system

Abstract

We provide the system. [Solution] Means for acquiring user pre-registration information and location information, Means for obtaining hazard maps and disaster information, A method for automatically generating individual evacuation guides based on acquired information using generation AI, A means of providing evacuation guides to users through audio and images, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a conventional disaster information providing system, evacuation information considering the circumstances and location information of individual users is not provided, making it difficult to make evacuation judgments. It is difficult to instantaneously determine an appropriate evacuation location and evacuation timing during a disaster, and it has been particularly difficult for the elderly and disabled to evacuate quickly. Against this background, there is an increasing need for an information providing system that promotes quick and appropriate evacuation actions according to individual situations.

Means for Solving the Problems

[0005] The present invention aims to solve the above problems by providing a system that includes means for acquiring user pre-registration information and location information, hazard maps and disaster information, means for automatically generating individual evacuation guides based on the acquired information using a generation AI, and means for providing evacuation guides to users via voice and images. Furthermore, by providing means for running a user evacuation simulation and providing the results, and means for receiving emergency alerts and automatically launching an application, the invention enables all users to take swift and accurate evacuation actions.

[0006] A "user" refers to an individual or legal entity that uses this system to receive disaster information and evacuation guides.

[0007] "Pre-registered information" refers to individual information that users enter into this system in advance, such as their address, ease of movement, and evacuation destination information.

[0008] "Location information" refers to data that identifies the user's current geographical location.

[0009] A "hazard map" refers to information that shows disaster risks and dangerous areas on a map.

[0010] "Disaster information" refers to breaking news provided when disasters such as earthquakes, tsunamis, typhoons, and floods occur.

[0011] "Generative AI" refers to artificial intelligence technology that automatically generates evacuation guides based on acquired information.

[0012] An "evacuation guide" refers to information automatically generated by a generation AI that instructs users on appropriate evacuation actions.

[0013] "Audio and images" refers to audio readings and visual displays that provide evacuation guides to users in an easy-to-understand manner.

[0014] "Evacuation simulation" refers to a function that simulates hypothetical evacuation actions based on pre-set information and a hazard map.

[0015] "Emergency notification" refers to emergency notification information transmitted when a disaster occurs.

[0016] "Application" refers to a software program for running this system on a smartphone or a dedicated terminal.

Brief Explanation of Drawings

[0017] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12]It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0018] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0019] First, the language used in the following description will be explained.

[0020] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0021] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0022] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0023] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0025] [First Embodiment]

[0026] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0027] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0028] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0030] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0033] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0035] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0036] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0037] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0038] This invention is an information system that provides disaster evacuation guides tailored to individual circumstances, and is used by users via a smartphone app or dedicated terminal. This system integrates the user's pre-registered information, location information, hazard maps, and disaster information, and provides personalized evacuation guides using a generating AI.

[0039] First, users enter pre-registration information such as their address, mobility status (e.g., able to walk, wheelchair user), and evacuation destination information through an app or dedicated device. The device sends this information to a server, which stores it in a database. This creates a detailed profile for each user.

[0040] If a disaster risk is suspected, the user can conduct an evacuation simulation. When a user requests an evacuation simulation, the terminal sends the request to the server. The server retrieves pre-registered information from the database along with the user's location information, and combines this with hazard map information. The generating AI uses this integrated data to perform an optimal evacuation simulation and sends the results to the terminal. The terminal provides the received results to the user in audio and image format.

[0041] Furthermore, when an emergency alert (area mail) is sent, the server receives this information and identifies users within the affected area. The server then sends an automatic application launch command to the identified users' devices. The devices receive this command and automatically launch the applications.

[0042] When the app is launched, the server uses a generation AI to create a personalized evacuation guide based on the user's latest location and pre-registered information. This guide includes details such as specific locations to evacuate to, evacuation routes, and timing of evacuation. The server sends the generated guide to the device, which then provides it to the user via audio and images.

[0043] For example, suppose User A lives at "1-1-1 Jingumae, Shibuya-ku, Tokyo" and has difficulty moving around, so has pre-registered a nearby park as an evacuation site. When a typhoon approaches, if the server receives an area email and determines that Shibuya-ku will be affected, the server immediately sends a command to User A's device to automatically activate the app. The device automatically activates the app, and the server generates and sends an evacuation guide (e.g., "Please evacuate to a higher place (nearby park) within 20 minutes"). The device reads the instructions aloud and displays the evacuation route on the screen. User A then quickly begins evacuation according to these instructions.

[0044] This system enables users to take swift and appropriate evacuation actions in the event of a disaster, and provides effective information, especially to users with low IT literacy, such as the elderly and people with disabilities.

[0045] The following describes the processing flow.

[0046] Step 1:

[0047] The user launches the app or dedicated device and enters pre-registered information such as their address, ease of movement, and evacuation destination information.

[0048] Step 2:

[0049] The device sends the pre-registration information entered to the server.

[0050] Step 3:

[0051] The server saves the pre-registration information it receives to the database.

[0052] Step 4:

[0053] The user requests an evacuation simulation, and the terminal sends that request to the server.

[0054] Step 5:

[0055] The server retrieves the user's location information and pre-registered information from the database, and integrates it with hazard map information.

[0056] Step 6:

[0057] The server uses AI generation to execute evacuation simulations and generate optimal evacuation simulation results.

[0058] Step 7:

[0059] The server sends the generated evacuation simulation results to the terminal.

[0060] Step 8:

[0061] The device provides the user with evacuation simulation results in audio and image format.

[0062] Step 9:

[0063] The server periodically monitors and receives emergency alerts (area mail).

[0064] Step 10:

[0065] The server analyzes the emergency alert and identifies the affected areas and users within those areas.

[0066] Step 11:

[0067] The server sends an automatic application launch command to user terminals within the affected area.

[0068] Step 12:

[0069] The device receives a command to automatically launch the app and starts the app automatically.

[0070] Step 13:

[0071] The server generates personalized evacuation guides using AI based on the user's latest location information and pre-registered information.

[0072] Step 14:

[0073] The server sends the generated evacuation guide to the terminal.

[0074] Step 15:

[0075] The device reads out evacuation instructions aloud and displays evacuation routes and locations on the screen.

[0076] Step 16:

[0077] The user quickly begins evacuation action by following the instructions on their device.

[0078] (Example 1)

[0079] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0080] Conventional evacuation guide systems have been limited to providing general information, making it difficult to offer specific evacuation guidance tailored to individual circumstances. Furthermore, users may not be able to take appropriate evacuation actions quickly during a disaster, posing a significant risk, especially for those with mobility difficulties such as the elderly and people with disabilities. Therefore, there was a need for a system that could consider each user's individual situation and provide optimal evacuation guidance in real time.

[0081] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0082] In this invention, the server includes means for acquiring user pre-registration information and location information, means for acquiring a map displaying disaster information and dangerous areas, means for automatically generating individual evacuation guides based on the acquired information using a generation AI, means for providing evacuation guides to users via voice and images, means for receiving emergency alerts and automatically launching the application, and means for executing an evacuation simulation for the user in the event of an anomaly and providing the results. This makes it possible to provide individually optimized evacuation guides to each user in real time.

[0083] "User pre-registration information" refers to personal information provided by the user in advance, such as address, mobility (e.g., able to walk, wheelchair user), and evacuation destination information.

[0084] "Location information" refers to information that indicates a user's geographical location, such as their current location or travel route.

[0085] "Disaster information" refers to information regarding the current situation and predictions of natural disasters such as typhoons, earthquakes, and floods.

[0086] A "map showing dangerous areas" is a map that visually indicates dangerous locations or areas with a high risk of disaster within a specific region.

[0087] "Generative AI" is an artificial intelligence model that analyzes diverse data and automatically generates optimal evacuation guides tailored to the individual circumstances of each user.

[0088] "Individualized evacuation guides" are pieces of information that provide specific evacuation instructions, such as where to evacuate, the route to take, and the timing of evacuation, based on each user's pre-registered information and location data.

[0089] "Means of providing information through audio and images" refers to means of communicating evacuation guides to users through audio messages, images, maps, and other visual information.

[0090] An "emergency alert" is a message used to quickly notify users in the affected area of ​​relevant information when a disaster occurs.

[0091] "Means for automatically launching applications" refers to a function that automatically starts a specific application on the user's device when an emergency alert is received.

[0092] "Means for performing evacuation simulations" refers to a function that simulates virtual evacuation actions based on the user's location information and pre-registered information during a disaster, and provides the results to the user.

[0093] This invention relates to an information system used by users via a smartphone app or dedicated terminal, which provides personalized evacuation guides during disasters. The system integrates the user's pre-registered information, location information, disaster information, and a map displaying dangerous areas, and generates evacuation guides using AI based on this information.

[0094] First, users enter pre-registration information, such as their address, mobility (e.g., walkable, wheelchair user), and evacuation destination information, using a smartphone app or a dedicated device. The device sends this information to a server, which stores it in a database. This process creates a detailed profile for each user.

[0095] If there is a risk of disaster, the user can request an evacuation simulation. When a user requests an evacuation simulation, the terminal sends the request to the server. The server retrieves pre-registered information from the database along with the user's location information, and combines this with disaster information and map information showing dangerous areas. The generating AI uses this integrated data to run an optimal evacuation simulation and sends the results to the terminal. The terminal provides the received results to the user in audio and image format.

[0096] For example, suppose User A lives at "1-1-1, a certain town in a certain ward of a certain city" and has pre-registered a nearby park as an evacuation site due to difficulty moving around. When a typhoon approaches, the server receives an area email and determines that the certain ward will be affected. The server immediately sends a command to User A's device to automatically activate the app. The device automatically activates the app, and the server generates and sends an evacuation guide (e.g., "Evacuate to a higher place (the nearby park) within 20 minutes"). The device reads the instructions aloud and displays the evacuation route on the screen. User A then quickly begins evacuation according to these instructions.

[0097] In addition, during emergencies, the server receives emergency alerts and identifies users in the affected area. The server sends an automatic app launch command to the identified user's device, and the device automatically launches the app upon receiving this command. Once the app is launched, the server uses AI generation based on the latest location information and pre-registered information to generate a personalized evacuation guide. This evacuation guide includes details such as specific places to evacuate, evacuation routes, and timing of evacuation. The server sends the generated guide to the device, which then provides it to the user via voice and images.

[0098] In this way, this system supports users in taking swift and appropriate evacuation actions during disasters, and provides effective information, especially to users with low IT literacy, such as the elderly and people with disabilities.

[0099] Examples of prompts for a generative AI model:

[0100] Based on the user's location and pre-registered information, propose the optimal evacuation route. Considering disaster information and maps showing hazardous areas, generate a detailed guide including evacuation locations, routes, and timing. Specific address: "1-1-1, [Town Name], [District Name], [City Name]", mobility: "Wheelchair accessible", evacuation destination: "Nearby park". Assume a disaster such as a typhoon.

[0101] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0102] Step 1:

[0103] Entering user pre-registration information

[0104] Users launch a smartphone app or dedicated device and enter their address, mobility status (e.g., able to walk, wheelchair user), and evacuation destination information. The entered data includes specific address, mode of transportation, and evacuation location.

[0105] Input: Address (e.g., "1-1-1, [town name], [district name], [city name]"), Accessibility (e.g., "Wheelchair accessible"), Evacuation destination (e.g., "Nearby park")

[0106] Output: Pre-registration information

[0107] Step 2:

[0108] Submitting and saving pre-registration information

[0109] The terminal sends the pre-registration information entered by the user to the server. The transmitted data is formed as a user profile.

[0110] The server stores the received information in a database and creates a detailed profile for each user. Various input data is stored in the profile.

[0111] Input: Pre-registration information

[0112] Output: Saved user profile

[0113] Step 3:

[0114] Request for evacuation simulation

[0115] In the event of a disaster risk, users can request an evacuation simulation via the app or a dedicated device. The request data includes their current location information.

[0116] The device sends this request to the server.

[0117] Input: Request, current location

[0118] Output: Evacuation Simulation Request

[0119] Step 4:

[0120] Execution of evacuation simulation

[0121] The server retrieves the user's current location and pre-registration information from the database. The retrieved data includes location information and profile information.

[0122] The server combines this with information from maps displaying disaster information and hazardous locations.

[0123] The generated AI model uses this integrated data to perform an optimal evacuation simulation. The generated evacuation simulation results include evacuation routes, destinations, and evacuation times.

[0124] Input: Current location information, pre-registration information, disaster information, hazardous area information

[0125] Output: Evacuation simulation results

[0126] Step 5:

[0127] Providing evacuation simulation results

[0128] The server sends the results of the evacuation simulation to the terminal. The transmitted data includes an evacuation guide.

[0129] The device provides the user with the received results in the form of audio and images. Specific actions include playing audio guides and displaying maps.

[0130] Input: Evacuation simulation results

[0131] Output: Audio guide, image display

[0132] Step 6:

[0133] Emergency alerts and automatic app launch

[0134] The server receives emergency alerts and analyzes their contents. The analysis data includes the type of disaster and the extent of its impact.

[0135] The server identifies users within the affected region, and this identification data includes a list of users.

[0136] The server sends an automatic application launch command to the specified user's device.

[0137] The device receives this command and automatically launches the app.

[0138] Input: Emergency alert, information on affected areas

[0139] Output: Auto-start command, launching the application

[0140] Step 7:

[0141] Generation and provision of individual evacuation guides

[0142] When the app is launched, the server uses a generation AI to create a personalized evacuation guide based on the user's latest location information and pre-registered information. The generated evacuation guide includes detailed evacuation instructions.

[0143] The server sends the generated evacuation guide to the terminal.

[0144] The device provides users with information via voice and images. Specifically, it provides navigation for evacuation routes and information about evacuation locations.

[0145] Input: Latest location information, pre-registration information

[0146] Output: Individual evacuation guide, audio guide, image display

[0147] (Application Example 1)

[0148] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0149] In modern society, a lack of adequate guidance for the rapid and safe evacuation of many people in the event of a disaster within a physical store is a significant problem. In particular, people with mobility difficulties (e.g., the elderly and wheelchair users) often struggle to find appropriate evacuation routes. Therefore, a comprehensive system is needed to support rapid and safe evacuation within physical stores.

[0150] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0151] In this invention, the server includes means for acquiring user pre-registration information and location information, means for acquiring hazard maps and disaster information, means for automatically generating individual evacuation guides based on the acquired information using a generation AI, means for providing evacuation guides to users via voice and images, means for receiving emergency alerts and automatically launching the application, and means for generating the optimal evacuation route based on pre-registration information and real-time location information and providing it via push notification. This enables quick and safe evacuation actions within physical stores, thereby protecting the safety of many people.

[0152] "User pre-registration information" refers to personal information such as the user's address, ease of movement, and evacuation destination, which the user enters and registers in advance through the application or a dedicated terminal.

[0153] "Location information" refers to information that indicates the user's current location, and is data mainly obtained using location measurement technologies such as GPS.

[0154] A "hazard map" is a map that visually displays the disaster risk in a specific area, indicating the likelihood of a disaster occurring and the extent of its impact.

[0155] "Disaster information" refers to real-time information about disasters such as earthquakes, typhoons, and floods, and is obtained from public institutions and meteorological data.

[0156] "Generative AI" refers to a system that utilizes artificial intelligence technology to analyze various input data and generate optimal output tailored to a specific purpose.

[0157] An "evacuation guide" is information that includes specific instructions, routes, and timing for users to evacuate safely.

[0158] "Provision via audio and images" refers to a method of presenting the generated evacuation guide to the user both aurally and visually.

[0159] An "emergency alert" is a notification designed to quickly and widely disseminate important information related to emergencies such as disasters.

[0160] "Automatic application startup" refers to a function that automatically launches relevant applications without user intervention when an emergency alert is received.

[0161] "Real-time location information" refers to information that acquires and updates the current location of the user in real time.

[0162] A "push notification" is an automated notification message that informs users that there is new information on their smartphone or tablet.

[0163] This invention relates to a system that provides optimal evacuation guidance to customers and store employees in the event of a disaster at a physical store. Based on the user's pre-registered information and real-time location information, an AI model is used to automatically generate individual evacuation guidance, which is then provided in both audio and image formats. The following describes a detailed embodiment of the system.

[0164] System Configuration

[0165] 1. Obtain user pre-registration information

[0166] Users enter and register personal data in advance via their smartphones or dedicated terminals, such as their address, mobility (e.g., able to walk, wheelchair user), and evacuation destination information. This creates a detailed profile for each user in the database.

[0167] 2. Acquisition of location information

[0168] The user's smartphone uses GPS to obtain location information in real time and transmit it to the server. Specifically, the smartphone app periodically transmits location information.

[0169] 3. Obtaining disaster information

[0170] The server regularly acquires and updates hazard maps and disaster information. This information is obtained from public institutions and meteorological data.

[0171] 4. Generation of evacuation guides using a generative AI model

[0172] The server integrates acquired user pre-registration information, real-time location information, hazard maps, and the latest disaster information, and generates individual evacuation guides using a generation AI model. The generated evacuation guides include details such as evacuation routes, specific places to evacuate to, and timing of evacuation.

[0173] 5. Provision of evacuation guides

[0174] The server sends the generated evacuation guide to the user's smartphone via push notification. The receiving device then provides the evacuation guide to the user through audio and images, encouraging prompt evacuation action.

[0175] 6. Response to Emergency Alerts

[0176] When an emergency alert is issued, the server receives this information and identifies users in the affected area. The server then sends an automatic application launch command to the identified users' smartphones. Once the application is launched, it generates and provides an evacuation guide again based on the user's latest location information and pre-registered information.

[0177] Hardware and software to be used

[0178] Server: Database management system (e.g., MySQL®), Generative AI model (e.g., TENSORFLOW®)

[0179] Client device: Smartphone application (e.g., React Native)

[0180] Communication: Internet connection, GPS

[0181] Specific example

[0182] For example, if an earthquake occurs in a shopping mall, this system will immediately acquire the location information of customers and provide the optimal evacuation route. For instance, it might issue specific instructions such as, "Please evacuate to a higher place (the second floor of the shopping mall) within 30 minutes."

[0183] Example of a prompt:

[0184] "Please tell me the best evacuation route from shopping mall A in the event of an earthquake."

[0185] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0186] Step 1:

[0187] Users input and register their address, mobility status (e.g., able to walk, wheelchair user), and evacuation destination information via their smartphone or a dedicated terminal. The terminal transmits this pre-registered information to a server and stores it in a database.

[0188] Input: User's address, ease of movement, evacuation destination information

[0189] Output: Information is stored in the user profile database on the server.

[0190] Step 2:

[0191] The device periodically uses GPS to obtain the user's real-time location information. The obtained location information is sent to the server and stored as the user's most recent location.

[0192] Input: User's current location information

[0193] Output: Latest user location information stored on the server

[0194] Step 3:

[0195] The server periodically retrieves hazard maps and the latest disaster information. Disaster information is collected using public institutions and weather data sources and stored in a database.

[0196] Input: Hazard map, disaster information

[0197] Output: Latest hazard maps and disaster information stored in the database

[0198] Step 4:

[0199] The user requests an evacuation simulation through the application. The device sends this request to the server. The server retrieves pre-registration information, real-time location information, the latest hazard maps, and disaster information from the database, and generates an individualized evacuation guide using a generation AI model. The generated evacuation guide is sent to the device.

[0200] Input: User evacuation simulation request, pre-registration information, location information, hazard map, disaster information

[0201] Output: Generated evacuation guide

[0202] Step 5:

[0203] The server sends an evacuation guide to the terminal. The terminal provides the received evacuation guide to the user in both audio and image formats.

[0204] Input: Evacuation guide sent from the server

[0205] Output: Audio and visual evacuation guide provided to the user.

[0206] Step 6:

[0207] The server receives an emergency alert. It identifies users in the affected area and sends an application auto-start command to the terminals of the relevant users.

[0208] Input: Breaking News

[0209] Output: Sending of automatic startup command

[0210] Step 7:

[0211] The application starts automatically. The server generates an evacuation guide using a regenerated AI model based on the latest location information and pre-registered information, and sends it to the device.

[0212] Input: Latest location information, pre-registration information

[0213] Output: Evacuation guide that is generated and sent

[0214] Step 8:

[0215] The device provides the user with received evacuation guides in audio and image formats, encouraging quick and appropriate evacuation actions.

[0216] Input: Evacuation guide sent from the server

[0217] Output: Audio and visual evacuation guide provided to the user.

[0218] Specific example

[0219] For example, if an earthquake occurs in a shopping mall, this system will immediately acquire the location information of the relevant users, generate an evacuation guide such as "Please head towards the nearest emergency exit," and send it via push notification.

[0220] Example of a prompt:

[0221] "Please tell me the best evacuation route from shopping mall A in the event of an earthquake."

[0222] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0223] This invention relates to an information system that provides evacuation guides during disasters, which includes a function to recognize the user's emotions and dynamically adjust the content of the evacuation guide based on those emotions. The system is used by the user via a smartphone app or a dedicated terminal.

[0224] First, users enter pre-registration information such as their address, mobility status (e.g., able to walk, wheelchair user), and evacuation destination information through an app or dedicated device. The device sends this information to a server, which stores it in a database. This creates a detailed profile for each user.

[0225] In the event of a disaster, the user's pre-registered information and latest location data are retrieved from the server. In addition, the server collects hazard maps and disaster information, and integrates this information using a generation AI. As a result, an individualized evacuation guide is generated.

[0226] A new element added to this system is the "emotion engine." The emotion engine analyzes the user's voice and facial expressions to recognize their current emotional state. The user sends data acquired by emotion recognition sensors to the terminal, which analyzes this data and sends the emotional information to the server. The server analyzes this information through the emotion engine, and the generating AI adjusts the evacuation guide to suit the user's emotions.

[0227] For example, if a user is feeling highly anxious, the evacuation guide will include specific and reassuring instructions, such as, "Don't worry. The evacuation shelter is very close. Please follow this path." Conversely, if the user is calm, concise and prompt evacuation instructions will be provided.

[0228] When an emergency alert (area mail) is sent, the server receives this information and identifies users in the affected area. The server then sends a command to automatically launch an application to the identified user's device. The device receives this command and automatically launches the application.

[0229] When the app is launched, the server uses a generative AI to generate a personalized evacuation guide based on the user's latest location and pre-registered information. This guide includes details such as specific places to evacuate, evacuation routes, and timing. Furthermore, the evacuation guide is dynamically adjusted based on the user's emotional state. For example, if the app detects that the user is in a "high-stress state," the evacuation instructions will become more detailed and reassuring.

[0230] As a concrete example, suppose User B lives at "1-1-1 Umeda, Kita-ku, Osaka City" and has difficulty moving around, so has pre-registered a nearby building as an evacuation site. When an earthquake occurs and the server receives an area email and determines that Kita-ku is affected, the server immediately sends a command to User B's device to automatically activate the app. The device automatically activates the app, the server analyzes User B's emotional state through the emotion engine, and the generating AI generates an evacuation guide (e.g., "Please stay calm. Please evacuate to a nearby building. This route is safe.") and sends it to the device. The device reads the evacuation guide aloud and displays the evacuation route on the screen. User B then quickly begins evacuation according to this.

[0231] This system takes into account the emotional state of users during a disaster, enabling swift and appropriate evacuation actions. It also effectively provides information to users with low IT literacy, particularly the elderly and people with disabilities.

[0232] The following describes the processing flow.

[0233] Step 1:

[0234] The user launches the app or dedicated device and enters pre-registered information such as their address, ease of movement, and evacuation destination information.

[0235] Step 2:

[0236] The device sends the pre-registration information entered to the server.

[0237] Step 3:

[0238] The server saves the pre-registration information it receives to the database.

[0239] Step 4:

[0240] The user uses emotion recognition sensors (e.g., wearable devices or cameras) to acquire emotional data.

[0241] Step 5:

[0242] The device sends the emotional data it has acquired to the server.

[0243] Step 6:

[0244] The server analyzes the received emotional data using an emotion engine to identify the user's current emotional state.

[0245] Step 7:

[0246] A disaster occurs, and the server receives an emergency alert (area mail).

[0247] Step 8:

[0248] The server analyzes the emergency alert and identifies the affected areas and users within those areas.

[0249] Step 9:

[0250] The server sends an automatic application launch command to user terminals within the affected area.

[0251] Step 10:

[0252] The device receives a command to automatically launch the app and starts the app automatically.

[0253] Step 11:

[0254] The server retrieves and integrates hazard maps and disaster information based on the user's latest location information and pre-registered information.

[0255] Step 12:

[0256] The server automatically generates individual evacuation guides based on information acquired using AI.

[0257] Step 13:

[0258] The server adjusts the content of the evacuation guide it generates, taking into account the user's emotional state. For example, if the user is feeling anxious, the guide will be modified to provide more reassurance.

[0259] Step 14:

[0260] The server sends the coordinated evacuation guide to the terminal.

[0261] Step 15:

[0262] The device reads aloud the evacuation guide it receives and displays evacuation routes and locations on the screen.

[0263] Step 16:

[0264] The user quickly begins evacuation action by following the instructions on their device.

[0265] As a concrete example, suppose User B lives at "1-1-1 Umeda, Kita-ku, Osaka City" and has difficulty moving around, so has pre-registered a nearby building as an evacuation site. When an earthquake occurs and the server receives an area email and determines that Kita-ku is affected, the server immediately sends a command to User B's device to automatically activate the app. The device automatically activates the app, the server analyzes User B's emotional state through the emotion engine, and the generating AI generates an evacuation guide (e.g., "Please stay calm. Please evacuate to a nearby building. This route is safe.") and sends it to the device. The device reads the evacuation guide aloud and displays the evacuation route on the screen. User B then quickly begins evacuation according to this.

[0266] (Example 2)

[0267] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0268] Conventional disaster evacuation guidance systems could only provide uniform evacuation instructions without considering the user's current emotional state. Therefore, it was difficult to provide appropriate evacuation guidance, especially for users with low IT literacy, such as the elderly and people with disabilities. Furthermore, a means of automatically launching the application was needed to encourage rapid evacuation action during a disaster.

[0269] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0270] In this invention, the server includes means for acquiring user pre-registration information and location information, means for acquiring hazard maps and disaster information, means for automatically generating individual evacuation guides based on the acquired information using a generation AI, means for providing evacuation guides to users via voice and images, means for analyzing voice and facial expression data to recognize the user's emotional state, and means for adjusting the content of the evacuation guides based on emotional information. This makes it possible to provide a rapid and appropriate evacuation guide that takes into account the user's emotional state in the event of a disaster.

[0271] "User pre-registration information" refers to detailed personal information such as address, ease of movement, and evacuation destination information that users of the evacuation guide system have registered in advance.

[0272] "Location information" refers to data that indicates the user's current geographical location, and is obtained using the GPS function of a smartphone, etc.

[0273] A "hazard map" is information that shows areas at risk of disaster and the extent of their impact on a map.

[0274] "Disaster information" refers to the latest information on disasters such as earthquakes, floods, and fires, and is data obtained from the Japan Meteorological Agency and disaster prevention organizations.

[0275] "Generative AI" refers to a system that uses artificial intelligence technology to automatically generate information such as evacuation guides.

[0276] An "individualized evacuation guide" is a user-specific evacuation instruction generated based on the user's pre-registered information and location data.

[0277] "Means of providing evacuation guides to users through audio and images" refers to methods for communicating generated evacuation guides to users using audio and images.

[0278] "Emotional state" refers to the psychological state that the user is currently experiencing (e.g., anxiety, fear, calmness, etc.).

[0279] The means for analyzing voice and facial expression data is a technology for analyzing voice and facial expression data obtained from a user to determine the emotional state.

[0280] The "emotional information" is data indicating the current emotional state of the user determined based on voice and facial expression data.

[0281] The means for adjusting the content of the evacuation guidance is a function for appropriately changing the instruction content of the evacuation guidance based on the user's emotional information.

[0282] The present invention is an information system that provides evacuation guidance during disasters, and includes a function for recognizing the user's emotions and dynamically adjusting the content of the evacuation guidance based on the results. The user uses this system using a smartphone application or a dedicated terminal.

[0283] First, the user inputs pre-registered information such as their address, mobility (e.g., walkable, wheelchair user, etc.), and evacuation destination information through a smartphone application or a dedicated terminal. The terminal transmits this information to the server, and the server creates a detailed profile for each user by storing it in the database.

[0284] When a disaster occurs, the user's pre-registered information and the latest location information are acquired from the server. The server collects the hazard map and the latest disaster information, and integrates this information using a generative AI (e.g., GPT-4 (registered trademark), etc.). As a result, an individual evacuation guidance is generated.

[0285] Furthermore, this system has an "emotion engine" and has a function for analyzing voice and facial expression data to recognize the user's current emotional state. The user uses a sensor for emotion recognition, and the acquired data is transmitted to the server through the terminal. The server analyzes this data using the emotion engine, and the generative AI adjusts the evacuation guidance in a form corresponding to the user's emotions.

[0286] Specifically, when the user is feeling strong anxiety, the evacuation guide includes specific and reassuring instructions (for example, "Please be calm. The evacuation location is very close. Please proceed along this road."). Conversely, if the user is calm, concise and rapid evacuation instructions are provided.

[0287] Also, when an emergency alert (area email) is sent, the server receives this information and identifies the users within the affected area. The server sends an instruction to automatically start the application to the terminals of the identified users, and the terminals receive this instruction and automatically start the application. In this case, based on the latest location information and pre-registered information of the users, the server uses a generative AI to generate individual evacuation guides. The evacuation guide is dynamically adjusted based on the user's emotional information.

[0288] As a specific example, assume that User B lives at "1-1-1 Umeda, Kita-ku, Osaka City" and uses a wheelchair, and has pre-registered a nearby building as an evacuation location. When an earthquake occurs and the server receives the area email and determines that Kita-ku is affected, the server immediately sends an instruction to automatically start the application to User B's terminal. The terminal automatically starts the application, and the server analyzes User B's emotional state through an emotion engine, and the generative AI generates an evacuation guide such as "Please stay calm. Evacuate to the nearby building. This route is safe." and sends it to the terminal. The terminal reads out the evacuation guide aloud and displays the evacuation route on the screen. User B starts the evacuation action promptly according to this.

[0289] Examples of prompt sentences are as follows:

[0290] "The user's address is 1-1-1 Umeda, Kita-ku, Osaka City, and the user uses a wheelchair. What kind of evacuation guide is appropriate to relieve the strong anxiety the user is feeling when an earthquake occurs?"

[0291] This makes it possible to provide quick and appropriate evacuation guidance that takes into account the user's emotional state. This system is expected to be particularly effective for users with low IT literacy, such as the elderly and people with disabilities.

[0292] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0293] Step 1:

[0294] Enter and submit pre-registration information

[0295] User: Open the smartphone app or dedicated device.

[0296] Input: Address, mobility (e.g., able to walk, wheelchair user, etc.), evacuation destination information, etc.

[0297] Specific actions: The user opens the app and enters the address "1-1-1 Umeda, Kita-ku, Osaka City", mobility status "Wheelchair user", and evacuation destination information.

[0298] Terminal: Sends the entered information to the server.

[0299] Data processing: Format the entered information and prepare it for transmission.

[0300] Output: Formatted pre-registration information.

[0301] Step 2:

[0302] Pre-registration information storage

[0303] Server: Saves the received pre-registration information to the database.

[0304] Input: Formatted pre-registration information.

[0305] Data processing: Convert received data into a format suitable for storage in the appropriate table within the database.

[0306] Output: User profiles stored in the database.

[0307] Specific operation: The server stores the address information of "1-1-1 Umeda, Kita-ku, Osaka City" and the mobility information of "wheelchair use" in the database.

[0308] Step 3:

[0309] Information acquisition during disasters

[0310] Server: Receive the latest disaster information when a disaster occurs.

[0311] Input: Hazard map and the latest disaster information.

[0312] Data processing: Integrate the disaster information and the hazard map to identify the affected area.

[0313] Output: Identification of the disaster-stricken area.

[0314] Specific operation: The server receives the earthquake early warning and identifies "Kita-ku, Osaka City" as the disaster area.

[0315] Step 4:

[0316] Acquisition of user information

[0317] Server: Obtain pre-registered information and the latest location information.

[0318] Input: User profile and location information from GPS.

[0319] Data processing: Combine the profile and location information to list the users in the affected area.

[0320] Output: List of users in the affected area.

[0321] Step 5:

[0322] Generating an evacuation guide

[0323] Server: Automatically generates individual evacuation guides using a generation AI.

[0324] Input: User pre-registration information, location information, disaster information.

[0325] Data processing: Generative AI integrates this information to calculate the optimal evacuation route and evacuation location.

[0326] Output: Individual evacuation guide.

[0327] Specific action: The server generates an evacuation guide that says, "Please evacuate to a nearby building. This route is safe."

[0328] Step 6:

[0329] Execution of emotion recognition

[0330] User: Uses a sensor for emotion recognition.

[0331] Input: Voice and facial expression data.

[0332] Terminal: Sends voice and facial expression data to the server.

[0333] Data processing: Convert audio and facial expression data into an analysis format.

[0334] Output: Data for analysis.

[0335] Server: Uses an emotion engine to analyze data and determine the user's emotional state.

[0336] Input: Data for analysis.

[0337] Data processing: The emotion engine analyzes voice and facial expressions to identify the emotional state.

[0338] Output: Emotional information.

[0339] Specific operation: The server analyzes the user's voice data and determines that the user is feeling "anxious".

[0340] Step 7:

[0341] Adjustment of evacuation guides

[0342] Server: Adjusts the content of the evacuation guide generated by the AI ​​based on emotional information.

[0343] Input: Emotional information and initial evacuation guide.

[0344] Data processing: Modify the evacuation guide to be more specific and reassuring, taking emotional information into account.

[0345] Output: Adjusted evacuation guide.

[0346] Specific action: The server will change its instructions to a reassuring message such as, "Please stay calm. Please evacuate to a nearby building."

[0347] Step 8:

[0348] Provision of evacuation guides

[0349] Server: Sends the coordinated evacuation guide to the user's terminal.

[0350] Input: Adjusted evacuation guide.

[0351] Output: Data sent to the terminal.

[0352] Specific action: The server sends a coordinated evacuation guide to the terminal.

[0353] Terminal: Displays the evacuation guide to the user and reads it aloud.

[0354] Input: Evacuation guide data from the server.

[0355] Output: Visual and auditory instructions to the user.

[0356] Specific actions: The device will play a voice command saying, "Please proceed this way," and display a detailed evacuation route on the screen.

[0357] Step 9:

[0358] Start of evacuation

[0359] User: Follow the instructions on your device and begin evacuation immediately.

[0360] Input: Instructions from the evacuation guide.

[0361] Output: Safe evacuation actions.

[0362] Specific actions: The user follows the voice instructions on the device and navigates the displayed route to a safe evacuation location.

[0363] (Application Example 2)

[0364] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0365] In food delivery operations, a challenge is providing delivery guides that reduce the stress and anxiety experienced by delivery drivers during deliveries, enabling them to perform their duties efficiently and with peace of mind. Traditional systems fail to provide instructions that take into account the emotional state of delivery drivers, resulting in decreased delivery efficiency and increased mental burden on drivers.

[0366] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0367] In this invention, the server includes means for acquiring user pre-registration information and location information, means for acquiring disaster information and delivery information, means using a generative AI that analyzes the acquired information and emotional data to generate individual delivery guides, means using an emotion engine that analyzes the emotions of delivery personnel and dynamically adjusts the delivery guide based on the results, and means for providing delivery guides in the form of voice and images. This provides an optimal delivery guide according to the emotional state of the delivery personnel, enabling them to perform their duties efficiently and with peace of mind.

[0368] "Pre-registration information" refers to data such as basic personal information, delivery address information, and mobility information that users provide in advance.

[0369] "Location information" refers to data that indicates the current location of the user or delivery person.

[0370] "Disaster information" refers to the latest information regarding natural disasters and emergencies.

[0371] "Delivery information" refers to information regarding the delivery address, the contents of the delivery, and the delivery schedule.

[0372] "Emotional data" refers to data that indicates the emotional state of the delivery person, and is obtained by analyzing facial expressions, voice, heart rate, etc.

[0373] "Generative AI" is artificial intelligence that generates individual delivery guides based on acquired information and data.

[0374] The "emotion engine" is software that analyzes the emotional state of delivery drivers and dynamically adjusts the instructions provided based on the results.

[0375] A "delivery guide" is information provided to delivery personnel during delivery, including route directions and work instructions.

[0376] A "delivery simulation" is a process that virtually executes the delivery process and analyzes the results.

[0377] The system realizing this invention has the function of acquiring and analyzing user pre-registration information, location information, disaster information, delivery information, and emotion data, and generating and providing personalized delivery guides. The main components of the system include a server, terminal, emotion engine, and generative AI model.

[0378] System Configuration and Data Processing

[0379] 1. Obtaining pre-registration information and location information.

[0380] Users register their basic information (e.g., address, mobility, delivery destination information) using their smartphones or dedicated terminals. This information is sent to a server and stored in a database. Location information is collected in real time using the GPS module built into the smartphone.

[0381] 2. Obtaining disaster information and delivery information

[0382] The server obtains disaster and delivery information in real time from external data provision services. This allows it to identify emergencies that may affect deliveries.

[0383] 3. Acquisition and analysis of emotional data

[0384] Emotional data from delivery drivers is collected through their smartphones' cameras, microphones, and heart rate sensors. The emotion engine analyzes this data to determine the driver's current emotional state. For example, it analyzes facial expressions and voice using open-source emotion recognition libraries (using OpenCV and TensorFlow).

[0385] 4. Generation of delivery guides using AI

[0386] The server uses a generation AI (for example, OpenAI®'s GPT-4) to integrate and analyze user pre-registration information, location information, disaster information, delivery information, and emotional data to generate individual delivery guides. The guide content is dynamically adjusted according to the emotional state of the delivery person and is designed to give the user a sense of security.

[0387] 5. Providing audio and visual guides.

[0388] The terminal provides the delivery driver with generated delivery guides via voice and images. This allows the driver to receive instructions visually and aurally, enabling them to deliver quickly and safely.

[0389] Specific example

[0390] For example, consider a situation where delivery driver A is handling multiple deliveries in bad weather and traffic congestion. The server, through its emotion engine, determines that delivery driver A is experiencing stress, and the generative AI generates a delivery guide like the following:

[0391] 1. Adjusting delivery routes

[0392] "Due to traffic congestion, we recommend this alternative route."

[0393] 2. Emotionally-based messages of support

[0394] "You look tired. Please hang in there a little longer. You've done a great job!"

[0395] 3. Simplification of instructions

[0396] If the delivery person is calm, they will provide simple instructions such as, "Your next delivery address is AA. Please follow the navigation."

[0397] Example of a prompt

[0398] Use the following prompts to input information into the invention's system and generate a response:

[0399] The user's latest emotional state data and location information have been retrieved. Based on the delivery destination list and current traffic conditions, calculate the optimal delivery route and generate the following instructions according to the user's emotional state: If the user is stressed, provide reassuring instructions; if calm, provide concise and quick instructions.

[0400] Emotional state: "High stress"

[0401] Current location: "City center"

[0402] Delivery destination list: ["Area A", "Area B"]

[0403] Traffic conditions: "Traffic congestion is occurring."

[0404] Instructions: (Generate appropriate instructions here)

[0405] This system allows delivery personnel to receive appropriate instructions and perform their duties efficiently and with peace of mind.

[0406] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0407] Step 1:

[0408] The terminal receives the user's pre-registered information (e.g., address, mobility, delivery destination information) through an input form and sends that information to the server.

[0409] Input: User's pre-registration information.

[0410] Output: User profile data stored on the server.

[0411] Operation: The user enters information using a smartphone app and sends it to the server. The server stores this information in a database.

[0412] Step 2:

[0413] The device uses a GPS module to collect real-time location information and transmit it to the server.

[0414] Input: The device's current location (GPS data).

[0415] Output: Real-time location information updated to the server.

[0416] Operation: The smartphone's GPS module acquires location information and sends it to the server. The server associates this information with the user profile in its database.

[0417] Step 3:

[0418] The server obtains disaster information and delivery information from external data provision services.

[0419] Input: Disaster information and delivery information.

[0420] Output: The latest disaster information and delivery information stored on the server.

[0421] Operation: The server sends requests to external data providers via an API to retrieve the latest disaster and delivery information. The retrieved information is stored in a database.

[0422] Step 4:

[0423] The device uses a camera, microphone, and heart rate sensor to collect emotional data from delivery drivers and transmit it to a server.

[0424] Input: Delivery person's facial expression data (camera), voice data (microphone), heart rate data (heart rate sensor).

[0425] Output: Emotional data sent to the server.

[0426] Operation: The device uses its camera, microphone, and heart rate sensor to collect and analyze data from the delivery person, then sends it to the server. The server uses an emotion engine to analyze this data.

[0427] Step 5:

[0428] The server uses a generation AI to integrate user pre-registration information, location information, disaster information, delivery information, and sentiment data to generate personalized delivery guides.

[0429] Inputs: User pre-registration information, location information, disaster information, delivery information, sentiment data.

[0430] Output: Individual delivery guides generated.

[0431] Operation: The server uses a generation AI (such as OpenAI's GPT-4) to analyze input data and generate a delivery guide. The generated guide is dynamically adjusted according to the delivery person's emotional state.

[0432] Step 6:

[0433] The terminal provides the generated delivery guide to the delivery person via voice and images.

[0434] Input: Generated individual delivery guide.

[0435] Output: Provision of delivery guides via audio and images.

[0436] Operation: The terminal provides delivery drivers with delivery guides received from the server using text-to-speech and navigation functions. Route guidance is displayed on the terminal's screen, and instructions are read aloud.

[0437] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0438] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0439] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0440] [Second Embodiment]

[0441] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0442] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0443] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0444] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0445] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0446] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0447] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0448] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0449] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0450] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0451] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0452] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0453] This invention is an information system that provides disaster evacuation guides tailored to individual circumstances, and is used by users via a smartphone app or dedicated terminal. This system integrates the user's pre-registered information, location information, hazard maps, and disaster information, and provides personalized evacuation guides using a generating AI.

[0454] First, users enter pre-registration information such as their address, mobility status (e.g., able to walk, wheelchair user), and evacuation destination information through an app or dedicated device. The device sends this information to a server, which stores it in a database. This creates a detailed profile for each user.

[0455] If a disaster risk is suspected, the user can conduct an evacuation simulation. When a user requests an evacuation simulation, the terminal sends the request to the server. The server retrieves pre-registered information from the database along with the user's location information, and combines this with hazard map information. The generating AI uses this integrated data to perform an optimal evacuation simulation and sends the results to the terminal. The terminal provides the received results to the user in audio and image format.

[0456] Furthermore, when an emergency alert (area mail) is sent, the server receives this information and identifies users within the affected area. The server then sends an automatic application launch command to the identified users' devices. The devices receive this command and automatically launch the applications.

[0457] When the app is launched, the server uses a generation AI to create a personalized evacuation guide based on the user's latest location and pre-registered information. This guide includes details such as specific locations to evacuate to, evacuation routes, and timing of evacuation. The server sends the generated guide to the device, which then provides it to the user via audio and images.

[0458] For example, suppose User A lives at "1-1-1 Jingumae, Shibuya-ku, Tokyo" and has difficulty moving around, so has pre-registered a nearby park as an evacuation site. When a typhoon approaches, if the server receives an area email and determines that Shibuya-ku will be affected, the server immediately sends a command to User A's device to automatically activate the app. The device automatically activates the app, and the server generates and sends an evacuation guide (e.g., "Please evacuate to a higher place (nearby park) within 20 minutes"). The device reads the instructions aloud and displays the evacuation route on the screen. User A then quickly begins evacuation according to these instructions.

[0459] This system enables users to take swift and appropriate evacuation actions in the event of a disaster, and provides effective information, especially to users with low IT literacy, such as the elderly and people with disabilities.

[0460] The following describes the processing flow.

[0461] Step 1:

[0462] The user launches the app or dedicated device and enters pre-registered information such as their address, ease of movement, and evacuation destination information.

[0463] Step 2:

[0464] The device sends the pre-registration information entered to the server.

[0465] Step 3:

[0466] The server saves the pre-registration information it receives to the database.

[0467] Step 4:

[0468] The user requests an evacuation simulation, and the terminal sends that request to the server.

[0469] Step 5:

[0470] The server retrieves the user's location information and pre-registered information from the database, and integrates it with hazard map information.

[0471] Step 6:

[0472] The server uses AI generation to execute evacuation simulations and generate optimal evacuation simulation results.

[0473] Step 7:

[0474] The server sends the generated evacuation simulation results to the terminal.

[0475] Step 8:

[0476] The device provides the user with evacuation simulation results in audio and image format.

[0477] Step 9:

[0478] The server periodically monitors and receives emergency alerts (area mail).

[0479] Step 10:

[0480] The server analyzes the emergency alert and identifies the affected areas and users within those areas.

[0481] Step 11:

[0482] The server sends an automatic application launch command to user terminals within the affected area.

[0483] Step 12:

[0484] The device receives a command to automatically launch the app and starts the app automatically.

[0485] Step 13:

[0486] The server generates personalized evacuation guides using AI based on the user's latest location information and pre-registered information.

[0487] Step 14:

[0488] The server sends the generated evacuation guide to the terminal.

[0489] Step 15:

[0490] The device reads out evacuation instructions aloud and displays evacuation routes and locations on the screen.

[0491] Step 16:

[0492] The user quickly begins evacuation action by following the instructions on their device.

[0493] (Example 1)

[0494] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0495] Conventional evacuation guide systems have been limited to providing general information, making it difficult to offer specific evacuation guidance tailored to individual circumstances. Furthermore, users may not be able to take appropriate evacuation actions quickly during a disaster, posing a significant risk, especially for those with mobility difficulties such as the elderly and people with disabilities. Therefore, there was a need for a system that could consider each user's individual situation and provide optimal evacuation guidance in real time.

[0496] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0497] In this invention, the server includes means for acquiring user pre-registration information and location information, means for acquiring a map displaying disaster information and dangerous areas, means for automatically generating individual evacuation guides based on the acquired information using a generation AI, means for providing evacuation guides to users via voice and images, means for receiving emergency alerts and automatically launching the application, and means for executing an evacuation simulation for the user in the event of an anomaly and providing the results. This makes it possible to provide individually optimized evacuation guides to each user in real time.

[0498] "User pre-registration information" refers to personal information provided by the user in advance, such as address, mobility (e.g., able to walk, wheelchair user), and evacuation destination information.

[0499] "Location information" refers to information that indicates a user's geographical location, such as their current location or travel route.

[0500] "Disaster information" refers to information regarding the current situation and predictions of natural disasters such as typhoons, earthquakes, and floods.

[0501] A "map showing dangerous areas" is a map that visually indicates dangerous locations or areas with a high risk of disaster within a specific region.

[0502] "Generative AI" is an artificial intelligence model that analyzes diverse data and automatically generates optimal evacuation guides tailored to the individual circumstances of each user.

[0503] "Individualized evacuation guides" are pieces of information that provide specific evacuation instructions, such as where to evacuate, the route to take, and the timing of evacuation, based on each user's pre-registered information and location data.

[0504] "Means of providing information through audio and images" refers to means of communicating evacuation guides to users through audio messages, images, maps, and other visual information.

[0505] An "emergency alert" is a message used to quickly notify users in the affected area of ​​relevant information when a disaster occurs.

[0506] "Means for automatically launching applications" refers to a function that automatically starts a specific application on the user's device when an emergency alert is received.

[0507] "Means for performing evacuation simulations" refers to a function that simulates virtual evacuation actions based on the user's location information and pre-registered information during a disaster, and provides the results to the user.

[0508] This invention relates to an information system used by users via a smartphone app or dedicated terminal, which provides personalized evacuation guides during disasters. The system integrates the user's pre-registered information, location information, disaster information, and a map displaying dangerous areas, and generates evacuation guides using AI based on this information.

[0509] First, users enter pre-registration information, such as their address, mobility (e.g., walkable, wheelchair user), and evacuation destination information, using a smartphone app or a dedicated device. The device sends this information to a server, which stores it in a database. This process creates a detailed profile for each user.

[0510] If there is a risk of disaster, the user can request an evacuation simulation. When a user requests an evacuation simulation, the terminal sends the request to the server. The server retrieves pre-registered information from the database along with the user's location information, and combines this with disaster information and map information showing dangerous areas. The generating AI uses this integrated data to run an optimal evacuation simulation and sends the results to the terminal. The terminal provides the received results to the user in audio and image format.

[0511] For example, suppose User A lives at "1-1-1, a certain town in a certain ward of a certain city" and has pre-registered a nearby park as an evacuation site due to difficulty moving around. When a typhoon approaches, the server receives an area email and determines that the certain ward will be affected. The server immediately sends a command to User A's device to automatically activate the app. The device automatically activates the app, and the server generates and sends an evacuation guide (e.g., "Evacuate to a higher place (the nearby park) within 20 minutes"). The device reads the instructions aloud and displays the evacuation route on the screen. User A then quickly begins evacuation according to these instructions.

[0512] In addition, during emergencies, the server receives emergency alerts and identifies users in the affected area. The server sends an automatic app launch command to the identified user's device, and the device automatically launches the app upon receiving this command. Once the app is launched, the server uses AI generation based on the latest location information and pre-registered information to generate a personalized evacuation guide. This evacuation guide includes details such as specific places to evacuate, evacuation routes, and timing of evacuation. The server sends the generated guide to the device, which then provides it to the user via voice and images.

[0513] In this way, this system supports users in taking swift and appropriate evacuation actions during disasters, and provides effective information, especially to users with low IT literacy, such as the elderly and people with disabilities.

[0514] Examples of prompts for a generative AI model:

[0515] Based on the user's location and pre-registered information, propose the optimal evacuation route. Considering disaster information and maps showing hazardous areas, generate a detailed guide including evacuation locations, routes, and timing. Specific address: "1-1-1, [Town Name], [District Name], [City Name]", mobility: "Wheelchair accessible", evacuation destination: "Nearby park". Assume a disaster such as a typhoon.

[0516] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0517] Step 1:

[0518] Entering user pre-registration information

[0519] Users launch a smartphone app or dedicated device and enter their address, mobility status (e.g., able to walk, wheelchair user), and evacuation destination information. The entered data includes specific address, mode of transportation, and evacuation location.

[0520] Input: Address (e.g., "1-1-1, [town name], [district name], [city name]"), Accessibility (e.g., "Wheelchair accessible"), Evacuation destination (e.g., "Nearby park")

[0521] Output: Pre-registration information

[0522] Step 2:

[0523] Submitting and saving pre-registration information

[0524] The terminal sends the pre-registration information entered by the user to the server. The transmitted data is formed as a user profile.

[0525] The server stores the received information in a database and creates a detailed profile for each user. Various input data is stored in the profile.

[0526] Input: Pre-registration information

[0527] Output: Saved user profile

[0528] Step 3:

[0529] Request for evacuation simulation

[0530] In the event of a disaster risk, users can request an evacuation simulation via the app or a dedicated device. The request data includes their current location information.

[0531] The device sends this request to the server.

[0532] Input: Request, current location

[0533] Output: Evacuation Simulation Request

[0534] Step 4:

[0535] Execution of evacuation simulation

[0536] The server retrieves the user's current location and pre-registration information from the database. The retrieved data includes location information and profile information.

[0537] The server combines this with information from maps displaying disaster information and hazardous locations.

[0538] The generated AI model uses this integrated data to perform an optimal evacuation simulation. The generated evacuation simulation results include evacuation routes, destinations, and evacuation times.

[0539] Input: Current location information, pre-registration information, disaster information, hazardous area information

[0540] Output: Evacuation simulation results

[0541] Step 5:

[0542] Providing evacuation simulation results

[0543] The server sends the results of the evacuation simulation to the terminal. The transmitted data includes an evacuation guide.

[0544] The device provides the user with the received results in the form of audio and images. Specific actions include playing audio guides and displaying maps.

[0545] Input: Evacuation simulation results

[0546] Output: Audio guide, image display

[0547] Step 6:

[0548] Emergency alerts and automatic app launch

[0549] The server receives emergency alerts and analyzes their contents. The analysis data includes the type of disaster and the extent of its impact.

[0550] The server identifies users within the affected region, and this identification data includes a list of users.

[0551] The server sends an automatic application launch command to the specified user's device.

[0552] The device receives this command and automatically launches the app.

[0553] Input: Emergency alert, information on affected areas

[0554] Output: Auto-start command, launching the application

[0555] Step 7:

[0556] Generation and provision of individual evacuation guides

[0557] When the app is launched, the server uses a generation AI to create a personalized evacuation guide based on the user's latest location information and pre-registered information. The generated evacuation guide includes detailed evacuation instructions.

[0558] The server sends the generated evacuation guide to the terminal.

[0559] The device provides users with information via voice and images. Specifically, it provides navigation for evacuation routes and information about evacuation locations.

[0560] Input: Latest location information, pre-registration information

[0561] Output: Individual evacuation guide, audio guide, image display

[0562] (Application Example 1)

[0563] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0564] In modern society, a lack of adequate guidance for the rapid and safe evacuation of many people in the event of a disaster within a physical store is a significant problem. In particular, people with mobility difficulties (e.g., the elderly and wheelchair users) often struggle to find appropriate evacuation routes. Therefore, a comprehensive system is needed to support rapid and safe evacuation within physical stores.

[0565] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0566] In this invention, the server includes means for acquiring user pre-registration information and location information, means for acquiring hazard maps and disaster information, means for automatically generating individual evacuation guides based on the acquired information using a generation AI, means for providing evacuation guides to users via voice and images, means for receiving emergency alerts and automatically launching the application, and means for generating the optimal evacuation route based on pre-registration information and real-time location information and providing it via push notification. This enables quick and safe evacuation actions within physical stores, thereby protecting the safety of many people.

[0567] "User pre-registration information" refers to personal information such as the user's address, ease of movement, and evacuation destination, which the user enters and registers in advance through the application or a dedicated terminal.

[0568] "Location information" refers to information that indicates the user's current location, and is data mainly obtained using location measurement technologies such as GPS.

[0569] A "hazard map" is a map that visually displays the disaster risk in a specific area, indicating the likelihood of a disaster occurring and the extent of its impact.

[0570] "Disaster information" refers to real-time information about disasters such as earthquakes, typhoons, and floods, and is obtained from public institutions and meteorological data.

[0571] "Generative AI" refers to a system that utilizes artificial intelligence technology to analyze various input data and generate optimal output tailored to a specific purpose.

[0572] An "evacuation guide" is information that includes specific instructions, routes, and timing for users to evacuate safely.

[0573] "Provision via audio and images" refers to a method of presenting the generated evacuation guide to the user both aurally and visually.

[0574] An "emergency alert" is a notification designed to quickly and widely disseminate important information related to emergencies such as disasters.

[0575] "Automatic application startup" refers to a function that automatically launches relevant applications without user intervention when an emergency alert is received.

[0576] "Real-time location information" refers to information that acquires and updates the current location of the user in real time.

[0577] A "push notification" is an automated notification message that informs users that there is new information on their smartphone or tablet.

[0578] This invention relates to a system that provides optimal evacuation guidance to customers and store employees in the event of a disaster at a physical store. Based on the user's pre-registered information and real-time location information, an AI model is used to automatically generate individual evacuation guidance, which is then provided in both audio and image formats. The following describes a detailed embodiment of the system.

[0579] System Configuration

[0580] 1. Obtain user pre-registration information

[0581] Users enter and register personal data in advance via their smartphones or dedicated terminals, such as their address, mobility (e.g., able to walk, wheelchair user), and evacuation destination information. This creates a detailed profile for each user in the database.

[0582] 2. Acquisition of location information

[0583] The user's smartphone uses GPS to obtain location information in real time and transmit it to the server. Specifically, the smartphone app periodically transmits location information.

[0584] 3. Obtaining disaster information

[0585] The server regularly acquires and updates hazard maps and disaster information. This information is obtained from public institutions and meteorological data.

[0586] 4. Generation of evacuation guides using a generative AI model

[0587] The server integrates acquired user pre-registration information, real-time location information, hazard maps, and the latest disaster information, and generates individual evacuation guides using a generation AI model. The generated evacuation guides include details such as evacuation routes, specific places to evacuate to, and timing of evacuation.

[0588] 5. Provision of evacuation guides

[0589] The server sends the generated evacuation guide to the user's smartphone via push notification. The receiving device then provides the evacuation guide to the user through audio and images, encouraging prompt evacuation action.

[0590] 6. Response to Emergency Alerts

[0591] When an emergency alert is issued, the server receives this information and identifies users in the affected area. The server then sends an automatic application launch command to the identified users' smartphones. Once the application is launched, it generates and provides an evacuation guide again based on the user's latest location information and pre-registered information.

[0592] Hardware and software to be used

[0593] Server: Database management system (e.g., MySQL), Generative AI model (e.g., TensorFlow)

[0594] Client device: Smartphone application (e.g., React Native)

[0595] Communication: Internet connection, GPS

[0596] Specific example

[0597] For example, if an earthquake occurs in a shopping mall, this system will immediately acquire the location information of customers and provide the optimal evacuation route. For instance, it might issue specific instructions such as, "Please evacuate to a higher place (the second floor of the shopping mall) within 30 minutes."

[0598] Example of a prompt:

[0599] "Please tell me the best evacuation route from shopping mall A in the event of an earthquake."

[0600] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0601] Step 1:

[0602] Users input and register their address, mobility status (e.g., able to walk, wheelchair user), and evacuation destination information via their smartphone or a dedicated terminal. The terminal transmits this pre-registered information to a server and stores it in a database.

[0603] Input: User's address, ease of movement, evacuation destination information

[0604] Output: Information is stored in the user profile database on the server.

[0605] Step 2:

[0606] The device periodically uses GPS to obtain the user's real-time location information. The obtained location information is sent to the server and stored as the user's most recent location.

[0607] Input: User's current location information

[0608] Output: Latest user location information stored on the server

[0609] Step 3:

[0610] The server periodically retrieves hazard maps and the latest disaster information. Disaster information is collected using public institutions and weather data sources and stored in a database.

[0611] Input: Hazard map, disaster information

[0612] Output: Latest hazard maps and disaster information stored in the database

[0613] Step 4:

[0614] The user requests an evacuation simulation through the application. The device sends this request to the server. The server retrieves pre-registration information, real-time location information, the latest hazard maps, and disaster information from the database, and generates an individualized evacuation guide using a generation AI model. The generated evacuation guide is sent to the device.

[0615] Input: User evacuation simulation request, pre-registration information, location information, hazard map, disaster information

[0616] Output: Generated evacuation guide

[0617] Step 5:

[0618] The server sends an evacuation guide to the terminal. The terminal provides the received evacuation guide to the user in both audio and image formats.

[0619] Input: Evacuation guide sent from the server

[0620] Output: Audio and visual evacuation guide provided to the user.

[0621] Step 6:

[0622] The server receives an emergency alert. It identifies users in the affected area and sends an application auto-start command to the terminals of the relevant users.

[0623] Input: Breaking News

[0624] Output: Sending of automatic startup command

[0625] Step 7:

[0626] The application starts automatically. The server generates an evacuation guide using a regenerated AI model based on the latest location information and pre-registered information, and sends it to the device.

[0627] Input: Latest location information, pre-registration information

[0628] Output: Evacuation guide that is generated and sent

[0629] Step 8:

[0630] The device provides the user with received evacuation guides in audio and image formats, encouraging quick and appropriate evacuation actions.

[0631] Input: Evacuation guide sent from the server

[0632] Output: Audio and visual evacuation guide provided to the user.

[0633] Specific example

[0634] For example, if an earthquake occurs in a shopping mall, this system will immediately acquire the location information of the relevant users, generate an evacuation guide such as "Please head towards the nearest emergency exit," and send it via push notification.

[0635] Example of a prompt:

[0636] "Please tell me the best evacuation route from shopping mall A in the event of an earthquake."

[0637] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0638] This invention relates to an information system that provides evacuation guides during disasters, which includes a function to recognize the user's emotions and dynamically adjust the content of the evacuation guide based on those emotions. The system is used by the user via a smartphone app or a dedicated terminal.

[0639] First, users enter pre-registration information such as their address, mobility status (e.g., able to walk, wheelchair user), and evacuation destination information through an app or dedicated device. The device sends this information to a server, which stores it in a database. This creates a detailed profile for each user.

[0640] In the event of a disaster, the user's pre-registered information and latest location data are retrieved from the server. In addition, the server collects hazard maps and disaster information, and integrates this information using a generation AI. As a result, an individualized evacuation guide is generated.

[0641] A new element added to this system is the "emotion engine." The emotion engine analyzes the user's voice and facial expressions to recognize their current emotional state. The user sends data acquired by emotion recognition sensors to the terminal, which analyzes this data and sends the emotional information to the server. The server analyzes this information through the emotion engine, and the generating AI adjusts the evacuation guide to suit the user's emotions.

[0642] For example, if a user is feeling highly anxious, the evacuation guide will include specific and reassuring instructions, such as, "Don't worry. The evacuation shelter is very close. Please follow this path." Conversely, if the user is calm, concise and prompt evacuation instructions will be provided.

[0643] When an emergency alert (area mail) is sent, the server receives this information and identifies users in the affected area. The server then sends a command to automatically launch an application to the identified user's device. The device receives this command and automatically launches the application.

[0644] When the app is launched, the server uses a generative AI to generate a personalized evacuation guide based on the user's latest location and pre-registered information. This guide includes details such as specific places to evacuate, evacuation routes, and timing. Furthermore, the evacuation guide is dynamically adjusted based on the user's emotional state. For example, if the app detects that the user is in a "high-stress state," the evacuation instructions will become more detailed and reassuring.

[0645] As a concrete example, suppose User B lives at "1-1-1 Umeda, Kita-ku, Osaka City" and has difficulty moving around, so has pre-registered a nearby building as an evacuation site. When an earthquake occurs and the server receives an area email and determines that Kita-ku is affected, the server immediately sends a command to User B's device to automatically activate the app. The device automatically activates the app, the server analyzes User B's emotional state through the emotion engine, and the generating AI generates an evacuation guide (e.g., "Please stay calm. Please evacuate to a nearby building. This route is safe.") and sends it to the device. The device reads the evacuation guide aloud and displays the evacuation route on the screen. User B then quickly begins evacuation according to this.

[0646] This system takes into account the emotional state of users during a disaster, enabling swift and appropriate evacuation actions. It also effectively provides information to users with low IT literacy, particularly the elderly and people with disabilities.

[0647] The following describes the processing flow.

[0648] Step 1:

[0649] The user launches the app or dedicated device and enters pre-registered information such as their address, ease of movement, and evacuation destination information.

[0650] Step 2:

[0651] The device sends the pre-registration information entered to the server.

[0652] Step 3:

[0653] The server saves the pre-registration information it receives to the database.

[0654] Step 4:

[0655] The user uses emotion recognition sensors (e.g., wearable devices or cameras) to acquire emotional data.

[0656] Step 5:

[0657] The device sends the emotional data it has acquired to the server.

[0658] Step 6:

[0659] The server analyzes the received emotional data using an emotion engine to identify the user's current emotional state.

[0660] Step 7:

[0661] A disaster occurs, and the server receives an emergency alert (area mail).

[0662] Step 8:

[0663] The server analyzes the emergency alert and identifies the affected areas and users within those areas.

[0664] Step 9:

[0665] The server sends an automatic application launch command to user terminals within the affected area.

[0666] Step 10:

[0667] The device receives a command to automatically launch the app and starts the app automatically.

[0668] Step 11:

[0669] The server retrieves and integrates hazard maps and disaster information based on the user's latest location information and pre-registered information.

[0670] Step 12:

[0671] The server automatically generates individual evacuation guides based on information acquired using AI.

[0672] Step 13:

[0673] The server adjusts the content of the evacuation guide it generates, taking into account the user's emotional state. For example, if the user is feeling anxious, the guide will be modified to provide more reassurance.

[0674] Step 14:

[0675] The server sends the coordinated evacuation guide to the terminal.

[0676] Step 15:

[0677] The device reads aloud the evacuation guide it receives and displays evacuation routes and locations on the screen.

[0678] Step 16:

[0679] The user quickly begins evacuation action by following the instructions on their device.

[0680] As a concrete example, suppose User B lives at "1-1-1 Umeda, Kita-ku, Osaka City" and has difficulty moving around, so has pre-registered a nearby building as an evacuation site. When an earthquake occurs and the server receives an area email and determines that Kita-ku is affected, the server immediately sends a command to User B's device to automatically activate the app. The device automatically activates the app, the server analyzes User B's emotional state through the emotion engine, and the generating AI generates an evacuation guide (e.g., "Please stay calm. Please evacuate to a nearby building. This route is safe.") and sends it to the device. The device reads the evacuation guide aloud and displays the evacuation route on the screen. User B then quickly begins evacuation according to this.

[0681] (Example 2)

[0682] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0683] Conventional disaster evacuation guidance systems could only provide uniform evacuation instructions without considering the user's current emotional state. Therefore, it was difficult to provide appropriate evacuation guidance, especially for users with low IT literacy, such as the elderly and people with disabilities. Furthermore, a means of automatically launching the application was needed to encourage rapid evacuation action during a disaster.

[0684] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0685] In this invention, the server includes means for acquiring user pre-registration information and location information, means for acquiring hazard maps and disaster information, means for automatically generating individual evacuation guides based on the acquired information using a generation AI, means for providing evacuation guides to users via voice and images, means for analyzing voice and facial expression data to recognize the user's emotional state, and means for adjusting the content of the evacuation guides based on emotional information. This makes it possible to provide a rapid and appropriate evacuation guide that takes into account the user's emotional state in the event of a disaster.

[0686] "User pre-registration information" refers to detailed personal information such as address, ease of movement, and evacuation destination information that users of the evacuation guide system have registered in advance.

[0687] "Location information" refers to data that indicates the user's current geographical location, and is obtained using the GPS function of a smartphone, etc.

[0688] A "hazard map" is information that shows areas at risk of disaster and the extent of their impact on a map.

[0689] "Disaster information" refers to the latest information on disasters such as earthquakes, floods, and fires, and is data obtained from the Japan Meteorological Agency and disaster prevention organizations.

[0690] "Generative AI" refers to a system that uses artificial intelligence technology to automatically generate information such as evacuation guides.

[0691] An "individualized evacuation guide" is a user-specific evacuation instruction generated based on the user's pre-registered information and location data.

[0692] "Means of providing evacuation guides to users through audio and images" refers to methods for communicating generated evacuation guides to users using audio and images.

[0693] "Emotional state" refers to the psychological state that the user is currently experiencing (e.g., anxiety, fear, calmness, etc.).

[0694] "Means for analyzing voice and facial expression data" refers to a technology that analyzes voice and facial expression data obtained from a user to determine their emotional state.

[0695] "Emotional information" refers to data that indicates the user's current emotional state, determined based on voice and facial expression data.

[0696] "Means for adjusting the content of the evacuation guide" refers to a function that appropriately modifies the instructions in the evacuation guide based on the user's emotional information.

[0697] This invention relates to an information system that provides evacuation guidance during disasters, and includes a function to recognize the user's emotions and dynamically adjust the content of the evacuation guidance based on those emotions. Users access this system using a smartphone app or a dedicated terminal.

[0698] First, users enter pre-registration information such as their address, mobility (e.g., able to walk, wheelchair user), and evacuation destination information via a smartphone app or dedicated terminal. The terminal sends this information to a server, which then stores it in a database to create a detailed profile for each user.

[0699] In the event of a disaster, the user's pre-registered information and latest location data are retrieved from the server. The server collects hazard maps and the latest disaster information, and integrates this information using a generation AI (e.g., GPT-4). As a result, individual evacuation guides are generated.

[0700] Furthermore, this system includes an "emotion engine" that analyzes voice and facial expression data to recognize the user's current emotional state. The user uses an emotion recognition sensor, and the acquired data is sent to the server via the terminal. The server uses the emotion engine to analyze this data, and a generating AI adjusts the evacuation guide to suit the user's emotions.

[0701] Specifically, if a user is feeling highly anxious, the evacuation guide will include specific and reassuring instructions (for example, "Don't worry. The evacuation shelter is very close. Follow this path."). Conversely, if the user is calm, concise and prompt evacuation instructions will be provided.

[0702] Furthermore, when an emergency alert (area mail) is sent, the server receives this information and identifies users in the affected area. The server sends an automatic application launch command to the identified user's device, and the device receives this command and automatically launches the app. In this case, the server uses a generation AI to generate individual evacuation guides based on the user's latest location information and pre-registered information. The evacuation guides are dynamically adjusted based on the user's emotional information.

[0703] As a concrete example, suppose User B lives at "1-1-1 Umeda, Kita-ku, Osaka City" and uses a wheelchair, so has pre-registered a nearby building as an evacuation site. When an earthquake occurs and the server receives an area email and determines that Kita-ku is affected, the server immediately sends a command to User B's device to automatically activate the app. The device automatically activates the app, and the server analyzes User B's emotional state through the emotion engine. The generating AI then generates an evacuation guide such as, "Please stay calm. Please evacuate to a nearby building. This route is safe," and sends it to the device. The device reads the evacuation guide aloud and displays the evacuation route on the screen. User B then quickly begins evacuation according to this guide.

[0704] Examples of prompt statements are as follows:

[0705] "The user's address is 1-1-1 Umeda, Kita-ku, Osaka City, and they use a wheelchair. What kind of evacuation guide would be appropriate to alleviate the strong anxiety the user feels in the event of an earthquake?"

[0706] This makes it possible to provide quick and appropriate evacuation guidance that takes into account the user's emotional state. This system is expected to be particularly effective for users with low IT literacy, such as the elderly and people with disabilities.

[0707] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0708] Step 1:

[0709] Enter and submit pre-registration information

[0710] User: Open the smartphone app or dedicated device.

[0711] Input: Address, mobility (e.g., able to walk, wheelchair user, etc.), evacuation destination information, etc.

[0712] Specific actions: The user opens the app and enters the address "1-1-1 Umeda, Kita-ku, Osaka City", mobility status "Wheelchair user", and evacuation destination information.

[0713] Terminal: Sends the entered information to the server.

[0714] Data processing: Format the entered information and prepare it for transmission.

[0715] Output: Formatted pre-registration information.

[0716] Step 2:

[0717] Pre-registration information storage

[0718] Server: Saves the received pre-registration information to the database.

[0719] Input: Formatted pre-registration information.

[0720] Data processing: Convert received data into a format suitable for storage in the appropriate table within the database.

[0721] Output: User profile stored in the database.

[0722] Specific operation: The server saves the address information "1-1-1 Umeda, Kita-ku, Osaka City" and mobility information "wheelchair user" to the database.

[0723] Step 3:

[0724] Information acquisition during a disaster

[0725] Server: Receives the latest disaster information when a disaster occurs.

[0726] Input: Hazard maps and the latest disaster information.

[0727] Data processing: Integrate disaster information and hazard maps to identify the scope of impact.

[0728] Output: Identification of the disaster-stricken area.

[0729] Specific action: The server receives an earthquake early warning and identifies "Kita Ward, Osaka City" as the affected area.

[0730] Step 4:

[0731] Retrieving user information

[0732] Server: Retrieves pre-registration information and the latest location information.

[0733] Input: User profile and location information from GPS.

[0734] Data processing: Combine profile and location information to list users within the affected area.

[0735] Output: List of affected users.

[0736] Step 5:

[0737] Generating an evacuation guide

[0738] Server: Automatically generates individual evacuation guides using a generation AI.

[0739] Input: User pre-registration information, location information, disaster information.

[0740] Data processing: Generative AI integrates this information to calculate the optimal evacuation route and evacuation location.

[0741] Output: Individual evacuation guide.

[0742] Specific action: The server generates an evacuation guide that says, "Please evacuate to a nearby building. This route is safe."

[0743] Step 6:

[0744] Execution of emotion recognition

[0745] User: Uses a sensor for emotion recognition.

[0746] Input: Voice and facial expression data.

[0747] Terminal: Sends voice and facial expression data to the server.

[0748] Data processing: Convert audio and facial expression data into an analysis format.

[0749] Output: Data for analysis.

[0750] Server: Uses an emotion engine to analyze data and determine the user's emotional state.

[0751] Input: Data for analysis.

[0752] Data processing: The emotion engine analyzes voice and facial expressions to identify the emotional state.

[0753] Output: Emotional information.

[0754] Specific operation: The server analyzes the user's voice data and determines that the user is feeling "anxious".

[0755] Step 7:

[0756] Adjustment of evacuation guides

[0757] Server: Adjusts the content of the evacuation guide generated by the AI ​​based on emotional information.

[0758] Input: Emotional information and initial evacuation guide.

[0759] Data processing: Modify the evacuation guide to be more specific and reassuring, taking emotional information into account.

[0760] Output: Adjusted evacuation guide.

[0761] Specific action: The server will change its instructions to a reassuring message such as, "Please stay calm. Please evacuate to a nearby building."

[0762] Step 8:

[0763] Provision of evacuation guides

[0764] Server: Sends the coordinated evacuation guide to the user's terminal.

[0765] Input: Adjusted evacuation guide.

[0766] Output: Data sent to the terminal.

[0767] Specific action: The server sends a coordinated evacuation guide to the terminal.

[0768] Terminal: Displays the evacuation guide to the user and reads it aloud.

[0769] Input: Evacuation guide data from the server.

[0770] Output: Visual and auditory instructions to the user.

[0771] Specific actions: The device will play a voice command saying, "Please proceed this way," and display a detailed evacuation route on the screen.

[0772] Step 9:

[0773] Start of evacuation

[0774] User: Follow the instructions on your device and begin evacuation immediately.

[0775] Input: Instructions from the evacuation guide.

[0776] Output: Safe evacuation actions.

[0777] Specific actions: The user follows the voice instructions on the device and navigates the displayed route to a safe evacuation location.

[0778] (Application Example 2)

[0779] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0780] In food delivery operations, a challenge is providing delivery guides that reduce the stress and anxiety experienced by delivery drivers during deliveries, enabling them to perform their duties efficiently and with peace of mind. Traditional systems fail to provide instructions that take into account the emotional state of delivery drivers, resulting in decreased delivery efficiency and increased mental burden on drivers.

[0781] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0782] In this invention, the server includes means for acquiring user pre-registration information and location information, means for acquiring disaster information and delivery information, means using a generative AI that analyzes the acquired information and emotional data to generate individual delivery guides, means using an emotion engine that analyzes the emotions of delivery personnel and dynamically adjusts the delivery guide based on the results, and means for providing delivery guides in the form of voice and images. This provides an optimal delivery guide according to the emotional state of the delivery personnel, enabling them to perform their duties efficiently and with peace of mind.

[0783] "Pre-registration information" refers to data such as basic personal information, delivery address information, and mobility information that users provide in advance.

[0784] "Location information" refers to data that indicates the current location of the user or delivery person.

[0785] "Disaster information" refers to the latest information regarding natural disasters and emergencies.

[0786] "Delivery information" refers to information regarding the delivery address, the contents of the delivery, and the delivery schedule.

[0787] "Emotional data" refers to data that indicates the emotional state of the delivery person, and is obtained by analyzing facial expressions, voice, heart rate, etc.

[0788] "Generative AI" is artificial intelligence that generates individual delivery guides based on acquired information and data.

[0789] The "emotion engine" is software that analyzes the emotional state of delivery drivers and dynamically adjusts the instructions provided based on the results.

[0790] A "delivery guide" is information provided to delivery personnel during delivery, including route directions and work instructions.

[0791] A "delivery simulation" is a process that virtually executes the delivery process and analyzes the results.

[0792] The system realizing this invention has the function of acquiring and analyzing user pre-registration information, location information, disaster information, delivery information, and emotion data, and generating and providing personalized delivery guides. The main components of the system include a server, terminal, emotion engine, and generative AI model.

[0793] System Configuration and Data Processing

[0794] 1. Obtaining pre-registration information and location information.

[0795] Users register their basic information (e.g., address, mobility, delivery destination information) using their smartphones or dedicated terminals. This information is sent to a server and stored in a database. Location information is collected in real time using the GPS module built into the smartphone.

[0796] 2. Obtaining disaster information and delivery information

[0797] The server obtains disaster and delivery information in real time from external data provision services. This allows it to identify emergencies that may affect deliveries.

[0798] 3. Acquisition and analysis of emotional data

[0799] Emotional data from delivery drivers is collected through their smartphones' cameras, microphones, and heart rate sensors. The emotion engine analyzes this data to determine the driver's current emotional state. For example, it analyzes facial expressions and voice using open-source emotion recognition libraries (using OpenCV and TensorFlow).

[0800] 4. Generation of delivery guides using AI

[0801] The server uses a generation AI (e.g., OpenAI's GPT-4) to integrate and analyze user pre-registration information, location information, disaster information, delivery information, and emotional data to generate personalized delivery guides. The guide content is dynamically adjusted according to the delivery person's emotional state and designed to provide users with a sense of security.

[0802] 5. Providing audio and visual guides.

[0803] The terminal provides the delivery driver with generated delivery guides via voice and images. This allows the driver to receive instructions visually and aurally, enabling them to deliver quickly and safely.

[0804] Specific example

[0805] For example, consider a situation where delivery driver A is handling multiple deliveries in bad weather and traffic congestion. The server, through its emotion engine, determines that delivery driver A is experiencing stress, and the generative AI generates a delivery guide like the following:

[0806] 1. Adjusting delivery routes

[0807] "Due to traffic congestion, we recommend this alternative route."

[0808] 2. Emotionally-based messages of support

[0809] "You look tired. Please hang in there a little longer. You've done a great job!"

[0810] 3. Simplification of instructions

[0811] If the delivery person is calm, they will provide simple instructions such as, "Your next delivery address is AA. Please follow the navigation."

[0812] Example of a prompt

[0813] Use the following prompts to input information into the invention's system and generate a response:

[0814] The user's latest emotional state data and location information have been retrieved. Based on the delivery destination list and current traffic conditions, calculate the optimal delivery route and generate the following instructions according to the user's emotional state: If the user is stressed, provide reassuring instructions; if calm, provide concise and quick instructions.

[0815] Emotional state: "High stress"

[0816] Current location: "City center"

[0817] Delivery destination list: ["Area A", "Area B"]

[0818] Traffic conditions: "Traffic congestion is occurring."

[0819] Instructions: (Generate appropriate instructions here)

[0820] This system allows delivery personnel to receive appropriate instructions and perform their duties efficiently and with peace of mind.

[0821] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0822] Step 1:

[0823] The terminal receives the user's pre-registered information (e.g., address, mobility, delivery destination information) through an input form and sends that information to the server.

[0824] Input: User's pre-registration information.

[0825] Output: User profile data stored on the server.

[0826] Operation: The user enters information using a smartphone app and sends it to the server. The server stores this information in a database.

[0827] Step 2:

[0828] The device uses a GPS module to collect real-time location information and transmit it to the server.

[0829] Input: The device's current location (GPS data).

[0830] Output: Real-time location information updated to the server.

[0831] Operation: The smartphone's GPS module acquires location information and sends it to the server. The server associates this information with the user profile in its database.

[0832] Step 3:

[0833] The server obtains disaster information and delivery information from external data provision services.

[0834] Input: Disaster information and delivery information.

[0835] Output: The latest disaster information and delivery information stored on the server.

[0836] Operation: The server sends requests to external data providers via an API to retrieve the latest disaster and delivery information. The retrieved information is stored in a database.

[0837] Step 4:

[0838] The device uses a camera, microphone, and heart rate sensor to collect emotional data from delivery drivers and transmit it to a server.

[0839] Input: Delivery person's facial expression data (camera), voice data (microphone), heart rate data (heart rate sensor).

[0840] Output: Emotional data sent to the server.

[0841] Operation: The device uses its camera, microphone, and heart rate sensor to collect and analyze data from the delivery person, then sends it to the server. The server uses an emotion engine to analyze this data.

[0842] Step 5:

[0843] The server uses a generation AI to integrate user pre-registration information, location information, disaster information, delivery information, and sentiment data to generate personalized delivery guides.

[0844] Inputs: User pre-registration information, location information, disaster information, delivery information, sentiment data.

[0845] Output: Individual delivery guides generated.

[0846] Operation: The server uses a generation AI (such as OpenAI's GPT-4) to analyze input data and generate a delivery guide. The generated guide is dynamically adjusted according to the delivery person's emotional state.

[0847] Step 6:

[0848] The terminal provides the generated delivery guide to the delivery person via voice and images.

[0849] Input: Generated individual delivery guide.

[0850] Output: Provision of delivery guides via audio and images.

[0851] Operation: The terminal provides delivery drivers with delivery guides received from the server using text-to-speech and navigation functions. Route guidance is displayed on the terminal's screen, and instructions are read aloud.

[0852] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0853] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0854] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0855] [Third Embodiment]

[0856] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0857] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0858] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0859] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0860] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0861] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0862] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0863] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0864] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0865] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0866] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0867] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0868] This invention is an information system that provides disaster evacuation guides tailored to individual circumstances, and is used by users via a smartphone app or dedicated terminal. This system integrates the user's pre-registered information, location information, hazard maps, and disaster information, and provides personalized evacuation guides using a generating AI.

[0869] First, users enter pre-registration information such as their address, mobility status (e.g., able to walk, wheelchair user), and evacuation destination information through an app or dedicated device. The device sends this information to a server, which stores it in a database. This creates a detailed profile for each user.

[0870] If a disaster risk is suspected, the user can conduct an evacuation simulation. When a user requests an evacuation simulation, the terminal sends the request to the server. The server retrieves pre-registered information from the database along with the user's location information, and combines this with hazard map information. The generating AI uses this integrated data to perform an optimal evacuation simulation and sends the results to the terminal. The terminal provides the received results to the user in audio and image format.

[0871] Furthermore, when an emergency alert (area mail) is sent, the server receives this information and identifies users within the affected area. The server then sends an automatic application launch command to the identified users' devices. The devices receive this command and automatically launch the applications.

[0872] When the app is launched, the server uses a generation AI to create a personalized evacuation guide based on the user's latest location and pre-registered information. This guide includes details such as specific locations to evacuate to, evacuation routes, and timing of evacuation. The server sends the generated guide to the device, which then provides it to the user via audio and images.

[0873] For example, suppose User A lives at "1-1-1 Jingumae, Shibuya-ku, Tokyo" and has difficulty moving around, so has pre-registered a nearby park as an evacuation site. When a typhoon approaches, if the server receives an area email and determines that Shibuya-ku will be affected, the server immediately sends a command to User A's device to automatically activate the app. The device automatically activates the app, and the server generates and sends an evacuation guide (e.g., "Please evacuate to a higher place (nearby park) within 20 minutes"). The device reads the instructions aloud and displays the evacuation route on the screen. User A then quickly begins evacuation according to these instructions.

[0874] This system enables users to take swift and appropriate evacuation actions in the event of a disaster, and provides effective information, especially to users with low IT literacy, such as the elderly and people with disabilities.

[0875] The following describes the processing flow.

[0876] Step 1:

[0877] The user launches the app or dedicated device and enters pre-registered information such as their address, ease of movement, and evacuation destination information.

[0878] Step 2:

[0879] The device sends the pre-registration information entered to the server.

[0880] Step 3:

[0881] The server saves the pre-registration information it receives to the database.

[0882] Step 4:

[0883] The user requests an evacuation simulation, and the terminal sends that request to the server.

[0884] Step 5:

[0885] The server retrieves the user's location information and pre-registered information from the database, and integrates it with hazard map information.

[0886] Step 6:

[0887] The server uses AI generation to execute evacuation simulations and generate optimal evacuation simulation results.

[0888] Step 7:

[0889] The server sends the generated evacuation simulation results to the terminal.

[0890] Step 8:

[0891] The device provides the user with evacuation simulation results in audio and image format.

[0892] Step 9:

[0893] The server periodically monitors and receives emergency alerts (area mail).

[0894] Step 10:

[0895] The server analyzes the emergency alert and identifies the affected areas and users within those areas.

[0896] Step 11:

[0897] The server sends an automatic application launch command to user terminals within the affected area.

[0898] Step 12:

[0899] The device receives a command to automatically launch the app and starts the app automatically.

[0900] Step 13:

[0901] The server generates personalized evacuation guides using AI based on the user's latest location information and pre-registered information.

[0902] Step 14:

[0903] The server sends the generated evacuation guide to the terminal.

[0904] Step 15:

[0905] The device reads out evacuation instructions aloud and displays evacuation routes and locations on the screen.

[0906] Step 16:

[0907] The user quickly begins evacuation action by following the instructions on their device.

[0908] (Example 1)

[0909] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0910] Conventional evacuation guide systems have been limited to providing general information, making it difficult to offer specific evacuation guidance tailored to individual circumstances. Furthermore, users may not be able to take appropriate evacuation actions quickly during a disaster, posing a significant risk, especially for those with mobility difficulties such as the elderly and people with disabilities. Therefore, there was a need for a system that could consider each user's individual situation and provide optimal evacuation guidance in real time.

[0911] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0912] In this invention, the server includes means for acquiring user pre-registration information and location information, means for acquiring a map displaying disaster information and dangerous areas, means for automatically generating individual evacuation guides based on the acquired information using a generation AI, means for providing evacuation guides to users via voice and images, means for receiving emergency alerts and automatically launching the application, and means for executing an evacuation simulation for the user in the event of an anomaly and providing the results. This makes it possible to provide individually optimized evacuation guides to each user in real time.

[0913] "User pre-registration information" refers to personal information provided by the user in advance, such as address, mobility (e.g., able to walk, wheelchair user), and evacuation destination information.

[0914] "Location information" refers to information that indicates a user's geographical location, such as their current location or travel route.

[0915] "Disaster information" refers to information regarding the current situation and predictions of natural disasters such as typhoons, earthquakes, and floods.

[0916] A "map showing dangerous areas" is a map that visually indicates dangerous locations or areas with a high risk of disaster within a specific region.

[0917] "Generative AI" is an artificial intelligence model that analyzes diverse data and automatically generates optimal evacuation guides tailored to the individual circumstances of each user.

[0918] "Individualized evacuation guides" are pieces of information that provide specific evacuation instructions, such as where to evacuate, the route to take, and the timing of evacuation, based on each user's pre-registered information and location data.

[0919] "Means of providing information through audio and images" refers to means of communicating evacuation guides to users through audio messages, images, maps, and other visual information.

[0920] An "emergency alert" is a message used to quickly notify users in the affected area of ​​relevant information when a disaster occurs.

[0921] "Means for automatically launching applications" refers to a function that automatically starts a specific application on the user's device when an emergency alert is received.

[0922] "Means for performing evacuation simulations" refers to a function that simulates virtual evacuation actions based on the user's location information and pre-registered information during a disaster, and provides the results to the user.

[0923] This invention relates to an information system used by users via a smartphone app or dedicated terminal, which provides personalized evacuation guides during disasters. The system integrates the user's pre-registered information, location information, disaster information, and a map displaying dangerous areas, and generates evacuation guides using AI based on this information.

[0924] First, users enter pre-registration information, such as their address, mobility (e.g., walkable, wheelchair user), and evacuation destination information, using a smartphone app or a dedicated device. The device sends this information to a server, which stores it in a database. This process creates a detailed profile for each user.

[0925] If there is a risk of disaster, the user can request an evacuation simulation. When a user requests an evacuation simulation, the terminal sends the request to the server. The server retrieves pre-registered information from the database along with the user's location information, and combines this with disaster information and map information showing dangerous areas. The generating AI uses this integrated data to run an optimal evacuation simulation and sends the results to the terminal. The terminal provides the received results to the user in audio and image format.

[0926] For example, suppose User A lives at "1-1-1, a certain town in a certain ward of a certain city" and has pre-registered a nearby park as an evacuation site due to difficulty moving around. When a typhoon approaches, the server receives an area email and determines that the certain ward will be affected. The server immediately sends a command to User A's device to automatically activate the app. The device automatically activates the app, and the server generates and sends an evacuation guide (e.g., "Evacuate to a higher place (the nearby park) within 20 minutes"). The device reads the instructions aloud and displays the evacuation route on the screen. User A then quickly begins evacuation according to these instructions.

[0927] In addition, during emergencies, the server receives emergency alerts and identifies users in the affected area. The server sends an automatic app launch command to the identified user's device, and the device automatically launches the app upon receiving this command. Once the app is launched, the server uses AI generation based on the latest location information and pre-registered information to generate a personalized evacuation guide. This evacuation guide includes details such as specific places to evacuate, evacuation routes, and timing of evacuation. The server sends the generated guide to the device, which then provides it to the user via voice and images.

[0928] In this way, this system supports users in taking swift and appropriate evacuation actions during disasters, and provides effective information, especially to users with low IT literacy, such as the elderly and people with disabilities.

[0929] Examples of prompts for a generative AI model:

[0930] Based on the user's location and pre-registered information, propose the optimal evacuation route. Considering disaster information and maps showing hazardous areas, generate a detailed guide including evacuation locations, routes, and timing. Specific address: "1-1-1, [Town Name], [District Name], [City Name]", mobility: "Wheelchair accessible", evacuation destination: "Nearby park". Assume a disaster such as a typhoon.

[0931] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0932] Step 1:

[0933] Entering user pre-registration information

[0934] Users launch a smartphone app or dedicated device and enter their address, mobility status (e.g., able to walk, wheelchair user), and evacuation destination information. The entered data includes specific address, mode of transportation, and evacuation location.

[0935] Input: Address (e.g., "1-1-1, [town name], [district name], [city name]"), Accessibility (e.g., "Wheelchair accessible"), Evacuation destination (e.g., "Nearby park")

[0936] Output: Pre-registration information

[0937] Step 2:

[0938] Submitting and saving pre-registration information

[0939] The terminal sends the pre-registration information entered by the user to the server. The transmitted data is formed as a user profile.

[0940] The server stores the received information in a database and creates a detailed profile for each user. Various input data is stored in the profile.

[0941] Input: Pre-registration information

[0942] Output: Saved user profile

[0943] Step 3:

[0944] Request for evacuation simulation

[0945] In the event of a disaster risk, users can request an evacuation simulation via the app or a dedicated device. The request data includes their current location information.

[0946] The device sends this request to the server.

[0947] Input: Request, current location

[0948] Output: Evacuation Simulation Request

[0949] Step 4:

[0950] Execution of evacuation simulation

[0951] The server retrieves the user's current location and pre-registration information from the database. The retrieved data includes location information and profile information.

[0952] The server combines this with information from maps displaying disaster information and hazardous locations.

[0953] The generated AI model uses this integrated data to perform an optimal evacuation simulation. The generated evacuation simulation results include evacuation routes, destinations, and evacuation times.

[0954] Input: Current location information, pre-registration information, disaster information, hazardous area information

[0955] Output: Evacuation simulation results

[0956] Step 5:

[0957] Providing evacuation simulation results

[0958] The server sends the results of the evacuation simulation to the terminal. The transmitted data includes an evacuation guide.

[0959] The device provides the user with the received results in the form of audio and images. Specific actions include playing audio guides and displaying maps.

[0960] Input: Evacuation simulation results

[0961] Output: Audio guide, image display

[0962] Step 6:

[0963] Emergency alerts and automatic app launch

[0964] The server receives emergency alerts and analyzes their contents. The analysis data includes the type of disaster and the extent of its impact.

[0965] The server identifies users within the affected region, and this identification data includes a list of users.

[0966] The server sends an automatic application launch command to the specified user's device.

[0967] The device receives this command and automatically launches the app.

[0968] Input: Emergency alert, information on affected areas

[0969] Output: Auto-start command, launching the application

[0970] Step 7:

[0971] Generation and provision of individual evacuation guides

[0972] When the app is launched, the server uses a generation AI to create a personalized evacuation guide based on the user's latest location information and pre-registered information. The generated evacuation guide includes detailed evacuation instructions.

[0973] The server sends the generated evacuation guide to the terminal.

[0974] The device provides users with information via voice and images. Specifically, it provides navigation for evacuation routes and information about evacuation locations.

[0975] Input: Latest location information, pre-registration information

[0976] Output: Individual evacuation guide, audio guide, image display

[0977] (Application Example 1)

[0978] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0979] In modern society, a lack of adequate guidance for the rapid and safe evacuation of many people in the event of a disaster within a physical store is a significant problem. In particular, people with mobility difficulties (e.g., the elderly and wheelchair users) often struggle to find appropriate evacuation routes. Therefore, a comprehensive system is needed to support rapid and safe evacuation within physical stores.

[0980] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0981] In this invention, the server includes means for acquiring user pre-registration information and location information, means for acquiring hazard maps and disaster information, means for automatically generating individual evacuation guides based on the acquired information using a generation AI, means for providing evacuation guides to users via voice and images, means for receiving emergency alerts and automatically launching the application, and means for generating the optimal evacuation route based on pre-registration information and real-time location information and providing it via push notification. This enables quick and safe evacuation actions within physical stores, thereby protecting the safety of many people.

[0982] "User pre-registration information" refers to personal information such as the user's address, ease of movement, and evacuation destination, which the user enters and registers in advance through the application or a dedicated terminal.

[0983] "Location information" refers to information that indicates the user's current location, and is data mainly obtained using location measurement technologies such as GPS.

[0984] A "hazard map" is a map that visually displays the disaster risk in a specific area, indicating the likelihood of a disaster occurring and the extent of its impact.

[0985] "Disaster information" refers to real-time information about disasters such as earthquakes, typhoons, and floods, and is obtained from public institutions and meteorological data.

[0986] "Generative AI" refers to a system that utilizes artificial intelligence technology to analyze various input data and generate optimal output tailored to a specific purpose.

[0987] An "evacuation guide" is information that includes specific instructions, routes, and timing for users to evacuate safely.

[0988] "Provision via audio and images" refers to a method of presenting the generated evacuation guide to the user both aurally and visually.

[0989] An "emergency alert" is a notification designed to quickly and widely disseminate important information related to emergencies such as disasters.

[0990] "Automatic application startup" refers to a function that automatically launches relevant applications without user intervention when an emergency alert is received.

[0991] "Real-time location information" refers to information that acquires and updates the current location of the user in real time.

[0992] A "push notification" is an automated notification message that informs users that there is new information on their smartphone or tablet.

[0993] This invention relates to a system that provides optimal evacuation guidance to customers and store employees in the event of a disaster at a physical store. Based on the user's pre-registered information and real-time location information, an AI model is used to automatically generate individual evacuation guidance, which is then provided in both audio and image formats. The following describes a detailed embodiment of the system.

[0994] System Configuration

[0995] 1. Obtain user pre-registration information

[0996] Users enter and register personal data in advance via their smartphones or dedicated terminals, such as their address, mobility (e.g., able to walk, wheelchair user), and evacuation destination information. This creates a detailed profile for each user in the database.

[0997] 2. Acquisition of location information

[0998] The user's smartphone uses GPS to obtain location information in real time and transmit it to the server. Specifically, the smartphone app periodically transmits location information.

[0999] 3. Obtaining disaster information

[1000] The server regularly acquires and updates hazard maps and disaster information. This information is obtained from public institutions and meteorological data.

[1001] 4. Generation of evacuation guides using a generative AI model

[1002] The server integrates acquired user pre-registration information, real-time location information, hazard maps, and the latest disaster information, and generates individual evacuation guides using a generation AI model. The generated evacuation guides include details such as evacuation routes, specific places to evacuate to, and timing of evacuation.

[1003] 5. Provision of evacuation guides

[1004] The server sends the generated evacuation guide to the user's smartphone via push notification. The receiving device then provides the evacuation guide to the user through audio and images, encouraging prompt evacuation action.

[1005] 6. Response to Emergency Alerts

[1006] When an emergency alert is issued, the server receives this information and identifies users in the affected area. The server then sends an automatic application launch command to the identified users' smartphones. Once the application is launched, it generates and provides an evacuation guide again based on the user's latest location information and pre-registered information.

[1007] Hardware and software to be used

[1008] Server: Database management system (e.g., MySQL), Generative AI model (e.g., TensorFlow)

[1009] Client device: Smartphone application (e.g., React Native)

[1010] Communication: Internet connection, GPS

[1011] Specific example

[1012] For example, if an earthquake occurs in a shopping mall, this system will immediately acquire the location information of customers and provide the optimal evacuation route. For instance, it might issue specific instructions such as, "Please evacuate to a higher place (the second floor of the shopping mall) within 30 minutes."

[1013] Example of a prompt:

[1014] "Please tell me the best evacuation route from shopping mall A in the event of an earthquake."

[1015] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1016] Step 1:

[1017] Users input and register their address, mobility status (e.g., able to walk, wheelchair user), and evacuation destination information via their smartphone or a dedicated terminal. The terminal transmits this pre-registered information to a server and stores it in a database.

[1018] Input: User's address, ease of movement, evacuation destination information

[1019] Output: Information is stored in the user profile database on the server.

[1020] Step 2:

[1021] The device periodically uses GPS to obtain the user's real-time location information. The obtained location information is sent to the server and stored as the user's most recent location.

[1022] Input: User's current location information

[1023] Output: Latest user location information stored on the server

[1024] Step 3:

[1025] The server periodically retrieves hazard maps and the latest disaster information. Disaster information is collected using public institutions and weather data sources and stored in a database.

[1026] Input: Hazard map, disaster information

[1027] Output: Latest hazard maps and disaster information stored in the database

[1028] Step 4:

[1029] The user requests an evacuation simulation through the application. The device sends this request to the server. The server retrieves pre-registration information, real-time location information, the latest hazard maps, and disaster information from the database, and generates an individualized evacuation guide using a generation AI model. The generated evacuation guide is sent to the device.

[1030] Input: User evacuation simulation request, pre-registration information, location information, hazard map, disaster information

[1031] Output: Generated evacuation guide

[1032] Step 5:

[1033] The server sends an evacuation guide to the terminal. The terminal provides the received evacuation guide to the user in both audio and image formats.

[1034] Input: Evacuation guide sent from the server

[1035] Output: Audio and visual evacuation guide provided to the user.

[1036] Step 6:

[1037] The server receives an emergency alert. It identifies users in the affected area and sends an application auto-start command to the terminals of the relevant users.

[1038] Input: Breaking News

[1039] Output: Sending of automatic startup command

[1040] Step 7:

[1041] The application starts automatically. The server generates an evacuation guide using a regenerated AI model based on the latest location information and pre-registered information, and sends it to the device.

[1042] Input: Latest location information, pre-registration information

[1043] Output: Evacuation guide that is generated and sent

[1044] Step 8:

[1045] The device provides the user with received evacuation guides in audio and image formats, encouraging quick and appropriate evacuation actions.

[1046] Input: Evacuation guide sent from the server

[1047] Output: Audio and visual evacuation guide provided to the user.

[1048] Specific example

[1049] For example, if an earthquake occurs in a shopping mall, this system will immediately acquire the location information of the relevant users, generate an evacuation guide such as "Please head towards the nearest emergency exit," and send it via push notification.

[1050] Example of a prompt:

[1051] "Please tell me the best evacuation route from shopping mall A in the event of an earthquake."

[1052] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1053] This invention relates to an information system that provides evacuation guides during disasters, which includes a function to recognize the user's emotions and dynamically adjust the content of the evacuation guide based on those emotions. The system is used by the user via a smartphone app or a dedicated terminal.

[1054] First, users enter pre-registration information such as their address, mobility status (e.g., able to walk, wheelchair user), and evacuation destination information through an app or dedicated device. The device sends this information to a server, which stores it in a database. This creates a detailed profile for each user.

[1055] In the event of a disaster, the user's pre-registered information and latest location data are retrieved from the server. In addition, the server collects hazard maps and disaster information, and integrates this information using a generation AI. As a result, an individualized evacuation guide is generated.

[1056] A new element added to this system is the "emotion engine." The emotion engine analyzes the user's voice and facial expressions to recognize their current emotional state. The user sends data acquired by emotion recognition sensors to the terminal, which analyzes this data and sends the emotional information to the server. The server analyzes this information through the emotion engine, and the generating AI adjusts the evacuation guide to suit the user's emotions.

[1057] For example, if a user is feeling highly anxious, the evacuation guide will include specific and reassuring instructions, such as, "Don't worry. The evacuation shelter is very close. Please follow this path." Conversely, if the user is calm, concise and prompt evacuation instructions will be provided.

[1058] When an emergency alert (area mail) is sent, the server receives this information and identifies users in the affected area. The server then sends a command to automatically launch an application to the identified user's device. The device receives this command and automatically launches the application.

[1059] When the app is launched, the server uses a generative AI to generate a personalized evacuation guide based on the user's latest location and pre-registered information. This guide includes details such as specific places to evacuate, evacuation routes, and timing. Furthermore, the evacuation guide is dynamically adjusted based on the user's emotional state. For example, if the app detects that the user is in a "high-stress state," the evacuation instructions will become more detailed and reassuring.

[1060] As a concrete example, suppose User B lives at "1-1-1 Umeda, Kita-ku, Osaka City" and has difficulty moving around, so has pre-registered a nearby building as an evacuation site. When an earthquake occurs and the server receives an area email and determines that Kita-ku is affected, the server immediately sends a command to User B's device to automatically activate the app. The device automatically activates the app, the server analyzes User B's emotional state through the emotion engine, and the generating AI generates an evacuation guide (e.g., "Please stay calm. Please evacuate to a nearby building. This route is safe.") and sends it to the device. The device reads the evacuation guide aloud and displays the evacuation route on the screen. User B then quickly begins evacuation according to this.

[1061] This system takes into account the emotional state of users during a disaster, enabling swift and appropriate evacuation actions. It also effectively provides information to users with low IT literacy, particularly the elderly and people with disabilities.

[1062] The following describes the processing flow.

[1063] Step 1:

[1064] The user launches the app or dedicated device and enters pre-registered information such as their address, ease of movement, and evacuation destination information.

[1065] Step 2:

[1066] The device sends the pre-registration information entered to the server.

[1067] Step 3:

[1068] The server saves the pre-registration information it receives to the database.

[1069] Step 4:

[1070] The user uses emotion recognition sensors (e.g., wearable devices or cameras) to acquire emotional data.

[1071] Step 5:

[1072] The device sends the emotional data it has acquired to the server.

[1073] Step 6:

[1074] The server analyzes the received emotional data using an emotion engine to identify the user's current emotional state.

[1075] Step 7:

[1076] A disaster occurs, and the server receives an emergency alert (area mail).

[1077] Step 8:

[1078] The server analyzes the emergency alert and identifies the affected areas and users within those areas.

[1079] Step 9:

[1080] The server sends an automatic application launch command to user terminals within the affected area.

[1081] Step 10:

[1082] The device receives a command to automatically launch the app and starts the app automatically.

[1083] Step 11:

[1084] The server retrieves and integrates hazard maps and disaster information based on the user's latest location information and pre-registered information.

[1085] Step 12:

[1086] The server automatically generates individual evacuation guides based on information acquired using AI.

[1087] Step 13:

[1088] The server adjusts the content of the evacuation guide it generates, taking into account the user's emotional state. For example, if the user is feeling anxious, the guide will be modified to provide more reassurance.

[1089] Step 14:

[1090] The server sends the coordinated evacuation guide to the terminal.

[1091] Step 15:

[1092] The device reads aloud the evacuation guide it receives and displays evacuation routes and locations on the screen.

[1093] Step 16:

[1094] The user quickly begins evacuation action by following the instructions on their device.

[1095] As a concrete example, suppose User B lives at "1-1-1 Umeda, Kita-ku, Osaka City" and has difficulty moving around, so has pre-registered a nearby building as an evacuation site. When an earthquake occurs and the server receives an area email and determines that Kita-ku is affected, the server immediately sends a command to User B's device to automatically activate the app. The device automatically activates the app, the server analyzes User B's emotional state through the emotion engine, and the generating AI generates an evacuation guide (e.g., "Please stay calm. Please evacuate to a nearby building. This route is safe.") and sends it to the device. The device reads the evacuation guide aloud and displays the evacuation route on the screen. User B then quickly begins evacuation according to this.

[1096] (Example 2)

[1097] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1098] Conventional disaster evacuation guidance systems could only provide uniform evacuation instructions without considering the user's current emotional state. Therefore, it was difficult to provide appropriate evacuation guidance, especially for users with low IT literacy, such as the elderly and people with disabilities. Furthermore, a means of automatically launching the application was needed to encourage rapid evacuation action during a disaster.

[1099] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1100] In this invention, the server includes means for acquiring user pre-registration information and location information, means for acquiring hazard maps and disaster information, means for automatically generating individual evacuation guides based on the acquired information using a generation AI, means for providing evacuation guides to users via voice and images, means for analyzing voice and facial expression data to recognize the user's emotional state, and means for adjusting the content of the evacuation guides based on emotional information. This makes it possible to provide a rapid and appropriate evacuation guide that takes into account the user's emotional state in the event of a disaster.

[1101] "User pre-registration information" refers to detailed personal information such as address, ease of movement, and evacuation destination information that users of the evacuation guide system have registered in advance.

[1102] "Location information" refers to data that indicates the user's current geographical location, and is obtained using the GPS function of a smartphone, etc.

[1103] A "hazard map" is information that shows areas at risk of disaster and the extent of their impact on a map.

[1104] "Disaster information" refers to the latest information on disasters such as earthquakes, floods, and fires, and is data obtained from the Japan Meteorological Agency and disaster prevention organizations.

[1105] "Generative AI" refers to a system that uses artificial intelligence technology to automatically generate information such as evacuation guides.

[1106] An "individualized evacuation guide" is a user-specific evacuation instruction generated based on the user's pre-registered information and location data.

[1107] "Means of providing evacuation guides to users through audio and images" refers to methods for communicating generated evacuation guides to users using audio and images.

[1108] "Emotional state" refers to the psychological state that the user is currently experiencing (e.g., anxiety, fear, calmness, etc.).

[1109] "Means for analyzing voice and facial expression data" refers to a technology that analyzes voice and facial expression data obtained from a user to determine their emotional state.

[1110] "Emotional information" refers to data that indicates the user's current emotional state, determined based on voice and facial expression data.

[1111] "Means for adjusting the content of the evacuation guide" refers to a function that appropriately modifies the instructions in the evacuation guide based on the user's emotional information.

[1112] This invention relates to an information system that provides evacuation guidance during disasters, and includes a function to recognize the user's emotions and dynamically adjust the content of the evacuation guidance based on those emotions. Users access this system using a smartphone app or a dedicated terminal.

[1113] First, users enter pre-registration information such as their address, mobility (e.g., able to walk, wheelchair user), and evacuation destination information via a smartphone app or dedicated terminal. The terminal sends this information to a server, which then stores it in a database to create a detailed profile for each user.

[1114] In the event of a disaster, the user's pre-registered information and latest location data are retrieved from the server. The server collects hazard maps and the latest disaster information, and integrates this information using a generation AI (e.g., GPT-4). As a result, individual evacuation guides are generated.

[1115] Furthermore, this system includes an "emotion engine" that analyzes voice and facial expression data to recognize the user's current emotional state. The user uses an emotion recognition sensor, and the acquired data is sent to the server via the terminal. The server uses the emotion engine to analyze this data, and a generating AI adjusts the evacuation guide to suit the user's emotions.

[1116] Specifically, if a user is feeling highly anxious, the evacuation guide will include specific and reassuring instructions (for example, "Don't worry. The evacuation shelter is very close. Follow this path."). Conversely, if the user is calm, concise and prompt evacuation instructions will be provided.

[1117] Furthermore, when an emergency alert (area mail) is sent, the server receives this information and identifies users in the affected area. The server sends an automatic application launch command to the identified user's device, and the device receives this command and automatically launches the app. In this case, the server uses a generation AI to generate individual evacuation guides based on the user's latest location information and pre-registered information. The evacuation guides are dynamically adjusted based on the user's emotional information.

[1118] As a concrete example, suppose User B lives at "1-1-1 Umeda, Kita-ku, Osaka City" and uses a wheelchair, so has pre-registered a nearby building as an evacuation site. When an earthquake occurs and the server receives an area email and determines that Kita-ku is affected, the server immediately sends a command to User B's device to automatically activate the app. The device automatically activates the app, and the server analyzes User B's emotional state through the emotion engine. The generating AI then generates an evacuation guide such as, "Please stay calm. Please evacuate to a nearby building. This route is safe," and sends it to the device. The device reads the evacuation guide aloud and displays the evacuation route on the screen. User B then quickly begins evacuation according to this guide.

[1119] Examples of prompt statements are as follows:

[1120] "The user's address is 1-1-1 Umeda, Kita-ku, Osaka City, and they use a wheelchair. What kind of evacuation guide would be appropriate to alleviate the strong anxiety the user feels in the event of an earthquake?"

[1121] This makes it possible to provide quick and appropriate evacuation guidance that takes into account the user's emotional state. This system is expected to be particularly effective for users with low IT literacy, such as the elderly and people with disabilities.

[1122] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1123] Step 1:

[1124] Enter and submit pre-registration information

[1125] User: Open the smartphone app or dedicated device.

[1126] Input: Address, mobility (e.g., able to walk, wheelchair user, etc.), evacuation destination information, etc.

[1127] Specific actions: The user opens the app and enters the address "1-1-1 Umeda, Kita-ku, Osaka City", mobility status "Wheelchair user", and evacuation destination information.

[1128] Terminal: Sends the entered information to the server.

[1129] Data processing: Format the entered information and prepare it for transmission.

[1130] Output: Formatted pre-registration information.

[1131] Step 2:

[1132] Pre-registration information storage

[1133] Server: Saves the received pre-registration information to the database.

[1134] Input: Formatted pre-registration information.

[1135] Data processing: Convert received data into a format suitable for storage in the appropriate table within the database.

[1136] Output: User profile stored in the database.

[1137] Specific operation: The server saves the address information "1-1-1 Umeda, Kita-ku, Osaka City" and mobility information "wheelchair user" to the database.

[1138] Step 3:

[1139] Information acquisition during a disaster

[1140] Server: Receives the latest disaster information when a disaster occurs.

[1141] Input: Hazard maps and the latest disaster information.

[1142] Data processing: Integrate disaster information and hazard maps to identify the scope of impact.

[1143] Output: Identification of the disaster-stricken area.

[1144] Specific action: The server receives an earthquake early warning and identifies "Kita Ward, Osaka City" as the affected area.

[1145] Step 4:

[1146] Retrieving user information

[1147] Server: Retrieves pre-registration information and the latest location information.

[1148] Input: User profile and location information from GPS.

[1149] Data processing: Combine profile and location information to list users within the affected area.

[1150] Output: List of affected users.

[1151] Step 5:

[1152] Generating an evacuation guide

[1153] Server: Automatically generates individual evacuation guides using a generation AI.

[1154] Input: User pre-registration information, location information, disaster information.

[1155] Data processing: Generative AI integrates this information to calculate the optimal evacuation route and evacuation location.

[1156] Output: Individual evacuation guide.

[1157] Specific action: The server generates an evacuation guide that says, "Please evacuate to a nearby building. This route is safe."

[1158] Step 6:

[1159] Execution of emotion recognition

[1160] User: Uses a sensor for emotion recognition.

[1161] Input: Voice and facial expression data.

[1162] Terminal: Sends voice and facial expression data to the server.

[1163] Data processing: Convert audio and facial expression data into an analysis format.

[1164] Output: Data for analysis.

[1165] Server: Uses an emotion engine to analyze data and determine the user's emotional state.

[1166] Input: Data for analysis.

[1167] Data processing: The emotion engine analyzes voice and facial expressions to identify the emotional state.

[1168] Output: Emotional information.

[1169] Specific operation: The server analyzes the user's voice data and determines that the user is feeling "anxious".

[1170] Step 7:

[1171] Adjustment of evacuation guides

[1172] Server: Adjusts the content of the evacuation guide generated by the AI ​​based on emotional information.

[1173] Input: Emotional information and initial evacuation guide.

[1174] Data processing: Modify the evacuation guide to be more specific and reassuring, taking emotional information into account.

[1175] Output: Adjusted evacuation guide.

[1176] Specific action: The server will change its instructions to a reassuring message such as, "Please stay calm. Please evacuate to a nearby building."

[1177] Step 8:

[1178] Provision of evacuation guides

[1179] Server: Sends the coordinated evacuation guide to the user's terminal.

[1180] Input: Adjusted evacuation guide.

[1181] Output: Data sent to the terminal.

[1182] Specific action: The server sends a coordinated evacuation guide to the terminal.

[1183] Terminal: Displays the evacuation guide to the user and reads it aloud.

[1184] Input: Evacuation guide data from the server.

[1185] Output: Visual and auditory instructions to the user.

[1186] Specific actions: The device will play a voice command saying, "Please proceed this way," and display a detailed evacuation route on the screen.

[1187] Step 9:

[1188] Start of evacuation

[1189] User: Follow the instructions on your device and begin evacuation immediately.

[1190] Input: Instructions from the evacuation guide.

[1191] Output: Safe evacuation actions.

[1192] Specific actions: The user follows the voice instructions on the device and navigates the displayed route to a safe evacuation location.

[1193] (Application Example 2)

[1194] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1195] In food delivery operations, a challenge is providing delivery guides that reduce the stress and anxiety experienced by delivery drivers during deliveries, enabling them to perform their duties efficiently and with peace of mind. Traditional systems fail to provide instructions that take into account the emotional state of delivery drivers, resulting in decreased delivery efficiency and increased mental burden on drivers.

[1196] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1197] In this invention, the server includes means for acquiring user pre-registration information and location information, means for acquiring disaster information and delivery information, means using a generative AI that analyzes the acquired information and emotional data to generate individual delivery guides, means using an emotion engine that analyzes the emotions of delivery personnel and dynamically adjusts the delivery guide based on the results, and means for providing delivery guides in the form of voice and images. This provides an optimal delivery guide according to the emotional state of the delivery personnel, enabling them to perform their duties efficiently and with peace of mind.

[1198] "Pre-registration information" refers to data such as basic personal information, delivery address information, and mobility information that users provide in advance.

[1199] "Location information" refers to data that indicates the current location of the user or delivery person.

[1200] "Disaster information" refers to the latest information regarding natural disasters and emergencies.

[1201] "Delivery information" refers to information regarding the delivery address, the contents of the delivery, and the delivery schedule.

[1202] "Emotional data" refers to data that indicates the emotional state of the delivery person, and is obtained by analyzing facial expressions, voice, heart rate, etc.

[1203] "Generative AI" is artificial intelligence that generates individual delivery guides based on acquired information and data.

[1204] The "emotion engine" is software that analyzes the emotional state of delivery drivers and dynamically adjusts the instructions provided based on the results.

[1205] A "delivery guide" is information provided to delivery personnel during delivery, including route directions and work instructions.

[1206] A "delivery simulation" is a process that virtually executes the delivery process and analyzes the results.

[1207] The system realizing this invention has the function of acquiring and analyzing user pre-registration information, location information, disaster information, delivery information, and emotion data, and generating and providing personalized delivery guides. The main components of the system include a server, terminal, emotion engine, and generative AI model.

[1208] System Configuration and Data Processing

[1209] 1. Obtaining pre-registration information and location information.

[1210] Users register their basic information (e.g., address, mobility, delivery destination information) using their smartphones or dedicated terminals. This information is sent to a server and stored in a database. Location information is collected in real time using the GPS module built into the smartphone.

[1211] 2. Obtaining disaster information and delivery information

[1212] The server obtains disaster and delivery information in real time from external data provision services. This allows it to identify emergencies that may affect deliveries.

[1213] 3. Acquisition and analysis of emotional data

[1214] Emotional data from delivery drivers is collected through their smartphones' cameras, microphones, and heart rate sensors. The emotion engine analyzes this data to determine the driver's current emotional state. For example, it analyzes facial expressions and voice using open-source emotion recognition libraries (using OpenCV and TensorFlow).

[1215] 4. Generation of delivery guides using AI

[1216] The server uses a generation AI (e.g., OpenAI's GPT-4) to integrate and analyze user pre-registration information, location information, disaster information, delivery information, and emotional data to generate personalized delivery guides. The guide content is dynamically adjusted according to the delivery person's emotional state and designed to provide users with a sense of security.

[1217] 5. Providing audio and visual guides.

[1218] The terminal provides the delivery driver with generated delivery guides via voice and images. This allows the driver to receive instructions visually and aurally, enabling them to deliver quickly and safely.

[1219] Specific example

[1220] For example, consider a situation where delivery driver A is handling multiple deliveries in bad weather and traffic congestion. The server, through its emotion engine, determines that delivery driver A is experiencing stress, and the generative AI generates a delivery guide like the following:

[1221] 1. Adjusting delivery routes

[1222] "Due to traffic congestion, we recommend this alternative route."

[1223] 2. Emotionally-based messages of support

[1224] "You look tired. Please hang in there a little longer. You've done a great job!"

[1225] 3. Simplification of instructions

[1226] If the delivery person is calm, they will provide simple instructions such as, "Your next delivery address is AA. Please follow the navigation."

[1227] Example of a prompt

[1228] Use the following prompts to input information into the invention's system and generate a response:

[1229] The user's latest emotional state data and location information have been retrieved. Based on the delivery destination list and current traffic conditions, calculate the optimal delivery route and generate the following instructions according to the user's emotional state: If the user is stressed, provide reassuring instructions; if calm, provide concise and quick instructions.

[1230] Emotional state: "High stress"

[1231] Current location: "City center"

[1232] Delivery destination list: ["Area A", "Area B"]

[1233] Traffic conditions: "Traffic congestion is occurring."

[1234] Instructions: (Generate appropriate instructions here)

[1235] This system allows delivery personnel to receive appropriate instructions and perform their duties efficiently and with peace of mind.

[1236] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1237] Step 1:

[1238] The terminal receives the user's pre-registered information (e.g., address, mobility, delivery destination information) through an input form and sends that information to the server.

[1239] Input: User's pre-registration information.

[1240] Output: User profile data stored on the server.

[1241] Operation: The user enters information using a smartphone app and sends it to the server. The server stores this information in a database.

[1242] Step 2:

[1243] The device uses a GPS module to collect real-time location information and transmit it to the server.

[1244] Input: The device's current location (GPS data).

[1245] Output: Real-time location information updated to the server.

[1246] Operation: The smartphone's GPS module acquires location information and sends it to the server. The server associates this information with the user profile in its database.

[1247] Step 3:

[1248] The server obtains disaster information and delivery information from external data provision services.

[1249] Input: Disaster information and delivery information.

[1250] Output: The latest disaster information and delivery information stored on the server.

[1251] Operation: The server sends requests to external data providers via an API to retrieve the latest disaster and delivery information. The retrieved information is stored in a database.

[1252] Step 4:

[1253] The device uses a camera, microphone, and heart rate sensor to collect emotional data from delivery drivers and transmit it to a server.

[1254] Input: Delivery person's facial expression data (camera), voice data (microphone), heart rate data (heart rate sensor).

[1255] Output: Emotional data sent to the server.

[1256] Operation: The device uses its camera, microphone, and heart rate sensor to collect and analyze data from the delivery person, then sends it to the server. The server uses an emotion engine to analyze this data.

[1257] Step 5:

[1258] The server uses a generation AI to integrate user pre-registration information, location information, disaster information, delivery information, and sentiment data to generate personalized delivery guides.

[1259] Inputs: User pre-registration information, location information, disaster information, delivery information, sentiment data.

[1260] Output: Individual delivery guides generated.

[1261] Operation: The server uses a generation AI (such as OpenAI's GPT-4) to analyze input data and generate a delivery guide. The generated guide is dynamically adjusted according to the delivery person's emotional state.

[1262] Step 6:

[1263] The terminal provides the generated delivery guide to the delivery person via voice and images.

[1264] Input: Generated individual delivery guide.

[1265] Output: Provision of delivery guides via audio and images.

[1266] Operation: The terminal provides delivery drivers with delivery guides received from the server using text-to-speech and navigation functions. Route guidance is displayed on the terminal's screen, and instructions are read aloud.

[1267] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1268] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1269] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1270] [Fourth Embodiment]

[1271] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1272] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1273] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1274] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1275] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1276] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1277] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1278] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1279] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1280] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1281] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1282] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1283] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1284] This invention is an information system that provides disaster evacuation guides tailored to individual circumstances, and is used by users via a smartphone app or dedicated terminal. This system integrates the user's pre-registered information, location information, hazard maps, and disaster information, and provides personalized evacuation guides using a generating AI.

[1285] First, users enter pre-registration information such as their address, mobility status (e.g., able to walk, wheelchair user), and evacuation destination information through an app or dedicated device. The device sends this information to a server, which stores it in a database. This creates a detailed profile for each user.

[1286] If a disaster risk is suspected, the user can conduct an evacuation simulation. When a user requests an evacuation simulation, the terminal sends the request to the server. The server retrieves pre-registered information from the database along with the user's location information, and combines this with hazard map information. The generating AI uses this integrated data to perform an optimal evacuation simulation and sends the results to the terminal. The terminal provides the received results to the user in audio and image format.

[1287] Furthermore, when an emergency alert (area mail) is sent, the server receives this information and identifies users within the affected area. The server then sends an automatic application launch command to the identified users' devices. The devices receive this command and automatically launch the applications.

[1288] When the app is launched, the server uses a generation AI to create a personalized evacuation guide based on the user's latest location and pre-registered information. This guide includes details such as specific locations to evacuate to, evacuation routes, and timing of evacuation. The server sends the generated guide to the device, which then provides it to the user via audio and images.

[1289] For example, suppose User A lives at "1-1-1 Jingumae, Shibuya-ku, Tokyo" and has difficulty moving around, so has pre-registered a nearby park as an evacuation site. When a typhoon approaches, if the server receives an area email and determines that Shibuya-ku will be affected, the server immediately sends a command to User A's device to automatically activate the app. The device automatically activates the app, and the server generates and sends an evacuation guide (e.g., "Please evacuate to a higher place (nearby park) within 20 minutes"). The device reads the instructions aloud and displays the evacuation route on the screen. User A then quickly begins evacuation according to these instructions.

[1290] This system enables users to take swift and appropriate evacuation actions in the event of a disaster, and provides effective information, especially to users with low IT literacy, such as the elderly and people with disabilities.

[1291] The following describes the processing flow.

[1292] Step 1:

[1293] The user launches the app or dedicated device and enters pre-registered information such as their address, ease of movement, and evacuation destination information.

[1294] Step 2:

[1295] The device sends the pre-registration information entered to the server.

[1296] Step 3:

[1297] The server saves the pre-registration information it receives to the database.

[1298] Step 4:

[1299] The user requests an evacuation simulation, and the terminal sends that request to the server.

[1300] Step 5:

[1301] The server retrieves the user's location information and pre-registered information from the database, and integrates it with hazard map information.

[1302] Step 6:

[1303] The server uses AI generation to execute evacuation simulations and generate optimal evacuation simulation results.

[1304] Step 7:

[1305] The server sends the generated evacuation simulation results to the terminal.

[1306] Step 8:

[1307] The device provides the user with evacuation simulation results in audio and image format.

[1308] Step 9:

[1309] The server periodically monitors and receives emergency alerts (area mail).

[1310] Step 10:

[1311] The server analyzes the emergency alert and identifies the affected areas and users within those areas.

[1312] Step 11:

[1313] The server sends an automatic application launch command to user terminals within the affected area.

[1314] Step 12:

[1315] The device receives a command to automatically launch the app and starts the app automatically.

[1316] Step 13:

[1317] The server generates personalized evacuation guides using AI based on the user's latest location information and pre-registered information.

[1318] Step 14:

[1319] The server sends the generated evacuation guide to the terminal.

[1320] Step 15:

[1321] The device reads out evacuation instructions aloud and displays evacuation routes and locations on the screen.

[1322] Step 16:

[1323] The user quickly begins evacuation action by following the instructions on their device.

[1324] (Example 1)

[1325] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1326] Conventional evacuation guide systems have been limited to providing general information, making it difficult to offer specific evacuation guidance tailored to individual circumstances. Furthermore, users may not be able to take appropriate evacuation actions quickly during a disaster, posing a significant risk, especially for those with mobility difficulties such as the elderly and people with disabilities. Therefore, there was a need for a system that could consider each user's individual situation and provide optimal evacuation guidance in real time.

[1327] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1328] In this invention, the server includes means for acquiring user pre-registration information and location information, means for acquiring a map displaying disaster information and dangerous areas, means for automatically generating individual evacuation guides based on the acquired information using a generation AI, means for providing evacuation guides to users via voice and images, means for receiving emergency alerts and automatically launching the application, and means for executing an evacuation simulation for the user in the event of an anomaly and providing the results. This makes it possible to provide individually optimized evacuation guides to each user in real time.

[1329] "User pre-registration information" refers to personal information provided by the user in advance, such as address, mobility (e.g., able to walk, wheelchair user), and evacuation destination information.

[1330] "Location information" refers to information that indicates a user's geographical location, such as their current location or travel route.

[1331] "Disaster information" refers to information regarding the current situation and predictions of natural disasters such as typhoons, earthquakes, and floods.

[1332] A "map showing dangerous areas" is a map that visually indicates dangerous locations or areas with a high risk of disaster within a specific region.

[1333] "Generative AI" is an artificial intelligence model that analyzes diverse data and automatically generates optimal evacuation guides tailored to the individual circumstances of each user.

[1334] "Individualized evacuation guides" are pieces of information that provide specific evacuation instructions, such as where to evacuate, the route to take, and the timing of evacuation, based on each user's pre-registered information and location data.

[1335] "Means of providing information through audio and images" refers to means of communicating evacuation guides to users through audio messages, images, maps, and other visual information.

[1336] An "emergency alert" is a message used to quickly notify users in the affected area of ​​relevant information when a disaster occurs.

[1337] "Means for automatically launching applications" refers to a function that automatically starts a specific application on the user's device when an emergency alert is received.

[1338] "Means for performing evacuation simulations" refers to a function that simulates virtual evacuation actions based on the user's location information and pre-registered information during a disaster, and provides the results to the user.

[1339] This invention relates to an information system used by users via a smartphone app or dedicated terminal, which provides personalized evacuation guides during disasters. The system integrates the user's pre-registered information, location information, disaster information, and a map displaying dangerous areas, and generates evacuation guides using AI based on this information.

[1340] First, users enter pre-registration information, such as their address, mobility (e.g., walkable, wheelchair user), and evacuation destination information, using a smartphone app or a dedicated device. The device sends this information to a server, which stores it in a database. This process creates a detailed profile for each user.

[1341] If there is a risk of disaster, the user can request an evacuation simulation. When a user requests an evacuation simulation, the terminal sends the request to the server. The server retrieves pre-registered information from the database along with the user's location information, and combines this with disaster information and map information showing dangerous areas. The generating AI uses this integrated data to run an optimal evacuation simulation and sends the results to the terminal. The terminal provides the received results to the user in audio and image format.

[1342] For example, suppose User A lives at "1-1-1, a certain town in a certain ward of a certain city" and has pre-registered a nearby park as an evacuation site due to difficulty moving around. When a typhoon approaches, the server receives an area email and determines that the certain ward will be affected. The server immediately sends a command to User A's device to automatically activate the app. The device automatically activates the app, and the server generates and sends an evacuation guide (e.g., "Evacuate to a higher place (the nearby park) within 20 minutes"). The device reads the instructions aloud and displays the evacuation route on the screen. User A then quickly begins evacuation according to these instructions.

[1343] In addition, during emergencies, the server receives emergency alerts and identifies users in the affected area. The server sends an automatic app launch command to the identified user's device, and the device automatically launches the app upon receiving this command. Once the app is launched, the server uses AI generation based on the latest location information and pre-registered information to generate a personalized evacuation guide. This evacuation guide includes details such as specific places to evacuate, evacuation routes, and timing of evacuation. The server sends the generated guide to the device, which then provides it to the user via voice and images.

[1344] In this way, this system supports users in taking swift and appropriate evacuation actions during disasters, and provides effective information, especially to users with low IT literacy, such as the elderly and people with disabilities.

[1345] Examples of prompts for a generative AI model:

[1346] Based on the user's location and pre-registered information, propose the optimal evacuation route. Considering disaster information and maps showing hazardous areas, generate a detailed guide including evacuation locations, routes, and timing. Specific address: "1-1-1, [Town Name], [District Name], [City Name]", mobility: "Wheelchair accessible", evacuation destination: "Nearby park". Assume a disaster such as a typhoon.

[1347] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1348] Step 1:

[1349] Entering user pre-registration information

[1350] Users launch a smartphone app or dedicated device and enter their address, mobility status (e.g., able to walk, wheelchair user), and evacuation destination information. The entered data includes specific address, mode of transportation, and evacuation location.

[1351] Input: Address (e.g., "1-1-1, [town name], [district name], [city name]"), Accessibility (e.g., "Wheelchair accessible"), Evacuation destination (e.g., "Nearby park")

[1352] Output: Pre-registration information

[1353] Step 2:

[1354] Submitting and saving pre-registration information

[1355] The terminal sends the pre-registration information entered by the user to the server. The transmitted data is formed as a user profile.

[1356] The server stores the received information in a database and creates a detailed profile for each user. Various input data is stored in the profile.

[1357] Input: Pre-registration information

[1358] Output: Saved user profile

[1359] Step 3:

[1360] Request for evacuation simulation

[1361] In the event of a disaster risk, users can request an evacuation simulation via the app or a dedicated device. The request data includes their current location information.

[1362] The device sends this request to the server.

[1363] Input: Request, current location

[1364] Output: Evacuation Simulation Request

[1365] Step 4:

[1366] Execution of evacuation simulation

[1367] The server retrieves the user's current location and pre-registration information from the database. The retrieved data includes location information and profile information.

[1368] The server combines this with information from maps displaying disaster information and hazardous locations.

[1369] The generated AI model uses this integrated data to perform an optimal evacuation simulation. The generated evacuation simulation results include evacuation routes, destinations, and evacuation times.

[1370] Input: Current location information, pre-registration information, disaster information, hazardous area information

[1371] Output: Evacuation simulation results

[1372] Step 5:

[1373] Providing evacuation simulation results

[1374] The server sends the results of the evacuation simulation to the terminal. The transmitted data includes an evacuation guide.

[1375] The device provides the user with the received results in the form of audio and images. Specific actions include playing audio guides and displaying maps.

[1376] Input: Evacuation simulation results

[1377] Output: Audio guide, image display

[1378] Step 6:

[1379] Emergency alerts and automatic app launch

[1380] The server receives emergency alerts and analyzes their contents. The analysis data includes the type of disaster and the extent of its impact.

[1381] The server identifies users within the affected region, and this identification data includes a list of users.

[1382] The server sends an automatic application launch command to the specified user's device.

[1383] The device receives this command and automatically launches the app.

[1384] Input: Emergency alert, information on affected areas

[1385] Output: Auto-start command, launching the application

[1386] Step 7:

[1387] Generation and provision of individual evacuation guides

[1388] When the app is launched, the server uses a generation AI to create a personalized evacuation guide based on the user's latest location information and pre-registered information. The generated evacuation guide includes detailed evacuation instructions.

[1389] The server sends the generated evacuation guide to the terminal.

[1390] The device provides users with information via voice and images. Specifically, it provides navigation for evacuation routes and information about evacuation locations.

[1391] Input: Latest location information, pre-registration information

[1392] Output: Individual evacuation guide, audio guide, image display

[1393] (Application Example 1)

[1394] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1395] In modern society, a lack of adequate guidance for the rapid and safe evacuation of many people in the event of a disaster within a physical store is a significant problem. In particular, people with mobility difficulties (e.g., the elderly and wheelchair users) often struggle to find appropriate evacuation routes. Therefore, a comprehensive system is needed to support rapid and safe evacuation within physical stores.

[1396] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1397] In this invention, the server includes means for acquiring user pre-registration information and location information, means for acquiring hazard maps and disaster information, means for automatically generating individual evacuation guides based on the acquired information using a generation AI, means for providing evacuation guides to users via voice and images, means for receiving emergency alerts and automatically launching the application, and means for generating the optimal evacuation route based on pre-registration information and real-time location information and providing it via push notification. This enables quick and safe evacuation actions within physical stores, thereby protecting the safety of many people.

[1398] "User pre-registration information" refers to personal information such as the user's address, ease of movement, and evacuation destination, which the user enters and registers in advance through the application or a dedicated terminal.

[1399] "Location information" refers to information that indicates the user's current location, and is data mainly obtained using location measurement technologies such as GPS.

[1400] A "hazard map" is a map that visually displays the disaster risk in a specific area, indicating the likelihood of a disaster occurring and the extent of its impact.

[1401] "Disaster information" refers to real-time information about disasters such as earthquakes, typhoons, and floods, and is obtained from public institutions and meteorological data.

[1402] "Generative AI" refers to a system that utilizes artificial intelligence technology to analyze various input data and generate optimal output tailored to a specific purpose.

[1403] An "evacuation guide" is information that includes specific instructions, routes, and timing for users to evacuate safely.

[1404] "Provision via audio and images" refers to a method of presenting the generated evacuation guide to the user both aurally and visually.

[1405] An "emergency alert" is a notification designed to quickly and widely disseminate important information related to emergencies such as disasters.

[1406] "Automatic application startup" refers to a function that automatically launches relevant applications without user intervention when an emergency alert is received.

[1407] "Real-time location information" refers to information that acquires and updates the current location of the user in real time.

[1408] A "push notification" is an automated notification message that informs users that there is new information on their smartphone or tablet.

[1409] This invention relates to a system that provides optimal evacuation guidance to customers and store employees in the event of a disaster at a physical store. Based on the user's pre-registered information and real-time location information, an AI model is used to automatically generate individual evacuation guidance, which is then provided in both audio and image formats. The following describes a detailed embodiment of the system.

[1410] System Configuration

[1411] 1. Obtain user pre-registration information

[1412] Users enter and register personal data in advance via their smartphones or dedicated terminals, such as their address, mobility (e.g., able to walk, wheelchair user), and evacuation destination information. This creates a detailed profile for each user in the database.

[1413] 2. Acquisition of location information

[1414] The user's smartphone uses GPS to obtain location information in real time and transmit it to the server. Specifically, the smartphone app periodically transmits location information.

[1415] 3. Obtaining disaster information

[1416] The server regularly acquires and updates hazard maps and disaster information. This information is obtained from public institutions and meteorological data.

[1417] 4. Generation of evacuation guides using a generative AI model

[1418] The server integrates acquired user pre-registration information, real-time location information, hazard maps, and the latest disaster information, and generates individual evacuation guides using a generation AI model. The generated evacuation guides include details such as evacuation routes, specific places to evacuate to, and timing of evacuation.

[1419] 5. Provision of evacuation guides

[1420] The server sends the generated evacuation guide to the user's smartphone via push notification. The receiving device then provides the evacuation guide to the user through audio and images, encouraging prompt evacuation action.

[1421] 6. Response to Emergency Alerts

[1422] When an emergency alert is issued, the server receives this information and identifies users in the affected area. The server then sends an automatic application launch command to the identified users' smartphones. Once the application is launched, it generates and provides an evacuation guide again based on the user's latest location information and pre-registered information.

[1423] Hardware and software to be used

[1424] Server: Database management system (e.g., MySQL), Generative AI model (e.g., TensorFlow)

[1425] Client device: Smartphone application (e.g., React Native)

[1426] Communication: Internet connection, GPS

[1427] Specific example

[1428] For example, if an earthquake occurs in a shopping mall, this system will immediately acquire the location information of customers and provide the optimal evacuation route. For instance, it might issue specific instructions such as, "Please evacuate to a higher place (the second floor of the shopping mall) within 30 minutes."

[1429] Example of a prompt:

[1430] "Please tell me the best evacuation route from shopping mall A in the event of an earthquake."

[1431] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1432] Step 1:

[1433] Users input and register their address, mobility status (e.g., able to walk, wheelchair user), and evacuation destination information via their smartphone or a dedicated terminal. The terminal transmits this pre-registered information to a server and stores it in a database.

[1434] Input: User's address, ease of movement, evacuation destination information

[1435] Output: Information is stored in the user profile database on the server.

[1436] Step 2:

[1437] The device periodically uses GPS to obtain the user's real-time location information. The obtained location information is sent to the server and stored as the user's most recent location.

[1438] Input: User's current location information

[1439] Output: Latest user location information stored on the server

[1440] Step 3:

[1441] The server periodically retrieves hazard maps and the latest disaster information. Disaster information is collected using public institutions and weather data sources and stored in a database.

[1442] Input: Hazard map, disaster information

[1443] Output: Latest hazard maps and disaster information stored in the database

[1444] Step 4:

[1445] The user requests an evacuation simulation through the application. The device sends this request to the server. The server retrieves pre-registration information, real-time location information, the latest hazard maps, and disaster information from the database, and generates an individualized evacuation guide using a generation AI model. The generated evacuation guide is sent to the device.

[1446] Input: User evacuation simulation request, pre-registration information, location information, hazard map, disaster information

[1447] Output: Generated evacuation guide

[1448] Step 5:

[1449] The server sends an evacuation guide to the terminal. The terminal provides the received evacuation guide to the user in both audio and image formats.

[1450] Input: Evacuation guide sent from the server

[1451] Output: Audio and visual evacuation guide provided to the user.

[1452] Step 6:

[1453] The server receives an emergency alert. It identifies users in the affected area and sends an application auto-start command to the terminals of the relevant users.

[1454] Input: Breaking News

[1455] Output: Sending of automatic startup command

[1456] Step 7:

[1457] The application starts automatically. The server generates an evacuation guide using a regenerated AI model based on the latest location information and pre-registered information, and sends it to the device.

[1458] Input: Latest location information, pre-registration information

[1459] Output: Evacuation guide that is generated and sent

[1460] Step 8:

[1461] The device provides the user with received evacuation guides in audio and image formats, encouraging quick and appropriate evacuation actions.

[1462] Input: Evacuation guide sent from the server

[1463] Output: Audio and visual evacuation guide provided to the user.

[1464] Specific example

[1465] For example, if an earthquake occurs in a shopping mall, this system will immediately acquire the location information of the relevant users, generate an evacuation guide such as "Please head towards the nearest emergency exit," and send it via push notification.

[1466] Example of a prompt:

[1467] "Please tell me the best evacuation route from shopping mall A in the event of an earthquake."

[1468] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1469] This invention relates to an information system that provides evacuation guides during disasters, which includes a function to recognize the user's emotions and dynamically adjust the content of the evacuation guide based on those emotions. The system is used by the user via a smartphone app or a dedicated terminal.

[1470] First, users enter pre-registration information such as their address, mobility status (e.g., able to walk, wheelchair user), and evacuation destination information through an app or dedicated device. The device sends this information to a server, which stores it in a database. This creates a detailed profile for each user.

[1471] In the event of a disaster, the user's pre-registered information and latest location data are retrieved from the server. In addition, the server collects hazard maps and disaster information, and integrates this information using a generation AI. As a result, an individualized evacuation guide is generated.

[1472] A new element added to this system is the "emotion engine." The emotion engine analyzes the user's voice and facial expressions to recognize their current emotional state. The user sends data acquired by emotion recognition sensors to the terminal, which analyzes this data and sends the emotional information to the server. The server analyzes this information through the emotion engine, and the generating AI adjusts the evacuation guide to suit the user's emotions.

[1473] For example, if a user is feeling highly anxious, the evacuation guide will include specific and reassuring instructions, such as, "Don't worry. The evacuation shelter is very close. Please follow this path." Conversely, if the user is calm, concise and prompt evacuation instructions will be provided.

[1474] When an emergency alert (area mail) is sent, the server receives this information and identifies users in the affected area. The server then sends a command to automatically launch an application to the identified user's device. The device receives this command and automatically launches the application.

[1475] When the app is launched, the server uses a generative AI to generate a personalized evacuation guide based on the user's latest location and pre-registered information. This guide includes details such as specific places to evacuate, evacuation routes, and timing. Furthermore, the evacuation guide is dynamically adjusted based on the user's emotional state. For example, if the app detects that the user is in a "high-stress state," the evacuation instructions will become more detailed and reassuring.

[1476] As a concrete example, suppose User B lives at "1-1-1 Umeda, Kita-ku, Osaka City" and has difficulty moving around, so has pre-registered a nearby building as an evacuation site. When an earthquake occurs and the server receives an area email and determines that Kita-ku is affected, the server immediately sends a command to User B's device to automatically activate the app. The device automatically activates the app, the server analyzes User B's emotional state through the emotion engine, and the generating AI generates an evacuation guide (e.g., "Please stay calm. Please evacuate to a nearby building. This route is safe.") and sends it to the device. The device reads the evacuation guide aloud and displays the evacuation route on the screen. User B then quickly begins evacuation according to this.

[1477] This system takes into account the emotional state of users during a disaster, enabling swift and appropriate evacuation actions. It also effectively provides information to users with low IT literacy, particularly the elderly and people with disabilities.

[1478] The following describes the processing flow.

[1479] Step 1:

[1480] The user launches the app or dedicated device and enters pre-registered information such as their address, ease of movement, and evacuation destination information.

[1481] Step 2:

[1482] The device sends the pre-registration information entered to the server.

[1483] Step 3:

[1484] The server saves the pre-registration information it receives to the database.

[1485] Step 4:

[1486] The user uses emotion recognition sensors (e.g., wearable devices or cameras) to acquire emotional data.

[1487] Step 5:

[1488] The device sends the emotional data it has acquired to the server.

[1489] Step 6:

[1490] The server analyzes the received emotional data using an emotion engine to identify the user's current emotional state.

[1491] Step 7:

[1492] A disaster occurs, and the server receives an emergency alert (area mail).

[1493] Step 8:

[1494] The server analyzes the emergency alert and identifies the affected areas and users within those areas.

[1495] Step 9:

[1496] The server sends an automatic application launch command to user terminals within the affected area.

[1497] Step 10:

[1498] The device receives a command to automatically launch the app and starts the app automatically.

[1499] Step 11:

[1500] The server retrieves and integrates hazard maps and disaster information based on the user's latest location information and pre-registered information.

[1501] Step 12:

[1502] The server automatically generates individual evacuation guides based on information acquired using AI.

[1503] Step 13:

[1504] The server adjusts the content of the evacuation guide it generates, taking into account the user's emotional state. For example, if the user is feeling anxious, the guide will be modified to provide more reassurance.

[1505] Step 14:

[1506] The server sends the coordinated evacuation guide to the terminal.

[1507] Step 15:

[1508] The device reads aloud the evacuation guide it receives and displays evacuation routes and locations on the screen.

[1509] Step 16:

[1510] The user quickly begins evacuation action by following the instructions on their device.

[1511] As a concrete example, suppose User B lives at "1-1-1 Umeda, Kita-ku, Osaka City" and has difficulty moving around, so has pre-registered a nearby building as an evacuation site. When an earthquake occurs and the server receives an area email and determines that Kita-ku is affected, the server immediately sends a command to User B's device to automatically activate the app. The device automatically activates the app, the server analyzes User B's emotional state through the emotion engine, and the generating AI generates an evacuation guide (e.g., "Please stay calm. Please evacuate to a nearby building. This route is safe.") and sends it to the device. The device reads the evacuation guide aloud and displays the evacuation route on the screen. User B then quickly begins evacuation according to this.

[1512] (Example 2)

[1513] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1514] Conventional disaster evacuation guidance systems could only provide uniform evacuation instructions without considering the user's current emotional state. Therefore, it was difficult to provide appropriate evacuation guidance, especially for users with low IT literacy, such as the elderly and people with disabilities. Furthermore, a means of automatically launching the application was needed to encourage rapid evacuation action during a disaster.

[1515] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1516] In this invention, the server includes means for acquiring user pre-registration information and location information, means for acquiring hazard maps and disaster information, means for automatically generating individual evacuation guides based on the acquired information using a generation AI, means for providing evacuation guides to users via voice and images, means for analyzing voice and facial expression data to recognize the user's emotional state, and means for adjusting the content of the evacuation guides based on emotional information. This makes it possible to provide a rapid and appropriate evacuation guide that takes into account the user's emotional state in the event of a disaster.

[1517] "User pre-registration information" refers to detailed personal information such as address, ease of movement, and evacuation destination information that users of the evacuation guide system have registered in advance.

[1518] "Location information" refers to data that indicates the user's current geographical location, and is obtained using the GPS function of a smartphone, etc.

[1519] A "hazard map" is information that shows areas at risk of disaster and the extent of their impact on a map.

[1520] "Disaster information" refers to the latest information on disasters such as earthquakes, floods, and fires, and is data obtained from the Japan Meteorological Agency and disaster prevention organizations.

[1521] "Generative AI" refers to a system that uses artificial intelligence technology to automatically generate information such as evacuation guides.

[1522] An "individualized evacuation guide" is a user-specific evacuation instruction generated based on the user's pre-registered information and location data.

[1523] "Means of providing evacuation guides to users through audio and images" refers to methods for communicating generated evacuation guides to users using audio and images.

[1524] "Emotional state" refers to the psychological state that the user is currently experiencing (e.g., anxiety, fear, calmness, etc.).

[1525] "Means for analyzing voice and facial expression data" refers to a technology that analyzes voice and facial expression data obtained from a user to determine their emotional state.

[1526] "Emotional information" refers to data that indicates the user's current emotional state, determined based on voice and facial expression data.

[1527] "Means for adjusting the content of the evacuation guide" refers to a function that appropriately modifies the instructions in the evacuation guide based on the user's emotional information.

[1528] This invention relates to an information system that provides evacuation guidance during disasters, and includes a function to recognize the user's emotions and dynamically adjust the content of the evacuation guidance based on those emotions. Users access this system using a smartphone app or a dedicated terminal.

[1529] First, users enter pre-registration information such as their address, mobility (e.g., able to walk, wheelchair user), and evacuation destination information via a smartphone app or dedicated terminal. The terminal sends this information to a server, which then stores it in a database to create a detailed profile for each user.

[1530] In the event of a disaster, the user's pre-registered information and latest location data are retrieved from the server. The server collects hazard maps and the latest disaster information, and integrates this information using a generation AI (e.g., GPT-4). As a result, individual evacuation guides are generated.

[1531] Furthermore, this system includes an "emotion engine" that analyzes voice and facial expression data to recognize the user's current emotional state. The user uses an emotion recognition sensor, and the acquired data is sent to the server via the terminal. The server uses the emotion engine to analyze this data, and a generating AI adjusts the evacuation guide to suit the user's emotions.

[1532] Specifically, if a user is feeling highly anxious, the evacuation guide will include specific and reassuring instructions (for example, "Don't worry. The evacuation shelter is very close. Follow this path."). Conversely, if the user is calm, concise and prompt evacuation instructions will be provided.

[1533] Furthermore, when an emergency alert (area mail) is sent, the server receives this information and identifies users in the affected area. The server sends an automatic application launch command to the identified user's device, and the device receives this command and automatically launches the app. In this case, the server uses a generation AI to generate individual evacuation guides based on the user's latest location information and pre-registered information. The evacuation guides are dynamically adjusted based on the user's emotional information.

[1534] As a concrete example, suppose User B lives at "1-1-1 Umeda, Kita-ku, Osaka City" and uses a wheelchair, so has pre-registered a nearby building as an evacuation site. When an earthquake occurs and the server receives an area email and determines that Kita-ku is affected, the server immediately sends a command to User B's device to automatically activate the app. The device automatically activates the app, and the server analyzes User B's emotional state through the emotion engine. The generating AI then generates an evacuation guide such as, "Please stay calm. Please evacuate to a nearby building. This route is safe," and sends it to the device. The device reads the evacuation guide aloud and displays the evacuation route on the screen. User B then quickly begins evacuation according to this guide.

[1535] Examples of prompt statements are as follows:

[1536] "The user's address is 1-1-1 Umeda, Kita-ku, Osaka City, and they use a wheelchair. What kind of evacuation guide would be appropriate to alleviate the strong anxiety the user feels in the event of an earthquake?"

[1537] This makes it possible to provide quick and appropriate evacuation guidance that takes into account the user's emotional state. This system is expected to be particularly effective for users with low IT literacy, such as the elderly and people with disabilities.

[1538] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1539] Step 1:

[1540] Enter and submit pre-registration information

[1541] User: Open the smartphone app or dedicated device.

[1542] Input: Address, mobility (e.g., able to walk, wheelchair user, etc.), evacuation destination information, etc.

[1543] Specific actions: The user opens the app and enters the address "1-1-1 Umeda, Kita-ku, Osaka City", mobility status "Wheelchair user", and evacuation destination information.

[1544] Terminal: Sends the entered information to the server.

[1545] Data processing: Format the entered information and prepare it for transmission.

[1546] Output: Formatted pre-registration information.

[1547] Step 2:

[1548] Pre-registration information storage

[1549] Server: Saves the received pre-registration information to the database.

[1550] Input: Formatted pre-registration information.

[1551] Data processing: Convert received data into a format suitable for storage in the appropriate table within the database.

[1552] Output: User profile stored in the database.

[1553] Specific operation: The server saves the address information "1-1-1 Umeda, Kita-ku, Osaka City" and mobility information "wheelchair user" to the database.

[1554] Step 3:

[1555] Information acquisition during a disaster

[1556] Server: Receives the latest disaster information when a disaster occurs.

[1557] Input: Hazard maps and the latest disaster information.

[1558] Data processing: Integrate disaster information and hazard maps to identify the scope of impact.

[1559] Output: Identification of the disaster-stricken area.

[1560] Specific action: The server receives an earthquake early warning and identifies "Kita Ward, Osaka City" as the affected area.

[1561] Step 4:

[1562] Retrieving user information

[1563] Server: Retrieves pre-registration information and the latest location information.

[1564] Input: User profile and location information from GPS.

[1565] Data processing: Combine profile and location information to list users within the affected area.

[1566] Output: List of affected users.

[1567] Step 5:

[1568] Generating an evacuation guide

[1569] Server: Automatically generates individual evacuation guides using a generation AI.

[1570] Input: User pre-registration information, location information, disaster information.

[1571] Data processing: Generative AI integrates this information to calculate the optimal evacuation route and evacuation location.

[1572] Output: Individual evacuation guide.

[1573] Specific action: The server generates an evacuation guide that says, "Please evacuate to a nearby building. This route is safe."

[1574] Step 6:

[1575] Execution of emotion recognition

[1576] User: Uses a sensor for emotion recognition.

[1577] Input: Voice and facial expression data.

[1578] Terminal: Sends voice and facial expression data to the server.

[1579] Data processing: Convert audio and facial expression data into an analysis format.

[1580] Output: Data for analysis.

[1581] Server: Uses an emotion engine to analyze data and determine the user's emotional state.

[1582] Input: Data for analysis.

[1583] Data processing: The emotion engine analyzes voice and facial expressions to identify the emotional state.

[1584] Output: Emotional information.

[1585] Specific operation: The server analyzes the user's voice data and determines that the user is feeling "anxious".

[1586] Step 7:

[1587] Adjustment of evacuation guides

[1588] Server: Adjusts the content of the evacuation guide generated by the AI ​​based on emotional information.

[1589] Input: Emotional information and initial evacuation guide.

[1590] Data processing: Modify the evacuation guide to be more specific and reassuring, taking emotional information into account.

[1591] Output: Adjusted evacuation guide.

[1592] Specific action: The server will change its instructions to a reassuring message such as, "Please stay calm. Please evacuate to a nearby building."

[1593] Step 8:

[1594] Provision of evacuation guides

[1595] Server: Sends the coordinated evacuation guide to the user's terminal.

[1596] Input: Adjusted evacuation guide.

[1597] Output: Data sent to the terminal.

[1598] Specific action: The server sends a coordinated evacuation guide to the terminal.

[1599] Terminal: Displays the evacuation guide to the user and reads it aloud.

[1600] Input: Evacuation guide data from the server.

[1601] Output: Visual and auditory instructions to the user.

[1602] Specific actions: The device will play a voice command saying, "Please proceed this way," and display a detailed evacuation route on the screen.

[1603] Step 9:

[1604] Start of evacuation

[1605] User: Follow the instructions on your device and begin evacuation immediately.

[1606] Input: Instructions from the evacuation guide.

[1607] Output: Safe evacuation actions.

[1608] Specific actions: The user follows the voice instructions on the device and navigates the displayed route to a safe evacuation location.

[1609] (Application Example 2)

[1610] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1611] In food delivery operations, a challenge is providing delivery guides that reduce the stress and anxiety experienced by delivery drivers during deliveries, enabling them to perform their duties efficiently and with peace of mind. Traditional systems fail to provide instructions that take into account the emotional state of delivery drivers, resulting in decreased delivery efficiency and increased mental burden on drivers.

[1612] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1613] In this invention, the server includes means for acquiring user pre-registration information and location information, means for acquiring disaster information and delivery information, means using a generative AI that analyzes the acquired information and emotional data to generate individual delivery guides, means using an emotion engine that analyzes the emotions of delivery personnel and dynamically adjusts the delivery guide based on the results, and means for providing delivery guides in the form of voice and images. This provides an optimal delivery guide according to the emotional state of the delivery personnel, enabling them to perform their duties efficiently and with peace of mind.

[1614] "Pre-registration information" refers to data such as basic personal information, delivery address information, and mobility information that users provide in advance.

[1615] "Location information" refers to data that indicates the current location of the user or delivery person.

[1616] "Disaster information" refers to the latest information regarding natural disasters and emergencies.

[1617] "Delivery information" refers to information regarding the delivery address, the contents of the delivery, and the delivery schedule.

[1618] "Emotional data" refers to data that indicates the emotional state of the delivery person, and is obtained by analyzing facial expressions, voice, heart rate, etc.

[1619] "Generative AI" is artificial intelligence that generates individual delivery guides based on acquired information and data.

[1620] The "emotion engine" is software that analyzes the emotional state of delivery drivers and dynamically adjusts the instructions provided based on the results.

[1621] A "delivery guide" is information provided to delivery personnel during delivery, including route directions and work instructions.

[1622] A "delivery simulation" is a process that virtually executes the delivery process and analyzes the results.

[1623] The system realizing this invention has the function of acquiring and analyzing user pre-registration information, location information, disaster information, delivery information, and emotion data, and generating and providing personalized delivery guides. The main components of the system include a server, terminal, emotion engine, and generative AI model.

[1624] System Configuration and Data Processing

[1625] 1. Obtaining pre-registration information and location information.

[1626] Users register their basic information (e.g., address, mobility, delivery destination information) using their smartphones or dedicated terminals. This information is sent to a server and stored in a database. Location information is collected in real time using the GPS module built into the smartphone.

[1627] 2. Obtaining disaster information and delivery information

[1628] The server obtains disaster and delivery information in real time from external data provision services. This allows it to identify emergencies that may affect deliveries.

[1629] 3. Acquisition and analysis of emotional data

[1630] Emotional data from delivery drivers is collected through their smartphones' cameras, microphones, and heart rate sensors. The emotion engine analyzes this data to determine the driver's current emotional state. For example, it analyzes facial expressions and voice using open-source emotion recognition libraries (using OpenCV and TensorFlow).

[1631] 4. Generation of delivery guides using AI

[1632] The server uses a generation AI (e.g., OpenAI's GPT-4) to integrate and analyze user pre-registration information, location information, disaster information, delivery information, and emotional data to generate personalized delivery guides. The guide content is dynamically adjusted according to the delivery person's emotional state and designed to provide users with a sense of security.

[1633] 5. Providing audio and visual guides.

[1634] The terminal provides the delivery driver with generated delivery guides via voice and images. This allows the driver to receive instructions visually and aurally, enabling them to deliver quickly and safely.

[1635] Specific example

[1636] For example, consider a situation where delivery driver A is handling multiple deliveries in bad weather and traffic congestion. The server, through its emotion engine, determines that delivery driver A is experiencing stress, and the generative AI generates a delivery guide like the following:

[1637] 1. Adjusting delivery routes

[1638] "Due to traffic congestion, we recommend this alternative route."

[1639] 2. Emotionally-based messages of support

[1640] "You look tired. Please hang in there a little longer. You've done a great job!"

[1641] 3. Simplification of instructions

[1642] If the delivery person is calm, they will provide simple instructions such as, "Your next delivery address is AA. Please follow the navigation."

[1643] Example of a prompt

[1644] Use the following prompts to input information into the invention's system and generate a response:

[1645] The user's latest emotional state data and location information have been retrieved. Based on the delivery destination list and current traffic conditions, calculate the optimal delivery route and generate the following instructions according to the user's emotional state: If the user is stressed, provide reassuring instructions; if calm, provide concise and quick instructions.

[1646] Emotional state: "High stress"

[1647] Current location: "City center"

[1648] Delivery destination list: ["Area A", "Area B"]

[1649] Traffic conditions: "Traffic congestion is occurring."

[1650] Instructions: (Generate appropriate instructions here)

[1651] This system allows delivery personnel to receive appropriate instructions and perform their duties efficiently and with peace of mind.

[1652] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1653] Step 1:

[1654] The terminal receives the user's pre-registered information (e.g., address, mobility, delivery destination information) through an input form and sends that information to the server.

[1655] Input: User's pre-registration information.

[1656] Output: User profile data stored on the server.

[1657] Operation: The user enters information using a smartphone app and sends it to the server. The server stores this information in a database.

[1658] Step 2:

[1659] The device uses a GPS module to collect real-time location information and transmit it to the server.

[1660] Input: The device's current location (GPS data).

[1661] Output: Real-time location information updated to the server.

[1662] Operation: The smartphone's GPS module acquires location information and sends it to the server. The server associates this information with the user profile in its database.

[1663] Step 3:

[1664] The server obtains disaster information and delivery information from external data provision services.

[1665] Input: Disaster information and delivery information.

[1666] Output: The latest disaster information and delivery information stored on the server.

[1667] Operation: The server sends requests to external data providers via an API to retrieve the latest disaster and delivery information. The retrieved information is stored in a database.

[1668] Step 4:

[1669] The device uses a camera, microphone, and heart rate sensor to collect emotional data from delivery drivers and transmit it to a server.

[1670] Input: Delivery person's facial expression data (camera), voice data (microphone), heart rate data (heart rate sensor).

[1671] Output: Emotional data sent to the server.

[1672] Operation: The device uses its camera, microphone, and heart rate sensor to collect and analyze data from the delivery person, then sends it to the server. The server uses an emotion engine to analyze this data.

[1673] Step 5:

[1674] The server uses a generation AI to integrate user pre-registration information, location information, disaster information, delivery information, and sentiment data to generate personalized delivery guides.

[1675] Inputs: User pre-registration information, location information, disaster information, delivery information, sentiment data.

[1676] Output: Individual delivery guides generated.

[1677] Operation: The server uses a generation AI (such as OpenAI's GPT-4) to analyze input data and generate a delivery guide. The generated guide is dynamically adjusted according to the delivery person's emotional state.

[1678] Step 6:

[1679] The terminal provides the generated delivery guide to the delivery person via voice and images.

[1680] Input: Generated individual delivery guide.

[1681] Output: Provision of delivery guides via audio and images.

[1682] Operation: The terminal provides delivery drivers with delivery guides received from the server using text-to-speech and navigation functions. Route guidance is displayed on the terminal's screen, and instructions are read aloud.

[1683] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1684] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1685] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1686] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1687] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1688] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1689] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1690] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1691] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1692] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1693] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1694] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1695] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1696] 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.

[1697] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1698] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1699] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1700] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1701] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1702] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1703] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[1704] The following is further disclosed regarding the embodiments described above.

[1705] (Claim 1)

[1706] A means of obtaining user pre-registration information and location information,

[1707] Means for obtaining hazard maps and disaster information,

[1708] A method for automatically generating individual evacuation guides based on acquired information using generation AI,

[1709] A means of providing evacuation guides to users through audio and images,

[1710] A system that includes this.

[1711] (Claim 2)

[1712] The system according to claim 1, further comprising means for performing a user evacuation simulation and providing the results thereof.

[1713] (Claim 3)

[1714] The system according to claim 1, further comprising means for receiving an emergency alert and automatically launching an application.

[1715] "Example 1"

[1716] (Claim 1)

[1717] A means of obtaining user pre-registration information and location information,

[1718] A means of obtaining a map that displays disaster information and dangerous locations,

[1719] A method for automatically generating individual evacuation guides based on acquired information using generation AI,

[1720] A means of providing evacuation guides to users through audio and images,

[1721] A means of receiving an emergency alert and automatically launching an application,

[1722] A means of running a user evacuation simulation in the event of an anomaly and providing the results,

[1723] A system that includes this.

[1724] (Claim 2)

[1725] The system according to claim 1, characterized in that it includes means for identifying the optimal evacuation route and evacuation location based on the evacuation simulation performed and notifying the user of these.

[1726] (Claim 3)

[1727] The system according to claim 1, characterized in that it includes means for providing an individualized evacuation guide in audio and image format, generated based on the user's latest location information and pre-registered information, when a disaster risk is detected.

[1728] "Application Example 1"

[1729] (Claim 1)

[1730] A means of obtaining user pre-registration information and location information,

[1731] Means for obtaining hazard maps and disaster information,

[1732] A method for automatically generating individual evacuation guides based on acquired information using generation AI,

[1733] A means of providing evacuation guides to users through audio and images,

[1734] A means of receiving an emergency alert and automatically launching an application,

[1735] A means of generating the optimal evacuation route based on pre-registered information and real-time location information and providing it via push notification,

[1736] A system that includes this.

[1737] (Claim 2)

[1738] The system according to claim 1, further comprising means for performing a user evacuation simulation and providing the results thereof.

[1739] (Claim 3)

[1740] The system according to claim 1, further comprising means for acquiring real-time location information and sending a request to a server when an emergency situation occurs, and means for generating an optimal evacuation guide using a generative AI model and providing it in voice and image.

[1741] "Example 2 of combining an emotion engine"

[1742] (Claim 1)

[1743] A means of obtaining user pre-registration information and location information,

[1744] Means for obtaining hazard maps and disaster information,

[1745] A method for automatically generating individual evacuation guides based on acquired information using generation AI,

[1746] A means of providing evacuation guides to users through audio and images,

[1747] A means for analyzing voice and facial expression data to recognize the user's emotional state,

[1748] A means of adjusting the content of evacuation guides based on emotional information,

[1749] A system that includes this.

[1750] (Claim 2)

[1751] The system according to claim 1, further comprising means for performing a user evacuation simulation and providing the results thereof.

[1752] (Claim 3)

[1753] The system according to claim 1, further comprising means for receiving an emergency alert and automatically launching an application.

[1754] "Application example 2 when combining with an emotional engine"

[1755] (Claim 1)

[1756] A means of obtaining user pre-registration information and location information,

[1757] Means for obtaining disaster information and delivery information,

[1758] A method using generative AI to generate individual delivery guides by analyzing acquired information and emotional data,

[1759] A method using an emotion engine that analyzes the emotions of delivery drivers and dynamically adjusts delivery guidelines based on the results,

[1760] Means of providing delivery guides via audio and images,

[1761] A system that includes this.

[1762] (Claim 2)

[1763] The system according to claim 1, further comprising means for performing a user delivery simulation and providing the results thereof.

[1764] (Claim 3)

[1765] The system according to claim 1, further comprising means for receiving an emergency alert and automatically launching an application. [Explanation of Symbols]

[1766] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of obtaining user pre-registration information and location information, Means for obtaining hazard maps and disaster information, A method for automatically generating individual evacuation guides based on acquired information using generation AI, A means of providing evacuation guides to users through audio and images, A system that includes this.

2. The system according to claim 1, further comprising means for performing a user evacuation simulation and providing the results thereof.

3. The system according to claim 1, further comprising means for receiving an emergency alert and automatically launching an application.

Images