the user's smartphone. This allows it to identify the user's emotional state (stress, anxiety, etc.). It receives facial images and voice data as input, generates user emotional information as output, and stores it in a database.

[0315] Step 4: Analyze the situation

[0316] The server integrates and analyzes the following data in a cloud environment:

[0317] 1. Disaster information (real-time disaster occurrence status)

[0318] 2. User location information

[0319] 3. User Emotion Information

[0320] The server also collects additional data such as transportation status, evacuation shelter congestion, road closure status, etc., and integrates these data to perform a comprehensive situation analysis. All data is integrated as input and a specific situation report for each user is generated as output. This report is used in the action guide generation step.

[0321] Step 5: Generate action guide

[0322] The server uses a generative AI model to generate appropriate action guidance based on the situation report. The generative AI model automatically generates optimal evacuation routes, evacuation locations, first aid methods, etc., taking into account past data, the current situation, and the user's emotions. It uses the situation report as input and generates an action guidance message as output. This message is used in the next notification step.

[0323] Step 6: Receiving and displaying data (terminal)

[0324] The device receives disaster information, action instructions, and emotional support messages sent from the server in the form of push notifications. The device receives notification data from the server as input and displays it to the user as output. Specifically, the device provides information to the user using a notification bar, a pop-up window, and voice guidance.

[0325] Step 7: App Usage and Behavior (Users)

[0326] The user launches the Disaster Evacuation Assistant app on their smartphone and checks the notification sent from the server. Specifically, the user confirms the instructions displayed on the screen and begins evacuation. The input is the notification on the app, and the output is the execution of appropriate actions, such as moving quickly to a designated evacuation site.

[0327] This series of processes enables users to take swift and appropriate evacuation actions in the event of a disaster, and by receiving support that takes into account their emotional state, they can reduce anxiety and stress.

[0328] (Application example 2)

[0329] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0330] In recent years, the frequency and scale of natural disasters have increased, making it important to provide prompt and appropriate evacuation guidance. However, conventional systems can only provide uniform guidance without taking into account the user's location information or emotional state, which makes it difficult for users to take appropriate action when they feel confused or anxious.

[0331] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0332] In this invention, the server includes means for receiving disaster information, means for acquiring location information, means for acquiring emotion information, means for analyzing the situation in the cloud, means for generating action guidance using a generative AI model, means for providing the generated action guidance to the user, and means for generating a voice notification. This makes it possible to provide optimal evacuation action guidance in real time that takes into account the user's location information and emotional state, and supports the user in taking prompt and appropriate action even when they feel confused or anxious.

[0333] "Means for receiving disaster information" refers to a function that obtains information in real time from external information sources when a natural disaster occurs.

[0334] "Means for obtaining location information" refers to a function that identifies the user's current geographic location using GPS or other location measurement methods.

[0335] The "means for acquiring emotional information" is a function for identifying and collecting the user's emotional state from facial expressions, voice, text, etc.

[0336] "Means for analyzing the situation using the cloud" is a function that uses cloud computing to comprehensively analyze collected data and evaluate current risks and appropriate actions.

[0337] "Means for generating action guidance using a generative AI model" is a function that utilizes AI technology to create action guidance customized for each user based on the analysis results.

[0338] The "means for providing the generated action guide to the user" is a function for notifying the user of the generated action guide in a form that the user can understand.

[0339] The "means for generating voice notification" is a function for converting text information into voice and providing a voice notification to the user in real time.

[0340] This invention is a system that receives disaster information, acquires and analyzes the user's location information and emotions, and generates and provides action guidance using a generative AI model. Specific embodiments of this system are described below.

[0341] Server Processing

[0342] The server first receives disaster information in real time from external sources, including disaster information providers such as the Japan Meteorological Agency and earthquake research institutes. The information received includes the type of disaster, the location of the disaster, the extent of its impact, and predicted damage.

[0343] The server then receives the location information sent from the user's smartphone and determines where the user is currently located. The location information is primarily obtained using GPS.

[0344] Furthermore, the server uses an emotion engine to recognize emotions from the user's facial expressions, voice tone, input text, etc. This allows it to identify the stress and anxiety the user is feeling during a disaster.

[0345] The server then integrates location information, disaster information, and emotional information on the cloud, and analyzes the status of public transport, the congestion of evacuation shelters, road closures, etc. Based on this, the specific situation the user is currently in can be grasped in real time.

[0346] Finally, the server generates optimal guidance for each user using a generative AI model, which predicts appropriate actions based on location, based on past data, the current situation, and the user's emotions.

[0347] Processing on the device (user's smartphone)

[0348] First, the device receives disaster information, action instructions, and support messages based on emotions sent from the server.

[0349] The device then displays instructions to the user using voice notifications, text messages, and map information, including responses based on emotions. For example, if the user is feeling anxious, the device will display a message saying, "Please stay calm. The current best evacuation route is ____."

[0350] The device then provides the generated action guidance to the user as a voice notification, using technologies such as Google Text-to-Speech (gTTS).

[0351] User Behavior

[0352] The user launches the Disaster Evacuation Assistant app on their smartphone, checks the notification, and takes appropriate action according to the specific instructions provided by the app.

[0353] Specific examples

[0354] Example 1: When an earthquake occurs

[0355] The server receives earthquake information from the Japan Meteorological Agency and analyzes the epicenter and seismic intensity. The server confirms that User A is located inside an office building and generates appropriate guidance for action. The server determines through the emotion engine that User A is feeling anxious. The device sends a notification to User A saying, "An earthquake has occurred. You are currently inside an office building, so please take cover under a desk and wait until the shaking stops. Please remain calm." The user follows the instructions and takes cover under a desk.

[0356] Example 2: When a flood or tsunami warning is issued

[0357] The server receives flood and tsunami warnings and identifies risk areas. The server confirms that User B's location is within the risk area and generates directions to a safe evacuation site. The server determines through the emotion engine that User B is confused. The device sends a notification to User B saying, "A tsunami warning has been issued. Your current location is dangerous. The nearest evacuation site is XX Park. Please move quickly. Do not panic, and act calmly." The user follows the instructions and heads to the designated evacuation site.

[0358] Examples of prompt statements

[0359] text

[0360] Current disaster information: A magnitude 7.5 earthquake occurred in Tokyo. The epicenter was in Tokyo Bay, and the seismic intensity was 5+.

[0361] User location: Latitude 35.6895, Longitude 139.6917 (i.e. Shinjuku Ward).

[0362] User's emotional state: Feeling very anxious.

[0363] Based on this information, generate the optimal action guide for the user.

[0364] This system helps users act quickly and appropriately in the event of a disaster, and by taking their emotions into consideration, it provides support that allows them to act with peace of mind.

[0365] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0366] Step 1:

[0367] The server receives disaster information in real time from external sources.

[0368] Input: Data from disaster information services such as the Japan Meteorological Agency and earthquake research institutes.

[0369] Data processing / calculation: Analyze the received data to identify the type of disaster, location, extent of impact, and predicted damage.

[0370] Output: Parsed disaster information.

[0371] Step 2:

[0372] The server receives location information sent from the user's smartphone and identifies the user's current location.

[0373] Input: GPS data from your smartphone.

[0374] Data processing / calculation: Location information is analyzed as latitude and longitude to identify a specific geographical location.

[0375] Output: User's current location (latitude and longitude).

[0376] Step 3:

[0377] The server uses an emotion engine to recognize emotions from the user's facial expressions, voice tone, input text, etc.

[0378] Input: User facial expression images, audio samples, and text data.

[0379] Data processing / calculation: The emotion engine analyzes the user's emotional state and recognizes emotions such as stress and anxiety.

[0380] Output: User's emotion information.

[0381] Step 4:

[0382] The server integrates location information, disaster information, and emotion information on the cloud to analyze the current situation.

[0383] Input: location information, disaster information, emotion information.

[0384] Data processing / calculation: Analyze the operation status of public transport, the congestion level of evacuation centres, road closure status, etc. to grasp the current situation in real time.

[0385] Output: Situation analysis results.

[0386] Step 5:

[0387] The server uses a generative AI model to generate optimal action guidance for each user.

[0388] Input: Situation analysis results, location information, emotion information.

[0389] Data processing / calculation: Generative AI models take into account past data, current conditions, and user emotions to predict and create appropriate action guidance based on location.

[0390] Output: Specific instructions for action.

[0391] Step 6:

[0392] The terminal receives the action guide sent from the server and provides it to the user.

[0393] Input: Action instructions from the server.

[0394] Data processing / calculation: Displaying action guidance as voice notifications, text messages, and map information.

[0395] Output: The action prompt displayed to the user.

[0396] Step 7:

[0397] The user launches the disaster evacuation assistant app on their smartphone and checks the notification.

[0398] Input: Notifications from your device.

[0399] Data processing / calculation: Check and understand the notification contents.

[0400] Output: Appropriate action according to user guidance.

[0401] Step 8:

[0402] The terminal provides the generated action guide to the user as a voice notification.

[0403] Input: Call-to-action text.

[0404] Data processing / calculation: Action instructions are converted into audio using Google Text-to-Speech (gTTS) and played back.

[0405] Output: The user receives a voice notification and understands the course of action.

[0406] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0407] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0408] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0409] [Second embodiment]

[0410] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0411] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0412] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0413] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0414] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0415] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0416] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0417] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0418] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0419] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0420] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0421] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0422] The present invention provides a system that receives disaster information, acquires and analyzes user location information, and generates and provides action guidance using a generative AI model. Specific embodiments of the system are described below.

[0423] Server Processing

[0424] 1. Receiving disaster information

[0425] The server accesses various disaster information services (e.g., the Japan Meteorological Agency and earthquake research institutes) and receives information on natural disasters in real time.

[0426] The received disaster information includes parameters such as type (earthquake, flood, tsunami, etc.), location of occurrence, affected area, and predicted damage situation.

[0427] 2. Collection of user location information

[0428] The server receives the location information sent from the user's smartphone.

[0429] This allows you to determine where the user is currently located in real time.

[0430] 3. Analyze the situation

[0431] The server integrates location information and received disaster information on the cloud and performs analysis taking into account factors such as the operation status of public transport, the occupancy level of evacuation centres, and road closure status.

[0432] 4. Generating action guidance

[0433] The server generates appropriate action guidance using a generative AI model, an algorithm that predicts optimal actions based on past disaster data and current situation data.

[0434] Processing on the device (user's smartphone)

[0435] 1. Receiving Data

[0436] The terminal receives disaster information and action instructions sent from the server.

[0437] Based on the received information, specific action instructions are created for the user.

[0438] 2. Displaying Data

[0439] The device displays action guidance to the user using voice notifications, text messages, and map information.

[0440] For example, when an earthquake occurs, a notification will be displayed saying, "You are currently inside an office building. Please hide under a desk and wait until the shaking stops."

[0441] User Behavior

[0442] 1. Using the app

[0443] The user launches the Disaster Evacuation Assistant app on their smartphone and checks the notification.

[0444] 2. Follow the instructions

[0445] Users take appropriate action by following the specific guidance provided by the app.

[0446] For example, when a flood warning is issued, people will follow the notification that says, "The nearest evacuation site is XX Park. Please move quickly," and head to the designated evacuation site.

[0447] Specific examples

[0448] Example 1: When an earthquake occurs

[0449] 1. The server receives earthquake information from the Japan Meteorological Agency and analyzes the epicenter and seismic intensity.

[0450] 2. The server confirms that User A is located within an office building and generates appropriate action guidance.

[0451] 3. The device sends a notification to User A saying, "An earthquake has occurred. You are currently in an office building. Please hide under a desk and wait until the shaking stops."

[0452] 4. The user follows the instructions and hides under the desk.

[0453] Example 2: When a flood or tsunami warning is issued

[0454] 1. The server receives flood and tsunami warnings and identifies risk areas.

[0455] 2. The server verifies that User B is located within the danger zone and generates directions to a safe evacuation site.

[0456] 3. The device sends a notification to User B saying, "A tsunami warning has been issued. Your current location is dangerous. The nearest evacuation site is XX Park. Please move quickly."

[0457] 4. The user follows the instructions and heads to the designated evacuation site.

[0458] This invention can ensure the safety of users in the event of a disaster and prevent disruption to social infrastructure.

[0459] The processing flow will be explained below.

[0460] Step 1:

[0461] The server accesses various disaster information services (e.g., the Japan Meteorological Agency, earthquake research institutes) and receives real-time information on natural disasters, including the type of disaster, the location of the disaster, the extent of its impact, and predicted damage.

[0462] Step 2:

[0463] The device periodically uses the GPS sensor to obtain the user's current location, which is then sent to a server in the background in preparation for a disaster.

[0464] Step 3:

[0465] The server stores the user's location information and disaster information in a database and compares it with the disaster information.

[0466] Step 4:

[0467] The server integrates the user's location information and disaster information on the cloud, and analyzes the status of public transport, the congestion level of evacuation centres, road closures, etc. This allows the user to grasp the specific situation they are currently in in real time.

[0468] Step 5:

[0469] The server uses a generative AI model to generate optimal guidance for each user, which predicts appropriate actions based on location based on past data and the current situation.

[0470] Step 6:

[0471] The server transmits the generated action guide to the user's terminal.

[0472] Step 7:

[0473] The device receives guidance from the server and notifies the user of the action instructions using voice, text, map information, etc. For example, when an earthquake occurs, the device will display a message saying, "You are currently in an office building. Please hide under a desk and wait until the shaking stops."

[0474] Step 8:

[0475] The user follows the notification on the device and takes the specified evacuation action. For example, in the event of a flood warning, the user will act according to instructions such as "The nearest evacuation site is XX Park. Please move quickly."

[0476] Step 9:

[0477] The server periodically updates the user's location information and new disaster information, and regenerates action guidance as necessary, allowing the user to always act based on the latest information.

[0478] By this specific processing, the present invention enables the user to take prompt and appropriate evacuation actions in the event of a disaster.

[0479] Example 1

[0480] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0481] When a natural disaster occurs, it is difficult to receive accurate disaster information in real time and generate and provide optimal action guidance based on the user's current location. There is also a need to provide users with accurate and prompt information and encourage safe evacuation. Existing systems often lack immediacy and accuracy because they receive disaster information, analyze the situation, and generate and present action guidance separately.

[0482] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0483] In this invention, the server includes means for receiving disaster information, means for acquiring location information, means for analyzing the situation in the cloud, means for generating action guidance using a generative AI model, means for providing the generated action guidance to the user, means for causing the user's terminal to receive the disaster information and action guidance, and means for displaying the received action guidance, thereby enabling the user to receive optimal action guidance in real time and take quick and safe evacuation actions.

[0484] "Means for receiving disaster information" refers to a device or system for obtaining disaster information in real time from meteorological agencies, research institutions, etc.

[0485] "Means for obtaining location information" means a device or system that uses GPS or other location measurement technology to determine a user's current location and transmits that information to a server.

[0486] "Means for analyzing situations in the cloud" refers to a device or system that uses a remote server accessible via the Internet to integrate and analyze collected data and assess the situation in real time.

[0487] "Means for generating action guidance using a generative AI model" refers to a device or system that uses artificial intelligence technology to generate optimal action guidance for a user based on past data and the current situation.

[0488] The "means for providing the generated action guide to the user" refers to a device or system for notifying the user of the generated action guide.

[0489] "Means for allowing a user's device to receive disaster information and action guidance" refers to a device or system for sending and receiving disaster information and action guidance to an electronic device such as a smartphone or tablet owned by the user.

[0490] The "means for displaying received action guidance" refers to a device or system that visually or audibly displays action guidance in the form of text, voice, map, or the like on the user's terminal.

[0491] This invention is a system that receives disaster information, acquires and analyzes user location information, and generates and provides action guidance using a generative AI model. Specific embodiments of this system are described below.

[0492] Server Processing

[0493] 1. Receiving disaster information

[0494] The server uses APIs provided by meteorological agencies and research institutions to collect the latest disaster information on earthquakes, floods, tsunamis, etc. in real time. Specifically, it obtains JSON-formatted data from various APIs and analyzes parameters such as type, location of occurrence, affected area, and predicted damage situation. This received data is stored in a database (such as an SQL database).

[0495] 2. Collection of user location information

[0496] The server collects location information (GPS data) sent from the user's smartphone in real time, which allows it to accurately identify the device's location and store it in a database.

[0497] 3. Analyze the situation

[0498] The server uses cloud services (e.g., Amazon Web Services, Microsoft Azure) to integrate and analyze location information and disaster information in real time. Specifically, it uses AWS Lambda functions and Azure Functions to perform analysis taking into account the operation status of public transportation, the occupancy level of evacuation shelters, road closure status, etc. This allows it to determine whether the user's current location will be affected by the disaster.

[0499] 4. Generating action guidance

[0500] The server generates appropriate action guidance using a generative AI model (e.g., OpenAI's GPT-4). For example, the prompt sentence is "Please generate the optimal action guidance for the user to safely evacuate from their current location." The generated action guidance is optimized using natural language processing and provided in an easy-to-understand format. The action guidance is generated in JSON format and sent to the user's device.

[0501] Processing on the device (user's smartphone)

[0502] 1. Receiving Data

[0503] The device receives disaster information and action instructions sent from the server via secure communication (e.g., HTTPS). This data is stored in a local database (e.g., SQLite) and prepared for immediate notification to the user.

[0504] 2. Displaying Data

[0505] The device visually displays the received guidance to the user. It displays map information along with text messages on the screen, and also provides voice notifications as needed. For example, in the event of an earthquake, the device displays evacuation locations on a map along with a message saying, "You are currently inside an office building. Please take cover under your desk and wait until the shaking stops."

[0506] User Behavior

[0507] 1. Using the app

[0508] Users launch the Disaster Evacuation Assistant app on their smartphones and check notifications. The app runs in the background, allowing them to respond immediately when a notification arrives. Users can check detailed disaster information and evacuation instructions within the app.

[0509] 2. Follow the instructions

[0510] Users take appropriate action by following the specific action instructions provided by the app. For example, when a flood warning is issued, users will follow the notification "The nearest evacuation site is XX Park. Please move quickly" and head to the designated evacuation site.

[0511] Example prompt

[0512] An example of a prompt sentence to input to a generative AI model is as follows:

[0513] "Generate optimal evacuation instructions based on the latest earthquake information and the user's location information."

[0514] "Please provide specific instructions on what to do if a flood warning is issued and the user's current location is in a risk area."

[0515] In this way, by ensuring the safety of users in the event of a disaster and providing prompt and accurate guidance on what to do, it is possible to prevent disruptions to social infrastructure.

[0516] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0517] Step 1:

[0518] Receiving disaster information

[0519] The server uses APIs provided by meteorological agencies and research institutions to collect the latest disaster information on earthquakes, floods, tsunamis, etc. in real time. Specifically, it obtains JSON-formatted data from various APIs and analyzes parameters such as type, location of occurrence, affected area, and predicted damage situation. The JSON data obtained from the API is used as input for the received data, and it is processed for storage in a database, extracting and saving the necessary items.

[0520] Step 2:

[0521] Collection of user location information

[0522] The server collects location information (GPS data) sent from the user's smartphone in real time. It inputs the GPS data sent from the user's device in JSON format, analyzes the data, and processes it to store it in a database. The analyzed location information is saved for each user ID, allowing the latest location to be checked at any time.

[0523] Step 3:

[0524] Analysis of the situation

[0525] The server uses cloud services (e.g., Amazon Web Services, Microsoft Azure) to integrate and analyze location information and disaster information in real time. Using AWS Lambda functions and Azure Functions, it performs analysis taking into account factors such as transportation operation status, evacuation shelter occupancy levels, and road closure status. Using the collected location information and disaster information as input, it determines whether specific users will be affected by the disaster. The results of this analysis are output as the necessity and priority of evacuation.

[0526] Step 4:

[0527] Generate action guide

[0528] The server generates appropriate action guidance using a generative AI model (e.g., OpenAI's GPT-4). For example, the server inputs a prompt statement such as "Please generate the optimal action guidance for the user to safely evacuate from their current location," and outputs the action guidance generated by the AI ​​model. The generated guidance is stored in a database and sent to the user's device in JSON format.

[0529] Step 5:

[0530] Receiving data

[0531] The device receives disaster information and action instructions sent from the server via secure communication (e.g., HTTPS communication). It takes the JSON-formatted action instructions sent from the server as input, stores them in a local database (e.g., SQLite), and prepares for immediate notification.

[0532] Step 6:

[0533] Viewing Data

[0534] The device visually displays the received guidance to the user. Using the guidance stored in a local database as input, the device displays map information along with text messages on the screen and, if necessary, provides audio notifications. For example, a message might read, "An earthquake has occurred. You are currently inside an office building. Please take cover under your desk and wait until the shaking stops."

[0535] Step 7:

[0536] Using the app

[0537] The user launches the Disaster Evacuation Assistant app on their smartphone and checks the notification. When the user launches the app, the latest action instructions sent from the server are displayed. User input is required, and detailed disaster information and evacuation instructions can be viewed within the app.

[0538] Step 8:

[0539] Follow the instructions

[0540] The user takes appropriate action according to the specific action guidance provided by the app. The user receives action guidance from the app as input and takes actual evacuation action based on that guidance. For example, when a flood warning is issued, the user will follow the notification "The nearest evacuation site is XX Park. Please move quickly" and head to the designated evacuation site.

[0541] (Application example 1)

[0542] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0543] In recent years, the increasing frequency and diversity of disasters has created a need for prompt and appropriate guidance. However, current systems sometimes fail to adequately ensure user safety. There is a need for technology that can obtain disaster information in real time and provide optimal guidance based on the user's current location. In addition, it is necessary to use a generative AI model to generate guidance based on prompts and provide it to users effectively using voice notifications, text messages, and map information.

[0544] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0545] In this invention, the server includes means for receiving disaster information, means for acquiring location information, means for analyzing the situation in the cloud, means for generating action guidance using a generative AI model, means for providing the generated action guidance to a user, means for identifying an optimal evacuation route and evacuation location based on the user's current location, means for inputting a prompt sentence to the generative AI model when generating the action guidance and generating optimal action guidance based on the prompt sentence, and means for providing the action guidance to the user using voice notification, text message, and map information. This enables quick and appropriate action guidance for users in the event of a disaster.

[0546] "Disaster information" refers to real-time information on natural disasters provided by the Japan Meteorological Agency, earthquake research institutes, and other organizations.

[0547] "Location information" is data indicating the current geographical coordinates obtained from a user's smartphone or the like.

[0548] The "cloud" is a distributed computing environment for managing, storing, and analyzing resources and data over the Internet.

[0549] A "generative AI model" is an artificial intelligence technology that incorporates an algorithm that generates behavioral guidance based on past data and current situational data.

[0550] "Action guidance" is information that instructs the user on the optimal action to take in the event of a disaster.

[0551] A "prompt statement" is a statement that specifies the task you want the generative AI model to perform and the requirements for the results you want it to generate.

[0552] An "evacuation route" is information about the optimal route for a user to move to a safe place.

[0553] An "evacuation site" is a designated assembly point that will ensure safety in the event of a disaster.

[0554] "Audio notification" is a method of conveying information to a user using audio.

[0555] "Text message" is a method of conveying action instructions and the like to a user using text information.

[0556] "Map information" is data for visually displaying geographical locations, routes, etc.

[0557] This system receives disaster information, acquires and analyzes the user's location information, and generates and provides action guidance using a generative AI model. This system consists of a server and a user's device.

[0558] Server Processing

[0559] The server first accesses various disaster information services and receives information on natural disasters in real time. The received disaster information includes parameters such as the type of disaster (earthquake, flood, tsunami, etc.), the location of the disaster, the extent of the impact, and the predicted damage situation. Next, it receives location information from the user's smartphone and identifies the user's current location in real time. The collected location information and the received disaster information are integrated in the cloud, and an analysis is performed taking into account the operation status of public transportation, the occupancy level of evacuation shelters, the status of road closures, etc.

[0560] The server then inputs a prompt into the generative AI model to generate appropriate guidance. This generative AI model predicts and generates optimal actions based on past disaster data and current situation data. For example, a prompt might be, "Based on the user's location information and the latest earthquake information, please generate optimal evacuation guidance for the user."

[0561] Processing on the device (user's smartphone)

[0562] The device receives disaster information and action instructions sent from the server. Based on the received information, specific action instructions are created for the user. This includes displaying action instructions to the user using voice notifications, text messages, and map information. For example, in the event of an earthquake, a notification may be displayed saying, "You are currently inside an office building. Please hide under a desk and wait until the shaking stops." Different action instructions are generated on the user's device based on the type of disaster and its impact, and are notified to the user in real time.

[0563] User Behavior

[0564] Users receive notifications from this system on their smartphones and are informed of appropriate action instructions. For example, if a flood warning is issued, the system notifies them by saying, "The nearest evacuation site is XX Park. Please move quickly." They can then head to the designated evacuation site. The guidance is provided as voice notifications, text messages, and map information, making it easy for users to understand intuitively.

[0565] Hardware and software used

[0566] This system uses a user's smartphone and a server computer. The program implementation uses the requests library for API access, the geopy library for processing geographic information, and the datetime module for timestamp processing.

[0567] With the above configuration, the present invention realizes prompt and appropriate guidance of actions to be taken by a user in the event of a disaster, thereby ensuring the safety of the user.

[0568] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0569] Step 1:

[0570] The server accesses various disaster information services and receives information on natural disasters in real time. As input, it accesses the APIs of the Japan Meteorological Agency and earthquake research institutes, and as output, it obtains data such as the type of disaster, the location of the occurrence, the extent of the impact, and the predicted damage situation. This allows the server to always have the latest disaster information.

[0571] Step 2:

[0572] The server receives current location information from the user's smartphone. The user provides location information through a smartphone app, and the server receives that location information as GPS data. The user's location data is sent as input, and the latitude and longitude data of the current location are obtained as output.

[0573] Step 3:

[0574] The server integrates the received disaster information and the user's location information on the cloud and analyzes the situation. Disaster information and the user's location information are integrated as input, and analytical data such as the distance between the disaster site and the user and the extent of the impact are obtained as output. This allows the risk level of the user's current location to be analyzed.

[0575] Step 4:

[0576] The server inputs a prompt into the generative AI model and generates optimal action guidance. For example, the prompt could be, "Please generate optimal evacuation guidance for the user based on the user's location information and the latest earthquake information." The prompt and analysis data are provided as input, and optimal action guidance is generated as output. The generative AI model runs an algorithm based on past disaster data and current situation data to predict and generate action guidance.

[0577] Step 5:

[0578] The server provides the generated action guidance to the user's smartphone. The action guidance is sent to the user as a voice notification, a text message, and map information. The generated action guidance is the input, and a notification is sent to the user's smartphone as the output. This allows the user to receive guidance to take prompt and appropriate action.

[0579] Step 6:

[0580] Users take appropriate action based on the guidance they receive through a smartphone app. For example, if a flood warning is issued, they follow the notification, "The nearest evacuation site is XX Park. Please move quickly." The user receives guidance as input and performs appropriate evacuation actions as output. The notification is provided as voice, text, and map information, making it easy for users to intuitively understand and act safely.

[0581] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0582] The present invention is a system that receives disaster information, acquires and analyzes the user's location information and emotions, and generates and provides action guidance using a generative AI model. Specific embodiments of the system are described below.

[0583] Server Processing

[0584] 1. Receiving disaster information

[0585] The server accesses various disaster information services (e.g., the Japan Meteorological Agency, earthquake research institutes) and receives real-time information on natural disasters, including the type of disaster, the location of the disaster, the extent of its impact, and predicted damage.

[0586] 2. Collection of user location information

[0587] The server receives location information sent from the user's smartphone and uses this to determine where the user is currently located.

[0588] 3. Collecting user emotion information

[0589] The server uses an emotion engine to recognize emotions from the user's facial expressions, voice tone, input text, etc. This allows it to identify the stress and anxiety the user is feeling during a disaster.

[0590] 4. Analyze the situation

[0591] The server integrates location information, disaster information, and emotional information on the cloud, and analyzes the status of public transport, the congestion of evacuation shelters, road closures, etc. Based on this, the specific situation the user is currently in can be grasped in real time.

[0592] 5. Generating action guidance

[0593] The server uses a generative AI model to generate optimal guidance for each user, which predicts appropriate actions based on location, based on past data, the current situation, and the user's emotions.

[0594] Processing on the device (user's smartphone)

[0595] 1. Receiving Data

[0596] The terminal receives disaster information, action instructions, and support messages based on emotions sent from the server.

[0597] 2. Displaying Data

[0598] The device will display guidance to the user using voice notifications, text messages, and map information, including responses based on emotions. For example, if the user is feeling anxious, the device will display a message saying, "Please stay calm. The current best evacuation route is XX."

[0599] User Behavior

[0600] 1. Using the app

[0601] The user launches the Disaster Evacuation Assistant app on their smartphone and checks the notification.

[0602] 2. Follow the instructions

[0603] The user takes appropriate action by following specific instructions provided by the app, such as "The nearest evacuation site is XX Park. Please move quickly."

[0604] Specific examples

[0605] Example 1: When an earthquake occurs

[0606] 1. The server receives earthquake information from the Japan Meteorological Agency and analyzes the epicenter and seismic intensity.

[0607] 2. The server confirms that User A is located within an office building and generates appropriate action guidance.

[0608] 3. The server determines through the emotion engine that User A is feeling anxious.

[0609] 4. The device sends a notification to User A saying, "An earthquake has occurred. You are currently inside an office building. Please hide under a desk and wait until the shaking stops. Please remain calm."

[0610] 5. The user follows the instructions and hides under the desk.

[0611] Example 2: When a flood or tsunami warning is issued

[0612] 1. The server receives flood and tsunami warnings and identifies risk areas.

[0613] 2. The server verifies that User B is located within the danger zone and generates directions to a safe evacuation site.

[0614] 3. The server, through its emotion engine, identifies that User B is confused.

[0615] 4. The device sends a notification to User B saying, "A tsunami warning has been issued. Your current location is dangerous. The nearest evacuation site is XX Park. Please move quickly. Do not panic, and act calmly."

[0616] 5. The user follows the instructions and heads to the designated evacuation site.

[0617] This invention enables users to take prompt and appropriate evacuation actions in the event of a disaster, and by taking into consideration the user's emotions, it is possible to provide support that allows users to act with greater peace of mind.

[0618] The processing flow will be explained below.

[0619] Step 1:

[0620] The server periodically sends requests to disaster information providers (e.g., the Japan Meteorological Agency, earthquake research institutes) to receive the latest disaster information. The received information includes the type of disaster (earthquake, flood, tsunami, etc.), the location of the disaster, the extent of the impact, and the predicted damage situation.

[0621] Step 2:

[0622] The device periodically uses the GPS sensor to obtain the user's current location, which is then automatically sent to the server and stored in a database.

[0623] Step 3:

[0624] The server uses an emotion engine to recognize the user's emotions in real time from the device's voice, camera, text input, etc. Emotions are evaluated based on multiple parameters, such as anxiety, stress, and calmness.

[0625] Step 4:

[0626] The server integrates location information, disaster information, and emotional information, and analyzes in real time the status of transportation within the affected area, the congestion level of evacuation centers, road closure status, and other information.

[0627] Step 5:

[0628] The server uses a generative AI model to generate optimal action guidance based on the user's current location and emotional state. The generative AI model predicts optimal actions by taking into account past disaster data, current disaster information, and the user's individual situation.

[0629] Step 6:

[0630] The server sends the generated action guide to the user's terminal, which includes specific evacuation routes, evacuation locations, and messages according to emotions.

[0631] Step 7:

[0632] The device will then provide the user with detailed instructions using voice notifications, text messages, and map information. For example, in the event of an earthquake, the device will display a message saying, "You are currently in an office building. Please take cover under a desk. Please remain calm and act accordingly."

[0633] Step 8:

[0634] The user follows the notification on the device and takes the evacuation action instructed. For example, in the case of a flood warning, the user follows the instruction "The nearest evacuation site is XX Park. Please move quickly."

[0635] Step 9:

[0636] The server continuously monitors the user's location and emotional information and regenerates new action guidance as necessary. This dynamic process helps users take appropriate actions even when the disaster situation changes.

[0637] By this specific processing, the present invention enables the user to take prompt and appropriate evacuation action in the event of a disaster, and furthermore, can provide mental care by taking into account the user's emotional state.

[0638] Example 2

[0639] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0640] When a natural disaster occurs, it is necessary to provide users with prompt and appropriate guidance. However, conventional systems identify evacuation routes using only the user's location information, and therefore do not provide personalized guidance that takes into account the user's emotional state, making it difficult to reduce the stress and anxiety users feel during a disaster. Therefore, it is necessary to provide guidance that also takes into account the user's emotional state.

[0641] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0642] In this invention, the server includes means for receiving disaster information, means for acquiring location information, means for acquiring emotion information, means for analyzing the situation in the cloud, means for generating action guidance using a generative AI model, and means for providing the generated action guidance to the user. This provides personalized action guidance based on the user's location information and emotional state, enabling the user to take optimal evacuation actions quickly and safely in the event of a disaster.

[0643] "Disaster information" refers to information about natural disasters (earthquakes, floods, tsunamis, etc.), including the type of disaster, the location of the disaster, the extent of the impact, and predicted damage.

[0644] "Location information" is information indicating the user's current location based on GPS data provided by the user's device.

[0645] "Emotional information" is information about a user's emotional state determined from the user's facial expression, voice tone, and input text, obtained using emotion recognition technology.

[0646] The "cloud" is an infrastructure that provides resources and services for storing, processing, and managing data over the Internet.

[0647] A "generative AI model" is a model that uses artificial intelligence to analyze and predict data, and automatically generates action guidance appropriate for the user.

[0648] "Action guidance" is information that instructs the user on specific actions to be taken in the event of a disaster, and includes evacuation routes, evacuation locations, messages according to the user's emotions, and the like.

[0649] "User" refers to a person who uses this system, and is an individual who should take evacuation action in the event of a disaster.

[0650] MODE FOR CARRYING OUT THE INVENTION

[0651] This invention is a system that receives disaster information, acquires and analyzes the user's location information and emotional information, and generates and provides action guidance using a generative AI model.

[0652] Server Processing

[0653] Receiving disaster information

[0654] The server sends API requests to multiple disaster information providers (e.g., the Japan Meteorological Agency, earthquake research institutes) to obtain disaster information. Disaster information includes the type of disaster, the location of the disaster, the extent of the impact, and predicted damage. The obtained information is stored in a database and updated in real time.

[0655] Collection of user location information

[0656] The server receives GPS data sent from the user's smartphone and identifies the user's current location. A dedicated app on the smartphone periodically sends location information to the server. This information is used to track the user's location in real time.

[0657] Collecting user emotion information

[0658] The server uses an emotion recognition engine (e.g., Microsoft Azure Cognitive Services, IBM Watson) to analyze facial images, voice, and input text acquired from the user's smartphone. This allows the server to identify the user's emotional state (stress, anxiety, etc.). For example, the server analyzes the user's facial expressions through the camera and evaluates the user's excitement level from their voice.

[0659] Analysis of the situation

[0660] The server consolidates and analyzes the following data in a cloud environment:

[0661] 1. Disaster information (real-time disaster occurrence status)

[0662] 2. User location information

[0663] 3. User Emotion Information

[0664] Furthermore, the system also collects information on the operation status of public transport (e.g., Google Maps API), information on the congestion of evacuation centres (data from government agencies), and road closure status (data from police and traffic information services) to perform comprehensive situation analysis, thereby enabling the system to grasp the specific situation of each user in real time.

[0665] Generate action guide

[0666] The server uses a generative AI model (e.g., OpenAI GPT-4) to generate action guidance based on the analysis data. The generative AI model takes into account past data, the current situation, and the user's emotions to automatically generate optimal evacuation routes, evacuation locations, first aid methods, etc. For example, it generates a message such as, "The nearest evacuation location is XX Park. Please evacuate immediately."

[0667] Processing on the device (user's smartphone)

[0668] Receiving data

[0669] The device receives disaster information, action instructions, and emotional support messages sent from the server in the form of push notifications.

[0670] Viewing Data

[0671] The terminal displays the following information to the user:

[0672] 1. Disaster Information: Breaking news on earthquakes, tsunami warnings, etc.

[0673] 2. Action Guide: Information on evacuation routes and evacuation shelters

[0674] 3. Emotional support messages: Messages of comfort and encouragement that correspond to the user's emotional state

[0675] This information is displayed in a notification bar or pop-up window, and is also accompanied by audio guidance, in an effort to reduce anxiety and confusion.

[0676] User Behavior

[0677] Using the app

[0678] The user launches the Disaster Evacuation Assistant app on their smartphone and checks the notification sent from the server. The app provides specific instructions on what actions the user should take and where to evacuate in the event of a disaster.

[0679] Follow the instructions

[0680] Users can take appropriate action based on the guidance provided by the app. For example, they can check the "route guidance to the nearest evacuation shelter" and take a safe route to the shelter.

[0681] Specific examples

[0682] Example 1: When an earthquake occurs

[0683] 1. The server receives earthquake information from the Japan Meteorological Agency and analyzes the epicenter and seismic intensity.

[0684] 2. The server confirms that User A is located within an office building and generates appropriate action guidance.

[0685] 3. The server determines through the emotion engine that User A is feeling anxious.

[0686] 4. The device sends a notification to User A saying, "An earthquake has occurred. You are currently inside an office building. Please hide under a desk and wait until the shaking stops. Please remain calm."

[0687] 5. The user follows the instructions and hides under the desk.

[0688] Example 2: When a flood or tsunami warning is issued

[0689] 1. The server receives flood and tsunami warnings and identifies risk areas.

[0690] 2. The server verifies that User B is located within the danger zone and generates directions to a safe evacuation site.

[0691] 3. The server, through its emotion engine, identifies that User B is confused.

[0692] 4. The device sends a notification to User B saying, "A tsunami warning has been issued. Your current location is dangerous. The nearest evacuation site is XX Park. Please move quickly. Do not panic, and act calmly."

[0693] 5. The user follows the instructions and heads to the designated evacuation site.

[0694] This invention makes it possible to support users so that they can act quickly and appropriately in the event of a disaster, and by providing support that takes into account their emotional state, it is possible to reduce the anxiety and stress of users.

[0695] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0696] Step 1: Receiving disaster information

[0697] The server sends an API request to the disaster information service to obtain disaster information in real time. This information includes the type of disaster, the location of the disaster, the extent of the impact, and the predicted damage situation, and is stored in a database. The API request is made as input, and disaster information data is obtained as output. This data is used in the next analysis step.

[0698] Step 2: Collect user location information

[0699] The server continuously receives GPS data from the user's smartphone. A dedicated app periodically sends location information to the server, which receives it and identifies the user's current location. The server receives the user's GPS data as input and stores the user's current location as output in a database. This location information is used for situation analysis.

[0700] Step 3: Collecting user emotion information

[0701] The server uses an emotion recognition engine to analyze facial images, voice, and input text acquired from the user's smartphone. This allows it to identify the user's emotional state (stress, anxiety, etc.). It receives facial images and voice data as input, generates user emotional information as output, and stores it in a database.

[0702] Step 4: Analyze the situation

[0703] The server integrates and analyzes the following data in a cloud environment:

[0704] 1. Disaster information (real-time disaster occurrence status)

[0705] 2. User location information

[0706] 3. User Emotion Information

[0707] The server also collects additional data such as transportation status, evacuation shelter congestion, road closure status, etc., and integrates these data to perform a comprehensive situation analysis. All data is integrated as input and a specific situation report for each user is generated as output. This report is used in the action guide generation step.

[0708] Step 5: Generate action guide

[0709] The server uses a generative AI model to generate appropriate action guidance based on the situation report. The generative AI model automatically generates optimal evacuation routes, evacuation locations, first aid methods, etc., taking into account past data, the current situation, and the user's emotions. It uses the situation report as input and generates an action guidance message as output. This message is used in the next notification step.

[0710] Step 6: Receiving and displaying data (terminal)

[0711] The device receives disaster information, action instructions, and emotional support messages sent from the server in the form of push notifications. The device receives notification data from the server as input and displays it to the user as output. Specifically, the device provides information to the user using a notification bar, a pop-up window, and voice guidance.

[0712] Step 7: App Usage and Behavior (Users)

[0713] The user launches the Disaster Evacuation Assistant app on their smartphone and checks the notification sent from the server. Specifically, the user confirms the instructions displayed on the screen and begins evacuation. The input is the notification on the app, and the output is the execution of appropriate actions, such as moving quickly to a designated evacuation site.

[0714] This series of processes enables users to take swift and appropriate evacuation actions in the event of a disaster, and by receiving support that takes into account their emotional state, they can reduce anxiety and stress.

[0715] (Application example 2)

[0716] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0717] In recent years, the frequency and scale of natural disasters have increased, making it important to provide prompt and appropriate evacuation guidance. However, conventional systems can only provide uniform guidance without taking into account the user's location information or emotional state, which makes it difficult for users to take appropriate action when they feel confused or anxious.

[0718] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0719] In this invention, the server includes means for receiving disaster information, means for acquiring location information, means for acquiring emotion information, means for analyzing the situation in the cloud, means for generating action guidance using a generative AI model, means for providing the generated action guidance to the user, and means for generating a voice notification. This makes it possible to provide optimal evacuation action guidance in real time that takes into account the user's location information and emotional state, and supports the user in taking prompt and appropriate action even when they feel confused or anxious.

[0720] "Means for receiving disaster information" refers to a function that obtains information in real time from external information sources when a natural disaster occurs.

[0721] "Means for obtaining location information" refers to a function that identifies the user's current geographic location using GPS or other location measurement methods.

[0722] The "means for acquiring emotional information" is a function for identifying and collecting the user's emotional state from facial expressions, voice, text, etc.

[0723] "Means for analyzing the situation using the cloud" is a function that uses cloud computing to comprehensively analyze collected data and evaluate current risks and appropriate actions.

[0724] "Means for generating action guidance using a generative AI model" is a function that utilizes AI technology to create action guidance customized for each user based on the analysis results.

[0725] The "means for providing the generated action guide to the user" is a function for notifying the user of the generated action guide in a form that the user can understand.

[0726] The "means for generating voice notification" is a function for converting text information into voice and providing a voice notification to the user in real time.

[0727] This invention is a system that receives disaster information, acquires and analyzes the user's location information and emotions, and generates and provides action guidance using a generative AI model. Specific embodiments of this system are described below.

[0728] Server Processing

[0729] The server first receives disaster information in real time from external sources, including disaster information providers such as the Japan Meteorological Agency and earthquake research institutes. The information received includes the type of disaster, the location of the disaster, the extent of its impact, and predicted damage.

[0730] The server then receives the location information sent from the user's smartphone and determines where the user is currently located. The location information is primarily obtained using GPS.

[0731] Furthermore, the server uses an emotion engine to recognize emotions from the user's facial expressions, voice tone, input text, etc. This allows it to identify the stress and anxiety the user is feeling during a disaster.

[0732] The server then integrates location information, disaster information, and emotional information on the cloud, and analyzes the status of public transport, the congestion of evacuation shelters, road closures, etc. Based on this, the specific situation the user is currently in can be grasped in real time.

[0733] Finally, the server generates optimal guidance for each user using a generative AI model, which predicts appropriate actions based on location, based on past data, the current situation, and the user's emotions.

[0734] Processing on the device (user's smartphone)

[0735] First, the device receives disaster information, action instructions, and support messages based on emotions sent from the server.

[0736] The device then displays instructions to the user using voice notifications, text messages, and map information, including responses based on emotions. For example, if the user is feeling anxious, the device will display a message saying, "Please stay calm. The current best evacuation route is ____."

[0737] The device then provides the generated action guidance to the user as a voice notification, using technologies such as Google Text-to-Speech (gTTS).

[0738] User Behavior

[0739] The user launches the Disaster Evacuation Assistant app on their smartphone, checks the notification, and takes appropriate action according to the specific instructions provided by the app.

[0740] Specific examples

[0741] Example 1: When an earthquake occurs

[0742] The server receives earthquake information from the Japan Meteorological Agency and analyzes the epicenter and seismic intensity. The server confirms that User A is located inside an office building and generates appropriate guidance for action. The server determines through the emotion engine that User A is feeling anxious. The device sends a notification to User A saying, "An earthquake has occurred. You are currently inside an office building, so please take cover under a desk and wait until the shaking stops. Please remain calm." The user follows the instructions and takes cover under a desk.

[0743] Example 2: When a flood or tsunami warning is issued

[0744] The server receives flood and tsunami warnings and identifies risk areas. The server confirms that User B's location is within the risk area and generates directions to a safe evacuation site. The server determines through the emotion engine that User B is confused. The device sends a notification to User B saying, "A tsunami warning has been issued. Your current location is dangerous. The nearest evacuation site is XX Park. Please move quickly. Do not panic, and act calmly." The user follows the instructions and heads to the designated evacuation site.

[0745] Examples of prompt statements

[0746] text

[0747] Current disaster information: A magnitude 7.5 earthquake occurred in Tokyo. The epicenter was in Tokyo Bay, and the seismic intensity was 5+.

[0748] User location: Latitude 35.6895, Longitude 139.6917 (i.e. Shinjuku Ward).

[0749] User's emotional state: Feeling very anxious.

[0750] Based on this information, generate the optimal action guide for the user.

[0751] This system helps users act quickly and appropriately in the event of a disaster, and by taking their emotions into consideration, it provides support that allows them to act with peace of mind.

[0752] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0753] Step 1:

[0754] The server receives disaster information in real time from external sources.

[0755] Input: Data from disaster information services such as the Japan Meteorological Agency and earthquake research institutes.

[0756] Data processing / calculation: Analyze the received data to identify the type of disaster, location, extent of impact, and predicted damage.

[0757] Output: Parsed disaster information.

[0758] Step 2:

[0759] The server receives location information sent from the user's smartphone and identifies the user's current location.

[0760] Input: GPS data from your smartphone.

[0761] Data processing / calculation: Location information is analyzed as latitude and longitude to identify a specific geographical location.

[0762] Output: User's current location (latitude and longitude).

[0763] Step 3:

[0764] The server uses an emotion engine to recognize emotions from the user's facial expressions, voice tone, input text, etc.

[0765] Input: User facial expression images, audio samples, and text data.

[0766] Data processing / calculation: The emotion engine analyzes the user's emotional state and recognizes emotions such as stress and anxiety.

[0767] Output: User's emotion information.

[0768] Step 4:

[0769] The server integrates location information, disaster information, and emotion information on the cloud to analyze the current situation.

[0770] Input: location information, disaster information, emotion information.

[0771] Data processing / calculation: Analyze the operation status of public transport, the congestion level of evacuation centres, road closure status, etc. to grasp the current situation in real time.

[0772] Output: Situation analysis results.

[0773] Step 5:

[0774] The server uses a generative AI model to generate optimal action guidance for each user.

[0775] Input: Situation analysis results, location information, emotion information.

[0776] Data processing / calculation: Generative AI models take into account past data, current conditions, and user emotions to predict and create appropriate action guidance based on location.

[0777] Output: Specific instructions for action.

[0778] Step 6:

[0779] The terminal receives the action guide sent from the server and provides it to the user.

[0780] Input: Action instructions from the server.

[0781] Data processing / calculation: Displaying action guidance as voice notifications, text messages, and map information.

[0782] Output: The action prompt displayed to the user.

[0783] Step 7:

[0784] The user launches the disaster evacuation assistant app on their smartphone and checks the notification.

[0785] Input: Notifications from your device.

[0786] Data processing / calculation: Check and understand the notification contents.

[0787] Output: Appropriate action according to user guidance.

[0788] Step 8:

[0789] The terminal provides the generated action guide to the user as a voice notification.

[0790] Input: Call-to-action text.

[0791] Data processing / calculation: Action instructions are converted into audio using Google Text-to-Speech (gTTS) and played back.

[0792] Output: The user receives a voice notification and understands the course of action.

[0793] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0794] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0795] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0796] [Third embodiment]

[0797] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0798] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0799] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0800] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0801] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0802] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0803] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0804] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0805] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0806] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0807] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0808] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0809] The present invention provides a system that receives disaster information, acquires and analyzes user location information, and generates and provides action guidance using a generative AI model. Specific embodiments of the system are described below.

[0810] Server Processing

[0811] 1. Receiving disaster information

[0812] The server accesses various disaster information services (e.g., the Japan Meteorological Agency and earthquake research institutes) and receives information on natural disasters in real time.

[0813] The received disaster information includes parameters such as type (earthquake, flood, tsunami, etc.), location of occurrence, affected area, and predicted damage situation.

[0814] 2. Collection of user location information

[0815] The server receives the location information sent from the user's smartphone.

[0816] This allows you to determine where the user is currently located in real time.

[0817] 3. Analyze the situation

[0818] The server integrates location information and received disaster information on the cloud and performs analysis taking into account factors such as the operation status of public transport, the occupancy level of evacuation centres, and road closure status.

[0819] 4. Generating action guidance

[0820] The server generates appropriate action guidance using a generative AI model, an algorithm that predicts optimal actions based on past disaster data and current situation data.

[0821] Processing on the device (user's smartphone)

[0822] 1. Receiving Data

[0823] The terminal receives disaster information and action instructions sent from the server.

[0824] Based on the received information, specific action instructions are created for the user.

[0825] 2. Displaying Data

[0826] The device displays action guidance to the user using voice notifications, text messages, and map information.

[0827] For example, when an earthquake occurs, a notification will be displayed saying, "You are currently inside an office building. Please hide under a desk and wait until the shaking stops."

[0828] User Behavior

[0829] 1. Using the app

[0830] The user launches the Disaster Evacuation Assistant app on their smartphone and checks the notification.

[0831] 2. Follow the instructions

[0832] Users take appropriate action by following the specific guidance provided by the app.

[0833] For example, when a flood warning is issued, people will follow the notification that says, "The nearest evacuation site is XX Park. Please move quickly," and head to the designated evacuation site.

[0834] Specific examples

[0835] Example 1: When an earthquake occurs

[0836] 1. The server receives earthquake information from the Japan Meteorological Agency and analyzes the epicenter and seismic intensity.

[0837] 2. The server confirms that User A is located within an office building and generates appropriate action guidance.

[0838] 3. The device sends a notification to User A saying, "An earthquake has occurred. You are currently in an office building. Please hide under a desk and wait until the shaking stops."

[0839] 4. The user follows the instructions and hides under the desk.

[0840] Example 2: When a flood or tsunami warning is issued

[0841] 1. The server receives flood and tsunami warnings and identifies risk areas.

[0842] 2. The server verifies that User B is located within the danger zone and generates directions to a safe evacuation site.

[0843] 3. The device sends a notification to User B saying, "A tsunami warning has been issued. Your current location is dangerous. The nearest evacuation site is XX Park. Please move quickly."

[0844] 4. The user follows the instructions and heads to the designated evacuation site.

[0845] This invention can ensure the safety of users in the event of a disaster and prevent disruption to social infrastructure.

[0846] The processing flow will be explained below.

[0847] Step 1:

[0848] The server accesses various disaster information services (e.g., the Japan Meteorological Agency, earthquake research institutes) and receives real-time information on natural disasters, including the type of disaster, the location of the disaster, the extent of its impact, and predicted damage.

[0849] Step 2:

[0850] The device periodically uses the GPS sensor to obtain the user's current location, which is then sent to a server in the background in preparation for a disaster.

[0851] Step 3:

[0852] The server stores the user's location information and disaster information in a database and compares it with the disaster information.

[0853] Step 4:

[0854] The server integrates the user's location information and disaster information on the cloud, and analyzes the status of public transport, the congestion level of evacuation shelters, road closures, etc. This allows the user to grasp the specific situation they are currently in in real time.

[0855] Step 5:

[0856] The server uses a generative AI model to generate optimal guidance for each user, which predicts appropriate actions based on location based on past data and the current situation.

[0857] Step 6:

[0858] The server transmits the generated action guide to the user's terminal.

[0859] Step 7:

[0860] The device receives guidance from the server and notifies the user of the action instructions using voice, text, map information, etc. For example, when an earthquake occurs, the device displays a message saying, "You are currently in an office building. Please hide under a desk and wait until the shaking stops."

[0861] Step 8:

[0862] The user follows the notification on the device and takes the specified evacuation action. For example, in the event of a flood warning, the user will act according to instructions such as "The nearest evacuation site is XX Park. Please move quickly."

[0863] Step 9:

[0864] The server periodically updates the user's location information and new disaster information, and regenerates action guidance as necessary, allowing the user to always act based on the latest information.

[0865] By this specific processing, the present invention enables the user to take prompt and appropriate evacuation actions in the event of a disaster.

[0866] Example 1

[0867] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0868] When a natural disaster occurs, it is difficult to receive accurate disaster information in real time and generate and provide optimal action guidance based on the user's current location. There is also a need to provide users with accurate and prompt information and encourage safe evacuation. Existing systems often lack immediacy and accuracy because they receive disaster information, analyze the situation, and generate and present action guidance separately.

[0869] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0870] In this invention, the server includes means for receiving disaster information, means for acquiring location information, means for analyzing the situation in the cloud, means for generating action guidance using a generative AI model, means for providing the generated action guidance to the user, means for causing the user's terminal to receive the disaster information and action guidance, and means for displaying the received action guidance, thereby enabling the user to receive optimal action guidance in real time and take quick and safe evacuation actions.

[0871] "Means for receiving disaster information" refers to a device or system for obtaining disaster information in real time from meteorological agencies, research institutions, etc.

[0872] "Means for obtaining location information" means a device or system that uses GPS or other location measurement technology to determine a user's current location and transmits that information to a server.

[0873] "Means for analyzing situations in the cloud" refers to a device or system that uses a remote server accessible via the Internet to integrate and analyze collected data and assess the situation in real time.

[0874] "Means for generating action guidance using a generative AI model" refers to a device or system that uses artificial intelligence technology to generate optimal action guidance for a user based on past data and the current situation.

[0875] The "means for providing the generated action guide to the user" refers to a device or system for notifying the user of the generated action guide.

[0876] "Means for allowing a user's device to receive disaster information and action guidance" refers to a device or system for sending and receiving disaster information and action guidance to an electronic device such as a smartphone or tablet owned by the user.

[0877] The "means for displaying received action guidance" refers to a device or system that visually or audibly displays action guidance in the form of text, voice, map, or the like on the user's terminal.

[0878] This invention is a system that receives disaster information, acquires and analyzes user location information, and generates and provides action guidance using a generative AI model. Specific embodiments of this system are described below.

[0879] Server Processing

[0880] 1. Receiving disaster information

[0881] The server uses APIs provided by meteorological agencies and research institutions to collect the latest disaster information on earthquakes, floods, tsunamis, etc. in real time. Specifically, it obtains JSON-formatted data from various APIs and analyzes parameters such as type, location of occurrence, affected area, and predicted damage situation. This received data is stored in a database (such as an SQL database).

[0882] 2. Collection of user location information

[0883] The server collects location information (GPS data) sent from the user's smartphone in real time, which allows it to accurately identify the device's location and store it in a database.

[0884] 3. Analyze the situation

[0885] The server uses cloud services (e.g., Amazon Web Services, Microsoft Azure) to integrate and analyze location information and disaster information in real time. Specifically, it uses AWS Lambda functions and Azure Functions to perform analysis taking into account the operation status of public transportation, the occupancy level of evacuation shelters, road closure status, etc. This allows it to determine whether the user's current location will be affected by the disaster.

[0886] 4. Generating action guidance

[0887] The server generates appropriate action guidance using a generative AI model (e.g., OpenAI's GPT-4). For example, the prompt sentence is "Please generate the optimal action guidance for the user to safely evacuate from their current location." The generated action guidance is optimized using natural language processing and provided in an easy-to-understand format. The action guidance is generated in JSON format and sent to the user's device.

[0888] Processing on the device (user's smartphone)

[0889] 1. Receiving Data

[0890] The device receives disaster information and action instructions sent from the server via secure communication (e.g., HTTPS). This data is stored in a local database (e.g., SQLite) and prepared for immediate notification to the user.

[0891] 2. Displaying Data

[0892] The device visually displays the received guidance to the user. It displays map information along with text messages on the screen, and also provides voice notifications as needed. For example, in the event of an earthquake, the device displays evacuation locations on a map along with a message saying, "You are currently inside an office building. Please take cover under your desk and wait until the shaking stops."

[0893] User Behavior

[0894] 1. Using the app

[0895] Users launch the Disaster Evacuation Assistant app on their smartphones and check notifications. The app runs in the background, allowing them to respond immediately when a notification arrives. Users can check detailed disaster information and evacuation instructions within the app.

[0896] 2. Follow the instructions

[0897] Users take appropriate action by following the specific action instructions provided by the app. For example, when a flood warning is issued, users will follow the notification "The nearest evacuation site is XX Park. Please move quickly" and head to the designated evacuation site.

[0898] Example prompt

[0899] An example of a prompt sentence to input to a generative AI model is as follows:

[0900] "Generate optimal evacuation instructions based on the latest earthquake information and the user's location information."

[0901] "Please provide specific instructions on what to do if a flood warning is issued and the user's current location is in a risk area."

[0902] In this way, by ensuring the safety of users in the event of a disaster and providing prompt and accurate guidance on what to do, it is possible to prevent disruptions to social infrastructure.

[0903] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0904] Step 1:

[0905] Receiving disaster information

[0906] The server uses APIs provided by meteorological agencies and research institutions to collect the latest disaster information on earthquakes, floods, tsunamis, etc. in real time. Specifically, it obtains JSON-formatted data from various APIs and analyzes parameters such as type, location of occurrence, affected area, and predicted damage situation. The JSON data obtained from the API is used as input for the received data, and it is processed for storage in a database, extracting and saving the necessary items.

[0907] Step 2:

[0908] Collection of user location information

[0909] The server collects location information (GPS data) sent from the user's smartphone in real time. It inputs the GPS data sent from the user's device in JSON format, analyzes the data, and processes it to store it in a database. The analyzed location information is saved for each user ID, allowing the latest location to be checked at any time.

[0910] Step 3:

[0911] Analysis of the situation

[0912] The server uses cloud services (e.g., Amazon Web Services, Microsoft Azure) to integrate and analyze location information and disaster information in real time. Using AWS Lambda functions and Azure Functions, it performs analysis taking into account factors such as transportation operation status, evacuation shelter occupancy levels, and road closure status. Using the collected location information and disaster information as input, it determines whether specific users will be affected by the disaster. The results of this analysis are output as the necessity and priority of evacuation.

[0913] Step 4:

[0914] Generate action guide

[0915] The server generates appropriate action guidance using a generative AI model (e.g., OpenAI's GPT-4). For example, the server inputs a prompt statement such as "Please generate the optimal action guidance for the user to safely evacuate from their current location," and outputs the action guidance generated by the AI ​​model. The generated guidance is stored in a database and sent to the user's device in JSON format.

[0916] Step 5:

[0917] Receiving data

[0918] The device receives disaster information and action instructions sent from the server via secure communication (e.g., HTTPS communication). It takes the JSON-formatted action instructions sent from the server as input, stores them in a local database (e.g., SQLite), and prepares for immediate notification.

[0919] Step 6:

[0920] Viewing Data

[0921] The device visually displays the received guidance to the user. Using the guidance stored in a local database as input, the device displays map information along with text messages on the screen and, if necessary, provides audio notifications. For example, a message might read, "An earthquake has occurred. You are currently inside an office building. Please take cover under your desk and wait until the shaking stops."

[0922] Step 7:

[0923] Using the app

[0924] The user launches the Disaster Evacuation Assistant app on their smartphone and checks the notification. When the user launches the app, the latest action instructions sent from the server are displayed. User input is required, and detailed disaster information and evacuation instructions can be viewed within the app.

[0925] Step 8:

[0926] Follow the instructions

[0927] The user takes appropriate action according to the specific action guidance provided by the app. The user receives action guidance from the app as input and takes actual evacuation action based on that guidance. For example, when a flood warning is issued, the user will follow the notification "The nearest evacuation site is XX Park. Please move quickly" and head to the designated evacuation site.

[0928] (Application example 1)

[0929] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0930] In recent years, the increasing frequency and diversity of disasters has created a need for prompt and appropriate guidance. However, current systems sometimes fail to adequately ensure user safety. There is a need for technology that can obtain disaster information in real time and provide optimal guidance based on the user's current location. In addition, it is necessary to use a generative AI model to generate guidance based on prompts and provide it to users effectively using voice notifications, text messages, and map information.

[0931] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0932] In this invention, the server includes means for receiving disaster information, means for acquiring location information, means for analyzing the situation in the cloud, means for generating action guidance using a generative AI model, means for providing the generated action guidance to a user, means for identifying an optimal evacuation route and evacuation location based on the user's current location, means for inputting a prompt sentence to the generative AI model when generating the action guidance and generating optimal action guidance based on the prompt sentence, and means for providing the action guidance to the user using voice notification, text message, and map information. This enables quick and appropriate action guidance for users in the event of a disaster.

[0933] "Disaster information" refers to real-time information on natural disasters provided by the Japan Meteorological Agency, earthquake research institutes, and other organizations.

[0934] "Location information" is data indicating the current geographical coordinates obtained from a user's smartphone or the like.

[0935] The "cloud" is a distributed computing environment for managing, storing, and analyzing resources and data over the Internet.

[0936] A "generative AI model" is an artificial intelligence technology that incorporates an algorithm that generates behavioral guidance based on past data and current situational data.

[0937] "Action guidance" is information that instructs the user on the optimal action to take in the event of a disaster.

[0938] A "prompt statement" is a statement that specifies the task you want the generative AI model to perform and the requirements for the results you want it to generate.

[0939] An "evacuation route" is information about the optimal route for a user to move to a safe place.

[0940] An "evacuation site" is a designated assembly point that will ensure safety in the event of a disaster.

[0941] "Audio notification" is a method of conveying information to a user using audio.

[0942] "Text message" is a method of conveying action instructions and the like to a user using text information.

[0943] "Map information" is data for visually displaying geographical locations, routes, etc.

[0944] This system receives disaster information, acquires and analyzes the user's location information, and generates and provides action guidance using a generative AI model. This system consists of a server and a user's device.

[0945] Server Processing

[0946] The server first accesses various disaster information services and receives information on natural disasters in real time. The received disaster information includes parameters such as the type of disaster (earthquake, flood, tsunami, etc.), the location of the disaster, the extent of the impact, and the predicted damage situation. Next, it receives location information from the user's smartphone and identifies the user's current location in real time. The collected location information and the received disaster information are integrated in the cloud, and an analysis is performed taking into account the operation status of public transportation, the occupancy level of evacuation shelters, the status of road closures, etc.

[0947] The server then inputs a prompt into the generative AI model to generate appropriate guidance. This generative AI model predicts and generates optimal actions based on past disaster data and current situation data. For example, a prompt might be, "Based on the user's location information and the latest earthquake information, please generate optimal evacuation guidance for the user."

[0948] Processing on the device (user's smartphone)

[0949] The device receives disaster information and action instructions sent from the server. Based on the received information, specific action instructions are created for the user. This includes displaying action instructions to the user using voice notifications, text messages, and map information. For example, in the event of an earthquake, a notification may be displayed saying, "You are currently inside an office building. Please hide under a desk and wait until the shaking stops." Different action instructions are generated on the user's device based on the type of disaster and its impact, and are notified to the user in real time.

[0950] User Behavior

[0951] Users receive notifications from this system on their smartphones and are informed of appropriate action instructions. For example, if a flood warning is issued, the system notifies them by saying, "The nearest evacuation site is XX Park. Please move quickly." They can then head to the designated evacuation site. The guidance is provided as voice notifications, text messages, and map information, making it easy for users to understand intuitively.

[0952] Hardware and software used

[0953] This system uses a user's smartphone and a server computer. The program implementation uses the requests library for API access, the geopy library for processing geographic information, and the datetime module for timestamp processing.

[0954] With the above configuration, the present invention realizes prompt and appropriate guidance of actions to be taken by a user in the event of a disaster, thereby ensuring the safety of the user.

[0955] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0956] Step 1:

[0957] The server accesses various disaster information services and receives information on natural disasters in real time. As input, it accesses the APIs of the Japan Meteorological Agency and earthquake research institutes, and as output, it obtains data such as the type of disaster, the location of the occurrence, the extent of the impact, and the predicted damage situation. This allows the server to always have the latest disaster information.

[0958] Step 2:

[0959] The server receives current location information from the user's smartphone. The user provides location information through a smartphone app, and the server receives that location information as GPS data. The user's location data is sent as input, and the latitude and longitude data of the current location are obtained as output.

[0960] Step 3:

[0961] The server integrates the received disaster information and the user's location information on the cloud and analyzes the situation. Disaster information and the user's location information are integrated as input, and analytical data such as the distance between the disaster site and the user and the extent of the impact are obtained as output. This allows the risk level of the user's current location to be analyzed.

[0962] Step 4:

[0963] The server inputs a prompt into the generative AI model and generates optimal action guidance. For example, the prompt could be, "Please generate optimal evacuation guidance for the user based on the user's location information and the latest earthquake information." The prompt and analysis data are provided as input, and optimal action guidance is generated as output. The generative AI model runs an algorithm based on past disaster data and current situation data to predict and generate action guidance.

[0964] Step 5:

[0965] The server provides the generated action guidance to the user's smartphone. The action guidance is sent to the user as a voice notification, a text message, and map information. The generated action guidance is the input, and a notification is sent to the user's smartphone as the output. This allows the user to receive guidance to take prompt and appropriate action.

[0966] Step 6:

[0967] Users take appropriate action based on the guidance they receive through a smartphone app. For example, if a flood warning is issued, they follow the notification, "The nearest evacuation site is XX Park. Please move quickly." The user receives guidance as input and performs appropriate evacuation actions as output. The notification is provided as voice, text, and map information, making it easy for users to intuitively understand and act safely.

[0968] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0969] The present invention is a system that receives disaster information, acquires and analyzes the user's location information and emotions, and generates and provides action guidance using a generative AI model. Specific embodiments of the system are described below.

[0970] Server Processing

[0971] 1. Receiving disaster information

[0972] The server accesses various disaster information services (e.g., the Japan Meteorological Agency, earthquake research institutes) and receives real-time information on natural disasters, including the type of disaster, the location of the disaster, the extent of its impact, and predicted damage.

[0973] 2. Collection of user location information

[0974] The server receives location information sent from the user's smartphone and uses this to determine where the user is currently located.

[0975] 3. Collecting user emotion information

[0976] The server uses an emotion engine to recognize emotions from the user's facial expressions, voice tone, input text, etc. This allows it to identify the stress and anxiety the user is feeling during a disaster.

[0977] 4. Analyze the situation

[0978] The server integrates location information, disaster information, and emotional information on the cloud, and analyzes the status of public transport, the congestion of evacuation shelters, road closures, etc. Based on this, the specific situation the user is currently in can be grasped in real time.

[0979] 5. Generating action guidance

[0980] The server uses a generative AI model to generate optimal guidance for each user, which predicts appropriate actions based on location, based on past data, the current situation, and the user's emotions.

[0981] Processing on the device (user's smartphone)

[0982] 1. Receiving Data

[0983] The terminal receives disaster information, action instructions, and support messages based on emotions sent from the server.

[0984] 2. Displaying Data

[0985] The device will display guidance to the user using voice notifications, text messages, and map information, including responses based on emotions. For example, if the user is feeling anxious, the device will display a message saying, "Please stay calm. The current best evacuation route is XX."

[0986] User Behavior

[0987] 1. Using the app

[0988] The user launches the Disaster Evacuation Assistant app on their smartphone and checks the notification.

[0989] 2. Follow the instructions

[0990] The user takes appropriate action by following specific instructions provided by the app, such as "The nearest evacuation site is XX Park. Please move quickly."

[0991] Specific examples

[0992] Example 1: When an earthquake occurs

[0993] 1. The server receives earthquake information from the Japan Meteorological Agency and analyzes the epicenter and seismic intensity.

[0994] 2. The server confirms that User A is located within an office building and generates appropriate action guidance.

[0995] 3. The server determines through the emotion engine that User A is feeling anxious.

[0996] 4. The device sends a notification to User A saying, "An earthquake has occurred. You are currently inside an office building. Please hide under a desk and wait until the shaking stops. Please remain calm."

[0997] 5. The user follows the instructions and hides under the desk.

[0998] Example 2: When a flood or tsunami warning is issued

[0999] 1. The server receives flood and tsunami warnings and identifies risk areas.

[1000] 2. The server verifies that User B is located within the danger zone and generates directions to a safe evacuation site.

[1001] 3. The server, through its emotion engine, identifies that User B is confused.

[1002] 4. The device sends a notification to User B saying, "A tsunami warning has been issued. Your current location is dangerous. The nearest evacuation site is XX Park. Please move quickly. Do not panic, and act calmly."

[1003] 5. The user follows the instructions and heads to the designated evacuation site.

[1004] This invention enables users to take prompt and appropriate evacuation actions in the event of a disaster, and by taking into consideration the user's emotions, it is possible to provide support that allows users to act with greater peace of mind.

[1005] The processing flow will be explained below.

[1006] Step 1:

[1007] The server periodically sends requests to disaster information providers (e.g., the Japan Meteorological Agency, earthquake research institutes) to receive the latest disaster information. The received information includes the type of disaster (earthquake, flood, tsunami, etc.), the location of the disaster, the extent of the impact, and the predicted damage situation.

[1008] Step 2:

[1009] The device periodically uses the GPS sensor to obtain the user's current location, which is then automatically sent to the server and stored in a database.

[1010] Step 3:

[1011] The server uses an emotion engine to recognize the user's emotions in real time from the device's voice, camera, text input, etc. Emotions are evaluated based on multiple parameters, such as anxiety, stress, and calmness.

[1012] Step 4:

[1013] The server integrates location information, disaster information, and emotional information, and analyzes in real time the status of transportation within the affected area, the congestion level of evacuation centers, road closure status, and other information.

[1014] Step 5:

[1015] The server uses a generative AI model to generate optimal action guidance based on the user's current location and emotional state. The generative AI model predicts optimal actions by taking into account past disaster data, current disaster information, and the user's individual situation.

[1016] Step 6:

[1017] The server sends the generated action guide to the user's terminal, which includes specific evacuation routes, evacuation locations, and messages according to emotions.

[1018] Step 7:

[1019] The device will then provide the user with detailed instructions using voice notifications, text messages, and map information. For example, in the event of an earthquake, the device will display a message saying, "You are currently in an office building. Please take cover under a desk. Please remain calm and act accordingly."

[1020] Step 8:

[1021] The user follows the notification on the device and takes the evacuation action instructed. For example, in the case of a flood warning, the user follows the instruction "The nearest evacuation site is XX Park. Please move quickly."

[1022] Step 9:

[1023] The server continuously monitors the user's location and emotional information and regenerates new action guidance as necessary. This dynamic process helps users take appropriate actions even when the disaster situation changes.

[1024] By this specific processing, the present invention enables the user to take prompt and appropriate evacuation action in the event of a disaster, and furthermore, can provide mental care by taking into account the user's emotional state.

[1025] Example 2

[1026] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1027] When a natural disaster occurs, it is necessary to provide users with prompt and appropriate guidance. However, conventional systems identify evacuation routes using only the user's location information, and therefore do not provide personalized guidance that takes into account the user's emotional state, making it difficult to reduce the stress and anxiety users feel during a disaster. Therefore, it is necessary to provide guidance that also takes into account the user's emotional state.

[1028] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1029] In this invention, the server includes means for receiving disaster information, means for acquiring location information, means for acquiring emotion information, means for analyzing the situation in the cloud, means for generating action guidance using a generative AI model, and means for providing the generated action guidance to the user. This provides personalized action guidance based on the user's location information and emotional state, enabling the user to take optimal evacuation actions quickly and safely in the event of a disaster.

[1030] "Disaster information" refers to information about natural disasters (earthquakes, floods, tsunamis, etc.), including the type of disaster, the location of the disaster, the extent of the impact, and predicted damage.

[1031] "Location information" is information indicating the user's current location based on GPS data provided by the user's device.

[1032] "Emotional information" is information about a user's emotional state determined from the user's facial expression, voice tone, and input text, obtained using emotion recognition technology.

[1033] The "cloud" is an infrastructure that provides resources and services for storing, processing, and managing data over the Internet.

[1034] A "generative AI model" is a model that uses artificial intelligence to analyze and predict data, and automatically generates action guidance appropriate for the user.

[1035] "Action guidance" is information that instructs the user on specific actions to be taken in the event of a disaster, and includes evacuation routes, evacuation locations, messages according to the user's emotions, and the like.

[1036] "User" refers to a person who uses this system, and is an individual who should take evacuation action in the event of a disaster.

[1037] MODE FOR CARRYING OUT THE INVENTION

[1038] This invention is a system that receives disaster information, acquires and analyzes the user's location information and emotional information, and generates and provides action guidance using a generative AI model.

[1039] Server Processing

[1040] Receiving disaster information

[1041] The server sends API requests to multiple disaster information providers (e.g., the Japan Meteorological Agency, earthquake research institutes) to obtain disaster information. Disaster information includes the type of disaster, the location of the disaster, the extent of the impact, and predicted damage. The obtained information is stored in a database and updated in real time.

[1042] Collection of user location information

[1043] The server receives GPS data sent from the user's smartphone and identifies the user's current location. A dedicated app on the smartphone periodically sends location information to the server. This information is used to track the user's location in real time.

[1044] Collecting user emotion information

[1045] The server uses an emotion recognition engine (e.g., Microsoft Azure Cognitive Services, IBM Watson) to analyze facial images, voice, and input text acquired from the user's smartphone. This allows the server to identify the user's emotional state (stress, anxiety, etc.). For example, the server analyzes the user's facial expressions through the camera and evaluates the user's excitement level from their voice.

[1046] Analysis of the situation

[1047] The server consolidates and analyzes the following data in a cloud environment:

[1048] 1. Disaster information (real-time disaster occurrence status)

[1049] 2. User location information

[1050] 3. User Emotion Information

[1051] Furthermore, the system also collects information on the operation status of public transport (e.g., Google Maps API), information on the congestion of evacuation centres (data from government agencies), and road closure status (data from police and traffic information services) to perform comprehensive situation analysis, thereby enabling the system to grasp the specific situation of each user in real time.

[1052] Generate action guide

[1053] The server uses a generative AI model (e.g., OpenAI GPT-4) to generate action guidance based on the analysis data. The generative AI model takes into account past data, the current situation, and the user's emotions to automatically generate optimal evacuation routes, evacuation locations, first aid methods, etc. For example, it generates a message such as, "The nearest evacuation location is XX Park. Please evacuate immediately."

[1054] Processing on the device (user's smartphone)

[1055] Receiving data

[1056] The device receives disaster information, action instructions, and emotional support messages sent from the server in the form of push notifications.

[1057] Viewing Data

[1058] The terminal displays the following information to the user:

[1059] 1. Disaster Information: Breaking news on earthquakes, tsunami warnings, etc.

[1060] 2. Action Guide: Information on evacuation routes and evacuation shelters

[1061] 3. Emotional support messages: Messages of comfort and encouragement that correspond to the user's emotional state

[1062] This information is displayed in a notification bar or pop-up window, and is also accompanied by audio guidance, in an effort to reduce anxiety and confusion.

[1063] User Behavior

[1064] Using the app

[1065] The user launches the Disaster Evacuation Assistant app on their smartphone and checks the notification sent from the server. The app provides specific instructions on what actions the user should take and where to evacuate in the event of a disaster.

[1066] Follow the instructions

[1067] Users can take appropriate action based on the guidance provided by the app. For example, they can check the "route guidance to the nearest evacuation shelter" and take a safe route to the shelter.

[1068] Specific examples

[1069] Example 1: When an earthquake occurs

[1070] 1. The server receives earthquake information from the Japan Meteorological Agency and analyzes the epicenter and seismic intensity.

[1071] 2. The server confirms that User A is located within an office building and generates appropriate action guidance.

[1072] 3. The server determines through the emotion engine that User A is feeling anxious.

[1073] 4. The device sends a notification to User A saying, "An earthquake has occurred. You are currently inside an office building. Please hide under a desk and wait until the shaking stops. Please remain calm."

[1074] 5. The user follows the instructions and hides under the desk.

[1075] Example 2: When a flood or tsunami warning is issued

[1076] 1. The server receives flood and tsunami warnings and identifies risk areas.

[1077] 2. The server verifies that User B is located within the danger zone and generates directions to a safe evacuation site.

[1078] 3. The server, through its emotion engine, identifies that User B is confused.

[1079] 4. The device sends a notification to User B saying, "A tsunami warning has been issued. Your current location is dangerous. The nearest evacuation site is XX Park. Please move quickly. Do not panic, and act calmly."

[1080] 5. The user follows the instructions and heads to the designated evacuation site.

[1081] This invention makes it possible to support users so that they can act quickly and appropriately in the event of a disaster, and by providing support that takes into account their emotional state, it is possible to reduce the anxiety and stress of users.

[1082] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1083] Step 1: Receiving disaster information

[1084] The server sends an API request to the disaster information service to obtain disaster information in real time. This information includes the type of disaster, the location of the disaster, the extent of the impact, and the predicted damage situation, and is stored in a database. The API request is made as input, and disaster information data is obtained as output. This data is used in the next analysis step.

[1085] Step 2: Collect user location information

[1086] The server continuously receives GPS data from the user's smartphone. A dedicated app periodically sends location information to the server, which receives it and identifies the user's current location. The server receives the user's GPS data as input and stores the user's current location as output in a database. This location information is used for situation analysis.

[1087] Step 3: Collecting user emotion information

[1088] The server uses an emotion recognition engine to analyze facial images, voice, and input text acquired from the user's smartphone. This allows it to identify the user's emotional state (stress, anxiety, etc.). It receives facial images and voice data as input, generates user emotional information as output, and stores it in a database.

[1089] Step 4: Analyze the situation

[1090] The server integrates and analyzes the following data in a cloud environment:

[1091] 1. Disaster information (real-time disaster occurrence status)

[1092] 2. User location information

[1093] 3. User Emotion Information

[1094] The server also collects additional data such as transportation status, evacuation shelter congestion, road closure status, etc., and integrates these data to perform a comprehensive situation analysis. All data is integrated as input and a specific situation report for each user is generated as output. This report is used in the action guide generation step.

[1095] Step 5: Generate action guide

[1096] The server uses a generative AI model to generate appropriate action guidance based on the situation report. The generative AI model automatically generates optimal evacuation routes, evacuation locations, first aid methods, etc., taking into account past data, the current situation, and the user's emotions. It uses the situation report as input and generates an action guidance message as output. This message is used in the next notification step.

[1097] Step 6: Receiving and displaying data (terminal)

[1098] The device receives disaster information, action instructions, and emotional support messages sent from the server in the form of push notifications. The device receives notification data from the server as input and displays it to the user as output. Specifically, the device provides information to the user using a notification bar, a pop-up window, and voice guidance.

[1099] Step 7: App Usage and Behavior (Users)

[1100] The user launches the Disaster Evacuation Assistant app on their smartphone and checks the notification sent from the server. Specifically, the user confirms the instructions displayed on the screen and begins evacuation. The input is the notification on the app, and the output is the execution of appropriate actions, such as moving quickly to a designated evacuation site.

[1101] This series of processes enables users to take swift and appropriate evacuation actions in the event of a disaster, and by receiving support that takes into account their emotional state, they can reduce anxiety and stress.

[1102] (Application example 2)

[1103] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1104] In recent years, the frequency and scale of natural disasters have increased, making it important to provide prompt and appropriate evacuation guidance. However, conventional systems can only provide uniform guidance without taking into account the user's location information or emotional state, which makes it difficult for users to take appropriate action when they feel confused or anxious.

[1105] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1106] In this invention, the server includes means for receiving disaster information, means for acquiring location information, means for acquiring emotion information, means for analyzing the situation in the cloud, means for generating action guidance using a generative AI model, means for providing the generated action guidance to the user, and means for generating a voice notification. This makes it possible to provide optimal evacuation action guidance in real time that takes into account the user's location information and emotional state, and supports the user in taking prompt and appropriate action even when they feel confused or anxious.

[1107] "Means for receiving disaster information" refers to a function that obtains information in real time from external information sources when a natural disaster occurs.

[1108] "Means for obtaining location information" refers to a function that identifies the user's current geographic location using GPS or other location measurement methods.

[1109] The "means for acquiring emotional information" is a function for identifying and collecting the user's emotional state from facial expressions, voice, text, etc.

[1110] "Means for analyzing the situation using the cloud" is a function that uses cloud computing to comprehensively analyze collected data and evaluate current risks and appropriate actions.

[1111] "Means for generating action guidance using a generative AI model" is a function that utilizes AI technology to create action guidance customized for each user based on the analysis results.

[1112] The "means for providing the generated action guide to the user" is a function for notifying the user of the generated action guide in a form that the user can understand.

[1113] The "means for generating voice notification" is a function for converting text information into voice and providing a voice notification to the user in real time.

[1114] This invention is a system that receives disaster information, acquires and analyzes the user's location information and emotions, and generates and provides action guidance using a generative AI model. Specific embodiments of this system are described below.

[1115] Server Processing

[1116] The server first receives disaster information in real time from external sources, including disaster information providers such as the Japan Meteorological Agency and earthquake research institutes. The information received includes the type of disaster, the location of the disaster, the extent of its impact, and predicted damage.

[1117] The server then receives the location information sent from the user's smartphone and determines where the user is currently located. The location information is primarily obtained using GPS.

[1118] Furthermore, the server uses an emotion engine to recognize emotions from the user's facial expressions, voice tone, input text, etc. This allows it to identify the stress and anxiety the user is feeling during a disaster.

[1119] The server then integrates location information, disaster information, and emotional information on the cloud, and analyzes the status of public transport, the congestion of evacuation shelters, road closures, etc. Based on this, the specific situation the user is currently in can be grasped in real time.

[1120] Finally, the server generates optimal guidance for each user using a generative AI model, which predicts appropriate actions based on location, based on past data, the current situation, and the user's emotions.

[1121] Processing on the device (user's smartphone)

[1122] First, the device receives disaster information, action instructions, and support messages based on emotions sent from the server.

[1123] The device then displays instructions to the user using voice notifications, text messages, and map information, including responses based on emotions. For example, if the user is feeling anxious, the device will display a message saying, "Please stay calm. The current best evacuation route is ____."

[1124] The device then provides the generated action guidance to the user as a voice notification, using technologies such as Google Text-to-Speech (gTTS).

[1125] User Behavior

[1126] The user launches the Disaster Evacuation Assistant app on their smartphone, checks the notification, and takes appropriate action according to the specific instructions provided by the app.

[1127] Specific examples

[1128] Example 1: When an earthquake occurs

[1129] The server receives earthquake information from the Japan Meteorological Agency and analyzes the epicenter and seismic intensity. The server confirms that User A is located inside an office building and generates appropriate guidance for action. The server determines through the emotion engine that User A is feeling anxious. The device sends a notification to User A saying, "An earthquake has occurred. You are currently inside an office building, so please take cover under a desk and wait until the shaking stops. Please remain calm." The user follows the instructions and takes cover under a desk.

[1130] Example 2: When a flood or tsunami warning is issued

[1131] The server receives flood and tsunami warnings and identifies risk areas. The server confirms that User B's location is within the risk area and generates directions to a safe evacuation site. The server determines through the emotion engine that User B is confused. The device sends a notification to User B saying, "A tsunami warning has been issued. Your current location is dangerous. The nearest evacuation site is XX Park. Please move quickly. Do not panic, and act calmly." The user follows the instructions and heads to the designated evacuation site.

[1132] Examples of prompt statements

[1133] text

[1134] Current disaster information: A magnitude 7.5 earthquake occurred in Tokyo. The epicenter was in Tokyo Bay, and the seismic intensity was 5+.

[1135] User location: Latitude 35.6895, Longitude 139.6917 (i.e. Shinjuku Ward).

[1136] User's emotional state: Feeling very anxious.

[1137] Based on this information, generate the optimal action guide for the user.

[1138] This system helps users act quickly and appropriately in the event of a disaster, and by taking their emotions into consideration, it provides support that allows them to act with peace of mind.

[1139] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1140] Step 1:

[1141] The server receives disaster information in real time from external sources.

[1142] Input: Data from disaster information services such as the Japan Meteorological Agency and earthquake research institutes.

[1143] Data processing / calculation: Analyze the received data to identify the type of disaster, location, extent of impact, and predicted damage.

[1144] Output: Parsed disaster information.

[1145] Step 2:

[1146] The server receives location information sent from the user's smartphone and identifies the user's current location.

[1147] Input: GPS data from your smartphone.

[1148] Data processing / calculation: Location information is analyzed as latitude and longitude to identify a specific geographical location.

[1149] Output: User's current location (latitude and longitude).

[1150] Step 3:

[1151] The server uses an emotion engine to recognize emotions from the user's facial expressions, voice tone, input text, etc.

[1152] Input: User facial expression images, audio samples, and text data.

[1153] Data processing / calculation: The emotion engine analyzes the user's emotional state and recognizes emotions such as stress and anxiety.

[1154] Output: User's emotion information.

[1155] Step 4:

[1156] The server integrates location information, disaster information, and emotion information on the cloud to analyze the current situation.

[1157] Input: location information, disaster information, emotion information.

[1158] Data processing / calculation: Analyze the operation status of public transport, the congestion level of evacuation centres, road closure status, etc. to grasp the current situation in real time.

[1159] Output: Situation analysis results.

[1160] Step 5:

[1161] The server uses a generative AI model to generate optimal action guidance for each user.

[1162] Input: Situation analysis results, location information, emotion information.

[1163] Data processing / calculation: Generative AI models take into account past data, current conditions, and user emotions to predict and create appropriate action guidance based on location.

[1164] Output: Specific instructions for action.

[1165] Step 6:

[1166] The terminal receives the action guide sent from the server and provides it to the user.

[1167] Input: Action instructions from the server.

[1168] Data processing / calculation: Displaying action guidance as voice notifications, text messages, and map information.

[1169] Output: The action prompt displayed to the user.

[1170] Step 7:

[1171] The user launches the disaster evacuation assistant app on their smartphone and checks the notification.

[1172] Input: Notifications from your device.

[1173] Data processing / calculation: Check and understand the notification contents.

[1174] Output: Appropriate action according to user guidance.

[1175] Step 8:

[1176] The terminal provides the generated action guide to the user as a voice notification.

[1177] Input: Call-to-action text.

[1178] Data processing / calculation: Action instructions are converted into audio using Google Text-to-Speech (gTTS) and played back.

[1179] Output: The user receives a voice notification and understands the course of action.

[1180] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1181] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1182] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1183] [Fourth embodiment]

[1184] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1185] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1186] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1187] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1188] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1189] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1190] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1191] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1192] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1193] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1194] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1195] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1196] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1197] The present invention provides a system that receives disaster information, acquires and analyzes user location information, and generates and provides action guidance using a generative AI model. Specific embodiments of the system are described below.

[1198] Server Processing

[1199] 1. Receiving disaster information

[1200] The server accesses various disaster information services (e.g., the Japan Meteorological Agency and earthquake research institutes) and receives information on natural disasters in real time.

[1201] The received disaster information includes parameters such as type (earthquake, flood, tsunami, etc.), location of occurrence, affected area, and predicted damage situation.

[1202] 2. Collection of user location information

[1203] The server receives the location information sent from the user's smartphone.

[1204] This allows you to determine where the user is currently located in real time.

[1205] 3. Analyze the situation

[1206] The server integrates location information and received disaster information on the cloud and performs analysis taking into account factors such as the operation status of public transport, the occupancy level of evacuation centres, and road closure status.

[1207] 4. Generating action guidance

[1208] The server generates appropriate action guidance using a generative AI model, an algorithm that predicts optimal actions based on past disaster data and current situation data.

[1209] Processing on the device (user's smartphone)

[1210] 1. Receiving Data

[1211] The terminal receives disaster information and action instructions sent from the server.

[1212] Based on the received information, specific action instructions are created for the user.

[1213] 2. Displaying Data

[1214] The device displays action guidance to the user using voice notifications, text messages, and map information.

[1215] For example, when an earthquake occurs, a notification will be displayed saying, "You are currently inside an office building. Please hide under a desk and wait until the shaking stops."

[1216] User Behavior

[1217] 1. Using the app

[1218] The user launches the Disaster Evacuation Assistant app on their smartphone and checks the notification.

[1219] 2. Follow the instructions

[1220] Users take appropriate action by following the specific guidance provided by the app.

[1221] For example, when a flood warning is issued, people will follow the notification that says, "The nearest evacuation site is XX Park. Please move quickly," and head to the designated evacuation site.

[1222] Specific examples

[1223] Example 1: When an earthquake occurs

[1224] 1. The server receives earthquake information from the Japan Meteorological Agency and analyzes the epicenter and seismic intensity.

[1225] 2. The server confirms that User A is located within an office building and generates appropriate action guidance.

[1226] 3. The device sends a notification to User A saying, "An earthquake has occurred. You are currently in an office building. Please hide under a desk and wait until the shaking stops."

[1227] 4. The user follows the instructions and hides under the desk.

[1228] Example 2: When a flood or tsunami warning is issued

[1229] 1. The server receives flood and tsunami warnings and identifies risk areas.

[1230] 2. The server verifies that User B is located within the danger zone and generates directions to a safe evacuation site.

[1231] 3. The device sends a notification to User B saying, "A tsunami warning has been issued. Your current location is dangerous. The nearest evacuation site is XX Park. Please move quickly."

[1232] 4. The user follows the instructions and heads to the designated evacuation site.

[1233] This invention can ensure the safety of users in the event of a disaster and prevent disruption to social infrastructure.

[1234] The processing flow will be explained below.

[1235] Step 1:

[1236] The server accesses various disaster information services (e.g., the Japan Meteorological Agency, earthquake research institutes) and receives real-time information on natural disasters, including the type of disaster, the location of the disaster, the extent of its impact, and predicted damage.

[1237] Step 2:

[1238] The device periodically uses the GPS sensor to obtain the user's current location, which is then sent to a server in the background in preparation for a disaster.

[1239] Step 3:

[1240] The server stores the user's location information and disaster information in a database and compares it with the disaster information.

[1241] Step 4:

[1242] The server integrates the user's location information and disaster information on the cloud, and analyzes the status of public transport, the congestion level of evacuation shelters, road closures, etc. This allows the user to grasp the specific situation they are currently in in real time.

[1243] Step 5:

[1244] The server uses a generative AI model to generate optimal guidance for each user, which predicts appropriate actions based on location based on past data and the current situation.

[1245] Step 6:

[1246] The server transmits the generated action guide to the user's terminal.

[1247] Step 7:

[1248] The device receives guidance from the server and notifies the user of the action instructions using voice, text, map information, etc. For example, when an earthquake occurs, the device displays a message saying, "You are currently in an office building. Please hide under a desk and wait until the shaking stops."

[1249] Step 8:

[1250] The user follows the notification on the device and takes the specified evacuation action. For example, in the event of a flood warning, the user will act according to instructions such as "The nearest evacuation site is XX Park. Please move quickly."

[1251] Step 9:

[1252] The server periodically updates the user's location information and new disaster information, and regenerates action guidance as necessary, allowing the user to always act based on the latest information.

[1253] By this specific processing, the present invention enables the user to take prompt and appropriate evacuation actions in the event of a disaster.

[1254] Example 1

[1255] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1256] When a natural disaster occurs, it is difficult to receive accurate disaster information in real time and generate and provide optimal action guidance based on the user's current location. There is also a need to provide users with accurate and prompt information and encourage safe evacuation. Existing systems often lack immediacy and accuracy because they receive disaster information, analyze the situation, and generate and present action guidance separately.

[1257] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1258] In this invention, the server includes means for receiving disaster information, means for acquiring location information, means for analyzing the situation in the cloud, means for generating action guidance using a generative AI model, means for providing the generated action guidance to the user, means for causing the user's terminal to receive the disaster information and action guidance, and means for displaying the received action guidance, thereby enabling the user to receive optimal action guidance in real time and take quick and safe evacuation actions.

[1259] "Means for receiving disaster information" refers to a device or system for obtaining disaster information in real time from meteorological agencies, research institutions, etc.

[1260] "Means for obtaining location information" means a device or system that uses GPS or other location measurement technology to determine a user's current location and transmits that information to a server.

[1261] "Means for analyzing situations in the cloud" refers to a device or system that uses a remote server accessible via the Internet to integrate and analyze collected data and assess the situation in real time.

[1262] "Means for generating action guidance using a generative AI model" refers to a device or system that uses artificial intelligence technology to generate optimal action guidance for a user based on past data and the current situation.

[1263] The "means for providing the generated action guide to the user" refers to a device or system for notifying the user of the generated action guide.

[1264] "Means for allowing a user's device to receive disaster information and action guidance" refers to a device or system for sending and receiving disaster information and action guidance to an electronic device such as a smartphone or tablet owned by the user.

[1265] The "means for displaying received action guidance" refers to a device or system that visually or audibly displays action guidance in the form of text, voice, map, or the like on the user's terminal.

[1266] This invention is a system that receives disaster information, acquires and analyzes user location information, and generates and provides action guidance using a generative AI model. Specific embodiments of this system are described below.

[1267] Server Processing

[1268] 1. Receiving disaster information

[1269] The server uses APIs provided by meteorological agencies and research institutions to collect the latest disaster information on earthquakes, floods, tsunamis, etc. in real time. Specifically, it obtains JSON-formatted data from various APIs and analyzes parameters such as type, location of occurrence, affected area, and predicted damage situation. This received data is stored in a database (such as an SQL database).

[1270] 2. Collection of user location information

[1271] The server collects location information (GPS data) sent from the user's smartphone in real time, which allows it to accurately identify the device's location and store it in a database.

[1272] 3. Analyze the situation

[1273] The server uses cloud services (e.g., Amazon Web Services, Microsoft Azure) to integrate and analyze location information and disaster information in real time. Specifically, it uses AWS Lambda functions and Azure Functions to perform analysis taking into account the operation status of public transportation, the occupancy level of evacuation shelters, road closure status, etc. This allows it to determine whether the user's current location will be affected by the disaster.

[1274] 4. Generating action guidance

[1275] The server generates appropriate action guidance using a generative AI model (e.g., OpenAI's GPT-4). For example, the prompt sentence is "Please generate the optimal action guidance for the user to safely evacuate from their current location." The generated action guidance is optimized using natural language processing and provided in an easy-to-understand format. The action guidance is generated in JSON format and sent to the user's device.

[1276] Processing on the device (user's smartphone)

[1277] 1. Receiving Data

[1278] The device receives disaster information and action instructions sent from the server via secure communication (e.g., HTTPS). This data is stored in a local database (e.g., SQLite) and prepared for immediate notification to the user.

[1279] 2. Displaying Data

[1280] The device visually displays the received guidance to the user. It displays map information along with text messages on the screen, and also provides voice notifications as needed. For example, in the event of an earthquake, the device displays evacuation locations on a map along with a message saying, "You are currently inside an office building. Please take cover under your desk and wait until the shaking stops."

[1281] User Behavior

[1282] 1. Using the app

[1283] Users launch the Disaster Evacuation Assistant app on their smartphones and check notifications. The app runs in the background, allowing them to respond immediately when a notification arrives. Users can check detailed disaster information and evacuation instructions within the app.

[1284] 2. Follow the instructions

[1285] Users take appropriate action by following the specific action instructions provided by the app. For example, when a flood warning is issued, users will follow the notification "The nearest evacuation site is XX Park. Please move quickly" and head to the designated evacuation site.

[1286] Example prompt

[1287] An example of a prompt sentence to input to a generative AI model is as follows:

[1288] "Generate optimal evacuation instructions based on the latest earthquake information and the user's location information."

[1289] "Please provide specific instructions on what to do if a flood warning is issued and the user's current location is in a risk area."

[1290] In this way, by ensuring the safety of users in the event of a disaster and providing prompt and accurate guidance on what to do, it is possible to prevent disruptions to social infrastructure.

[1291] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1292] Step 1:

[1293] Receiving disaster information

[1294] The server uses APIs provided by meteorological agencies and research institutions to collect the latest disaster information on earthquakes, floods, tsunamis, etc. in real time. Specifically, it obtains JSON-formatted data from various APIs and analyzes parameters such as type, location of occurrence, affected area, and predicted damage situation. The JSON data obtained from the API is used as input for the received data, and it is processed for storage in a database, extracting and saving the necessary items.

[1295] Step 2:

[1296] Collection of user location information

[1297] The server collects location information (GPS data) sent from the user's smartphone in real time. It inputs the GPS data sent from the user's device in JSON format, analyzes the data, and processes it to store it in a database. The analyzed location information is saved for each user ID, allowing the latest location to be checked at any time.

[1298] Step 3:

[1299] Analysis of the situation

[1300] The server uses cloud services (e.g., Amazon Web Services, Microsoft Azure) to integrate and analyze location information and disaster information in real time. Using AWS Lambda functions and Azure Functions, it performs analysis taking into account factors such as transportation operation status, evacuation shelter occupancy levels, and road closure status. Using the collected location information and disaster information as input, it determines whether specific users will be affected by the disaster. The results of this analysis are output as the necessity and priority of evacuation.

[1301] Step 4:

[1302] Generate action guide

[1303] The server generates appropriate action guidance using a generative AI model (e.g., OpenAI's GPT-4). For example, the server inputs a prompt statement such as "Please generate the optimal action guidance for the user to safely evacuate from their current location," and outputs the action guidance generated by the AI ​​model. The generated guidance is stored in a database and sent to the user's device in JSON format.

[1304] Step 5:

[1305] Receiving data

[1306] The device receives disaster information and action instructions sent from the server via secure communication (e.g., HTTPS communication). It takes the JSON-formatted action instructions sent from the server as input, stores them in a local database (e.g., SQLite), and prepares for immediate notification.

[1307] Step 6:

[1308] Viewing Data

[1309] The device visually displays the received guidance to the user. Using the guidance stored in a local database as input, the device displays map information along with text messages on the screen and, if necessary, provides audio notifications. For example, a message might read, "An earthquake has occurred. You are currently inside an office building. Please take cover under your desk and wait until the shaking stops."

[1310] Step 7:

[1311] Using the app

[1312] The user launches the Disaster Evacuation Assistant app on their smartphone and checks the notification. When the user launches the app, the latest action instructions sent from the server are displayed. User input is required, and detailed disaster information and evacuation instructions can be viewed within the app.

[1313] Step 8:

[1314] Follow the instructions

[1315] The user takes appropriate action according to the specific action guidance provided by the app. The user receives action guidance from the app as input and takes actual evacuation action based on that guidance. For example, when a flood warning is issued, the user will follow the notification "The nearest evacuation site is XX Park. Please move quickly" and head to the designated evacuation site.

[1316] (Application example 1)

[1317] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1318] In recent years, the increasing frequency and diversity of disasters has created a need for prompt and appropriate guidance. However, current systems sometimes fail to adequately ensure user safety. There is a need for technology that can obtain disaster information in real time and provide optimal guidance based on the user's current location. In addition, it is necessary to use a generative AI model to generate guidance based on prompts and provide it to users effectively using voice notifications, text messages, and map information.

[1319] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1320] In this invention, the server includes means for receiving disaster information, means for acquiring location information, means for analyzing the situation in the cloud, means for generating action guidance using a generative AI model, means for providing the generated action guidance to a user, means for identifying an optimal evacuation route and evacuation location based on the user's current location, means for inputting a prompt sentence to the generative AI model when generating the action guidance and generating optimal action guidance based on the prompt sentence, and means for providing the action guidance to the user using voice notification, text message, and map information. This enables quick and appropriate action guidance for users in the event of a disaster.

[1321] "Disaster information" refers to real-time information on natural disasters provided by the Japan Meteorological Agency, earthquake research institutes, and other organizations.

[1322] "Location information" is data indicating the current geographical coordinates obtained from a user's smartphone or the like.

[1323] The "cloud" is a distributed computing environment for managing, storing, and analyzing resources and data over the Internet.

[1324] A "generative AI model" is an artificial intelligence technology that incorporates an algorithm that generates behavioral guidance based on past data and current situational data.

[1325] "Action guidance" is information that instructs the user on the optimal action to take in the event of a disaster.

[1326] A "prompt statement" is a statement that specifies the task you want the generative AI model to perform and the requirements for the results you want it to generate.

[1327] An "evacuation route" is information about the optimal route for a user to move to a safe place.

[1328] An "evacuation site" is a designated assembly point that will ensure safety in the event of a disaster.

[1329] "Audio notification" is a method of conveying information to a user using audio.

[1330] "Text message" is a method of conveying action instructions and the like to a user using text information.

[1331] "Map information" is data for visually displaying geographical locations, routes, etc.

[1332] This system receives disaster information, acquires and analyzes the user's location information, and generates and provides action guidance using a generative AI model. This system consists of a server and a user's device.

[1333] Server Processing

[1334] The server first accesses various disaster information services and receives information on natural disasters in real time. The received disaster information includes parameters such as the type of disaster (earthquake, flood, tsunami, etc.), the location of the disaster, the extent of the impact, and the predicted damage situation. Next, it receives location information from the user's smartphone and identifies the user's current location in real time. The collected location information and the received disaster information are integrated in the cloud, and an analysis is performed taking into account the operation status of public transportation, the occupancy level of evacuation shelters, the status of road closures, etc.

[1335] The server then inputs a prompt into the generative AI model to generate appropriate guidance. This generative AI model predicts and generates optimal actions based on past disaster data and current situation data. For example, a prompt might be, "Based on the user's location information and the latest earthquake information, please generate optimal evacuation guidance for the user."

[1336] Processing on the device (user's smartphone)

[1337] The device receives disaster information and action instructions sent from the server. Based on the received information, specific action instructions are created for the user. This includes displaying action instructions to the user using voice notifications, text messages, and map information. For example, in the event of an earthquake, a notification may be displayed saying, "You are currently inside an office building. Please hide under a desk and wait until the shaking stops." Different action instructions are generated on the user's device based on the type of disaster and its impact, and are notified to the user in real time.

[1338] User Behavior

[1339] Users receive notifications from this system on their smartphones and are informed of appropriate action instructions. For example, if a flood warning is issued, the system notifies them by saying, "The nearest evacuation site is XX Park. Please move quickly." They can then head to the designated evacuation site. The guidance is provided as voice notifications, text messages, and map information, making it easy for users to understand intuitively.

[1340] Hardware and software used

[1341] This system uses a user's smartphone and a server computer. The program implementation uses the requests library for API access, the geopy library for processing geographic information, and the datetime module for timestamp processing.

[1342] With the above configuration, the present invention realizes prompt and appropriate guidance of actions to be taken by a user in the event of a disaster, thereby ensuring the safety of the user.

[1343] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1344] Step 1:

[1345] The server accesses various disaster information services and receives information on natural disasters in real time. As input, it accesses the APIs of the Japan Meteorological Agency and earthquake research institutes, and as output, it obtains data such as the type of disaster, the location of the occurrence, the extent of the impact, and the predicted damage situation. This allows the server to always have the latest disaster information.

[1346] Step 2:

[1347] The server receives current location information from the user's smartphone. The user provides location information through a smartphone app, and the server receives that location information as GPS data. The user's location data is sent as input, and the latitude and longitude data of the current location are obtained as output.

[1348] Step 3:

[1349] The server integrates the received disaster information and the user's location information on the cloud and analyzes the situation. Disaster information and the user's location information are integrated as input, and analytical data such as the distance between the disaster site and the user and the extent of the impact are obtained as output. This allows the risk level of the user's current location to be analyzed.

[1350] Step 4:

[1351] The server inputs a prompt into the generative AI model and generates optimal action guidance. For example, the prompt could be, "Please generate optimal evacuation guidance for the user based on the user's location information and the latest earthquake information." The prompt and analysis data are provided as input, and optimal action guidance is generated as output. The generative AI model runs an algorithm based on past disaster data and current situation data to predict and generate action guidance.

[1352] Step 5:

[1353] The server provides the generated action guidance to the user's smartphone. The action guidance is sent to the user as a voice notification, a text message, and map information. The generated action guidance is the input, and a notification is sent to the user's smartphone as the output. This allows the user to receive guidance to take prompt and appropriate action.

[1354] Step 6:

[1355] Users take appropriate action based on the guidance they receive through a smartphone app. For example, if a flood warning is issued, they follow the notification, "The nearest evacuation site is XX Park. Please move quickly." The user receives guidance as input and performs appropriate evacuation actions as output. The notification is provided as voice, text, and map information, making it easy for users to intuitively understand and act safely.

[1356] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1357] The present invention is a system that receives disaster information, acquires and analyzes the user's location information and emotions, and generates and provides action guidance using a generative AI model. Specific embodiments of the system are described below.

[1358] Server Processing

[1359] 1. Receiving disaster information

[1360] The server accesses various disaster information services (e.g., the Japan Meteorological Agency, earthquake research institutes) and receives real-time information on natural disasters, including the type of disaster, the location of the disaster, the extent of its impact, and predicted damage.

[1361] 2. Collection of user location information

[1362] The server receives location information sent from the user's smartphone and uses this to determine where the user is currently located.

[1363] 3. Collecting user emotion information

[1364] The server uses an emotion engine to recognize emotions from the user's facial expressions, voice tone, input text, etc. This allows it to identify the stress and anxiety the user is feeling during a disaster.

[1365] 4. Analyze the situation

[1366] The server integrates location information, disaster information, and emotional information on the cloud, and analyzes the status of public transport, the congestion of evacuation shelters, road closures, etc. Based on this, the specific situation the user is currently in can be grasped in real time.

[1367] 5. Generating action guidance

[1368] The server uses a generative AI model to generate optimal guidance for each user, which predicts appropriate actions based on location, based on past data, the current situation, and the user's emotions.

[1369] Processing on the device (user's smartphone)

[1370] 1. Receiving Data

[1371] The terminal receives disaster information, action instructions, and support messages based on emotions sent from the server.

[1372] 2. Displaying Data

[1373] The device will display guidance to the user using voice notifications, text messages, and map information, including responses based on emotions. For example, if the user is feeling anxious, the device will display a message saying, "Please stay calm. The current best evacuation route is XX."

[1374] User Behavior

[1375] 1. Using the app

[1376] The user launches the Disaster Evacuation Assistant app on their smartphone and checks the notification.

[1377] 2. Follow the instructions

[1378] The user takes appropriate action by following specific instructions provided by the app, such as "The nearest evacuation site is XX Park. Please move quickly."

[1379] Specific examples

[1380] Example 1: When an earthquake occurs

[1381] 1. The server receives earthquake information from the Japan Meteorological Agency and analyzes the epicenter and seismic intensity.

[1382] 2. The server confirms that User A is located within an office building and generates appropriate action guidance.

[1383] 3. The server determines through the emotion engine that User A is feeling anxious.

[1384] 4. The device sends a notification to User A saying, "An earthquake has occurred. You are currently inside an office building. Please hide under a desk and wait until the shaking stops. Please remain calm."

[1385] 5. The user follows the instructions and hides under the desk.

[1386] Example 2: When a flood or tsunami warning is issued

[1387] 1. The server receives flood and tsunami warnings and identifies risk areas.

[1388] 2. The server verifies that User B is located within the danger zone and generates directions to a safe evacuation site.

[1389] 3. The server, through its emotion engine, identifies that User B is confused.

[1390] 4. The device sends a notification to User B saying, "A tsunami warning has been issued. Your current location is dangerous. The nearest evacuation site is XX Park. Please move quickly. Do not panic, and act calmly."

[1391] 5. The user follows the instructions and heads to the designated evacuation site.

[1392] This invention enables users to take prompt and appropriate evacuation actions in the event of a disaster, and by taking into consideration the user's emotions, it is possible to provide support that allows users to act with greater peace of mind.

[1393] The processing flow will be explained below.

[1394] Step 1:

[1395] The server periodically sends requests to disaster information providers (e.g., the Japan Meteorological Agency, earthquake research institutes) to receive the latest disaster information. The received information includes the type of disaster (earthquake, flood, tsunami, etc.), the location of the disaster, the extent of the impact, and the predicted damage situation.

[1396] Step 2:

[1397] The device periodically uses the GPS sensor to obtain the user's current location, which is then automatically sent to the server and stored in a database.

[1398] Step 3:

[1399] The server uses an emotion engine to recognize the user's emotions in real time from the device's voice, camera, text input, etc. Emotions are evaluated based on multiple parameters, such as anxiety, stress, and calmness.

[1400] Step 4:

[1401] The server integrates location information, disaster information, and emotional information, and analyzes in real time the status of transportation within the affected area, the congestion level of evacuation centers, road closure status, and other information.

[1402] Step 5:

[1403] The server uses a generative AI model to generate optimal action guidance based on the user's current location and emotional state. The generative AI model predicts optimal actions by taking into account past disaster data, current disaster information, and the user's individual situation.

[1404] Step 6:

[1405] The server sends the generated action guide to the user's terminal, which includes specific evacuation routes, evacuation locations, and messages according to emotions.

[1406] Step 7:

[1407] The device will then provide the user with detailed instructions using voice notifications, text messages, and map information. For example, in the event of an earthquake, the device will display a message saying, "You are currently in an office building. Please take cover under a desk. Please remain calm and act accordingly."

[1408] Step 8:

[1409] The user follows the notification on the device and takes the evacuation action instructed. For example, in the case of a flood warning, the user follows the instruction "The nearest evacuation site is XX Park. Please move quickly."

[1410] Step 9:

[1411] The server continuously monitors the user's location and emotional information and regenerates new action guidance as necessary. This dynamic process helps users take appropriate actions even when the disaster situation changes.

[1412] By this specific processing, the present invention enables the user to take prompt and appropriate evacuation action in the event of a disaster, and furthermore, can provide mental care by taking into account the user's emotional state.

[1413] Example 2

[1414] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1415] When a natural disaster occurs, it is necessary to provide users with prompt and appropriate guidance. However, conventional systems identify evacuation routes using only the user's location information, and therefore do not provide personalized guidance that takes into account the user's emotional state, making it difficult to reduce the stress and anxiety users feel during a disaster. Therefore, it is necessary to provide guidance that also takes into account the user's emotional state.

[1416] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1417] In this invention, the server includes means for receiving disaster information, means for acquiring location information, means for acquiring emotion information, means for analyzing the situation in the cloud, means for generating action guidance using a generative AI model, and means for providing the generated action guidance to the user. This provides personalized action guidance based on the user's location information and emotional state, enabling the user to take optimal evacuation actions quickly and safely in the event of a disaster.

[1418] "Disaster information" refers to information about natural disasters (earthquakes, floods, tsunamis, etc.), including the type of disaster, the location of the disaster, the extent of the impact, and predicted damage.

[1419] "Location information" is information indicating the user's current location based on GPS data provided by the user's device.

[1420] "Emotional information" is information about a user's emotional state determined from the user's facial expression, voice tone, and input text, obtained using emotion recognition technology.

[1421] The "cloud" is an infrastructure that provides resources and services for storing, processing, and managing data over the Internet.

[1422] A "generative AI model" is a model that uses artificial intelligence to analyze and predict data, and automatically generates action guidance appropriate for the user.

[1423] "Action guidance" is information that instructs the user on specific actions to be taken in the event of a disaster, and includes evacuation routes, evacuation locations, messages according to the user's emotions, and the like.

[1424] "User" refers to a person who uses this system, and is an individual who should take evacuation action in the event of a disaster.

[1425] MODE FOR CARRYING OUT THE INVENTION

[1426] This invention is a system that receives disaster information, acquires and analyzes the user's location information and emotional information, and generates and provides action guidance using a generative AI model.

[1427] Server Processing

[1428] Receiving disaster information

[1429] The server sends API requests to multiple disaster information providers (e.g., the Japan Meteorological Agency, earthquake research institutes) to obtain disaster information. Disaster information includes the type of disaster, the location of the disaster, the extent of the impact, and predicted damage. The obtained information is stored in a database and updated in real time.

[1430] Collection of user location information

[1431] The server receives GPS data sent from the user's smartphone and identifies the user's current location. A dedicated app on the smartphone periodically sends location information to the server. This information is used to track the user's location in real time.

[1432] Collecting user emotion information

[1433] The server uses an emotion recognition engine (e.g., Microsoft Azure Cognitive Services, IBM Watson) to analyze facial images, voice, and input text acquired from the user's smartphone. This allows the server to identify the user's emotional state (stress, anxiety, etc.). For example, the server analyzes the user's facial expressions through the camera and evaluates the user's excitement level from their voice.

[1434] Analysis of the situation

[1435] The server consolidates and analyzes the following data in a cloud environment:

[1436] 1. Disaster information (real-time disaster occurrence status)

[1437] 2. User location information

[1438] 3. User Emotion Information

[1439] Furthermore, the system also collects information on the operation status of public transport (e.g., Google Maps API), information on the congestion of evacuation centres (data from government agencies), and road closure status (data from police and traffic information services) to perform comprehensive situation analysis, thereby enabling the system to grasp the specific situation of each user in real time.

[1440] Generate action guide

[1441] The server uses a generative AI model (e.g., OpenAI GPT-4) to generate action guidance based on the analysis data. The generative AI model takes into account past data, the current situation, and the user's emotions to automatically generate optimal evacuation routes, evacuation locations, first aid methods, etc. For example, it generates a message such as, "The nearest evacuation location is XX Park. Please evacuate immediately."

[1442] Processing on the device (user's smartphone)

[1443] Receiving data

[1444] The device receives disaster information, action instructions, and emotional support messages sent from the server in the form of push notifications.

[1445] Viewing Data

[1446] The terminal displays the following information to the user:

[1447] 1. Disaster Information: Breaking news on earthquakes, tsunami warnings, etc.

[1448] 2. Action Guide: Information on evacuation routes and evacuation shelters

[1449] 3. Emotional support messages: Messages of comfort and encouragement that correspond to the user's emotional state

[1450] This information is displayed in a notification bar or pop-up window, and is also accompanied by audio guidance, in an effort to reduce anxiety and confusion.

[1451] User Behavior

[1452] Using the app

[1453] The user launches the Disaster Evacuation Assistant app on their smartphone and checks the notification sent from the server. The app provides specific instructions on what actions the user should take and where to evacuate in the event of a disaster.

[1454] Follow the instructions

[1455] Users can take appropriate action based on the guidance provided by the app. For example, they can check the "route guidance to the nearest evacuation shelter" and take a safe route to the shelter.

[1456] Specific examples

[1457] Example 1: When an earthquake occurs

[1458] 1. The server receives earthquake information from the Japan Meteorological Agency and analyzes the epicenter and seismic intensity.

[1459] 2. The server confirms that User A is located within an office building and generates appropriate action guidance.

[1460] 3. The server determines through the emotion engine that User A is feeling anxious.

[1461] 4. The device sends a notification to User A saying, "An earthquake has occurred. You are currently inside an office building. Please hide under a desk and wait until the shaking stops. Please remain calm."

[1462] 5. The user follows the instructions and hides under the desk.

[1463] Example 2: When a flood or tsunami warning is issued

[1464] 1. The server receives flood and tsunami warnings and identifies risk areas.

[1465] 2. The server verifies that User B is located within the danger zone and generates directions to a safe evacuation site.

[1466] 3. The server, through its emotion engine, identifies that User B is confused.

[1467] 4. The device sends a notification to User B saying, "A tsunami warning has been issued. Your current location is dangerous. The nearest evacuation site is XX Park. Please move quickly. Do not panic, and act calmly."

[1468] 5. The user follows the instructions and heads to the designated evacuation site.

[1469] This invention makes it possible to support users so that they can act quickly and appropriately in the event of a disaster, and by providing support that takes into account their emotional state, it is possible to reduce the anxiety and stress of users.

[1470] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1471] Step 1: Receiving disaster information

[1472] The server sends an API request to the disaster information service to obtain disaster information in real time. This information includes the type of disaster, the location of the disaster, the extent of the impact, and the predicted damage situation, and is stored in a database. The API request is made as input, and disaster information data is obtained as output. This data is used in the next analysis step.

[1473] Step 2: Collect user location information

[1474] The server continuously receives GPS data from the user's smartphone. A dedicated app periodically sends location information to the server, which receives it and identifies the user's current location. The server receives the user's GPS data as input and stores the user's current location as output in a database. This location information is used for situation analysis.

[1475] Step 3: Collecting user emotion information

[1476] The server uses an emotion recognition engine to analyze facial images, voice, and input text acquired from the user's smartphone. This allows it to identify the user's emotional state (stress, anxiety, etc.). It receives facial images and voice data as input, generates user emotional information as output, and stores it in a database.

[1477] Step 4: Analyze the situation

[1478] The server integrates and analyzes the following data in a cloud environment:

[1479] 1. Disaster information (real-time disaster occurrence status)

[1480] 2. User location information

[1481] 3. User Emotion Information

[1482] The server also collects additional data such as transportation status, evacuation shelter congestion, road closure status, etc., and integrates these data to perform a comprehensive situation analysis. All data is integrated as input and a specific situation report for each user is generated as output. This report is used in the action guide generation step.

[1483] Step 5: Generate action guide

[1484] The server uses a generative AI model to generate appropriate action guidance based on the situation report. The generative AI model automatically generates optimal evacuation routes, evacuation locations, first aid methods, etc., taking into account past data, the current situation, and the user's emotions. It uses the situation report as input and generates an action guidance message as output. This message is used in the next notification step.

[1485] Step 6: Receiving and displaying data (terminal)

[1486] The device receives disaster information, action instructions, and emotional support messages sent from the server in the form of push notifications. The device receives notification data from the server as input and displays it to the user as output. Specifically, the device provides information to the user using a notification bar, a pop-up window, and voice guidance.

[1487] Step 7: App Usage and Behavior (Users)

[1488] The user launches the Disaster Evacuation Assistant app on their smartphone and checks the notification sent from the server. Specifically, the user confirms the instructions displayed on the screen and begins evacuation. The input is the notification on the app, and the output is the execution of appropriate actions, such as moving quickly to a designated evacuation site.

[1489] This series of processes enables users to take swift and appropriate evacuation actions in the event of a disaster, and by receiving support that takes into account their emotional state, they can reduce anxiety and stress.

[1490] (Application example 2)

[1491] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1492] In recent years, the frequency and scale of natural disasters have increased, making it important to provide prompt and appropriate evacuation guidance. However, conventional systems can only provide uniform guidance without taking into account the user's location information or emotional state, which makes it difficult for users to take appropriate action when they feel confused or anxious.

[1493] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1494] In this invention, the server includes means for receiving disaster information, means for acquiring location information, means for acquiring emotion information, means for analyzing the situation in the cloud, means for generating action guidance using a generative AI model, means for providing the generated action guidance to the user, and means for generating a voice notification. This makes it possible to provide optimal evacuation action guidance in real time that takes into account the user's location information and emotional state, and supports the user in taking prompt and appropriate action even when they feel confused or anxious.

[1495] "Means for receiving disaster information" refers to a function that obtains information in real time from external information sources when a natural disaster occurs.

[1496] "Means for obtaining location information" refers to a function that identifies the user's current geographic location using GPS or other location measurement methods.

[1497] The "means for acquiring emotional information" is a function for identifying and collecting the user's emotional state from facial expressions, voice, text, etc.

[1498] "Means for analyzing the situation using the cloud" is a function that uses cloud computing to comprehensively analyze collected data and evaluate current risks and appropriate actions.

[1499] "Means for generating action guidance using a generative AI model" is a function that utilizes AI technology to create action guidance customized for each user based on the analysis results.

[1500] The "means for providing the generated action guide to the user" is a function for notifying the user of the generated action guide in a form that the user can understand.

[1501] The "means for generating voice notification" is a function for converting text information into voice and providing a voice notification to the user in real time.

[1502] This invention is a system that receives disaster information, acquires and analyzes the user's location information and emotions, and generates and provides action guidance using a generative AI model. Specific embodiments of this system are described below.

[1503] Server Processing

[1504] The server first receives disaster information in real time from external sources, including disaster information providers such as the Japan Meteorological Agency and earthquake research institutes. The information received includes the type of disaster, the location of the disaster, the extent of its impact, and predicted damage.

[1505] The server then receives the location information sent from the user's smartphone and determines where the user is currently located. The location information is primarily obtained using GPS.

[1506] Furthermore, the server uses an emotion engine to recognize emotions from the user's facial expressions, voice tone, input text, etc. This allows it to identify the stress and anxiety the user is feeling during a disaster.

[1507] The server then integrates location information, disaster information, and emotional information on the cloud, and analyzes the status of public transport, the congestion of evacuation shelters, road closures, etc. Based on this, the specific situation the user is currently in can be grasped in real time.

[1508] Finally, the server generates optimal guidance for each user using a generative AI model, which predicts appropriate actions based on location, based on past data, the current situation, and the user's emotions.

[1509] Processing on the device (user's smartphone)

[1510] First, the device receives disaster information, action instructions, and support messages based on emotions sent from the server.

[1511] The device then displays instructions to the user using voice notifications, text messages, and map information, including responses based on emotions. For example, if the user is feeling anxious, the device will display a message saying, "Please stay calm. The current best evacuation route is ____."

[1512] The device then provides the generated action guidance to the user as a voice notification, using technologies such as Google Text-to-Speech (gTTS).

[1513] User Behavior

[1514] The user launches the Disaster Evacuation Assistant app on their smartphone, checks the notification, and takes appropriate action according to the specific instructions provided by the app.

[1515] Specific examples

[1516] Example 1: When an earthquake occurs

[1517] The server receives earthquake information from the Japan Meteorological Agency and analyzes the epicenter and seismic intensity. The server confirms that User A is located inside an office building and generates appropriate guidance for action. The server determines through the emotion engine that User A is feeling anxious. The device sends a notification to User A saying, "An earthquake has occurred. You are currently inside an office building, so please take cover under a desk and wait until the shaking stops. Please remain calm." The user follows the instructions and takes cover under a desk.

[1518] Example 2: When a flood or tsunami warning is issued

[1519] The server receives flood and tsunami warnings and identifies risk areas. The server confirms that User B's location is within the risk area and generates directions to a safe evacuation site. The server determines through the emotion engine that User B is confused. The device sends a notification to User B saying, "A tsunami warning has been issued. Your current location is dangerous. The nearest evacuation site is XX Park. Please move quickly. Do not panic, and act calmly." The user follows the instructions and heads to the designated evacuation site.

[1520] Examples of prompt statements

[1521] text

[1522] Current disaster information: A magnitude 7.5 earthquake occurred in Tokyo. The epicenter was in Tokyo Bay, and the seismic intensity was 5+.

[1523] User location: Latitude 35.6895, Longitude 139.6917 (i.e. Shinjuku Ward).

[1524] User's emotional state: Feeling very anxious.

[1525] Based on this information, generate the optimal action guide for the user.

[1526] This system helps users act quickly and appropriately in the event of a disaster, and by taking their emotions into consideration, it provides support that allows them to act with peace of mind.

[1527] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1528] Step 1:

[1529] The server receives disaster information in real time from external sources.

[1530] Input: Data from disaster information services such as the Japan Meteorological Agency and earthquake research institutes.

[1531] Data processing / calculation: Analyze the received data to identify the type of disaster, location, extent of impact, and predicted damage.

[1532] Output: Parsed disaster information.

[1533] Step 2:

[1534] The server receives location information sent from the user's smartphone and identifies the user's current location.

[1535] Input: GPS data from your smartphone.

[1536] Data processing / calculation: Location information is analyzed as latitude and longitude to identify a specific geographical location.

[1537] Output: User's current location (latitude and longitude).

[1538] Step 3:

[1539] The server uses an emotion engine to recognize emotions from the user's facial expressions, voice tone, input text, etc.

[1540] Input: User facial expression images, audio samples, and text data.

[1541] Data processing / calculation: The emotion engine analyzes the user's emotional state and recognizes emotions such as stress and anxiety.

[1542] Output: User's emotion information.

[1543] Step 4:

[1544] The server integrates location information, disaster information, and emotion information on the cloud to analyze the current situation.

[1545] Input: location information, disaster information, emotion information.

[1546] Data processing / calculation: Analyze the operation status of public transport, the congestion level of evacuation centres, road closure status, etc. to grasp the current situation in real time.

[1547] Output: Situation analysis results.

[1548] Step 5:

[1549] The server uses a generative AI model to generate optimal action guidance for each user.

[1550] Input: Situation analysis results, location information, emotion information.

[1551] Data processing / calculation: Generative AI models take into account past data, current conditions, and user emotions to predict and create appropriate action guidance based on location.

[1552] Output: Specific instructions for action.

[1553] Step 6:

[1554] The terminal receives the action guide sent from the server and provides it to the user.

[1555] Input: Action instructions from the server.

[1556] Data processing / calculation: Displaying action guidance as voice notifications, text messages, and map information.

[1557] Output: The action prompt displayed to the user.

[1558] Step 7:

[1559] The user launches the disaster evacuation assistant app on their smartphone and checks the notification.

[1560] Input: Notifications from your device.

[1561] Data processing / calculation: Check and understand the notification contents.

[1562] Output: Appropriate action according to user guidance.

[1563] Step 8:

[1564] The terminal provides the generated action guide to the user as a voice notification.

[1565] Input: Call-to-action text.

[1566] Data processing / calculation: Action instructions are converted into audio using Google Text-to-Speech (gTTS) and played back.

[1567] Output: The user receives a voice notification and understands the course of action.

[1568] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1569] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1570] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1571] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1572] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1573] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1574] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1575] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1576] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1577] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1578] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1579] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1580] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1581] 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.

[1582] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1583] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1584] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1585] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1586] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1587] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1588] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1589] The following is further disclosed regarding the above embodiment.

[1590] (Claim 1)

[1591] means for receiving disaster information;

[1592] A means for acquiring location information;

[1593] A means of analyzing the situation in the cloud,

[1594] A means for generating action guidance using a generative AI model;

[1595] means for providing the generated action guide to a user;

[1596] A system including:

[1597] (Claim 2)

[1598] 10. The system of claim 1, further comprising means for identifying an optimal evacuation route and evacuation location based on the user's current location.

[1599] (Claim 3)

[1600] The system according to claim 1, further comprising means for generating different action guidance based on the type of disaster and its impact.

[1601] "Example 1"

[1602] (Claim 1)

[1603] means for receiving disaster information;

[1604] A means for acquiring location information;

[1605] A means of analyzing the situation in the cloud,

[1606] A means for generating action guidance using a generative AI model;

[1607] means for providing the generated action guide to a user;

[1608] means for causing a user terminal to receive disaster information and action guidance;

[1609] means for displaying the received action guidance;

[1610] A system including:

[1611] (Claim 2)

[1612] 10. The system of claim 1, further comprising means for identifying an optimal evacuation route and evacuation location based on the user's current location.

[1613] (Claim 3)

[1614] The system according to claim 1, further comprising means for generating different action guidance based on the type of disaster and its impact.

[1615] "Application Example 1"

[1616] (Claim 1)

[1617] means for receiving disaster information;

[1618] A means for acquiring location information;

[1619] A means of analyzing the situation in the cloud,

[1620] A means for generating action guidance using a generative AI model;

[1621] means for providing the generated action guide to a user;

[1622] means for identifying an optimal evacuation route and evacuation location based on the user's current location;

[1623] A means for inputting a prompt sentence to the generation AI model when generating action guidance, and generating optimal action guidance based on the prompt sentence;

[1624] A system including:

[1625] (Claim 2)

[1626] The system according to claim 1, further comprising means for generating different action guidance based on the type of disaster and its impact.

[1627] (Claim 3)

[1628] 10. The system of claim 1, further comprising means for providing navigational guidance to the user using voice notifications, text messages, and map information.

[1629] "Example 2: Combining Emotion Engines"

[1630] (Claim 1)

[1631] means for receiving disaster information;

[1632] A means for acquiring location information;

[1633] A means for acquiring emotion information;

[1634] A means of analyzing the situation in the cloud,

[1635] A means for generating action guidance using a generative AI model;

[1636] means for providing the generated action guide to a user;

[1637] A system including:

[1638] (Claim 2)

[1639] 10. The system of claim 1, further comprising means for identifying an optimal evacuation route and evacuation location based on the user's current location.

[1640] (Claim 3)

[1641] The system according to claim 1, further comprising means for generating different action guidance based on the type of disaster and its impact.

[1642] "Application example 2 when combining emotion engines"

[1643] (Claim 1)

[1644] means for receiving disaster information;

[1645] A means for acquiring location information;

[1646] A means for acquiring emotion information;

[1647] A means of analyzing the situation in the cloud,

[1648] A means for generating action guidance using a generative AI model;

[1649] means for providing the generated action guide to a user;

[1650] means for generating an audio notification;

[1651] A system including:

[1652] (Claim 2)

[1653] 10. The system of claim 1, further comprising means for identifying an optimal evacuation route and evacuation location based on the user's current location.

[1654] (Claim 3)

[1655] The system according to claim 1, further comprising means for generating different action guidance based on the type of disaster and its impact. [Explanation of symbols]

[1656] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving disaster information; A means for acquiring location information; A means of analyzing the situation in the cloud, A means for generating action guidance using a generative AI model; means for providing the generated action guide to a user; A system including:

2. The system of claim 1 further comprising means for identifying an optimal evacuation route and evacuation location based on the user's current location.

3. The system according to claim 1 , further comprising means for generating different action guidance based on the type of disaster and its impact.

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