System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving an emergency notification; means for acquiring location information; means for identifying a target area based on the received emergency notification and the acquired location information; means for collecting relevant information from a cloud; means for generating a prompt based on the collected information; means for generating an action guidance based on the generated prompt; and means for notifying a user of the generated action guidance.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Disaster prevention measures in disaster-prone countries still face many challenges, including a lack of information transmission speed and appropriate instructions for action. In particular, during large-scale disasters such as a major earthquake directly beneath the Tokyo metropolitan area or a Nankai Trough earthquake, confusion occurs due to transportation disruptions and crowded evacuation sites. It is crucial to ensure that people can act quickly and safely in such situations, and conventional methods are currently not sufficient to achieve this. [Means for solving the problem]

[0005] In order to solve the above problems, the present invention provides the following means. First, a means for receiving emergency notifications in the event of a disaster is provided. Next, a means for acquiring the user's current location using GPS, Wi-Fi, or cell tower information is provided. Furthermore, a means for identifying the target area based on the acquired location information and the received emergency notification is provided. A means for collecting related information such as traffic conditions and evacuation sites from the cloud is provided, and a prompt is generated based on the collected information. Finally, a means for generating specific action guidance using the generated prompt and notifying the user is provided. In this way, a system is provided that supports users in taking appropriate action quickly and safely in the event of a disaster.

[0006] "Emergency Notification" refers to breaking news and warning information sent in the event of a natural disaster or other emergency.

[0007] "Location Information" means data about your current location obtained using GPS, Wi-Fi, or cell tower information.

[0008] "Affected Area" refers to the affected area based on the emergency notification received and the location information obtained.

[0009] The "cloud" refers to external servers and data centers that store, manage, and process data over the Internet.

[0010] A "prompt" is a request in the form of an instruction or question input to a generative AI model, which is generated based on the information provided.

[0011] "Action guidance" refers to specific action instructions for the user that are generated based on the prompt. [Brief explanation of the drawings]

[0012] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0013] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0016] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0017] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0018] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0020] [First embodiment]

[0021] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0022] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0023] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0025] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0028] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0032] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0033] The present invention is a system aimed at ensuring the safety of users in emergency situations, and includes the following elements: A server, terminals, and users work together to support prompt and appropriate action in the event of a natural disaster or other emergency.

[0034] System configuration

[0035] server

[0036] The server receives and analyzes emergency notifications, including earthquake alerts, heavy rain and flood warnings, and other alerts. It analyzes the received notifications to identify the type of disaster that requires a response. It then receives location information from the user's device and identifies the affected area. It then uses the cloud to collect relevant information, such as traffic conditions, evacuation locations, and the extent of impact in the area, and uses this information to generate prompts using a generative AI model.

[0037] The prompts generated by the generative AI model are converted into specific action instructions, which are then sent to the device and notified to the user.

[0038] Terminal

[0039] The device has the ability to obtain the user's current location using GPS, Wi-Fi, or cell tower information. When a disaster notification is received, the device immediately obtains the device's location information and sends it to the server. The device also receives action instructions sent from the server and notifies the user of them. Notification methods include pop-up messages, audio alerts, and vibrations.

[0040] user

[0041] Users take appropriate action based on notifications from their devices. For example, in the event of an earthquake, users will take shelter under a desk until the shaking subsides, then head to a designated evacuation site. In the event of a flood, users will always move toward an evacuation site and choose a safe route. To confirm whether users have followed the instructions, devices continuously update their location information and connect with the server.

[0042] Program processing flow

[0043] Specific examples of earthquakes

[0044] For example, if an earthquake occurs, the system operates as follows: The server receives an earthquake early warning from the Japan Meteorological Agency and analyzes the information to identify the occurrence of the earthquake. Next, it obtains the user's current location information from the device and sends it to the server. The server uses this location information to determine whether the user is close to the epicenter. If the user is close to the epicenter, the server collects relevant information such as traffic conditions and evacuation locations from the cloud and inputs "safety instructions for users close to the epicenter" as a prompt into the generative AI model.

[0045] The generative AI model generates action guidance such as "Hold shelter under a desk until the shaking stops, then head to a designated evacuation site." This is received by the server and sent to the device. The device then displays this action guidance to the user as a pop-up message. The user then follows this to take safe action.

[0046] Specific examples of floods

[0047] The same applies when a flood warning is issued. The server receives and analyzes the heavy rain and flood warning from the Japan Meteorological Agency. The device then obtains the user's current location information and sends it to the server. The server uses the location information to determine whether the user is in an area affected by flooding. If it determines that the user is affected, the server collects information from the cloud, such as evacuation sites and road traffic conditions. Based on the generated prompt, specific action instructions are generated, such as "The evacuation site is XX Elementary School. Please evacuate by following the specified route." The action instructions are sent to the device, and the user is notified via a pop-up message or voice alert.

[0048] In this way, the system helps users take early and appropriate action in the event of a disaster. By working together, each element can provide quick and accurate evacuation instructions and ensure user safety.

[0049] The processing flow will be explained below.

[0050] Step 1:

[0051] Server: Receives emergency earthquake alerts, heavy rain and flood warnings, and other disaster information via API. Analyzes the various disaster information and identifies the type of disaster.

[0052] Step 2:

[0053] Device: When disaster information is received, the application will immediately launch and obtain the user's current location using GPS, Wi-Fi, or cell tower information.

[0054] Step 3:

[0055] Device: The acquired location information is sent to the server. The user's location information is encrypted and sent in a secure manner to protect privacy.

[0056] Step 4:

[0057] Server: Based on the received location information, the server determines whether the user is in an area affected by the disaster. This determination is based on the distance from the epicenter and the predicted inundation areas.

[0058] Step 5:

[0059] Server: If a user is determined to be in an affected area, relevant information such as transportation status, evacuation locations, and the extent of the impact on the area is collected from the cloud.

[0060] Step 6:

[0061] Server: Based on the collected information, it generates prompts to be passed to the generative AI model. Specifically, it creates prompts including the user's current location, the nearest evacuation site, and traffic conditions.

[0062] Step 7:

[0063] Server: Inputs prompts to the generative AI model and generates specific instructions for action, such as "An earthquake has occurred. Take shelter under a desk, and once the shaking has stopped, head to the designated evacuation site."

[0064] Step 8:

[0065] Server: Sends the generated action guide to the terminal. The action guide is formatted in a way that is intuitively understandable to the user.

[0066] Step 9:

[0067] Device: Notify the user of the action to be taken, using pop-up messages, audio alerts, vibrations, etc.

[0068] Step 10:

[0069] User: Receives notifications from the device and acts accordingly. For example, in the event of an earthquake, the user may take shelter under a desk and then head to a designated evacuation site. Location information during evacuation is periodically updated by the device and sent to the server.

[0070] Step 11:

[0071] Server: Receives the user's location information and checks whether the evacuation was successful. Provides continuous support until the evacuation is complete.

[0072] Example 1

[0073] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0074] The challenge is the lack of means to take prompt and appropriate action in the event of a natural disaster or other emergency. Conventional systems lack the ability to not only receive emergency notifications but also to immediately instruct users on what specific actions to take based on those notifications. This makes it difficult for users to take appropriate action when faced with a dangerous situation.

[0075] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0076] In this invention, the server includes means for receiving an emergency notification, means for acquiring location information, means for identifying a target area based on the received emergency notification and the acquired location information, means for collecting related information from the cloud, means for generating a prompt sentence based on the collected information, a generation AI model means for generating action guidance based on the generated prompt sentence, means for notifying the user of the generated action guidance, and means for monitoring the user's actions and updating the location information. This enables the user to take appropriate action immediately in the event of a disaster, and provides quick and accurate evacuation instructions.

[0077] "Emergency Notification" means alerts and bulletins that provide real-time information about natural disasters and other emergencies.

[0078] "Location information" refers to the geographic coordinates of a user's current location, and is data obtained using technologies such as GPS, Wi-Fi, and cell tower information.

[0079] "Affected Area" means an area potentially affected by a disaster or emergency, as identified based on received emergency notifications and acquired location information.

[0080] "Cloud" refers to a virtual environment that provides data storage and computing resources using remote servers and services over the Internet.

[0081] "Related information" refers to information necessary for disaster prevention, such as traffic conditions, evacuation sites, and the extent of impact on the area, obtained from the cloud.

[0082] A "prompt" is an instruction based on specific conditions that is input to a generative AI model.

[0083] A "generative AI model" refers to an artificial intelligence model that automatically generates specific action instructions for users to take based on the input prompt text.

[0084] "Action guidance" refers to instructions generated by a generative AI model that indicate specific actions a user should take in an emergency.

[0085] "Notification" refers to a means of presenting information via a terminal to convey the generated action guidance to the user.

[0086] "Monitoring behavior" refers to the act of continuously obtaining location information and sending it to a server to confirm that the user is behaving appropriately in accordance with instructions.

[0087] The present invention is a system designed to ensure the safety of users in emergency situations. To this end, a server, terminals, and users work together to support prompt and appropriate action in the event of a natural disaster or other emergency. The configuration and operation of this system are as follows.

[0088] System configuration

[0089] server

[0090] The server has the function of receiving emergency notifications. It uses an API to receive and analyze emergency notifications (earthquake alerts, heavy rain and flood warnings, etc.) from the Japan Meteorological Agency and disaster prevention organizations in real time. The server analyzes the received notifications to identify the type of disaster, and then determines the affected area based on the location information.

[0091] In addition, the server collects relevant information from the cloud, such as traffic conditions, evacuation sites, and the extent of impact in the area. This information is obtained using cloud services such as Google Maps API. Based on the collected information, it generates prompt sentences to be input into the generative AI model.

[0092] For example, generate the following prompt:

[0093] "Generate safety instructions for users near the epicenter."

[0094] "Generate safe behavior instructions for users affected by flooding."

[0095] The generative AI model takes these prompts as input and generates specific instructions for the user to take, which are then received by the server and sent to the device.

[0096] Terminal

[0097] The device has the ability to obtain the user's current location. This is done using GPS, Wi-Fi, and cell tower information. When an emergency notification is received, the device immediately obtains the location information and sends it to the server. It also has the ability to receive action instructions sent from the server and notify the user.

[0098] Notification methods include pop-up messages, audio alerts, and vibrations. For example, a pop-up message will be displayed with instructions such as "Stay under a desk until the shaking stops, then head to a designated evacuation site."

[0099] user

[0100] Users can take appropriate action based on notifications from their devices. Specifically, in the event of an earthquake, users can take shelter under a desk until the shaking subsides and then head to a designated evacuation site. In the event of a flood, users can move toward a designated evacuation site and choose a safe route.

[0101] Additionally, to ensure that users follow the instructions, the device continuously updates and transmits location information to the server, allowing the server to monitor users' movements and determine whether they have followed the instructions.

[0102] Specific actions

[0103] When an earthquake occurs, the server receives an earthquake early warning from the Japan Meteorological Agency and analyzes the information to identify the occurrence of the earthquake. Next, the server obtains the user's current location information from the device and sends it to the server. Based on this location information, the server determines whether the user is near the epicenter.

[0104] If the user is close to the epicenter, the server collects relevant information such as traffic conditions and evacuation locations from the cloud and inputs "safety instructions for users close to the epicenter" as a prompt into the generative AI model. The generative AI model generates action instructions such as "take shelter under a desk until the shaking stops, then head to the designated evacuation location." The server receives this and sends it to the device. The device displays this action instruction to the user as a pop-up message, and the user follows it to take safe actions.

[0105] The same applies when a flood warning is issued. The server receives and analyzes the heavy rain and flood warning from the Japan Meteorological Agency. The device then obtains the user's current location information and sends it to the server. The server uses the location information to determine whether the user is in an area affected by flooding. If it determines that the user is affected, the server collects information from the cloud, such as evacuation sites and road traffic conditions. Based on the generated prompt, specific action instructions are generated, such as "The evacuation site is XX Elementary School. Please evacuate by following the specified route." The action instructions are sent to the device, and the user is notified via a pop-up message or voice alert.

[0106] In this way, the system helps users take early and appropriate action in the event of a disaster. By linking together the various elements, the system can provide quick and accurate evacuation instructions and ensure the safety of users.

[0107] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0108] Step 1:

[0109] Receive emergency notifications

[0110] The server receives emergency notifications from the Japan Meteorological Agency and disaster prevention organizations. The input is emergency alert data obtained from the Japan Meteorological Agency's API, and the output is analyzed disaster information. The server uses the API to stream data in real time and receive notifications. This data is sent to the analysis module, which identifies the type of disaster.

[0111] Specific behavior:

[0112] The server obtains earthquake alerts, heavy rain and flood warnings, etc. from the Japan Meteorological Agency's API.

[0113] The acquired data is analyzed to identify the type of disaster, such as "earthquake" or "flood."

[0114] Step 2:

[0115] Obtaining user location information

[0116] The device obtains the user's current location and sends it to the server. The input is location data (GPS, Wi-Fi, cell tower information) obtained from the device's sensors, and the output is location information sent to the server. The device activates location services to obtain GPS data. If the GPS signal is insufficient, the device complements the location using Wi-Fi network and cell tower information.

[0117] Specific behavior:

[0118] The device uses location services to obtain the user's current location.

[0119] The acquired location information is sent to the server via API.

[0120] Step 3:

[0121] Identifying target areas

[0122] The server identifies areas that may be affected by a disaster based on the emergency notification received and the acquired location information. The input is the emergency notification and the user's location information, and the output is the result of identifying the affected area. The server integrates this information and evaluates the extent of the impact.

[0123] Specific behavior:

[0124] The server combines the user's location information with disaster information to identify affected areas.

[0125] Information about the identified target area is sent to a cloud service.

[0126] Step 4:

[0127] Gathering relevant information

[0128] The server collects relevant information such as traffic conditions, evacuation sites, and the extent of impact on the area from the cloud. The input is a request to the cloud service, and the output is the collected relevant information. The server obtains the necessary information in real time using the Google Maps API, etc.

[0129] Specific behavior:

[0130] The server calls the API of the cloud service to obtain information on traffic conditions and evacuation locations.

[0131] The acquired information is stored in an internal database and used to generate prompts.

[0132] Step 5:

[0133] Generate prompt statement

[0134] Based on the relevant information collected by the server, a prompt sentence is generated to be input to the generative AI model. The input is the relevant information data, and the output is the prompt sentence. The prompt generation module creates a prompt sentence such as "Safety instructions for users near the epicenter."

[0135] Specific behavior:

[0136] The server parses the relevant information and generates the appropriate prompt.

[0137] The generated prompt sentence is sent to the API of the generative AI model.

[0138] Step 6:

[0139] Generate action guide

[0140] The generative AI model generates action instructions based on the prompt text. The input is the prompt text, and the output is specific action instructions. The generative AI model analyzes the prompt text and generates instructions such as "Hide under a desk until the shaking stops, then head to the designated evacuation site."

[0141] Specific behavior:

[0142] The generative AI model receives the prompt and analyzes it.

[0143] The instruction content is generated in JSON format or similar and returned to the server.

[0144] Step 7:

[0145] notification

[0146] The device receives the action instructions sent from the server and notifies the user. The input is the action instructions from the server, and the output is the notification content to the user. The device displays the action instructions to the user as a pop-up message or audio alert.

[0147] Specific behavior:

[0148] The terminal receives the action guide from the server.

[0149] The user will be notified of the received action instructions via a pop-up message, audio alert, or vibration.

[0150] Step 8:

[0151] User behavior confirmation

[0152] The device monitors the user's behavior and continuously updates and sends location information to the server. The input is the user's location and the output is the updated information sent to the server. The device retrieves location information at regular intervals to check whether the user is moving according to instructions.

[0153] Specific behavior:

[0154] The device will update its location using location services at regular intervals.

[0155] Send updated location information to the server and check user activity.

[0156] By following the above processing steps, the system helps users take prompt and appropriate action in the event of a disaster. Detailed data processing and calculation at each step provide advanced support.

[0157] (Application example 1)

[0158] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0159] Although self-driving vehicles are becoming more common in modern society, systems for taking prompt and appropriate action in the event of a natural disaster or emergency are still not fully in place. As a result, users of self-driving vehicles may become confused in the event of an emergency, making it difficult for them to take appropriate evacuation actions. In particular, the lack of appropriate real-time guidance leaves users with concerns about their safety.

[0160] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0161] In this invention, the server includes means for receiving an emergency notification, means for acquiring location information, means for identifying a target area based on the received emergency notification and the acquired location information, means for collecting related information from a cloud, means for generating a prompt based on the collected information, means for generating action guidance based on the generated prompt, means for notifying a user of the generated action guidance, and means for displaying guidance on safe actions to take in an emergency on a display in the autonomous vehicle or on a smartphone. This enables users of autonomous vehicles to take appropriate actions in real time in an emergency, thereby ensuring the safety of the users.

[0162] "Emergency Notification" means information notifying people of natural disasters or other emergencies.

[0163] "Location Information" means information about the current location of a user or device obtained using GPS, Wi-Fi, or cell tower information.

[0164] The "target area" is an area identified based on emergency notification and acquired location information.

[0165] A "cloud" is a collection of computing resources delivered over the Internet.

[0166] A "prompt" is an instruction sentence that is input to a generative AI model to generate action guidance.

[0167] "Action guidance" is information that instructs the user on specific actions to be taken in an emergency.

[0168] An "autonomous vehicle" is a vehicle that has the ability to drive autonomously without human intervention.

[0169] A "display" is a screen or monitor for displaying information.

[0170] A "smartphone" is a portable information device with advanced computing power and connectivity.

[0171] A "generative AI model" is an artificial intelligence algorithm that generates responses or instructions in natural language based on data.

[0172] This invention is a system for autonomous vehicles that aims to ensure user safety in the event of a natural disaster or other emergency. It integrates various elements, such as emergency notification, location information, target area, cloud computing, prompts, action guidance, autonomous vehicles, displays, smartphones, and generative AI models, to build a system that supports prompt and appropriate actions.

[0173] System configuration

[0174] server

[0175] The server has the following functions:

[0176] 1. Emergency notification analysis function

[0177] The server receives and analyzes emergency notifications about natural disasters and emergencies, such as earthquake alerts, heavy rain and flood warnings, and other alerts from public organizations such as the Japan Meteorological Agency.

[0178] 2. Location information acquisition function

[0179] Current location information is acquired through the GPS module in the autonomous vehicle, allowing the user's location to be tracked in real time.

[0180] 3. Ability to identify target areas

[0181] Based on the emergency notification received and the location information obtained, the affected areas of the emergency are identified.

[0182] 4. Function to collect related information

[0183] Use cloud services (e.g., AWS and Google Cloud) to collect relevant information such as traffic conditions, evacuation sites, and road traffic conditions in real time.

[0184] 5. Prompt generation function using generative AI models

[0185] Based on the information collected from the cloud, a generative AI model (e.g., GPT-4) generates prompts, which are used to instruct appropriate actions in emergencies.

[0186] 6. Function to generate action guides

[0187] Based on the generated prompt text, an action guide is generated that indicates the specific action the user should take.

[0188] 7. Function to notify users of action guidance

[0189] The generated guidance is displayed and notified to the user on the display and smartphone inside the autonomous vehicle, using pop-up messages and audio alerts to ensure the user is informed.

[0190] Terminal

[0191] The terminal installed in the autonomous vehicle has the following functions:

[0192] 1. Location information acquisition function

[0193] Location information is obtained using a GPS module and sent to the server.

[0194] 2. Action guide notification function

[0195] The system notifies the user of the action instructions sent from the server, displaying messages on the screen and also using audio alerts.

[0196] Usage examples and prompt statements

[0197] As a specific example of use, the system behavior when a flood warning occurs is shown below.

[0198] 1. The server receives a heavy rain and flood warning from the Japan Meteorological Agency.

[0199] 2. The GPS module in the autonomous vehicle acquires the current location information and sends it to the server.

[0200] 3. The server uses location information to determine if the user is in an area affected by flooding.

[0201] 4. The server collects information on evacuation sites and road traffic conditions from the cloud.

[0202] 5. Enter the following prompt into the generative AI model:

[0203] The user is located at point △△ in ○○ city and a flood warning has been issued. Please guide them to safe evacuation routes and evacuation locations.

[0204] 6. The generative AI model generates the instruction, "Flood warning: Please evacuate from your current location to △△ Junior High School. The specific route is via □□ Street."

[0205] 7. The server sends this instruction to a display inside the autonomous vehicle, which then issues a voice alert saying, "Flood warning issued, please follow evacuation instructions."

[0206] In this way, the system of the present invention assists users in taking prompt and appropriate action in an emergency, thereby ensuring their safety.

[0207] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0208] Step 1:

[0209] Receive emergency notifications

[0210] The server receives emergency notifications about natural disasters and other emergencies. For example, it receives real-time earthquake alerts and heavy rain and flood warnings from public organizations such as the Japan Meteorological Agency. The input is the emergency notification data, and the output is the analysis results. The server analyzes this data and identifies the type of disaster and its urgency.

[0211] Step 2:

[0212] Obtaining location information

[0213] The terminal uses the GPS module in the autonomous vehicle to obtain its current location information. The input is the GPS signal, and the output is the location coordinate data. The terminal then sends this location information to the server.

[0214] Step 3:

[0215] Identifying target areas

[0216] The server identifies the affected area based on the received emergency notification and the acquired location information. The input is the analysis result of the emergency notification and the location coordinate data, and the output is the affected area data. The server uses this data to determine whether the user is in the affected area.

[0217] Step 4:

[0218] Gathering relevant information

[0219] The server uses cloud services to collect relevant information such as traffic conditions, evacuation shelters, and road traffic conditions. The input is the target area data, and the output is the collected relevant information. Specifically, the server obtains the necessary data using a cloud API.

[0220] Step 5:

[0221] Prompt Generation

[0222] The server generates a prompt sentence from a generative AI model based on the collected information. The input is the collected relevant information, and the output is the prompt sentence. The server generates a prompt to instruct the generative AI model (e.g., GPT-4) to take a specific action.

[0223] Step 6:

[0224] Generate action guide

[0225] The server generates specific instructions based on the generated prompt. The input is the prompt, and the output is instructions. A generative AI model is used to generate instructions such as "Flood warning: Please evacuate from your current location to △△ Junior High School."

[0226] Step 7:

[0227] Action guide notifications

[0228] The terminal displays and notifies the generated action guidance on the display or smartphone inside the autonomous vehicle. The input is the action guidance, and the output is a notification to the user. Specifically, a message is displayed on the display and an audio alert is also used to ensure that the user is informed.

[0229] This allows users to take appropriate action in real time in an emergency and ensure safety.

[0230] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0231] This invention is a system aimed at ensuring the safety and psychological stability of users in emergency situations, and includes the following elements: A server, a terminal, an emotion engine, and a user work together to support swift and appropriate action in natural disasters and other emergency situations, and also provide appropriate instructions according to emotions.

[0232] System configuration

[0233] server

[0234] The server receives emergency notifications and analyzes their contents. These include earthquake alerts, heavy rain and flood warnings, and other alerts. The server analyzes the received notifications and identifies the type of disaster that requires a response. It then receives location information from the user's device and identifies the affected area. It then uses the cloud to collect relevant information such as traffic conditions, evacuation sites, and the extent of impact in the area, and uses this information to have the generative AI model generate prompts. The prompts generated by the generative AI model are converted into specific action instructions and sent to the device.

[0235] Terminal

[0236] The device has the ability to obtain the user's current location using GPS, Wi-Fi, or cell tower information. When a disaster notification is received, the device immediately obtains the device's location information and sends it to the server. The device also receives action instructions sent from the server and notifies the user of them. Notification methods include pop-up messages, audio alerts, and vibrations.

[0237] Emotion Engine

[0238] The emotion engine is designed to recognize the user's emotions. It determines the user's emotional state using voice data, text data, or other input means. Based on the determined emotion, it adjusts the presentation of action guidance and the information provided. The emotion engine is built into the server or terminal.

[0239] user

[0240] The user takes appropriate action based on notifications from the device. For example, in the event of an earthquake, the user will take shelter under a desk until the shaking subsides, then head to a designated evacuation site. In the event of a flood, the user will move toward the evacuation site and choose a safe route. Furthermore, the user will act while maintaining psychological stability by following the guidance provided by the emotion engine based on their emotions. Location information during evacuation is periodically updated by the device and linked to the server.

[0241] Program processing flow

[0242] Specific examples of earthquakes

[0243] For example, if an earthquake occurs, the system operates as follows: The server receives an earthquake early warning from the Japan Meteorological Agency and analyzes the information to identify the occurrence of the earthquake. Next, it obtains the user's current location information from the device and sends it to the server. The server uses this location information to determine whether the user is close to the epicenter. If the user is close to the epicenter, the server collects relevant information such as traffic conditions and evacuation locations from the cloud. It inputs "safety instructions for users close to the epicenter" as a prompt into the generative AI model, and uses the emotion engine to check the user's emotional state.

[0244] By combining the generative AI model with the emotion engine, action guidance such as "Hold shelter under a desk until the shaking stops, then head to a designated evacuation site" is generated. If the emotion engine recognizes the user's anxiety, it can include additional reassuring information such as "Don't worry. These are steps to ensure your safety." The server sends the generated action guidance to the device, which displays it to the user as a pop-up message. The user then follows the instructions to take safe actions.

[0245] Specific examples of floods

[0246] The same applies when a flood warning is issued. The server receives and analyzes heavy rain and flood warnings from the Japan Meteorological Agency. The device then obtains its current location information and sends it to the server. The server uses the location information to determine whether the user is in an area affected by flooding. If it determines that the user is affected, the server collects information from the cloud, such as evacuation sites and road traffic conditions. Based on the generated prompt, specific action instructions are generated, such as "The evacuation site is XX Elementary School. Please evacuate by following the specified route." If the emotion engine detects stress or anxiety in the user at this time, a message offering reassurance, such as "Please remain calm. The route to the evacuation site is safe," is included.

[0247] The generated action instructions are sent to the device and notified to the user via pop-up messages and voice alerts, allowing the user to safely move towards the designated evacuation site.

[0248] In this way, the system helps users take early and appropriate action in the event of a disaster. In addition, by providing guidance that is tailored to the user's emotional state, it can reduce psychological stress and support more effective evacuation. By working together, each element can provide quick and accurate evacuation instructions, ensuring the user's safety and psychological stability.

[0249] The processing flow will be explained below.

[0250] Step 1:

[0251] Server: Receives emergency earthquake alerts, heavy rain and flood warnings, and other disaster information via API. Analyzes the various disaster information and identifies the type of disaster.

[0252] Step 2:

[0253] Device: When disaster information is received, the application will immediately launch and obtain the user's current location using GPS, Wi-Fi, or cell tower information.

[0254] Step 3:

[0255] Device: The acquired location information is sent to the server. The user's location information is encrypted and sent in a secure manner to protect privacy.

[0256] Step 4:

[0257] Server: Based on the received location information, the server determines whether the user is in an area affected by the disaster. This determination is based on the distance from the epicenter and the predicted inundation areas.

[0258] Step 5:

[0259] Server: If a user is determined to be in an affected area, relevant information such as transportation status, evacuation locations, and the extent of the impact on the area is collected from the cloud.

[0260] Step 6:

[0261] Server: Based on the collected information, it generates prompts to be passed to the generative AI model. Specifically, it creates prompts including the user's current location, the nearest evacuation site, and traffic conditions.

[0262] Step 7:

[0263] Server: Inputs prompts to the generative AI model and generates specific instructions for action, such as "An earthquake has occurred. Take shelter under a desk, and once the shaking has stopped, head to the designated evacuation site."

[0264] Step 8:

[0265] Device: Receives the generated action guide and activates the emotion engine, which analyzes the user's voice and text data to determine their emotional state.

[0266] Step 9:

[0267] Terminal: After the emotion engine determines the user's emotional state, it sends the result to the server.

[0268] Step 10:

[0269] Server: Adjust the guidance based on the received emotion data. For example, if the user expresses anxiety, include additional reassurance such as "Don't worry, these are the steps we're taking to ensure your safety."

[0270] Step 11:

[0271] Server: Sends tailored action instructions to the device, conveying them to the user via pop-up messages, audio alerts, vibrations, etc.

[0272] Step 12:

[0273] Device: Provides instructions to the user. For example, it displays something like, "The evacuation site is XX Elementary School. Follow the designated route and evacuate safely. Rest assured, the situation is understood."

[0274] Step 13:

[0275] User: Receives notifications from the device and acts accordingly. For example, in the event of an earthquake, the user may take shelter under a desk and then head to a designated evacuation site. Location information during evacuation is periodically updated by the device and sent to the server.

[0276] Step 14:

[0277] Server: Receives the user's location information and checks whether the evacuation was successful. Provides continuous support until the evacuation is complete.

[0278] Example 2

[0279] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0280] In the event of a natural disaster or other emergency, the challenge is to help users act quickly and appropriately while maintaining psychological stability. Current systems provide emergency notifications and acquire location information, but do not provide action guidance that takes into account the user's emotional state, which can lead to situations where users' anxiety and confusion cannot be completely alleviated.

[0281] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for receiving an emergency notification, a means for acquiring location information, a means for identifying a target area based on the received emergency notification and the acquired location information, a means for collecting related information from the cloud, a means for generating a prompt sentence based on the collected information, a means for generating action guidance based on the generated prompt sentence, a means for determining the user's emotional state, a means for adjusting the action guidance based on the emotional state, and a means for notifying the user of the generated action guidance. This makes it possible to provide quick and appropriate action guidance that takes the user's emotional state into consideration, and to support appropriate action in the event of a disaster while maintaining the user's psychological stability.

[0282] "Emergency Notification" refers to alerts and bulletins issued in the event of a natural disaster or other emergency.

[0283] "Location Information" means data that indicates a user's current geographic location, such as data obtained from GPS, Wi-Fi, or cell tower information.

[0284] "Target area" refers to an area that is likely to be affected by a disaster, as identified based on emergency notifications and location information.

[0285] "Cloud" refers to storage and computing resources for providing and sharing large amounts of data over the Internet.

[0286] "Relevant information" refers to auxiliary data necessary for disaster response, such as traffic conditions, evacuation sites, and the extent of impact on the area.

[0287] A "prompt" is an instruction given to a generative AI model to generate an output in response to a specific question or instruction to act.

[0288] "Action instructions" refer to specific steps of action that the user should take, generated based on the prompt text.

[0289] "User's emotional state" refers to the psychological state determined from the user's voice data, text data, etc.

[0290] "Means for adjusting action guidance based on emotional state" refers to a function for changing the content and expression of action guidance according to the user's emotional state.

[0291] The present invention relates to a system for ensuring user safety and psychological stability in emergency situations, which functions in cooperation with a server, a terminal, an emotion engine, and a user. In this system, the server receives emergency notifications, acquires location information, collects related information from the cloud, and uses a generative AI model to generate prompts and action guidance for the user. The system also includes a means for determining the user's emotional state using the emotion engine and adjusting the action guidance based on the emotional state.

[0292] server

[0293] The server receives emergency notifications from the Japan Meteorological Agency and other organizations. Emergency notifications include earthquake alerts and heavy rain and flood warnings. The server analyzes the received emergency notifications to identify the type of disaster. Next, it receives the user's location information obtained from the device and identifies the affected area. It also collects related information from the cloud, such as traffic conditions, evacuation sites, and the extent of impact on the area. A generative AI model is used based on this collected information to generate prompt text. For example, prompt texts such as "safety instructions for users near the epicenter" and "evacuation routes for users affected by flooding" are generated. Specific action instructions are created from these prompt texts, and the content is adjusted taking into account the user's emotional state.

[0294] An example prompt for a generative AI model is:

[0295] "Safety instructions for users near the epicenter"

[0296] Example prompt for a generative AI model: "What is the best course of action for a user near the epicenter? Hide under a desk until the shaking stops, then head to a designated evacuation site."

[0297] "Providing reassuring information"

[0298] Example prompt input for the generative AI model: "Please provide reassurance information to reduce user anxiety. Rest assured, these are steps to ensure your safety."

[0299] The server transmits the generated action guide to the terminal and notifies the user.

[0300] Terminal

[0301] The device obtains the user's current location using GPS, Wi-Fi, and cell tower information. When a disaster notification is received, it immediately obtains the location information and sends it to the server. It also receives action instructions sent from the server and notifies the user. Notification methods include pop-up messages, audio alerts, and vibrations. When the user begins to take action, the device periodically updates the location information and sends it to the server. This allows the server to continue to know the user's location in real time.

[0302] Emotion Engine

[0303] The emotion engine uses voice and text data to determine the user's emotional state. It uses speech recognition software and natural language processing technology to analyze the emotions expressed in what the user says and writes. For example, if the user is feeling very anxious, it will add reassuring content. The emotion engine is built into a server or device and often runs on the server to perform advanced analysis. The generated action guidance is adjusted based on the data obtained by the emotion engine.

[0304] user

[0305] Users follow the notifications from their devices and take the actions instructed. For example, in the event of an earthquake, they would follow instructions such as "Hold shelter under a desk until the shaking stops, then head to the designated evacuation site." In the event of a flood, they would follow instructions such as "The evacuation site is XX Elementary School. Please evacuate by following the designated route." While the disaster continues, the device periodically sends its location information to the server, and new instructions are sent to the user when necessary.

[0306] In this way, the system helps users act quickly and appropriately in the event of a natural disaster or other emergency, providing psychological stability. The above configuration ensures the safety and security of users.

[0307] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0308] Specific example in the case of an earthquake

[0309] Step 1:

[0310] The server receives an emergency earthquake alert from the Japan Meteorological Agency.

[0311] Input: Earthquake Early Warning data from the Japan Meteorological Agency.

[0312] Processing: The server's data receiving module analyzes the earthquake early warning data.

[0313] Output: Earthquake occurrence information (occurrence time, epicenter, seismic intensity, etc.).

[0314] Step 2:

[0315] The device receives a disaster notification and obtains the user's current location information.

[0316] Input: Emergency earthquake alert sent from the server.

[0317] Processing: The device's location acquisition module acquires the user's current location using GPS, Wi-Fi, and cell tower information.

[0318] Output: Current location of the user (latitude, longitude).

[0319] Step 3:

[0320] The current location information acquired by the terminal is sent to the server.

[0321] Input: The user's current location.

[0322] Processing: The device sends location information to the server.

[0323] Output: The location information received by the server.

[0324] Step 4:

[0325] The server uses location information to determine whether the user is close to the epicenter.

[0326] Input: Earthquake occurrence information, user's current location information.

[0327] Processing: The server's analysis module calculates the distance between the user's location and the epicenter and evaluates the impact.

[0328] Output: The result of determining whether the user is close or far from the epicenter.

[0329] Step 5:

[0330] The server collects relevant information such as traffic conditions and evacuation locations from the cloud.

[0331] Input: Information about the area of ​​interest.

[0332] Processing: The server collects the necessary information from the traffic database and evacuation site database via the cloud API.

[0333] Output: Information on traffic conditions, evacuation locations, and local impact.

[0334] Step 6:

[0335] The server generates prompt sentences for the generative AI model based on the collected information.

[0336] Input: Earthquake occurrence information, user location information, and related information.

[0337] Processing: The generative AI model is given a prompt: "Safety instructions for users near the epicenter."

[0338] Output: Prompt text "Safety instructions for users near the epicenter."

[0339] Step 7:

[0340] The server generates action guidance using the generative AI model.

[0341] Input: Prompt text "Safety instructions for users near the epicenter."

[0342] Processing: The generative AI model generates specific action instructions based on the prompt sentence.

[0343] Output: Instructions for action: "Take shelter under a desk until the shaking stops, then proceed to the designated evacuation site."

[0344] Step 8:

[0345] The server uses an emotion engine to determine the user's emotional state.

[0346] Input: User voice and text data.

[0347] Processing: The emotion engine analyzes the data and determines the user's anxiety or stress.

[0348] Output: The user's emotional state (calm, anxious, stressed, etc.).

[0349] Step 9:

[0350] The server adjusts the user's behavioral guidance based on the user's emotional state.

[0351] Input: Generated action guide, user's emotional state.

[0352] Treatment: If anxiety is recognized, add an additional reassuring message to the action guide.

[0353] Output: Action instructions: "Take shelter under a desk until the shaking stops, then proceed to a designated evacuation site. Rest assured, these are procedures to ensure your safety."

[0354] Step 10:

[0355] The server transmits the generated action guide to the terminal.

[0356] Input: Coordinated behavioral guidance.

[0357] Processing: Send action instructions to the terminal.

[0358] Output: Action instructions received by the device.

[0359] Step 11:

[0360] The device notifies the user of action instructions.

[0361] Input: Action guide.

[0362] Action: Notify action instructions using pop-up messages, audio alerts, and vibrations.

[0363] Output: The call to action received by the user.

[0364] Step 12:

[0365] The user takes the indicated action.

[0366] Input: Action guide.

[0367] Action: The user "takes shelter under a desk until the shaking stops, then heads to a designated evacuation location."

[0368] Output: The user takes action to evacuate to a safe place.

[0369] Step 13:

[0370] The device periodically updates the user's current location and sends it to the server.

[0371] Input: The user's new location.

[0372] Processing: Periodically obtain location information and send it to the server.

[0373] Output: The latest location information received by the server.

[0374] Step 14:

[0375] The server generates additional action guidance as needed and transmits it to the terminal.

[0376] Input: Latest location and emergency notification updates.

[0377] Processing: Using the generative AI model, new action guidance is generated and sent to the device.

[0378] Output: New action instructions received by the device.

[0379] In this way, a system is constructed that supports users in taking prompt and appropriate action in the event of a disaster through specific actions at each step.

[0380] (Application example 2)

[0381] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0382] In the event of a natural disaster or other emergency, users are required to take prompt and appropriate action. However, conventional systems are limited to emergency notifications and location information acquisition, and lack guidance based on the user's individual emotional state, which can lead to anxiety and panic. Furthermore, it is difficult to convert the collected information into appropriate action guidance, which can result in delayed notification to users.

[0383] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0384] In this invention, the server includes means for receiving an emergency notification, means for acquiring location information, means for identifying a target area based on the received emergency notification and the acquired location information, means for collecting related information from the cloud, means for generating a prompt sentence based on the collected information, means for generating action guidance from the prompt sentence using a generative AI model, means for detecting the user's emotional state and adjusting the action guidance according to that state, and means for notifying the user of the generated action guidance. This allows the user to receive prompt and accurate action guidance in an emergency, and by being provided with individual instructions according to the user's emotional state, the user can act safely while maintaining psychological stability.

[0385] "Emergency Notification" means a notification regarding a natural disaster or other emergency.

[0386] "Location Information" means data about a user's current location obtained using GPS, Wi-Fi, or cell tower information.

[0387] "Target Area" means an area potentially affected by a disaster, as identified based on emergency notification and location information.

[0388] The "cloud" is a distributed infrastructure of resources, data storage, and computing power delivered over the internet.

[0389] "Relevant information" is information necessary for dealing with an emergency, such as traffic conditions, evacuation sites, and the extent of impact on the area.

[0390] A "prompt sentence" is a collection of terms and sentences that are input into a generative AI model and serve as the source data for generating action guidance.

[0391] A "generative AI model" is an artificial intelligence model that generates appropriate action guidance from a prompt sentence.

[0392] "Action guidance" is information that provides specific instructions on what actions a user should take in an emergency.

[0393] "Emotional state" refers to the user's psychological state as sensed using voice data, text data, or other input means.

[0394] The "emotion engine" is a function that recognizes the user's emotional state and adjusts behavioral guidance.

[0395] The present invention is a system for ensuring the safety of users and providing psychological stability in emergency situations. The system includes the following components: a server, a terminal, an emotion engine, and a user.

[0396] server

[0397] The server has the function of receiving emergency notifications and analyzing their contents. Emergency notifications include earthquake alerts, heavy rain and flood warnings, and other warnings. When this notification is received, the server analyzes its contents and identifies the type of disaster that requires response.

[0398] The server then retrieves the user's location from the device and identifies the target area, which can be obtained using GPS, Wi-Fi, or cell tower information.

[0399] The server then uses the cloud to collect relevant information such as traffic conditions, evacuation sites, and the extent of local impact. Based on this information, the server uses a generative AI model to generate a prompt. The generated prompt is text data in the format "Notification: Earthquake Warning, Location: Tokyo, Emotion: Fear." Using this prompt as input, the generative AI model generates specific action instructions and adjusts those instructions.

[0400] Terminal

[0401] The device has the ability to obtain the user's current location. It obtains location information using GPS, Wi-Fi, or cell tower information and sends it to the server. The device also receives action instructions sent from the server and notifies the user of them. Notification methods include pop-up messages, audio alerts, and vibrations.

[0402] Emotion Engine

[0403] The emotion engine has the function of recognizing the user's emotional state and adjusting the action guidance. It determines the user's emotional state using voice data, text data, or other input means. Based on the determined emotion, it adjusts the expression and content of the generated action guidance to provide the user with appropriate instructions.

[0404] Specific examples

[0405] When an earthquake occurs, the server receives an earthquake early warning from the Japan Meteorological Agency and analyzes the information to identify the occurrence of the earthquake. The device then obtains the user's current location information and sends it to the server. The server uses this location information to determine whether the user is near the epicenter.

[0406] If the user is close to the epicenter, the server collects relevant information such as traffic conditions and evacuation sites from the cloud. The generated prompt is "Notification: Earthquake warning, Location: Tokyo, Emotion: Fear." This prompt is input into the generative AI model, and the emotion engine is used to check the user's emotional state.

[0407] By combining the generative AI model with the emotion engine, the system can generate action instructions such as, "Hold shelter under a desk until the shaking stops, then proceed to a designated evacuation site." Furthermore, if the emotion engine recognizes the user's anxiety, it can include additional reassurance information such as, "Don't worry. These are steps to ensure your safety."

[0408] In this way, the system helps users take early and appropriate action in the event of a disaster. It also provides guidance tailored to the user's emotional state, reducing psychological stress and supporting more effective evacuation.

[0409] In the embodiment of the present invention, the above elements function in cooperation with each other to provide quick and accurate evacuation instructions and ensure the safety and psychological stability of the user.

[0410] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0411] Step 1:

[0412] The server receives emergency notifications. Specifically, it receives emergency notifications such as earthquake alerts and flood warnings from the Japan Meteorological Agency and other public organizations in real time. The input data is the content of the emergency notification, and the output is the emergency notification data to be analyzed. The server analyzes the necessary information from this notification data and identifies the type and details of the disaster.

[0413] Step 2:

[0414] The device obtains the user's location information. Specifically, it obtains the current location using GPS, Wi-Fi, or cell tower information and sends it to the server. The input data is the user's location information, and the output is the user's latitude and longitude data.

[0415] Step 3:

[0416] The server identifies the affected area based on the received emergency notification and the acquired location information. The server compares the user's location with the extent of the disaster's impact and determines whether the user is in an area affected by the disaster. The input data is the emergency notification and location information, and the output is data identifying the affected area.

[0417] Step 4:

[0418] The server collects relevant information from the cloud, such as traffic conditions, evacuation sites, and the extent of impact on the area, and integrates it within the server. The input data is specific data for the target area, and the output is the collected relevant information.

[0419] Step 5:

[0420] The server generates a prompt sentence based on the collected related information. The generated prompt sentence is text data to be input into the generative AI model. An example of a prompt sentence is "Notification: Earthquake warning, Location: Tokyo, Emotion: Fear." The input data is the collected related information, and the output is the generated prompt sentence.

[0421] Step 6:

[0422] The server uses a generative AI model to generate action instructions from prompts. The input data is the generated prompt, and specific action instructions are output through data calculations. For example, "Please take shelter under a desk until the shaking stops, then head to the designated evacuation site."

[0423] Step 7:

[0424] The server detects the user's emotional state and adjusts the action guidance accordingly. Specifically, it analyzes the user's emotions from voice and text data, and the emotion engine adjusts the action guidance based on the results. The input data is the user's emotional state, and the output is the adjusted action guidance.

[0425] Step 8:

[0426] The terminal notifies the user of the generated action guidance using a pop-up message, a voice alert, and vibration. The input data is the adjusted action guidance, and the output is displayed as a notification to the user.

[0427] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0428] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0429] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0430] [Second embodiment]

[0431] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0432] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0433] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0434] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0435] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0436] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0437] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0438] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0439] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0440] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0441] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0442] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0443] The present invention is a system aimed at ensuring the safety of users in emergency situations, and includes the following elements: A server, terminals, and users work together to support prompt and appropriate action in the event of a natural disaster or other emergency.

[0444] System configuration

[0445] server

[0446] The server receives and analyzes emergency notifications, including earthquake alerts, heavy rain and flood warnings, and other alerts. It analyzes the received notifications to identify the type of disaster that requires a response. It then receives location information from the user's device and identifies the affected area. It then uses the cloud to collect relevant information, such as traffic conditions, evacuation locations, and the extent of impact in the area, and uses this information to generate prompts using a generative AI model.

[0447] The prompts generated by the generative AI model are converted into specific action instructions, which are then sent to the device and notified to the user.

[0448] Terminal

[0449] The device has the ability to obtain the user's current location using GPS, Wi-Fi, or cell tower information. When a disaster notification is received, the device immediately obtains the device's location information and sends it to the server. The device also receives action instructions sent from the server and notifies the user of them. Notification methods include pop-up messages, audio alerts, and vibrations.

[0450] user

[0451] Users take appropriate action based on notifications from their devices. For example, in the event of an earthquake, users will take shelter under a desk until the shaking subsides, then head to a designated evacuation site. In the event of a flood, users will always move toward an evacuation site and choose a safe route. To confirm whether users have followed the instructions, devices continuously update their location information and connect with the server.

[0452] Program processing flow

[0453] Specific examples of earthquakes

[0454] For example, if an earthquake occurs, the system operates as follows: The server receives an earthquake early warning from the Japan Meteorological Agency and analyzes the information to identify the occurrence of the earthquake. Next, it obtains the user's current location information from the device and sends it to the server. The server uses this location information to determine whether the user is close to the epicenter. If the user is close to the epicenter, the server collects relevant information such as traffic conditions and evacuation locations from the cloud and inputs "safety instructions for users close to the epicenter" as a prompt into the generative AI model.

[0455] The generative AI model generates action guidance such as "Hold shelter under a desk until the shaking stops, then head to a designated evacuation site." This is received by the server and sent to the device. The device then displays this action guidance to the user as a pop-up message. The user then follows this to take safe action.

[0456] Specific examples of floods

[0457] The same applies when a flood warning is issued. The server receives and analyzes the heavy rain and flood warning from the Japan Meteorological Agency. The device then obtains the user's current location information and sends it to the server. The server uses the location information to determine whether the user is in an area affected by flooding. If it determines that the user is affected, the server collects information from the cloud, such as evacuation sites and road traffic conditions. Based on the generated prompt, specific action instructions are generated, such as "The evacuation site is XX Elementary School. Please evacuate by following the specified route." The action instructions are sent to the device, and the user is notified via a pop-up message or voice alert.

[0458] In this way, the system helps users take early and appropriate action in the event of a disaster. By working together, each element can provide quick and accurate evacuation instructions and ensure user safety.

[0459] The processing flow will be explained below.

[0460] Step 1:

[0461] Server: Receives emergency earthquake alerts, heavy rain and flood warnings, and other disaster information via API. Analyzes the various disaster information and identifies the type of disaster.

[0462] Step 2:

[0463] Device: When disaster information is received, the application will immediately launch and obtain the user's current location using GPS, Wi-Fi, or cell tower information.

[0464] Step 3:

[0465] Device: The acquired location information is sent to the server. The user's location information is encrypted and sent in a secure manner to protect privacy.

[0466] Step 4:

[0467] Server: Based on the received location information, the server determines whether the user is in an area affected by the disaster. This determination is based on the distance from the epicenter and the predicted inundation areas.

[0468] Step 5:

[0469] Server: If a user is determined to be in an affected area, relevant information such as transportation status, evacuation locations, and the extent of the impact on the area is collected from the cloud.

[0470] Step 6:

[0471] Server: Based on the collected information, it generates prompts to be passed to the generative AI model. Specifically, it creates prompts including the user's current location, the nearest evacuation site, and traffic conditions.

[0472] Step 7:

[0473] Server: Inputs prompts to the generative AI model and generates specific instructions for action, such as "An earthquake has occurred. Take shelter under a desk, and once the shaking has stopped, head to the designated evacuation site."

[0474] Step 8:

[0475] Server: Sends the generated action guide to the terminal. The action guide is formatted in a way that is intuitively understandable to the user.

[0476] Step 9:

[0477] Device: Notify the user of the action to be taken, using pop-up messages, audio alerts, vibrations, etc.

[0478] Step 10:

[0479] User: Receives notifications from the device and acts accordingly. For example, in the event of an earthquake, the user may take shelter under a desk and then head to a designated evacuation site. Location information during evacuation is periodically updated by the device and sent to the server.

[0480] Step 11:

[0481] Server: Receives the user's location information and checks whether the evacuation was successful. Provides continuous support until the evacuation is complete.

[0482] Example 1

[0483] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0484] The challenge is the lack of means to take prompt and appropriate action in the event of a natural disaster or other emergency. Conventional systems lack the ability to not only receive emergency notifications but also to immediately instruct users on what specific actions to take based on those notifications. This makes it difficult for users to take appropriate action when faced with a dangerous situation.

[0485] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0486] In this invention, the server includes means for receiving an emergency notification, means for acquiring location information, means for identifying a target area based on the received emergency notification and the acquired location information, means for collecting related information from the cloud, means for generating a prompt sentence based on the collected information, a generation AI model means for generating action guidance based on the generated prompt sentence, means for notifying the user of the generated action guidance, and means for monitoring the user's actions and updating the location information. This enables the user to take appropriate action immediately in the event of a disaster, and provides quick and accurate evacuation instructions.

[0487] "Emergency Notification" means alerts and bulletins that provide real-time information about natural disasters and other emergencies.

[0488] "Location information" refers to the geographic coordinates of a user's current location, and is data obtained using technologies such as GPS, Wi-Fi, and cell tower information.

[0489] "Affected Area" means an area potentially affected by a disaster or emergency, as identified based on received emergency notifications and acquired location information.

[0490] "Cloud" refers to a virtual environment that provides data storage and computing resources using remote servers and services over the Internet.

[0491] "Related information" refers to information necessary for disaster prevention, such as traffic conditions, evacuation sites, and the extent of impact on the area, obtained from the cloud.

[0492] A "prompt" is an instruction based on specific conditions that is input to a generative AI model.

[0493] A "generative AI model" refers to an artificial intelligence model that automatically generates specific action instructions for users to take based on the input prompt text.

[0494] "Action guidance" refers to instructions generated by a generative AI model that indicate specific actions a user should take in an emergency.

[0495] "Notification" refers to a means of presenting information via a terminal to convey the generated action guidance to the user.

[0496] "Monitoring behavior" refers to the act of continuously obtaining location information and sending it to a server to confirm that the user is behaving appropriately in accordance with instructions.

[0497] The present invention is a system designed to ensure the safety of users in emergency situations. To this end, a server, terminals, and users work together to support prompt and appropriate action in the event of a natural disaster or other emergency. The configuration and operation of this system are as follows.

[0498] System configuration

[0499] server

[0500] The server has the function of receiving emergency notifications. It uses an API to receive and analyze emergency notifications (earthquake alerts, heavy rain and flood warnings, etc.) from the Japan Meteorological Agency and disaster prevention organizations in real time. The server analyzes the received notifications to identify the type of disaster, and then determines the affected area based on the location information.

[0501] In addition, the server collects relevant information from the cloud, such as traffic conditions, evacuation sites, and the extent of impact in the area. This information is obtained using cloud services such as Google Maps API. Based on the collected information, it generates prompt sentences to be input into the generative AI model.

[0502] For example, generate the following prompt:

[0503] "Generate safety instructions for users near the epicenter."

[0504] "Generate safe behavior instructions for users affected by flooding."

[0505] The generative AI model takes these prompts as input and generates specific instructions for the user to take, which are then received by the server and sent to the device.

[0506] Terminal

[0507] The device has the ability to obtain the user's current location. This is done using GPS, Wi-Fi, and cell tower information. When an emergency notification is received, the device immediately obtains the location information and sends it to the server. It also has the ability to receive action instructions sent from the server and notify the user.

[0508] Notification methods include pop-up messages, audio alerts, and vibrations. For example, a pop-up message will be displayed with instructions such as "Stay under a desk until the shaking stops, then head to a designated evacuation site."

[0509] user

[0510] Users can take appropriate action based on notifications from their devices. Specifically, in the event of an earthquake, users can take shelter under a desk until the shaking subsides and then head to a designated evacuation site. In the event of a flood, users can move toward a designated evacuation site and choose a safe route.

[0511] Additionally, to ensure that users follow the instructions, the device continuously updates and transmits location information to the server, allowing the server to monitor users' movements and determine whether they have followed the instructions.

[0512] Specific actions

[0513] When an earthquake occurs, the server receives an earthquake early warning from the Japan Meteorological Agency and analyzes the information to identify the occurrence of the earthquake. Next, the server obtains the user's current location information from the device and sends it to the server. Based on this location information, the server determines whether the user is near the epicenter.

[0514] If the user is close to the epicenter, the server collects relevant information such as traffic conditions and evacuation locations from the cloud and inputs "safety instructions for users close to the epicenter" as a prompt into the generative AI model. The generative AI model generates action instructions such as "take shelter under a desk until the shaking stops, then head to the designated evacuation location." The server receives this and sends it to the device. The device displays this action instruction to the user as a pop-up message, and the user follows it to take safe actions.

[0515] The same applies when a flood warning is issued. The server receives and analyzes the heavy rain and flood warning from the Japan Meteorological Agency. The device then obtains the user's current location information and sends it to the server. The server uses the location information to determine whether the user is in an area affected by flooding. If it determines that the user is affected, the server collects information from the cloud, such as evacuation sites and road traffic conditions. Based on the generated prompt, specific action instructions are generated, such as "The evacuation site is XX Elementary School. Please evacuate by following the specified route." The action instructions are sent to the device, and the user is notified via a pop-up message or voice alert.

[0516] In this way, the system helps users take early and appropriate action in the event of a disaster. By linking together the various elements, the system can provide quick and accurate evacuation instructions and ensure the safety of users.

[0517] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0518] Step 1:

[0519] Receive emergency notifications

[0520] The server receives emergency notifications from the Japan Meteorological Agency and disaster prevention organizations. The input is emergency alert data obtained from the Japan Meteorological Agency's API, and the output is analyzed disaster information. The server uses the API to stream data in real time and receive notifications. This data is sent to the analysis module, which identifies the type of disaster.

[0521] Specific behavior:

[0522] The server obtains earthquake alerts, heavy rain and flood warnings, etc. from the Japan Meteorological Agency's API.

[0523] The acquired data is analyzed to identify the type of disaster, such as "earthquake" or "flood."

[0524] Step 2:

[0525] Obtaining user location information

[0526] The device obtains the user's current location and sends it to the server. The input is location data (GPS, Wi-Fi, cell tower information) obtained from the device's sensors, and the output is location information sent to the server. The device activates location services to obtain GPS data. If the GPS signal is insufficient, the device complements the location using Wi-Fi network and cell tower information.

[0527] Specific behavior:

[0528] The device uses location services to obtain the user's current location.

[0529] The acquired location information is sent to the server via API.

[0530] Step 3:

[0531] Identifying target areas

[0532] The server identifies areas that may be affected by a disaster based on the emergency notification received and the acquired location information. The input is the emergency notification and the user's location information, and the output is the result of identifying the affected area. The server integrates this information and evaluates the extent of the impact.

[0533] Specific behavior:

[0534] The server combines the user's location information with disaster information to identify affected areas.

[0535] Information about the identified target area is sent to a cloud service.

[0536] Step 4:

[0537] Gathering relevant information

[0538] The server collects relevant information such as traffic conditions, evacuation sites, and the extent of impact on the area from the cloud. The input is a request to the cloud service, and the output is the collected relevant information. The server obtains the necessary information in real time using the Google Maps API, etc.

[0539] Specific behavior:

[0540] The server calls the API of the cloud service to obtain information on traffic conditions and evacuation locations.

[0541] The acquired information is stored in an internal database and used to generate prompts.

[0542] Step 5:

[0543] Generate prompt statement

[0544] Based on the relevant information collected by the server, a prompt sentence is generated to be input to the generative AI model. The input is the relevant information data, and the output is the prompt sentence. The prompt generation module creates a prompt sentence such as "Safety instructions for users near the epicenter."

[0545] Specific behavior:

[0546] The server parses the relevant information and generates the appropriate prompt.

[0547] The generated prompt sentence is sent to the API of the generative AI model.

[0548] Step 6:

[0549] Generate action guide

[0550] The generative AI model generates action instructions based on the prompt text. The input is the prompt text, and the output is specific action instructions. The generative AI model analyzes the prompt text and generates instructions such as "Hide under a desk until the shaking stops, then head to the designated evacuation site."

[0551] Specific behavior:

[0552] The generative AI model receives the prompt and analyzes it.

[0553] The instruction content is generated in JSON format or similar and returned to the server.

[0554] Step 7:

[0555] notification

[0556] The device receives the action instructions sent from the server and notifies the user. The input is the action instructions from the server, and the output is the notification content to the user. The device displays the action instructions to the user as a pop-up message or audio alert.

[0557] Specific behavior:

[0558] The terminal receives the action guide from the server.

[0559] The user will be notified of the received action instructions via a pop-up message, audio alert, or vibration.

[0560] Step 8:

[0561] User behavior confirmation

[0562] The device monitors the user's behavior and continuously updates and sends location information to the server. The input is the user's location and the output is the updated information sent to the server. The device retrieves location information at regular intervals to check whether the user is moving according to instructions.

[0563] Specific behavior:

[0564] The device will update its location using location services at regular intervals.

[0565] Send updated location information to the server and check user activity.

[0566] By following the above processing steps, the system helps users take prompt and appropriate action in the event of a disaster. Detailed data processing and calculation at each step provide advanced support.

[0567] (Application example 1)

[0568] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0569] Although self-driving vehicles are becoming more common in modern society, systems for taking prompt and appropriate action in the event of a natural disaster or emergency are still not fully in place. As a result, users of self-driving vehicles may become confused in the event of an emergency, making it difficult for them to take appropriate evacuation actions. In particular, the lack of appropriate real-time guidance leaves users with concerns about their safety.

[0570] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0571] In this invention, the server includes means for receiving an emergency notification, means for acquiring location information, means for identifying a target area based on the received emergency notification and the acquired location information, means for collecting related information from a cloud, means for generating a prompt based on the collected information, means for generating action guidance based on the generated prompt, means for notifying a user of the generated action guidance, and means for displaying guidance on safe actions to take in an emergency on a display in the autonomous vehicle or on a smartphone. This enables users of autonomous vehicles to take appropriate actions in real time in an emergency, thereby ensuring the safety of the users.

[0572] "Emergency Notification" means information notifying people of natural disasters or other emergencies.

[0573] "Location Information" means information about the current location of a user or device obtained using GPS, Wi-Fi, or cell tower information.

[0574] The "target area" is an area identified based on emergency notification and acquired location information.

[0575] A "cloud" is a collection of computing resources delivered over the Internet.

[0576] A "prompt" is an instruction sentence that is input to a generative AI model to generate action guidance.

[0577] "Action guidance" is information that instructs the user on specific actions to be taken in an emergency.

[0578] An "autonomous vehicle" is a vehicle that has the ability to drive autonomously without human intervention.

[0579] A "display" is a screen or monitor for displaying information.

[0580] A "smartphone" is a portable information device with advanced computing power and connectivity.

[0581] A "generative AI model" is an artificial intelligence algorithm that generates responses or instructions in natural language based on data.

[0582] This invention is a system for autonomous vehicles that aims to ensure user safety in the event of a natural disaster or other emergency. It integrates various elements, such as emergency notification, location information, target area, cloud computing, prompts, action guidance, autonomous vehicles, displays, smartphones, and generative AI models, to build a system that supports prompt and appropriate actions.

[0583] System configuration

[0584] server

[0585] The server has the following functions:

[0586] 1. Emergency notification analysis function

[0587] The server receives and analyzes emergency notifications about natural disasters and emergencies, such as earthquake alerts, heavy rain and flood warnings, and other alerts from public organizations such as the Japan Meteorological Agency.

[0588] 2. Location information acquisition function

[0589] Current location information is acquired through the GPS module in the autonomous vehicle, allowing the user's location to be tracked in real time.

[0590] 3. Ability to identify target areas

[0591] Based on the emergency notification received and the location information obtained, the affected areas of the emergency are identified.

[0592] 4. Function to collect related information

[0593] Use cloud services (e.g., AWS and Google Cloud) to collect relevant information such as traffic conditions, evacuation sites, and road traffic conditions in real time.

[0594] 5. Prompt generation function using generative AI models

[0595] Based on the information collected from the cloud, a generative AI model (e.g., GPT-4) generates prompts, which are used to instruct appropriate actions in emergencies.

[0596] 6. Function to generate action guides

[0597] Based on the generated prompt text, an action guide is generated that indicates the specific action the user should take.

[0598] 7. Function to notify users of action guidance

[0599] The generated guidance is displayed and notified to the user on the display and smartphone inside the autonomous vehicle, using pop-up messages and audio alerts to ensure the user is informed.

[0600] Terminal

[0601] The terminal installed in the autonomous vehicle has the following functions:

[0602] 1. Location information acquisition function

[0603] Location information is obtained using a GPS module and sent to the server.

[0604] 2. Action guide notification function

[0605] The system notifies the user of the action instructions sent from the server, displaying messages on the screen and also using audio alerts.

[0606] Usage examples and prompt statements

[0607] As a specific example of use, the system behavior when a flood warning occurs is shown below.

[0608] 1. The server receives a heavy rain and flood warning from the Japan Meteorological Agency.

[0609] 2. The GPS module in the autonomous vehicle acquires the current location information and sends it to the server.

[0610] 3. The server uses location information to determine if the user is in an area affected by flooding.

[0611] 4. The server collects information on evacuation sites and road traffic conditions from the cloud.

[0612] 5. Enter the following prompt into the generative AI model:

[0613] The user is located at point △△ in ○○ city and a flood warning has been issued. Please guide them to safe evacuation routes and evacuation locations.

[0614] 6. The generative AI model generates the instruction, "Flood warning: Please evacuate from your current location to △△ Junior High School. The specific route is via □□ Street."

[0615] 7. The server sends this instruction to a display inside the autonomous vehicle, which then issues a voice alert saying, "Flood warning issued, please follow evacuation instructions."

[0616] In this way, the system of the present invention assists users in taking prompt and appropriate action in an emergency, thereby ensuring their safety.

[0617] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0618] Step 1:

[0619] Receive emergency notifications

[0620] The server receives emergency notifications about natural disasters and other emergencies. For example, it receives real-time earthquake alerts and heavy rain and flood warnings from public organizations such as the Japan Meteorological Agency. The input is the emergency notification data, and the output is the analysis results. The server analyzes this data and identifies the type of disaster and its urgency.

[0621] Step 2:

[0622] Obtaining location information

[0623] The terminal uses the GPS module in the autonomous vehicle to obtain its current location information. The input is the GPS signal, and the output is the location coordinate data. The terminal then sends this location information to the server.

[0624] Step 3:

[0625] Identifying target areas

[0626] The server identifies the affected area based on the received emergency notification and the acquired location information. The input is the analysis result of the emergency notification and the location coordinate data, and the output is the affected area data. The server uses this data to determine whether the user is in the affected area.

[0627] Step 4:

[0628] Gathering relevant information

[0629] The server uses cloud services to collect relevant information such as traffic conditions, evacuation shelters, and road traffic conditions. The input is the target area data, and the output is the collected relevant information. Specifically, the server obtains the necessary data using a cloud API.

[0630] Step 5:

[0631] Prompt Generation

[0632] The server generates a prompt sentence from a generative AI model based on the collected information. The input is the collected relevant information, and the output is the prompt sentence. The server generates a prompt to instruct the generative AI model (e.g., GPT-4) to take a specific action.

[0633] Step 6:

[0634] Generate action guide

[0635] The server generates specific instructions based on the generated prompt. The input is the prompt, and the output is instructions. A generative AI model is used to generate instructions such as "Flood warning: Please evacuate from your current location to △△ Junior High School."

[0636] Step 7:

[0637] Action guide notifications

[0638] The terminal displays and notifies the generated action guidance on the display or smartphone inside the autonomous vehicle. The input is the action guidance, and the output is a notification to the user. Specifically, a message is displayed on the display and an audio alert is also used to ensure that the user is informed.

[0639] This allows users to take appropriate action in real time in an emergency and ensure safety.

[0640] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0641] This invention is a system aimed at ensuring the safety and psychological stability of users in emergency situations, and includes the following elements: A server, a terminal, an emotion engine, and a user work together to support swift and appropriate action in natural disasters and other emergency situations, and also provide appropriate instructions according to emotions.

[0642] System configuration

[0643] server

[0644] The server receives emergency notifications and analyzes their contents. These include earthquake alerts, heavy rain and flood warnings, and other alerts. The server analyzes the received notifications and identifies the type of disaster that requires a response. It then receives location information from the user's device and identifies the affected area. It then uses the cloud to collect relevant information such as traffic conditions, evacuation sites, and the extent of impact in the area, and uses this information to have the generative AI model generate prompts. The prompts generated by the generative AI model are converted into specific action instructions and sent to the device.

[0645] Terminal

[0646] The device has the ability to obtain the user's current location using GPS, Wi-Fi, or cell tower information. When a disaster notification is received, the device immediately obtains the device's location information and sends it to the server. The device also receives action instructions sent from the server and notifies the user of them. Notification methods include pop-up messages, audio alerts, and vibrations.

[0647] Emotion Engine

[0648] The emotion engine is designed to recognize the user's emotions. It determines the user's emotional state using voice data, text data, or other input means. Based on the determined emotion, it adjusts the presentation of action guidance and the information provided. The emotion engine is built into the server or terminal.

[0649] user

[0650] The user takes appropriate action based on notifications from the device. For example, in the event of an earthquake, the user will take shelter under a desk until the shaking subsides, then head to a designated evacuation site. In the event of a flood, the user will move toward the evacuation site and choose a safe route. Furthermore, the user will act while maintaining psychological stability by following the guidance provided by the emotion engine based on their emotions. Location information during evacuation is periodically updated by the device and linked to the server.

[0651] Program processing flow

[0652] Specific examples of earthquakes

[0653] For example, if an earthquake occurs, the system operates as follows: The server receives an earthquake early warning from the Japan Meteorological Agency and analyzes the information to identify the occurrence of the earthquake. Next, it obtains the user's current location information from the device and sends it to the server. The server uses this location information to determine whether the user is close to the epicenter. If the user is close to the epicenter, the server collects relevant information such as traffic conditions and evacuation locations from the cloud. It inputs "safety instructions for users close to the epicenter" as a prompt into the generative AI model, and uses the emotion engine to check the user's emotional state.

[0654] By combining the generative AI model with the emotion engine, action guidance such as "Hold shelter under a desk until the shaking stops, then head to a designated evacuation site" is generated. If the emotion engine recognizes the user's anxiety, it can include additional reassuring information such as "Don't worry. These are steps to ensure your safety." The server sends the generated action guidance to the device, which displays it to the user as a pop-up message. The user then follows the instructions to take safe actions.

[0655] Specific examples of floods

[0656] The same applies when a flood warning is issued. The server receives and analyzes heavy rain and flood warnings from the Japan Meteorological Agency. The device then obtains its current location information and sends it to the server. The server uses the location information to determine whether the user is in an area affected by flooding. If it determines that the user is affected, the server collects information from the cloud, such as evacuation sites and road traffic conditions. Based on the generated prompt, specific action instructions are generated, such as "The evacuation site is XX Elementary School. Please evacuate by following the specified route." If the emotion engine detects stress or anxiety in the user at this time, a message offering reassurance, such as "Please remain calm. The route to the evacuation site is safe," is included.

[0657] The generated action instructions are sent to the device and notified to the user via pop-up messages and voice alerts, allowing the user to safely move towards the designated evacuation site.

[0658] In this way, the system helps users take early and appropriate action in the event of a disaster. In addition, by providing guidance that is tailored to the user's emotional state, it can reduce psychological stress and support more effective evacuation. By working together, each element can provide quick and accurate evacuation instructions, ensuring the user's safety and psychological stability.

[0659] The processing flow will be explained below.

[0660] Step 1:

[0661] Server: Receives emergency earthquake alerts, heavy rain and flood warnings, and other disaster information via API. Analyzes the various disaster information and identifies the type of disaster.

[0662] Step 2:

[0663] Device: When disaster information is received, the application will immediately launch and obtain the user's current location using GPS, Wi-Fi, or cell tower information.

[0664] Step 3:

[0665] Device: The acquired location information is sent to the server. The user's location information is encrypted and sent in a secure manner to protect privacy.

[0666] Step 4:

[0667] Server: Based on the received location information, the server determines whether the user is in an area affected by the disaster. This determination is based on the distance from the epicenter and the predicted inundation areas.

[0668] Step 5:

[0669] Server: If a user is determined to be in an affected area, relevant information such as transportation status, evacuation locations, and the extent of the impact on the area is collected from the cloud.

[0670] Step 6:

[0671] Server: Based on the collected information, it generates prompts to be passed to the generative AI model. Specifically, it creates prompts including the user's current location, the nearest evacuation site, and traffic conditions.

[0672] Step 7:

[0673] Server: Inputs prompts to the generative AI model and generates specific instructions for action, such as "An earthquake has occurred. Take shelter under a desk, and once the shaking has stopped, head to the designated evacuation site."

[0674] Step 8:

[0675] Device: Receives the generated action guide and activates the emotion engine, which analyzes the user's voice and text data to determine their emotional state.

[0676] Step 9:

[0677] Terminal: After the emotion engine determines the user's emotional state, it sends the result to the server.

[0678] Step 10:

[0679] Server: Adjust the guidance based on the received emotion data. For example, if the user expresses anxiety, include additional reassurance such as "Don't worry, these are the steps we're taking to ensure your safety."

[0680] Step 11:

[0681] Server: Sends tailored action instructions to the device, conveying them to the user via pop-up messages, audio alerts, vibrations, etc.

[0682] Step 12:

[0683] Device: Provides instructions to the user. For example, it displays something like, "The evacuation site is XX Elementary School. Follow the designated route and evacuate safely. Rest assured, the situation is understood."

[0684] Step 13:

[0685] User: Receives notifications from the device and acts accordingly. For example, in the event of an earthquake, the user may take shelter under a desk and then head to a designated evacuation site. Location information during evacuation is periodically updated by the device and sent to the server.

[0686] Step 14:

[0687] Server: Receives the user's location information and checks whether the evacuation was successful. Provides continuous support until the evacuation is complete.

[0688] Example 2

[0689] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0690] In the event of a natural disaster or other emergency, the challenge is to help users act quickly and appropriately while maintaining psychological stability. Current systems provide emergency notifications and acquire location information, but do not provide action guidance that takes into account the user's emotional state, which can lead to situations where users' anxiety and confusion cannot be completely alleviated.

[0691] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for receiving an emergency notification, a means for acquiring location information, a means for identifying a target area based on the received emergency notification and the acquired location information, a means for collecting related information from the cloud, a means for generating a prompt sentence based on the collected information, a means for generating action guidance based on the generated prompt sentence, a means for determining the user's emotional state, a means for adjusting the action guidance based on the emotional state, and a means for notifying the user of the generated action guidance. This makes it possible to provide quick and appropriate action guidance that takes the user's emotional state into consideration, and to support appropriate action in the event of a disaster while maintaining the user's psychological stability.

[0692] "Emergency Notification" refers to alerts and bulletins issued in the event of a natural disaster or other emergency.

[0693] "Location Information" means data that indicates a user's current geographic location, such as data obtained from GPS, Wi-Fi, or cell tower information.

[0694] "Target area" refers to an area that is likely to be affected by a disaster, as identified based on emergency notifications and location information.

[0695] "Cloud" refers to storage and computing resources for providing and sharing large amounts of data over the Internet.

[0696] "Relevant information" refers to auxiliary data necessary for disaster response, such as traffic conditions, evacuation sites, and the extent of impact on the area.

[0697] A "prompt" is an instruction given to a generative AI model to generate an output in response to a specific question or instruction to act.

[0698] "Action instructions" refer to specific steps of action that the user should take, generated based on the prompt text.

[0699] "User's emotional state" refers to the psychological state determined from the user's voice data, text data, etc.

[0700] "Means for adjusting action guidance based on emotional state" refers to a function for changing the content and expression of action guidance according to the user's emotional state.

[0701] The present invention relates to a system for ensuring user safety and psychological stability in emergency situations, which functions in cooperation with a server, a terminal, an emotion engine, and a user. In this system, the server receives emergency notifications, acquires location information, collects related information from the cloud, and uses a generative AI model to generate prompts and action guidance for the user. The system also includes a means for determining the user's emotional state using the emotion engine and adjusting the action guidance based on the emotional state.

[0702] server

[0703] The server receives emergency notifications from the Japan Meteorological Agency and other organizations. Emergency notifications include earthquake alerts and heavy rain and flood warnings. The server analyzes the received emergency notifications to identify the type of disaster. Next, it receives the user's location information obtained from the device and identifies the affected area. It also collects related information from the cloud, such as traffic conditions, evacuation sites, and the extent of impact on the area. A generative AI model is used based on this collected information to generate prompt text. For example, prompt texts such as "safety instructions for users near the epicenter" and "evacuation routes for users affected by flooding" are generated. Specific action instructions are created from these prompt texts, and the content is adjusted taking into account the user's emotional state.

[0704] An example prompt for a generative AI model is:

[0705] "Safety instructions for users near the epicenter"

[0706] Example prompt for a generative AI model: "What is the best course of action for a user near the epicenter? Hide under a desk until the shaking stops, then head to a designated evacuation site."

[0707] "Providing reassuring information"

[0708] Example prompt input for the generative AI model: "Please provide reassurance information to reduce user anxiety. Rest assured, these are steps to ensure your safety."

[0709] The server transmits the generated action guide to the terminal and notifies the user.

[0710] Terminal

[0711] The device obtains the user's current location using GPS, Wi-Fi, and cell tower information. When a disaster notification is received, it immediately obtains the location information and sends it to the server. It also receives action instructions sent from the server and notifies the user. Notification methods include pop-up messages, audio alerts, and vibrations. When the user begins to take action, the device periodically updates the location information and sends it to the server. This allows the server to continue to know the user's location in real time.

[0712] Emotion Engine

[0713] The emotion engine uses voice and text data to determine the user's emotional state. It uses speech recognition software and natural language processing technology to analyze the emotions expressed in what the user says and writes. For example, if the user is feeling very anxious, it will add reassuring content. The emotion engine is built into a server or device and often runs on the server to perform advanced analysis. The generated action guidance is adjusted based on the data obtained by the emotion engine.

[0714] user

[0715] Users follow the notifications from their devices and take the actions instructed. For example, in the event of an earthquake, they would follow instructions such as "Hold shelter under a desk until the shaking stops, then head to the designated evacuation site." In the event of a flood, they would follow instructions such as "The evacuation site is XX Elementary School. Please evacuate by following the designated route." While the disaster continues, the device periodically sends its location information to the server, and new instructions are sent to the user when necessary.

[0716] In this way, the system helps users act quickly and appropriately in the event of a natural disaster or other emergency, providing psychological stability. The above configuration ensures the safety and security of users.

[0717] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0718] Specific example in the case of an earthquake

[0719] Step 1:

[0720] The server receives an emergency earthquake alert from the Japan Meteorological Agency.

[0721] Input: Earthquake Early Warning data from the Japan Meteorological Agency.

[0722] Processing: The server's data receiving module analyzes the earthquake early warning data.

[0723] Output: Earthquake occurrence information (occurrence time, epicenter, seismic intensity, etc.).

[0724] Step 2:

[0725] The device receives a disaster notification and obtains the user's current location information.

[0726] Input: Emergency earthquake alert sent from the server.

[0727] Processing: The device's location acquisition module acquires the user's current location using GPS, Wi-Fi, and cell tower information.

[0728] Output: Current location of the user (latitude, longitude).

[0729] Step 3:

[0730] The current location information acquired by the terminal is sent to the server.

[0731] Input: The user's current location.

[0732] Processing: The device sends location information to the server.

[0733] Output: The location information received by the server.

[0734] Step 4:

[0735] The server uses location information to determine whether the user is close to the epicenter.

[0736] Input: Earthquake occurrence information, user's current location information.

[0737] Processing: The server's analysis module calculates the distance between the user's location and the epicenter and evaluates the impact.

[0738] Output: The result of determining whether the user is close or far from the epicenter.

[0739] Step 5:

[0740] The server collects relevant information such as traffic conditions and evacuation locations from the cloud.

[0741] Input: Information about the area of ​​interest.

[0742] Processing: The server collects the necessary information from the traffic database and evacuation site database via the cloud API.

[0743] Output: Information on traffic conditions, evacuation locations, and local impact.

[0744] Step 6:

[0745] The server generates prompt sentences for the generative AI model based on the collected information.

[0746] Input: Earthquake occurrence information, user location information, and related information.

[0747] Processing: The generative AI model is given a prompt: "Safety instructions for users near the epicenter."

[0748] Output: Prompt text "Safety instructions for users near the epicenter."

[0749] Step 7:

[0750] The server generates action guidance using the generative AI model.

[0751] Input: Prompt text "Safety instructions for users near the epicenter."

[0752] Processing: The generative AI model generates specific action instructions based on the prompt sentence.

[0753] Output: Instructions for action: "Take shelter under a desk until the shaking stops, then proceed to the designated evacuation site."

[0754] Step 8:

[0755] The server uses an emotion engine to determine the user's emotional state.

[0756] Input: User voice and text data.

[0757] Processing: The emotion engine analyzes the data and determines the user's anxiety or stress.

[0758] Output: The user's emotional state (calm, anxious, stressed, etc.).

[0759] Step 9:

[0760] The server adjusts the user's behavioral guidance based on the user's emotional state.

[0761] Input: Generated action guide, user's emotional state.

[0762] Treatment: If anxiety is recognized, add an additional reassuring message to the action guide.

[0763] Output: Action instructions: "Take shelter under a desk until the shaking stops, then proceed to a designated evacuation site. Rest assured, these are procedures to ensure your safety."

[0764] Step 10:

[0765] The server transmits the generated action guide to the terminal.

[0766] Input: Coordinated behavioral guidance.

[0767] Processing: Send action instructions to the terminal.

[0768] Output: Action instructions received by the device.

[0769] Step 11:

[0770] The device notifies the user of action instructions.

[0771] Input: Action guide.

[0772] Action: Notify action instructions using pop-up messages, audio alerts, and vibrations.

[0773] Output: The call to action received by the user.

[0774] Step 12:

[0775] The user takes the indicated action.

[0776] Input: Action guide.

[0777] Action: The user "takes shelter under a desk until the shaking stops, then heads to a designated evacuation location."

[0778] Output: The user takes action to evacuate to a safe place.

[0779] Step 13:

[0780] The device periodically updates the user's current location and sends it to the server.

[0781] Input: The user's new location.

[0782] Processing: Periodically obtain location information and send it to the server.

[0783] Output: The latest location information received by the server.

[0784] Step 14:

[0785] The server generates additional action guidance as needed and transmits it to the terminal.

[0786] Input: Latest location and emergency notification updates.

[0787] Processing: Using the generative AI model, new action guidance is generated and sent to the device.

[0788] Output: New action instructions received by the device.

[0789] In this way, a system is constructed that supports users in taking prompt and appropriate action in the event of a disaster through specific actions at each step.

[0790] (Application example 2)

[0791] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0792] In the event of a natural disaster or other emergency, users are required to take prompt and appropriate action. However, conventional systems are limited to emergency notifications and location information acquisition, and lack guidance based on the user's individual emotional state, which can lead to anxiety and panic. Furthermore, it is difficult to convert the collected information into appropriate action guidance, which can result in delayed notification to users.

[0793] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0794] In this invention, the server includes means for receiving an emergency notification, means for acquiring location information, means for identifying a target area based on the received emergency notification and the acquired location information, means for collecting related information from the cloud, means for generating a prompt sentence based on the collected information, means for generating action guidance from the prompt sentence using a generative AI model, means for detecting the user's emotional state and adjusting the action guidance according to that state, and means for notifying the user of the generated action guidance. This allows the user to receive prompt and accurate action guidance in an emergency, and by being provided with individual instructions according to the user's emotional state, the user can act safely while maintaining psychological stability.

[0795] "Emergency Notification" means a notification regarding a natural disaster or other emergency.

[0796] "Location Information" means data about a user's current location obtained using GPS, Wi-Fi, or cell tower information.

[0797] "Target Area" means an area potentially affected by a disaster, as identified based on emergency notification and location information.

[0798] The "cloud" is a distributed infrastructure of resources, data storage, and computing power delivered over the internet.

[0799] "Relevant information" is information necessary for dealing with an emergency, such as traffic conditions, evacuation sites, and the extent of impact on the area.

[0800] A "prompt sentence" is a collection of terms and sentences that are input into a generative AI model and serve as the source data for generating action guidance.

[0801] A "generative AI model" is an artificial intelligence model that generates appropriate action guidance from a prompt sentence.

[0802] "Action guidance" is information that provides specific instructions on what actions a user should take in an emergency.

[0803] "Emotional state" refers to the user's psychological state as sensed using voice data, text data, or other input means.

[0804] The "emotion engine" is a function that recognizes the user's emotional state and adjusts behavioral guidance.

[0805] The present invention is a system for ensuring the safety of users and providing psychological stability in emergency situations. The system includes the following components: a server, a terminal, an emotion engine, and a user.

[0806] server

[0807] The server has the function of receiving emergency notifications and analyzing their contents. Emergency notifications include earthquake alerts, heavy rain and flood warnings, and other warnings. When this notification is received, the server analyzes its contents and identifies the type of disaster that requires response.

[0808] The server then retrieves the user's location from the device and identifies the target area, which can be obtained using GPS, Wi-Fi, or cell tower information.

[0809] The server then uses the cloud to collect relevant information such as traffic conditions, evacuation sites, and the extent of local impact. Based on this information, the server uses a generative AI model to generate a prompt. The generated prompt is text data in the format "Notification: Earthquake Warning, Location: Tokyo, Emotion: Fear." Using this prompt as input, the generative AI model generates specific action instructions and adjusts those instructions.

[0810] Terminal

[0811] The device has the ability to obtain the user's current location. It obtains location information using GPS, Wi-Fi, or cell tower information and sends it to the server. The device also receives action instructions sent from the server and notifies the user of them. Notification methods include pop-up messages, audio alerts, and vibrations.

[0812] Emotion Engine

[0813] The emotion engine has the function of recognizing the user's emotional state and adjusting the action guidance. It determines the user's emotional state using voice data, text data, or other input means. Based on the determined emotion, it adjusts the expression and content of the generated action guidance to provide the user with appropriate instructions.

[0814] Specific examples

[0815] When an earthquake occurs, the server receives an earthquake early warning from the Japan Meteorological Agency and analyzes the information to identify the occurrence of the earthquake. The device then obtains the user's current location information and sends it to the server. The server uses this location information to determine whether the user is near the epicenter.

[0816] If the user is close to the epicenter, the server collects relevant information such as traffic conditions and evacuation sites from the cloud. The generated prompt is "Notification: Earthquake warning, Location: Tokyo, Emotion: Fear." This prompt is input into the generative AI model, and the emotion engine is used to check the user's emotional state.

[0817] By combining the generative AI model with the emotion engine, the system can generate action instructions such as, "Hold shelter under a desk until the shaking stops, then proceed to a designated evacuation site." Furthermore, if the emotion engine recognizes the user's anxiety, it can include additional reassurance information such as, "Don't worry. These are steps to ensure your safety."

[0818] In this way, the system helps users take early and appropriate action in the event of a disaster. It also provides guidance tailored to the user's emotional state, reducing psychological stress and supporting more effective evacuation.

[0819] In the embodiment of the present invention, the above elements function in cooperation with each other to provide quick and accurate evacuation instructions and ensure the safety and psychological stability of the user.

[0820] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0821] Step 1:

[0822] The server receives emergency notifications. Specifically, it receives emergency notifications such as earthquake alerts and flood warnings from the Japan Meteorological Agency and other public organizations in real time. The input data is the content of the emergency notification, and the output is the emergency notification data to be analyzed. The server analyzes the necessary information from this notification data and identifies the type and details of the disaster.

[0823] Step 2:

[0824] The device obtains the user's location information. Specifically, it obtains the current location using GPS, Wi-Fi, or cell tower information and sends it to the server. The input data is the user's location information, and the output is the user's latitude and longitude data.

[0825] Step 3:

[0826] The server identifies the affected area based on the received emergency notification and the acquired location information. The server compares the user's location with the extent of the disaster's impact and determines whether the user is in an area affected by the disaster. The input data is the emergency notification and location information, and the output is data identifying the affected area.

[0827] Step 4:

[0828] The server collects relevant information from the cloud, such as traffic conditions, evacuation sites, and the extent of impact on the area, and integrates it within the server. The input data is specific data for the target area, and the output is the collected relevant information.

[0829] Step 5:

[0830] The server generates a prompt sentence based on the collected related information. The generated prompt sentence is text data to be input into the generative AI model. An example of a prompt sentence is "Notification: Earthquake warning, Location: Tokyo, Emotion: Fear." The input data is the collected related information, and the output is the generated prompt sentence.

[0831] Step 6:

[0832] The server uses a generative AI model to generate action instructions from prompts. The input data is the generated prompt, and specific action instructions are output through data calculations. For example, "Please take shelter under a desk until the shaking stops, then head to the designated evacuation site."

[0833] Step 7:

[0834] The server detects the user's emotional state and adjusts the action guidance accordingly. Specifically, it analyzes the user's emotions from voice and text data, and the emotion engine adjusts the action guidance based on the results. The input data is the user's emotional state, and the output is the adjusted action guidance.

[0835] Step 8:

[0836] The terminal notifies the user of the generated action guidance using a pop-up message, a voice alert, and vibration. The input data is the adjusted action guidance, and the output is displayed as a notification to the user.

[0837] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0838] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0839] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0840] [Third embodiment]

[0841] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0842] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0843] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0844] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0845] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0846] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0847] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0848] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0849] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0850] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0851] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0852] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0853] The present invention is a system aimed at ensuring the safety of users in emergency situations, and includes the following elements: A server, terminals, and users work together to support prompt and appropriate action in the event of a natural disaster or other emergency.

[0854] System configuration

[0855] server

[0856] The server receives and analyzes emergency notifications, including earthquake alerts, heavy rain and flood warnings, and other alerts. It analyzes the received notifications to identify the type of disaster that requires a response. It then receives location information from the user's device and identifies the affected area. It then uses the cloud to collect relevant information, such as traffic conditions, evacuation locations, and the extent of impact in the area, and uses this information to generate prompts using a generative AI model.

[0857] The prompts generated by the generative AI model are converted into specific action instructions, which are then sent to the device and notified to the user.

[0858] Terminal

[0859] The device has the ability to obtain the user's current location using GPS, Wi-Fi, or cell tower information. When a disaster notification is received, the device immediately obtains the device's location information and sends it to the server. The device also receives action instructions sent from the server and notifies the user of them. Notification methods include pop-up messages, audio alerts, and vibrations.

[0860] user

[0861] Users take appropriate action based on notifications from their devices. For example, in the event of an earthquake, users will take shelter under a desk until the shaking subsides, then head to a designated evacuation site. In the event of a flood, users will always move toward an evacuation site and choose a safe route. To confirm whether users have followed the instructions, devices continuously update their location information and connect with the server.

[0862] Program processing flow

[0863] Specific examples of earthquakes

[0864] For example, if an earthquake occurs, the system operates as follows: The server receives an earthquake early warning from the Japan Meteorological Agency and analyzes the information to identify the occurrence of the earthquake. Next, it obtains the user's current location information from the device and sends it to the server. The server uses this location information to determine whether the user is close to the epicenter. If the user is close to the epicenter, the server collects relevant information such as traffic conditions and evacuation locations from the cloud and inputs "safety instructions for users close to the epicenter" as a prompt into the generative AI model.

[0865] The generative AI model generates action guidance such as "Hold shelter under a desk until the shaking stops, then head to a designated evacuation site." This is received by the server and sent to the device. The device then displays this action guidance to the user as a pop-up message. The user then follows this to take safe action.

[0866] Specific examples of floods

[0867] The same applies when a flood warning is issued. The server receives and analyzes the heavy rain and flood warning from the Japan Meteorological Agency. The device then obtains the user's current location information and sends it to the server. The server uses the location information to determine whether the user is in an area affected by flooding. If it determines that the user is affected, the server collects information from the cloud, such as evacuation sites and road traffic conditions. Based on the generated prompt, specific action instructions are generated, such as "The evacuation site is XX Elementary School. Please evacuate by following the specified route." The action instructions are sent to the device, and the user is notified via a pop-up message or voice alert.

[0868] In this way, the system helps users take early and appropriate action in the event of a disaster. By working together, each element can provide quick and accurate evacuation instructions and ensure user safety.

[0869] The processing flow will be explained below.

[0870] Step 1:

[0871] Server: Receives emergency earthquake alerts, heavy rain and flood warnings, and other disaster information via API. Analyzes the various disaster information and identifies the type of disaster.

[0872] Step 2:

[0873] Device: When disaster information is received, the application will immediately launch and obtain the user's current location using GPS, Wi-Fi, or cell tower information.

[0874] Step 3:

[0875] Device: The acquired location information is sent to the server. The user's location information is encrypted and sent in a secure manner to protect privacy.

[0876] Step 4:

[0877] Server: Based on the received location information, the server determines whether the user is in an area affected by the disaster. This determination is based on the distance from the epicenter and the predicted inundation areas.

[0878] Step 5:

[0879] Server: If a user is determined to be in an affected area, relevant information such as transportation status, evacuation locations, and the extent of the impact on the area is collected from the cloud.

[0880] Step 6:

[0881] Server: Based on the collected information, it generates prompts to be passed to the generative AI model. Specifically, it creates prompts including the user's current location, the nearest evacuation site, and traffic conditions.

[0882] Step 7:

[0883] Server: Inputs prompts to the generative AI model and generates specific instructions for action, such as "An earthquake has occurred. Take shelter under a desk, and once the shaking has stopped, head to the designated evacuation site."

[0884] Step 8:

[0885] Server: Sends the generated action guide to the terminal. The action guide is formatted in a way that is intuitively understandable to the user.

[0886] Step 9:

[0887] Device: Notify the user of the action to be taken, using pop-up messages, audio alerts, vibrations, etc.

[0888] Step 10:

[0889] User: Receives notifications from the device and acts accordingly. For example, in the event of an earthquake, the user may take shelter under a desk and then head to a designated evacuation site. Location information during evacuation is periodically updated by the device and sent to the server.

[0890] Step 11:

[0891] Server: Receives the user's location information and checks whether the evacuation was successful. Provides continuous support until the evacuation is complete.

[0892] Example 1

[0893] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0894] The challenge is the lack of means to take prompt and appropriate action in the event of a natural disaster or other emergency. Conventional systems lack the ability to not only receive emergency notifications but also to immediately instruct users on what specific actions to take based on those notifications. This makes it difficult for users to take appropriate action when faced with a dangerous situation.

[0895] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0896] In this invention, the server includes means for receiving an emergency notification, means for acquiring location information, means for identifying a target area based on the received emergency notification and the acquired location information, means for collecting related information from the cloud, means for generating a prompt sentence based on the collected information, a generation AI model means for generating action guidance based on the generated prompt sentence, means for notifying the user of the generated action guidance, and means for monitoring the user's actions and updating the location information. This enables the user to take appropriate action immediately in the event of a disaster, and provides quick and accurate evacuation instructions.

[0897] "Emergency Notification" means alerts and bulletins that provide real-time information about natural disasters and other emergencies.

[0898] "Location information" refers to the geographic coordinates of a user's current location, and is data obtained using technologies such as GPS, Wi-Fi, and cell tower information.

[0899] "Affected Area" means an area potentially affected by a disaster or emergency, as identified based on received emergency notifications and acquired location information.

[0900] "Cloud" refers to a virtual environment that provides data storage and computing resources using remote servers and services over the Internet.

[0901] "Related information" refers to information necessary for disaster prevention, such as traffic conditions, evacuation sites, and the extent of impact on the area, obtained from the cloud.

[0902] A "prompt" is an instruction based on specific conditions that is input to a generative AI model.

[0903] A "generative AI model" refers to an artificial intelligence model that automatically generates specific action instructions for users to take based on the input prompt text.

[0904] "Action guidance" refers to instructions generated by a generative AI model that indicate specific actions a user should take in an emergency.

[0905] "Notification" refers to a means of presenting information via a terminal to convey the generated action guidance to the user.

[0906] "Monitoring behavior" refers to the act of continuously obtaining location information and sending it to a server to confirm that the user is behaving appropriately in accordance with instructions.

[0907] The present invention is a system designed to ensure the safety of users in emergency situations. To this end, a server, terminals, and users work together to support prompt and appropriate action in the event of a natural disaster or other emergency. The configuration and operation of this system are as follows.

[0908] System configuration

[0909] server

[0910] The server has the function of receiving emergency notifications. It uses an API to receive and analyze emergency notifications (earthquake alerts, heavy rain and flood warnings, etc.) from the Japan Meteorological Agency and disaster prevention organizations in real time. The server analyzes the received notifications to identify the type of disaster, and then determines the affected area based on the location information.

[0911] In addition, the server collects relevant information from the cloud, such as traffic conditions, evacuation sites, and the extent of impact in the area. This information is obtained using cloud services such as Google Maps API. Based on the collected information, it generates prompt sentences to be input into the generative AI model.

[0912] For example, generate the following prompt:

[0913] "Generate safety instructions for users near the epicenter."

[0914] "Generate safe behavior instructions for users affected by flooding."

[0915] The generative AI model takes these prompts as input and generates specific instructions for the user to take, which are then received by the server and sent to the device.

[0916] Terminal

[0917] The device has the ability to obtain the user's current location. This is done using GPS, Wi-Fi, and cell tower information. When an emergency notification is received, the device immediately obtains the location information and sends it to the server. It also has the ability to receive action instructions sent from the server and notify the user.

[0918] Notification methods include pop-up messages, audio alerts, and vibrations. For example, a pop-up message will be displayed with instructions such as "Stay under a desk until the shaking stops, then head to a designated evacuation site."

[0919] user

[0920] Users can take appropriate action based on notifications from their devices. Specifically, in the event of an earthquake, users can take shelter under a desk until the shaking subsides and then head to a designated evacuation site. In the event of a flood, users can move toward a designated evacuation site and choose a safe route.

[0921] Additionally, to ensure that users follow the instructions, the device continuously updates and transmits location information to the server, allowing the server to monitor users' movements and determine whether they have followed the instructions.

[0922] Specific actions

[0923] When an earthquake occurs, the server receives an earthquake early warning from the Japan Meteorological Agency and analyzes the information to identify the occurrence of the earthquake. Next, the server obtains the user's current location information from the device and sends it to the server. Based on this location information, the server determines whether the user is near the epicenter.

[0924] If the user is close to the epicenter, the server collects relevant information such as traffic conditions and evacuation locations from the cloud and inputs "safety instructions for users close to the epicenter" as a prompt into the generative AI model. The generative AI model generates action instructions such as "take shelter under a desk until the shaking stops, then head to the designated evacuation location." The server receives this and sends it to the device. The device displays this action instruction to the user as a pop-up message, and the user follows it to take safe actions.

[0925] The same applies when a flood warning is issued. The server receives and analyzes the heavy rain and flood warning from the Japan Meteorological Agency. The device then obtains the user's current location information and sends it to the server. The server uses the location information to determine whether the user is in an area affected by flooding. If it determines that the user is affected, the server collects information from the cloud, such as evacuation sites and road traffic conditions. Based on the generated prompt, specific action instructions are generated, such as "The evacuation site is XX Elementary School. Please evacuate by following the specified route." The action instructions are sent to the device, and the user is notified via a pop-up message or voice alert.

[0926] In this way, the system helps users take early and appropriate action in the event of a disaster. By linking together the various elements, the system can provide quick and accurate evacuation instructions and ensure the safety of users.

[0927] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0928] Step 1:

[0929] Receive emergency notifications

[0930] The server receives emergency notifications from the Japan Meteorological Agency and disaster prevention organizations. The input is emergency alert data obtained from the Japan Meteorological Agency's API, and the output is analyzed disaster information. The server uses the API to stream data in real time and receive notifications. This data is sent to the analysis module, which identifies the type of disaster.

[0931] Specific behavior:

[0932] The server obtains earthquake alerts, heavy rain and flood warnings, etc. from the Japan Meteorological Agency's API.

[0933] The acquired data is analyzed to identify the type of disaster, such as "earthquake" or "flood."

[0934] Step 2:

[0935] Obtaining user location information

[0936] The device obtains the user's current location and sends it to the server. The input is location data (GPS, Wi-Fi, cell tower information) obtained from the device's sensors, and the output is location information sent to the server. The device activates location services to obtain GPS data. If the GPS signal is insufficient, the device complements the location using Wi-Fi network and cell tower information.

[0937] Specific behavior:

[0938] The device uses location services to obtain the user's current location.

[0939] The acquired location information is sent to the server via API.

[0940] Step 3:

[0941] Identifying target areas

[0942] The server identifies areas that may be affected by a disaster based on the emergency notification received and the acquired location information. The input is the emergency notification and the user's location information, and the output is the result of identifying the affected area. The server integrates this information and evaluates the extent of the impact.

[0943] Specific behavior:

[0944] The server combines the user's location information with disaster information to identify affected areas.

[0945] Information about the identified target area is sent to a cloud service.

[0946] Step 4:

[0947] Gathering relevant information

[0948] The server collects relevant information such as traffic conditions, evacuation sites, and the extent of impact on the area from the cloud. The input is a request to the cloud service, and the output is the collected relevant information. The server obtains the necessary information in real time using the Google Maps API, etc.

[0949] Specific behavior:

[0950] The server calls the API of the cloud service to obtain information on traffic conditions and evacuation locations.

[0951] The acquired information is stored in an internal database and used to generate prompts.

[0952] Step 5:

[0953] Generate prompt statement

[0954] Based on the relevant information collected by the server, a prompt sentence is generated to be input to the generative AI model. The input is the relevant information data, and the output is the prompt sentence. The prompt generation module creates a prompt sentence such as "Safety instructions for users near the epicenter."

[0955] Specific behavior:

[0956] The server parses the relevant information and generates the appropriate prompt.

[0957] The generated prompt sentence is sent to the API of the generative AI model.

[0958] Step 6:

[0959] Generate action guide

[0960] The generative AI model generates action instructions based on the prompt text. The input is the prompt text, and the output is specific action instructions. The generative AI model analyzes the prompt text and generates instructions such as "Hide under a desk until the shaking stops, then head to the designated evacuation site."

[0961] Specific behavior:

[0962] The generative AI model receives the prompt and analyzes it.

[0963] The instruction content is generated in JSON format or similar and returned to the server.

[0964] Step 7:

[0965] notification

[0966] The device receives the action instructions sent from the server and notifies the user. The input is the action instructions from the server, and the output is the notification content to the user. The device displays the action instructions to the user as a pop-up message or audio alert.

[0967] Specific behavior:

[0968] The terminal receives the action guide from the server.

[0969] The user will be notified of the received action instructions via a pop-up message, audio alert, or vibration.

[0970] Step 8:

[0971] User behavior confirmation

[0972] The device monitors the user's behavior and continuously updates and sends location information to the server. The input is the user's location and the output is the updated information sent to the server. The device retrieves location information at regular intervals to check whether the user is moving according to instructions.

[0973] Specific behavior:

[0974] The device will update its location using location services at regular intervals.

[0975] Send updated location information to the server and check user activity.

[0976] By following the above processing steps, the system helps users take prompt and appropriate action in the event of a disaster. Detailed data processing and calculation at each step provide advanced support.

[0977] (Application example 1)

[0978] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0979] Although self-driving vehicles are becoming more common in modern society, systems for taking prompt and appropriate action in the event of a natural disaster or emergency are still not fully in place. As a result, users of self-driving vehicles may become confused in the event of an emergency, making it difficult for them to take appropriate evacuation actions. In particular, the lack of appropriate real-time guidance leaves users with concerns about their safety.

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

[0981] In this invention, the server includes means for receiving an emergency notification, means for acquiring location information, means for identifying a target area based on the received emergency notification and the acquired location information, means for collecting related information from a cloud, means for generating a prompt based on the collected information, means for generating action guidance based on the generated prompt, means for notifying a user of the generated action guidance, and means for displaying guidance on safe actions to take in an emergency on a display in the autonomous vehicle or on a smartphone. This enables users of autonomous vehicles to take appropriate actions in real time in an emergency, thereby ensuring the safety of the users.

[0982] "Emergency Notification" means information notifying people of natural disasters or other emergencies.

[0983] "Location Information" means information about the current location of a user or device obtained using GPS, Wi-Fi, or cell tower information.

[0984] The "target area" is an area identified based on emergency notification and acquired location information.

[0985] A "cloud" is a collection of computing resources delivered over the Internet.

[0986] A "prompt" is an instruction sentence that is input to a generative AI model to generate action guidance.

[0987] "Action guidance" is information that instructs the user on specific actions to be taken in an emergency.

[0988] An "autonomous vehicle" is a vehicle that has the ability to drive autonomously without human intervention.

[0989] A "display" is a screen or monitor for displaying information.

[0990] A "smartphone" is a portable information device with advanced computing power and connectivity.

[0991] A "generative AI model" is an artificial intelligence algorithm that generates responses or instructions in natural language based on data.

[0992] This invention is a system for autonomous vehicles that aims to ensure user safety in the event of a natural disaster or other emergency. It integrates various elements, such as emergency notification, location information, target area, cloud computing, prompts, action guidance, autonomous vehicles, displays, smartphones, and generative AI models, to build a system that supports prompt and appropriate actions.

[0993] System configuration

[0994] server

[0995] The server has the following functions:

[0996] 1. Emergency notification analysis function

[0997] The server receives and analyzes emergency notifications about natural disasters and emergencies, such as earthquake alerts, heavy rain and flood warnings, and other alerts from public organizations such as the Japan Meteorological Agency.

[0998] 2. Location information acquisition function

[0999] Current location information is acquired through the GPS module in the autonomous vehicle, allowing the user's location to be tracked in real time.

[1000] 3. Ability to identify target areas

[1001] Based on the emergency notification received and the location information obtained, the affected areas of the emergency are identified.

[1002] 4. Function to collect related information

[1003] Use cloud services (e.g., AWS and Google Cloud) to collect relevant information such as traffic conditions, evacuation sites, and road traffic conditions in real time.

[1004] 5. Prompt generation function using generative AI models

[1005] Based on the information collected from the cloud, a generative AI model (e.g., GPT-4) generates prompts, which are used to instruct appropriate actions in emergencies.

[1006] 6. Function to generate action guides

[1007] Based on the generated prompt text, an action guide is generated that indicates the specific action the user should take.

[1008] 7. Function to notify users of action guidance

[1009] The generated guidance is displayed and notified to the user on the display and smartphone inside the autonomous vehicle, using pop-up messages and audio alerts to ensure the user is informed.

[1010] Terminal

[1011] The terminal installed in the autonomous vehicle has the following functions:

[1012] 1. Location information acquisition function

[1013] Location information is obtained using a GPS module and sent to the server.

[1014] 2. Action guide notification function

[1015] The system notifies the user of the action instructions sent from the server, displaying messages on the screen and also using audio alerts.

[1016] Usage examples and prompt statements

[1017] As a specific example of use, the system behavior when a flood warning occurs is shown below.

[1018] 1. The server receives a heavy rain and flood warning from the Japan Meteorological Agency.

[1019] 2. The GPS module in the autonomous vehicle acquires the current location information and sends it to the server.

[1020] 3. The server uses location information to determine if the user is in an area affected by flooding.

[1021] 4. The server collects information on evacuation sites and road traffic conditions from the cloud.

[1022] 5. Enter the following prompt into the generative AI model:

[1023] The user is located at point △△ in ○○ city and a flood warning has been issued. Please guide them to safe evacuation routes and evacuation locations.

[1024] 6. The generative AI model generates the instruction, "Flood warning: Please evacuate from your current location to △△ Junior High School. The specific route is via □□ Street."

[1025] 7. The server sends this instruction to a display inside the autonomous vehicle, which then issues a voice alert saying, "Flood warning issued, please follow evacuation instructions."

[1026] In this way, the system of the present invention assists users in taking prompt and appropriate action in an emergency, thereby ensuring their safety.

[1027] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1028] Step 1:

[1029] Receive emergency notifications

[1030] The server receives emergency notifications about natural disasters and other emergencies. For example, it receives real-time earthquake alerts and heavy rain and flood warnings from public organizations such as the Japan Meteorological Agency. The input is the emergency notification data, and the output is the analysis results. The server analyzes this data and identifies the type of disaster and its urgency.

[1031] Step 2:

[1032] Obtaining location information

[1033] The terminal uses the GPS module in the autonomous vehicle to obtain its current location information. The input is the GPS signal, and the output is the location coordinate data. The terminal then sends this location information to the server.

[1034] Step 3:

[1035] Identifying target areas

[1036] The server identifies the affected area based on the received emergency notification and the acquired location information. The input is the analysis result of the emergency notification and the location coordinate data, and the output is the affected area data. The server uses this data to determine whether the user is in the affected area.

[1037] Step 4:

[1038] Gathering relevant information

[1039] The server uses cloud services to collect relevant information such as traffic conditions, evacuation shelters, and road traffic conditions. The input is the target area data, and the output is the collected relevant information. Specifically, the server obtains the necessary data using a cloud API.

[1040] Step 5:

[1041] Prompt Generation

[1042] The server generates a prompt sentence from a generative AI model based on the collected information. The input is the collected relevant information, and the output is the prompt sentence. The server generates a prompt to instruct the generative AI model (e.g., GPT-4) to take a specific action.

[1043] Step 6:

[1044] Generate action guide

[1045] The server generates specific instructions based on the generated prompt. The input is the prompt, and the output is instructions. A generative AI model is used to generate instructions such as "Flood warning: Please evacuate from your current location to △△ Junior High School."

[1046] Step 7:

[1047] Action guide notifications

[1048] The terminal displays and notifies the generated action guidance on the display or smartphone inside the autonomous vehicle. The input is the action guidance, and the output is a notification to the user. Specifically, a message is displayed on the display and an audio alert is also used to ensure that the user is informed.

[1049] This allows users to take appropriate action in real time in an emergency and ensure safety.

[1050] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1051] This invention is a system aimed at ensuring the safety and psychological stability of users in emergency situations, and includes the following elements: A server, a terminal, an emotion engine, and a user work together to support swift and appropriate action in natural disasters and other emergency situations, and also provide appropriate instructions according to emotions.

[1052] System configuration

[1053] server

[1054] The server receives emergency notifications and analyzes their contents. These include earthquake alerts, heavy rain and flood warnings, and other alerts. The server analyzes the received notifications and identifies the type of disaster that requires a response. It then receives location information from the user's device and identifies the affected area. It then uses the cloud to collect relevant information such as traffic conditions, evacuation sites, and the extent of impact in the area, and uses this information to have the generative AI model generate prompts. The prompts generated by the generative AI model are converted into specific action instructions and sent to the device.

[1055] Terminal

[1056] The device has the ability to obtain the user's current location using GPS, Wi-Fi, or cell tower information. When a disaster notification is received, the device immediately obtains the device's location information and sends it to the server. The device also receives action instructions sent from the server and notifies the user of them. Notification methods include pop-up messages, audio alerts, and vibrations.

[1057] Emotion Engine

[1058] The emotion engine is designed to recognize the user's emotions. It determines the user's emotional state using voice data, text data, or other input means. Based on the determined emotion, it adjusts the presentation of action guidance and the information provided. The emotion engine is built into the server or terminal.

[1059] user

[1060] The user takes appropriate action based on notifications from the device. For example, in the event of an earthquake, the user will take shelter under a desk until the shaking subsides, then head to a designated evacuation site. In the event of a flood, the user will move toward the evacuation site and choose a safe route. Furthermore, the user will act while maintaining psychological stability by following the guidance provided by the emotion engine based on their emotions. Location information during evacuation is periodically updated by the device and linked to the server.

[1061] Program processing flow

[1062] Specific examples of earthquakes

[1063] For example, if an earthquake occurs, the system operates as follows: The server receives an earthquake early warning from the Japan Meteorological Agency and analyzes the information to identify the occurrence of the earthquake. Next, it obtains the user's current location information from the device and sends it to the server. The server uses this location information to determine whether the user is close to the epicenter. If the user is close to the epicenter, the server collects relevant information such as traffic conditions and evacuation locations from the cloud. It inputs "safety instructions for users close to the epicenter" as a prompt into the generative AI model, and uses the emotion engine to check the user's emotional state.

[1064] By combining the generative AI model with the emotion engine, action guidance such as "Hold shelter under a desk until the shaking stops, then head to a designated evacuation site" is generated. If the emotion engine recognizes the user's anxiety, it can include additional reassuring information such as "Don't worry. These are steps to ensure your safety." The server sends the generated action guidance to the device, which displays it to the user as a pop-up message. The user then follows the instructions to take safe actions.

[1065] Specific examples of floods

[1066] The same applies when a flood warning is issued. The server receives and analyzes heavy rain and flood warnings from the Japan Meteorological Agency. The device then obtains its current location information and sends it to the server. The server uses the location information to determine whether the user is in an area affected by flooding. If it determines that the user is affected, the server collects information from the cloud, such as evacuation sites and road traffic conditions. Based on the generated prompt, specific action instructions are generated, such as "The evacuation site is XX Elementary School. Please evacuate by following the specified route." If the emotion engine detects stress or anxiety in the user at this time, a message offering reassurance, such as "Please remain calm. The route to the evacuation site is safe," is included.

[1067] The generated action instructions are sent to the device and notified to the user via pop-up messages and voice alerts, allowing the user to safely move towards the designated evacuation site.

[1068] In this way, the system helps users take early and appropriate action in the event of a disaster. In addition, by providing guidance that is tailored to the user's emotional state, it can reduce psychological stress and support more effective evacuation. By working together, each element can provide quick and accurate evacuation instructions, ensuring the user's safety and psychological stability.

[1069] The processing flow will be explained below.

[1070] Step 1:

[1071] Server: Receives emergency earthquake alerts, heavy rain and flood warnings, and other disaster information via API. Analyzes the various disaster information and identifies the type of disaster.

[1072] Step 2:

[1073] Device: When disaster information is received, the application will immediately launch and obtain the user's current location using GPS, Wi-Fi, or cell tower information.

[1074] Step 3:

[1075] Device: The acquired location information is sent to the server. The user's location information is encrypted and sent in a secure manner to protect privacy.

[1076] Step 4:

[1077] Server: Based on the received location information, the server determines whether the user is in an area affected by the disaster. This determination is based on the distance from the epicenter and the predicted inundation areas.

[1078] Step 5:

[1079] Server: If a user is determined to be in an affected area, relevant information such as transportation status, evacuation locations, and the extent of the impact on the area is collected from the cloud.

[1080] Step 6:

[1081] Server: Based on the collected information, it generates prompts to be passed to the generative AI model. Specifically, it creates prompts including the user's current location, the nearest evacuation site, and traffic conditions.

[1082] Step 7:

[1083] Server: Inputs prompts to the generative AI model and generates specific instructions for action, such as "An earthquake has occurred. Take shelter under a desk, and once the shaking has stopped, head to the designated evacuation site."

[1084] Step 8:

[1085] Device: Receives the generated action guide and activates the emotion engine, which analyzes the user's voice and text data to determine their emotional state.

[1086] Step 9:

[1087] Terminal: After the emotion engine determines the user's emotional state, it sends the result to the server.

[1088] Step 10:

[1089] Server: Adjust the guidance based on the received emotion data. For example, if the user expresses anxiety, include additional reassurance such as "Don't worry, these are the steps we're taking to ensure your safety."

[1090] Step 11:

[1091] Server: Sends tailored action instructions to the device, conveying them to the user via pop-up messages, audio alerts, vibrations, etc.

[1092] Step 12:

[1093] Device: Provides instructions to the user. For example, it displays something like, "The evacuation site is XX Elementary School. Follow the designated route and evacuate safely. Rest assured, the situation is understood."

[1094] Step 13:

[1095] User: Receives notifications from the device and acts accordingly. For example, in the event of an earthquake, the user may take shelter under a desk and then head to a designated evacuation site. Location information during evacuation is periodically updated by the device and sent to the server.

[1096] Step 14:

[1097] Server: Receives the user's location information and checks whether the evacuation was successful. Provides continuous support until the evacuation is complete.

[1098] Example 2

[1099] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1100] In the event of a natural disaster or other emergency, the challenge is to help users act quickly and appropriately while maintaining psychological stability. Current systems provide emergency notifications and acquire location information, but do not provide action guidance that takes into account the user's emotional state, which can lead to situations where users' anxiety and confusion cannot be completely alleviated.

[1101] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for receiving an emergency notification, a means for acquiring location information, a means for identifying a target area based on the received emergency notification and the acquired location information, a means for collecting related information from the cloud, a means for generating a prompt sentence based on the collected information, a means for generating action guidance based on the generated prompt sentence, a means for determining the user's emotional state, a means for adjusting the action guidance based on the emotional state, and a means for notifying the user of the generated action guidance. This makes it possible to provide quick and appropriate action guidance that takes the user's emotional state into consideration, and to support appropriate action in the event of a disaster while maintaining the user's psychological stability.

[1102] "Emergency Notification" refers to alerts and bulletins issued in the event of a natural disaster or other emergency.

[1103] "Location Information" means data that indicates a user's current geographic location, such as data obtained from GPS, Wi-Fi, or cell tower information.

[1104] "Target area" refers to an area that is likely to be affected by a disaster, as identified based on emergency notifications and location information.

[1105] "Cloud" refers to storage and computing resources for providing and sharing large amounts of data over the Internet.

[1106] "Relevant information" refers to auxiliary data necessary for disaster response, such as traffic conditions, evacuation sites, and the extent of impact on the area.

[1107] A "prompt" is an instruction given to a generative AI model to generate an output in response to a specific question or instruction to act.

[1108] "Action instructions" refer to specific steps of action that the user should take, generated based on the prompt text.

[1109] "User's emotional state" refers to the psychological state determined from the user's voice data, text data, etc.

[1110] "Means for adjusting action guidance based on emotional state" refers to a function for changing the content and expression of action guidance according to the user's emotional state.

[1111] The present invention relates to a system for ensuring user safety and psychological stability in emergency situations, which functions in cooperation with a server, a terminal, an emotion engine, and a user. In this system, the server receives emergency notifications, acquires location information, collects related information from the cloud, and uses a generative AI model to generate prompts and action guidance for the user. The system also includes a means for determining the user's emotional state using the emotion engine and adjusting the action guidance based on the emotional state.

[1112] server

[1113] The server receives emergency notifications from the Japan Meteorological Agency and other organizations. Emergency notifications include earthquake alerts and heavy rain and flood warnings. The server analyzes the received emergency notifications to identify the type of disaster. Next, it receives the user's location information obtained from the device and identifies the affected area. It also collects related information from the cloud, such as traffic conditions, evacuation sites, and the extent of impact on the area. A generative AI model is used based on this collected information to generate prompt text. For example, prompt texts such as "safety instructions for users near the epicenter" and "evacuation routes for users affected by flooding" are generated. Specific action instructions are created from these prompt texts, and the content is adjusted taking into account the user's emotional state.

[1114] An example prompt for a generative AI model is:

[1115] "Safety instructions for users near the epicenter"

[1116] Example prompt for a generative AI model: "What is the best course of action for a user near the epicenter? Hide under a desk until the shaking stops, then head to a designated evacuation site."

[1117] "Providing reassuring information"

[1118] Example prompt input for the generative AI model: "Please provide reassurance information to reduce user anxiety. Rest assured, these are steps to ensure your safety."

[1119] The server transmits the generated action guide to the terminal and notifies the user.

[1120] Terminal

[1121] The device obtains the user's current location using GPS, Wi-Fi, and cell tower information. When a disaster notification is received, it immediately obtains the location information and sends it to the server. It also receives action instructions sent from the server and notifies the user. Notification methods include pop-up messages, audio alerts, and vibrations. When the user begins to take action, the device periodically updates the location information and sends it to the server. This allows the server to continue to know the user's location in real time.

[1122] Emotion Engine

[1123] The emotion engine uses voice and text data to determine the user's emotional state. It uses speech recognition software and natural language processing technology to analyze the emotions expressed in what the user says and writes. For example, if the user is feeling very anxious, it will add reassuring content. The emotion engine is built into a server or device and often runs on the server to perform advanced analysis. The generated action guidance is adjusted based on the data obtained by the emotion engine.

[1124] user

[1125] Users follow the notifications from their devices and take the actions instructed. For example, in the event of an earthquake, they would follow instructions such as "Hold shelter under a desk until the shaking stops, then head to the designated evacuation site." In the event of a flood, they would follow instructions such as "The evacuation site is XX Elementary School. Please evacuate by following the designated route." While the disaster continues, the device periodically sends its location information to the server, and new instructions are sent to the user when necessary.

[1126] In this way, the system helps users act quickly and appropriately in the event of a natural disaster or other emergency, providing psychological stability. The above configuration ensures the safety and security of users.

[1127] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1128] Specific example in the case of an earthquake

[1129] Step 1:

[1130] The server receives an emergency earthquake alert from the Japan Meteorological Agency.

[1131] Input: Earthquake Early Warning data from the Japan Meteorological Agency.

[1132] Processing: The server's data receiving module analyzes the earthquake early warning data.

[1133] Output: Earthquake occurrence information (occurrence time, epicenter, seismic intensity, etc.).

[1134] Step 2:

[1135] The device receives a disaster notification and obtains the user's current location information.

[1136] Input: Emergency earthquake alert sent from the server.

[1137] Processing: The device's location acquisition module acquires the user's current location using GPS, Wi-Fi, and cell tower information.

[1138] Output: Current location of the user (latitude, longitude).

[1139] Step 3:

[1140] The current location information acquired by the terminal is sent to the server.

[1141] Input: The user's current location.

[1142] Processing: The device sends location information to the server.

[1143] Output: The location information received by the server.

[1144] Step 4:

[1145] The server uses location information to determine whether the user is close to the epicenter.

[1146] Input: Earthquake occurrence information, user's current location information.

[1147] Processing: The server's analysis module calculates the distance between the user's location and the epicenter and evaluates the impact.

[1148] Output: The result of determining whether the user is close or far from the epicenter.

[1149] Step 5:

[1150] The server collects relevant information such as traffic conditions and evacuation locations from the cloud.

[1151] Input: Information about the area of ​​interest.

[1152] Processing: The server collects the necessary information from the traffic database and evacuation site database via the cloud API.

[1153] Output: Information on traffic conditions, evacuation locations, and local impact.

[1154] Step 6:

[1155] The server generates prompt sentences for the generative AI model based on the collected information.

[1156] Input: Earthquake occurrence information, user location information, and related information.

[1157] Processing: The generative AI model is given a prompt: "Safety instructions for users near the epicenter."

[1158] Output: Prompt text "Safety instructions for users near the epicenter."

[1159] Step 7:

[1160] The server generates action guidance using the generative AI model.

[1161] Input: Prompt text "Safety instructions for users near the epicenter."

[1162] Processing: The generative AI model generates specific action instructions based on the prompt sentence.

[1163] Output: Instructions for action: "Take shelter under a desk until the shaking stops, then proceed to the designated evacuation site."

[1164] Step 8:

[1165] The server uses an emotion engine to determine the user's emotional state.

[1166] Input: User voice and text data.

[1167] Processing: The emotion engine analyzes the data and determines the user's anxiety or stress.

[1168] Output: The user's emotional state (calm, anxious, stressed, etc.).

[1169] Step 9:

[1170] The server adjusts the user's behavioral guidance based on the user's emotional state.

[1171] Input: Generated action guide, user's emotional state.

[1172] Treatment: If anxiety is recognized, add an additional reassuring message to the action guide.

[1173] Output: Action instructions: "Take shelter under a desk until the shaking stops, then proceed to a designated evacuation site. Rest assured, these are procedures to ensure your safety."

[1174] Step 10:

[1175] The server transmits the generated action guide to the terminal.

[1176] Input: Coordinated behavioral guidance.

[1177] Processing: Send action instructions to the terminal.

[1178] Output: Action instructions received by the device.

[1179] Step 11:

[1180] The device notifies the user of action instructions.

[1181] Input: Action guide.

[1182] Action: Notify action instructions using pop-up messages, audio alerts, and vibrations.

[1183] Output: The call to action received by the user.

[1184] Step 12:

[1185] The user takes the indicated action.

[1186] Input: Action guide.

[1187] Action: The user "takes shelter under a desk until the shaking stops, then heads to a designated evacuation location."

[1188] Output: The user takes action to evacuate to a safe place.

[1189] Step 13:

[1190] The device periodically updates the user's current location and sends it to the server.

[1191] Input: The user's new location.

[1192] Processing: Periodically obtain location information and send it to the server.

[1193] Output: The latest location information received by the server.

[1194] Step 14:

[1195] The server generates additional action guidance as needed and transmits it to the terminal.

[1196] Input: Latest location and emergency notification updates.

[1197] Processing: Using the generative AI model, new action guidance is generated and sent to the device.

[1198] Output: New action instructions received by the device.

[1199] In this way, a system is constructed that supports users in taking prompt and appropriate action in the event of a disaster through specific actions at each step.

[1200] (Application example 2)

[1201] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1202] In the event of a natural disaster or other emergency, users are required to take prompt and appropriate action. However, conventional systems are limited to emergency notifications and location information acquisition, and lack guidance based on the user's individual emotional state, which can lead to anxiety and panic. Furthermore, it is difficult to convert the collected information into appropriate action guidance, which can result in delayed notification to users.

[1203] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1204] In this invention, the server includes means for receiving an emergency notification, means for acquiring location information, means for identifying a target area based on the received emergency notification and the acquired location information, means for collecting related information from the cloud, means for generating a prompt sentence based on the collected information, means for generating action guidance from the prompt sentence using a generative AI model, means for detecting the user's emotional state and adjusting the action guidance according to that state, and means for notifying the user of the generated action guidance. This allows the user to receive prompt and accurate action guidance in an emergency, and by being provided with individual instructions according to the user's emotional state, the user can act safely while maintaining psychological stability.

[1205] "Emergency Notification" means a notification regarding a natural disaster or other emergency.

[1206] "Location Information" means data about a user's current location obtained using GPS, Wi-Fi, or cell tower information.

[1207] "Target Area" means an area potentially affected by a disaster, as identified based on emergency notification and location information.

[1208] The "cloud" is a distributed infrastructure of resources, data storage, and computing power delivered over the internet.

[1209] "Relevant information" is information necessary for dealing with an emergency, such as traffic conditions, evacuation sites, and the extent of impact on the area.

[1210] A "prompt sentence" is a collection of terms and sentences that are input into a generative AI model and serve as the source data for generating action guidance.

[1211] A "generative AI model" is an artificial intelligence model that generates appropriate action guidance from a prompt sentence.

[1212] "Action guidance" is information that provides specific instructions on what actions a user should take in an emergency.

[1213] "Emotional state" refers to the user's psychological state as sensed using voice data, text data, or other input means.

[1214] The "emotion engine" is a function that recognizes the user's emotional state and adjusts behavioral guidance.

[1215] The present invention is a system for ensuring the safety of users and providing psychological stability in emergency situations. The system includes the following components: a server, a terminal, an emotion engine, and a user.

[1216] server

[1217] The server has the function of receiving emergency notifications and analyzing their contents. Emergency notifications include earthquake alerts, heavy rain and flood warnings, and other warnings. When this notification is received, the server analyzes its contents and identifies the type of disaster that requires response.

[1218] The server then retrieves the user's location from the device and identifies the target area, which can be obtained using GPS, Wi-Fi, or cell tower information.

[1219] The server then uses the cloud to collect relevant information such as traffic conditions, evacuation sites, and the extent of local impact. Based on this information, the server uses a generative AI model to generate a prompt. The generated prompt is text data in the format "Notification: Earthquake Warning, Location: Tokyo, Emotion: Fear." Using this prompt as input, the generative AI model generates specific action instructions and adjusts those instructions.

[1220] Terminal

[1221] The device has the ability to obtain the user's current location. It obtains location information using GPS, Wi-Fi, or cell tower information and sends it to the server. The device also receives action instructions sent from the server and notifies the user of them. Notification methods include pop-up messages, audio alerts, and vibrations.

[1222] Emotion Engine

[1223] The emotion engine has the function of recognizing the user's emotional state and adjusting the action guidance. It determines the user's emotional state using voice data, text data, or other input means. Based on the determined emotion, it adjusts the expression and content of the generated action guidance to provide the user with appropriate instructions.

[1224] Specific examples

[1225] When an earthquake occurs, the server receives an earthquake early warning from the Japan Meteorological Agency and analyzes the information to identify the occurrence of the earthquake. The device then obtains the user's current location information and sends it to the server. The server uses this location information to determine whether the user is near the epicenter.

[1226] If the user is close to the epicenter, the server collects relevant information such as traffic conditions and evacuation sites from the cloud. The generated prompt is "Notification: Earthquake warning, Location: Tokyo, Emotion: Fear." This prompt is input into the generative AI model, and the emotion engine is used to check the user's emotional state.

[1227] By combining the generative AI model with the emotion engine, the system can generate action instructions such as, "Hold shelter under a desk until the shaking stops, then proceed to a designated evacuation site." Furthermore, if the emotion engine recognizes the user's anxiety, it can include additional reassurance information such as, "Don't worry. These are steps to ensure your safety."

[1228] In this way, the system helps users take early and appropriate action in the event of a disaster. It also provides guidance tailored to the user's emotional state, reducing psychological stress and supporting more effective evacuation.

[1229] In the embodiment of the present invention, the above elements function in cooperation with each other to provide quick and accurate evacuation instructions and ensure the safety and psychological stability of the user.

[1230] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1231] Step 1:

[1232] The server receives emergency notifications. Specifically, it receives emergency notifications such as earthquake alerts and flood warnings from the Japan Meteorological Agency and other public organizations in real time. The input data is the content of the emergency notification, and the output is the emergency notification data to be analyzed. The server analyzes the necessary information from this notification data and identifies the type and details of the disaster.

[1233] Step 2:

[1234] The device obtains the user's location information. Specifically, it obtains the current location using GPS, Wi-Fi, or cell tower information and sends it to the server. The input data is the user's location information, and the output is the user's latitude and longitude data.

[1235] Step 3:

[1236] The server identifies the affected area based on the received emergency notification and the acquired location information. The server compares the user's location with the extent of the disaster's impact and determines whether the user is in an area affected by the disaster. The input data is the emergency notification and location information, and the output is data identifying the affected area.

[1237] Step 4:

[1238] The server collects relevant information from the cloud, such as traffic conditions, evacuation sites, and the extent of impact on the area, and integrates it within the server. The input data is specific data for the target area, and the output is the collected relevant information.

[1239] Step 5:

[1240] The server generates a prompt sentence based on the collected related information. The generated prompt sentence is text data to be input into the generative AI model. An example of a prompt sentence is "Notification: Earthquake warning, Location: Tokyo, Emotion: Fear." The input data is the collected related information, and the output is the generated prompt sentence.

[1241] Step 6:

[1242] The server uses a generative AI model to generate action instructions from prompts. The input data is the generated prompt, and specific action instructions are output through data calculations. For example, "Please take shelter under a desk until the shaking stops, then head to the designated evacuation site."

[1243] Step 7:

[1244] The server detects the user's emotional state and adjusts the action guidance accordingly. Specifically, it analyzes the user's emotions from voice and text data, and the emotion engine adjusts the action guidance based on the results. The input data is the user's emotional state, and the output is the adjusted action guidance.

[1245] Step 8:

[1246] The terminal notifies the user of the generated action guidance using a pop-up message, a voice alert, and vibration. The input data is the adjusted action guidance, and the output is displayed as a notification to the user.

[1247] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1248] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1249] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1250] [Fourth embodiment]

[1251] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1252] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1253] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1254] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1255] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1256] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1257] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1258] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1259] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1260] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1261] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1262] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1263] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1264] The present invention is a system aimed at ensuring the safety of users in emergency situations, and includes the following elements: A server, terminals, and users work together to support prompt and appropriate action in the event of a natural disaster or other emergency.

[1265] System configuration

[1266] server

[1267] The server receives and analyzes emergency notifications, including earthquake alerts, heavy rain and flood warnings, and other alerts. It analyzes the received notifications to identify the type of disaster that requires a response. It then receives location information from the user's device and identifies the affected area. It then uses the cloud to collect relevant information, such as traffic conditions, evacuation locations, and the extent of impact in the area, and uses this information to generate prompts using a generative AI model.

[1268] The prompts generated by the generative AI model are converted into specific action instructions, which are then sent to the device and notified to the user.

[1269] Terminal

[1270] The device has the ability to obtain the user's current location using GPS, Wi-Fi, or cell tower information. When a disaster notification is received, the device immediately obtains the device's location information and sends it to the server. The device also receives action instructions sent from the server and notifies the user of them. Notification methods include pop-up messages, audio alerts, and vibrations.

[1271] user

[1272] Users take appropriate action based on notifications from their devices. For example, in the event of an earthquake, users will take shelter under a desk until the shaking subsides, then head to a designated evacuation site. In the event of a flood, users will always move toward an evacuation site and choose a safe route. To confirm whether users have followed the instructions, devices continuously update their location information and connect with the server.

[1273] Program processing flow

[1274] Specific examples of earthquakes

[1275] For example, if an earthquake occurs, the system operates as follows: The server receives an earthquake early warning from the Japan Meteorological Agency and analyzes the information to identify the occurrence of the earthquake. Next, it obtains the user's current location information from the device and sends it to the server. The server uses this location information to determine whether the user is close to the epicenter. If the user is close to the epicenter, the server collects relevant information such as traffic conditions and evacuation locations from the cloud and inputs "safety instructions for users close to the epicenter" as a prompt into the generative AI model.

[1276] The generative AI model generates action guidance such as "Hold shelter under a desk until the shaking stops, then head to a designated evacuation site." This is received by the server and sent to the device. The device then displays this action guidance to the user as a pop-up message. The user then follows this to take safe action.

[1277] Specific examples of floods

[1278] The same applies when a flood warning is issued. The server receives and analyzes the heavy rain and flood warning from the Japan Meteorological Agency. The device then obtains the user's current location information and sends it to the server. The server uses the location information to determine whether the user is in an area affected by flooding. If it determines that the user is affected, the server collects information from the cloud, such as evacuation sites and road traffic conditions. Based on the generated prompt, specific action instructions are generated, such as "The evacuation site is XX Elementary School. Please evacuate by following the specified route." The action instructions are sent to the device, and the user is notified via a pop-up message or voice alert.

[1279] In this way, the system helps users take early and appropriate action in the event of a disaster. By working together, each element can provide quick and accurate evacuation instructions and ensure user safety.

[1280] The processing flow will be explained below.

[1281] Step 1:

[1282] Server: Receives emergency earthquake alerts, heavy rain and flood warnings, and other disaster information via API. Analyzes the various disaster information and identifies the type of disaster.

[1283] Step 2:

[1284] Device: When disaster information is received, the application will immediately launch and obtain the user's current location using GPS, Wi-Fi, or cell tower information.

[1285] Step 3:

[1286] Device: The acquired location information is sent to the server. The user's location information is encrypted and sent in a secure manner to protect privacy.

[1287] Step 4:

[1288] Server: Based on the received location information, the server determines whether the user is in an area affected by the disaster. This determination is based on the distance from the epicenter and the predicted inundation areas.

[1289] Step 5:

[1290] Server: If a user is determined to be in an affected area, relevant information such as transportation status, evacuation locations, and the extent of the impact on the area is collected from the cloud.

[1291] Step 6:

[1292] Server: Based on the collected information, it generates prompts to be passed to the generative AI model. Specifically, it creates prompts including the user's current location, the nearest evacuation site, and traffic conditions.

[1293] Step 7:

[1294] Server: Inputs prompts to the generative AI model and generates specific instructions for action, such as "An earthquake has occurred. Take shelter under a desk, and once the shaking has stopped, head to the designated evacuation site."

[1295] Step 8:

[1296] Server: Sends the generated action guide to the terminal. The action guide is formatted in a way that is intuitively understandable to the user.

[1297] Step 9:

[1298] Device: Notify the user of the action to be taken, using pop-up messages, audio alerts, vibrations, etc.

[1299] Step 10:

[1300] User: Receives notifications from the device and acts accordingly. For example, in the event of an earthquake, the user may take shelter under a desk and then head to a designated evacuation site. Location information during evacuation is periodically updated by the device and sent to the server.

[1301] Step 11:

[1302] Server: Receives the user's location information and checks whether the evacuation was successful. Provides continuous support until the evacuation is complete.

[1303] Example 1

[1304] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1305] The challenge is the lack of means to take prompt and appropriate action in the event of a natural disaster or other emergency. Conventional systems lack the ability to not only receive emergency notifications but also to immediately instruct users on what specific actions to take based on those notifications. This makes it difficult for users to take appropriate action when faced with a dangerous situation.

[1306] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1307] In this invention, the server includes means for receiving an emergency notification, means for acquiring location information, means for identifying a target area based on the received emergency notification and the acquired location information, means for collecting related information from the cloud, means for generating a prompt sentence based on the collected information, a generation AI model means for generating action guidance based on the generated prompt sentence, means for notifying the user of the generated action guidance, and means for monitoring the user's actions and updating the location information. This enables the user to take appropriate action immediately in the event of a disaster, and provides quick and accurate evacuation instructions.

[1308] "Emergency Notification" means alerts and bulletins that provide real-time information about natural disasters and other emergencies.

[1309] "Location information" refers to the geographic coordinates of a user's current location, and is data obtained using technologies such as GPS, Wi-Fi, and cell tower information.

[1310] "Affected Area" means an area potentially affected by a disaster or emergency, as identified based on received emergency notifications and acquired location information.

[1311] "Cloud" refers to a virtual environment that provides data storage and computing resources using remote servers and services over the Internet.

[1312] "Related information" refers to information necessary for disaster prevention, such as traffic conditions, evacuation sites, and the extent of impact on the area, obtained from the cloud.

[1313] A "prompt" is an instruction based on specific conditions that is input to a generative AI model.

[1314] A "generative AI model" refers to an artificial intelligence model that automatically generates specific action instructions for users to take based on the input prompt text.

[1315] "Action guidance" refers to instructions generated by a generative AI model that indicate specific actions a user should take in an emergency.

[1316] "Notification" refers to a means of presenting information via a terminal to convey the generated action guidance to the user.

[1317] "Monitoring behavior" refers to the act of continuously obtaining location information and sending it to a server to confirm that the user is behaving appropriately in accordance with instructions.

[1318] The present invention is a system designed to ensure the safety of users in emergency situations. To this end, a server, terminals, and users work together to support prompt and appropriate action in the event of a natural disaster or other emergency. The configuration and operation of this system are as follows.

[1319] System configuration

[1320] server

[1321] The server has the function of receiving emergency notifications. It uses an API to receive and analyze emergency notifications (earthquake alerts, heavy rain and flood warnings, etc.) from the Japan Meteorological Agency and disaster prevention organizations in real time. The server analyzes the received notifications to identify the type of disaster, and then determines the affected area based on the location information.

[1322] In addition, the server collects relevant information from the cloud, such as traffic conditions, evacuation sites, and the extent of impact in the area. This information is obtained using cloud services such as Google Maps API. Based on the collected information, it generates prompt sentences to be input into the generative AI model.

[1323] For example, generate the following prompt:

[1324] "Generate safety instructions for users near the epicenter."

[1325] "Generate safe behavior instructions for users affected by flooding."

[1326] The generative AI model takes these prompts as input and generates specific instructions for the user to take, which are then received by the server and sent to the device.

[1327] Terminal

[1328] The device has the ability to obtain the user's current location. This is done using GPS, Wi-Fi, and cell tower information. When an emergency notification is received, the device immediately obtains the location information and sends it to the server. It also has the ability to receive action instructions sent from the server and notify the user.

[1329] Notification methods include pop-up messages, audio alerts, and vibrations. For example, a pop-up message will be displayed with instructions such as "Stay under a desk until the shaking stops, then head to a designated evacuation site."

[1330] user

[1331] Users can take appropriate action based on notifications from their devices. Specifically, in the event of an earthquake, users can take shelter under a desk until the shaking subsides and then head to a designated evacuation site. In the event of a flood, users can move toward a designated evacuation site and choose a safe route.

[1332] Additionally, to ensure that users follow the instructions, the device continuously updates and transmits location information to the server, allowing the server to monitor users' movements and determine whether they have followed the instructions.

[1333] Specific actions

[1334] When an earthquake occurs, the server receives an earthquake early warning from the Japan Meteorological Agency and analyzes the information to identify the occurrence of the earthquake. Next, the server obtains the user's current location information from the device and sends it to the server. Based on this location information, the server determines whether the user is near the epicenter.

[1335] If the user is close to the epicenter, the server collects relevant information such as traffic conditions and evacuation locations from the cloud and inputs "safety instructions for users close to the epicenter" as a prompt into the generative AI model. The generative AI model generates action instructions such as "take shelter under a desk until the shaking stops, then head to the designated evacuation location." The server receives this and sends it to the device. The device displays this action instruction to the user as a pop-up message, and the user follows it to take safe actions.

[1336] The same applies when a flood warning is issued. The server receives and analyzes the heavy rain and flood warning from the Japan Meteorological Agency. The device then obtains the user's current location information and sends it to the server. The server uses the location information to determine whether the user is in an area affected by flooding. If it determines that the user is affected, the server collects information from the cloud, such as evacuation sites and road traffic conditions. Based on the generated prompt, specific action instructions are generated, such as "The evacuation site is XX Elementary School. Please evacuate by following the specified route." The action instructions are sent to the device, and the user is notified via a pop-up message or voice alert.

[1337] In this way, the system helps users take early and appropriate action in the event of a disaster. By linking together the various elements, the system can provide quick and accurate evacuation instructions and ensure the safety of users.

[1338] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1339] Step 1:

[1340] Receive emergency notifications

[1341] The server receives emergency notifications from the Japan Meteorological Agency and disaster prevention organizations. The input is emergency alert data obtained from the Japan Meteorological Agency's API, and the output is analyzed disaster information. The server uses the API to stream data in real time and receive notifications. This data is sent to the analysis module, which identifies the type of disaster.

[1342] Specific behavior:

[1343] The server obtains earthquake alerts, heavy rain and flood warnings, etc. from the Japan Meteorological Agency's API.

[1344] The acquired data is analyzed to identify the type of disaster, such as "earthquake" or "flood."

[1345] Step 2:

[1346] Obtaining user location information

[1347] The device obtains the user's current location and sends it to the server. The input is location data (GPS, Wi-Fi, cell tower information) obtained from the device's sensors, and the output is location information sent to the server. The device activates location services to obtain GPS data. If the GPS signal is insufficient, the device complements the location using Wi-Fi network and cell tower information.

[1348] Specific behavior:

[1349] The device uses location services to obtain the user's current location.

[1350] The acquired location information is sent to the server via API.

[1351] Step 3:

[1352] Identifying target areas

[1353] The server identifies areas that may be affected by a disaster based on the emergency notification received and the acquired location information. The input is the emergency notification and the user's location information, and the output is the result of identifying the affected area. The server integrates this information and evaluates the extent of the impact.

[1354] Specific behavior:

[1355] The server combines the user's location information with disaster information to identify affected areas.

[1356] Information about the identified target area is sent to a cloud service.

[1357] Step 4:

[1358] Gathering relevant information

[1359] The server collects relevant information such as traffic conditions, evacuation sites, and the extent of impact on the area from the cloud. The input is a request to the cloud service, and the output is the collected relevant information. The server obtains the necessary information in real time using the Google Maps API, etc.

[1360] Specific behavior:

[1361] The server calls the API of the cloud service to obtain information on traffic conditions and evacuation locations.

[1362] The acquired information is stored in an internal database and used to generate prompts.

[1363] Step 5:

[1364] Generate prompt statement

[1365] Based on the relevant information collected by the server, a prompt sentence is generated to be input to the generative AI model. The input is the relevant information data, and the output is the prompt sentence. The prompt generation module creates a prompt sentence such as "Safety instructions for users near the epicenter."

[1366] Specific behavior:

[1367] The server parses the relevant information and generates the appropriate prompt.

[1368] The generated prompt sentence is sent to the API of the generative AI model.

[1369] Step 6:

[1370] Generate action guide

[1371] The generative AI model generates action instructions based on the prompt text. The input is the prompt text, and the output is specific action instructions. The generative AI model analyzes the prompt text and generates instructions such as "Hide under a desk until the shaking stops, then head to the designated evacuation site."

[1372] Specific behavior:

[1373] The generative AI model receives the prompt and analyzes it.

[1374] The instruction content is generated in JSON format or similar and returned to the server.

[1375] Step 7:

[1376] notification

[1377] The device receives the action instructions sent from the server and notifies the user. The input is the action instructions from the server, and the output is the notification content to the user. The device displays the action instructions to the user as a pop-up message or audio alert.

[1378] Specific behavior:

[1379] The terminal receives the action guide from the server.

[1380] The user will be notified of the received action instructions via a pop-up message, audio alert, or vibration.

[1381] Step 8:

[1382] User behavior confirmation

[1383] The device monitors the user's behavior and continuously updates and sends location information to the server. The input is the user's location and the output is the updated information sent to the server. The device retrieves location information at regular intervals to check whether the user is moving according to instructions.

[1384] Specific behavior:

[1385] The device will update its location using location services at regular intervals.

[1386] Send updated location information to the server and check user activity.

[1387] By following the above processing steps, the system helps users take prompt and appropriate action in the event of a disaster. Detailed data processing and calculation at each step provide advanced support.

[1388] (Application example 1)

[1389] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1390] Although self-driving vehicles are becoming more common in modern society, systems for taking prompt and appropriate action in the event of a natural disaster or emergency are still not fully in place. As a result, users of self-driving vehicles may become confused in the event of an emergency, making it difficult for them to take appropriate evacuation actions. In particular, the lack of appropriate real-time guidance leaves users with concerns about their safety.

[1391] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1392] In this invention, the server includes means for receiving an emergency notification, means for acquiring location information, means for identifying a target area based on the received emergency notification and the acquired location information, means for collecting related information from a cloud, means for generating a prompt based on the collected information, means for generating action guidance based on the generated prompt, means for notifying a user of the generated action guidance, and means for displaying guidance on safe actions to take in an emergency on a display in the autonomous vehicle or on a smartphone. This enables users of autonomous vehicles to take appropriate actions in real time in an emergency, thereby ensuring the safety of the users.

[1393] "Emergency Notification" means information notifying people of natural disasters or other emergencies.

[1394] "Location Information" means information about the current location of a user or device obtained using GPS, Wi-Fi, or cell tower information.

[1395] The "target area" is an area identified based on emergency notification and acquired location information.

[1396] A "cloud" is a collection of computing resources delivered over the Internet.

[1397] A "prompt" is an instruction sentence that is input to a generative AI model to generate action guidance.

[1398] "Action guidance" is information that instructs the user on specific actions to be taken in an emergency.

[1399] An "autonomous vehicle" is a vehicle that has the ability to drive autonomously without human intervention.

[1400] A "display" is a screen or monitor for displaying information.

[1401] A "smartphone" is a portable information device with advanced computing power and connectivity.

[1402] A "generative AI model" is an artificial intelligence algorithm that generates responses or instructions in natural language based on data.

[1403] This invention is a system for autonomous vehicles that aims to ensure user safety in the event of a natural disaster or other emergency. It integrates various elements, such as emergency notification, location information, target area, cloud computing, prompts, action guidance, autonomous vehicles, displays, smartphones, and generative AI models, to build a system that supports prompt and appropriate actions.

[1404] System configuration

[1405] server

[1406] The server has the following functions:

[1407] 1. Emergency notification analysis function

[1408] The server receives and analyzes emergency notifications about natural disasters and emergencies, such as earthquake alerts, heavy rain and flood warnings, and other alerts from public organizations such as the Japan Meteorological Agency.

[1409] 2. Location information acquisition function

[1410] Current location information is acquired through the GPS module in the autonomous vehicle, allowing the user's location to be tracked in real time.

[1411] 3. Ability to identify target areas

[1412] Based on the emergency notification received and the location information obtained, the affected areas of the emergency are identified.

[1413] 4. Function to collect related information

[1414] Use cloud services (e.g., AWS and Google Cloud) to collect relevant information such as traffic conditions, evacuation sites, and road traffic conditions in real time.

[1415] 5. Prompt generation function using generative AI models

[1416] Based on the information collected from the cloud, a generative AI model (e.g., GPT-4) generates prompts, which are used to instruct appropriate actions in emergencies.

[1417] 6. Function to generate action guides

[1418] Based on the generated prompt text, an action guide is generated that indicates the specific action the user should take.

[1419] 7. Function to notify users of action guidance

[1420] The generated guidance is displayed and notified to the user on the display and smartphone inside the autonomous vehicle, using pop-up messages and audio alerts to ensure the user is informed.

[1421] Terminal

[1422] The terminal installed in the autonomous vehicle has the following functions:

[1423] 1. Location information acquisition function

[1424] Location information is obtained using a GPS module and sent to the server.

[1425] 2. Action guide notification function

[1426] The system notifies the user of the action instructions sent from the server, displaying messages on the screen and also using audio alerts.

[1427] Usage examples and prompt statements

[1428] As a specific example of use, the system behavior when a flood warning occurs is shown below.

[1429] 1. The server receives a heavy rain and flood warning from the Japan Meteorological Agency.

[1430] 2. The GPS module in the autonomous vehicle acquires the current location information and sends it to the server.

[1431] 3. The server uses location information to determine if the user is in an area affected by flooding.

[1432] 4. The server collects information on evacuation sites and road traffic conditions from the cloud.

[1433] 5. Enter the following prompt into the generative AI model:

[1434] The user is located at point △△ in ○○ city and a flood warning has been issued. Please guide them to safe evacuation routes and evacuation locations.

[1435] 6. The generative AI model generates the instruction, "Flood warning: Please evacuate from your current location to △△ Junior High School. The specific route is via □□ Street."

[1436] 7. The server sends this instruction to a display inside the autonomous vehicle, which then issues a voice alert saying, "Flood warning issued, please follow evacuation instructions."

[1437] In this way, the system of the present invention assists users in taking prompt and appropriate action in an emergency, thereby ensuring their safety.

[1438] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1439] Step 1:

[1440] Receive emergency notifications

[1441] The server receives emergency notifications about natural disasters and other emergencies. For example, it receives real-time earthquake alerts and heavy rain and flood warnings from public organizations such as the Japan Meteorological Agency. The input is the emergency notification data, and the output is the analysis results. The server analyzes this data and identifies the type of disaster and its urgency.

[1442] Step 2:

[1443] Obtaining location information

[1444] The terminal uses the GPS module in the autonomous vehicle to obtain its current location information. The input is the GPS signal, and the output is the location coordinate data. The terminal then sends this location information to the server.

[1445] Step 3:

[1446] Identifying target areas

[1447] The server identifies the affected area based on the received emergency notification and the acquired location information. The input is the analysis result of the emergency notification and the location coordinate data, and the output is the affected area data. The server uses this data to determine whether the user is in the affected area.

[1448] Step 4:

[1449] Gathering relevant information

[1450] The server uses cloud services to collect relevant information such as traffic conditions, evacuation shelters, and road traffic conditions. The input is the target area data, and the output is the collected relevant information. Specifically, the server obtains the necessary data using a cloud API.

[1451] Step 5:

[1452] Prompt Generation

[1453] The server generates a prompt sentence from a generative AI model based on the collected information. The input is the collected relevant information, and the output is the prompt sentence. The server generates a prompt to instruct the generative AI model (e.g., GPT-4) to take a specific action.

[1454] Step 6:

[1455] Generate action guide

[1456] The server generates specific instructions based on the generated prompt. The input is the prompt, and the output is instructions. A generative AI model is used to generate instructions such as "Flood warning: Please evacuate from your current location to △△ Junior High School."

[1457] Step 7:

[1458] Action guide notifications

[1459] The terminal displays and notifies the generated action guidance on the display or smartphone inside the autonomous vehicle. The input is the action guidance, and the output is a notification to the user. Specifically, a message is displayed on the display and an audio alert is also used to ensure that the user is informed.

[1460] This allows users to take appropriate action in real time in an emergency and ensure safety.

[1461] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1462] This invention is a system aimed at ensuring the safety and psychological stability of users in emergency situations, and includes the following elements: A server, a terminal, an emotion engine, and a user work together to support swift and appropriate action in natural disasters and other emergency situations, and also provide appropriate instructions according to emotions.

[1463] System configuration

[1464] server

[1465] The server receives emergency notifications and analyzes their contents. These include earthquake alerts, heavy rain and flood warnings, and other alerts. The server analyzes the received notifications and identifies the type of disaster that requires a response. It then receives location information from the user's device and identifies the affected area. It then uses the cloud to collect relevant information such as traffic conditions, evacuation sites, and the extent of impact in the area, and uses this information to have the generative AI model generate prompts. The prompts generated by the generative AI model are converted into specific action instructions and sent to the device.

[1466] Terminal

[1467] The device has the ability to obtain the user's current location using GPS, Wi-Fi, or cell tower information. When a disaster notification is received, the device immediately obtains the device's location information and sends it to the server. The device also receives action instructions sent from the server and notifies the user of them. Notification methods include pop-up messages, audio alerts, and vibrations.

[1468] Emotion Engine

[1469] The emotion engine is designed to recognize the user's emotions. It determines the user's emotional state using voice data, text data, or other input means. Based on the determined emotion, it adjusts the presentation of action guidance and the information provided. The emotion engine is built into the server or terminal.

[1470] user

[1471] The user takes appropriate action based on notifications from the device. For example, in the event of an earthquake, the user will take shelter under a desk until the shaking subsides, then head to a designated evacuation site. In the event of a flood, the user will move toward the evacuation site and choose a safe route. Furthermore, the user will act while maintaining psychological stability by following the guidance provided by the emotion engine based on their emotions. Location information during evacuation is periodically updated by the device and linked to the server.

[1472] Program processing flow

[1473] Specific examples of earthquakes

[1474] For example, if an earthquake occurs, the system operates as follows: The server receives an earthquake early warning from the Japan Meteorological Agency and analyzes the information to identify the occurrence of the earthquake. Next, it obtains the user's current location information from the device and sends it to the server. The server uses this location information to determine whether the user is close to the epicenter. If the user is close to the epicenter, the server collects relevant information such as traffic conditions and evacuation locations from the cloud. It inputs "safety instructions for users close to the epicenter" as a prompt into the generative AI model, and uses the emotion engine to check the user's emotional state.

[1475] By combining the generative AI model with the emotion engine, action guidance such as "Hold shelter under a desk until the shaking stops, then head to a designated evacuation site" is generated. If the emotion engine recognizes the user's anxiety, it can include additional reassuring information such as "Don't worry. These are steps to ensure your safety." The server sends the generated action guidance to the device, which displays it to the user as a pop-up message. The user then follows the instructions to take safe actions.

[1476] Specific examples of floods

[1477] The same applies when a flood warning is issued. The server receives and analyzes heavy rain and flood warnings from the Japan Meteorological Agency. The device then obtains its current location information and sends it to the server. The server uses the location information to determine whether the user is in an area affected by flooding. If it determines that the user is affected, the server collects information from the cloud, such as evacuation sites and road traffic conditions. Based on the generated prompt, specific action instructions are generated, such as "The evacuation site is XX Elementary School. Please evacuate by following the specified route." If the emotion engine detects stress or anxiety in the user at this time, a message offering reassurance, such as "Please remain calm. The route to the evacuation site is safe," is included.

[1478] The generated action instructions are sent to the device and notified to the user via pop-up messages and voice alerts, allowing the user to safely move towards the designated evacuation site.

[1479] In this way, the system helps users take early and appropriate action in the event of a disaster. In addition, by providing guidance that is tailored to the user's emotional state, it can reduce psychological stress and support more effective evacuation. By working together, each element can provide quick and accurate evacuation instructions, ensuring the user's safety and psychological stability.

[1480] The processing flow will be explained below.

[1481] Step 1:

[1482] Server: Receives emergency earthquake alerts, heavy rain and flood warnings, and other disaster information via API. Analyzes the various disaster information and identifies the type of disaster.

[1483] Step 2:

[1484] Device: When disaster information is received, the application will immediately launch and obtain the user's current location using GPS, Wi-Fi, or cell tower information.

[1485] Step 3:

[1486] Device: The acquired location information is sent to the server. The user's location information is encrypted and sent in a secure manner to protect privacy.

[1487] Step 4:

[1488] Server: Based on the received location information, the server determines whether the user is in an area affected by the disaster. This determination is based on the distance from the epicenter and the predicted inundation areas.

[1489] Step 5:

[1490] Server: If a user is determined to be in an affected area, relevant information such as transportation status, evacuation locations, and the extent of the impact on the area is collected from the cloud.

[1491] Step 6:

[1492] Server: Based on the collected information, it generates prompts to be passed to the generative AI model. Specifically, it creates prompts including the user's current location, the nearest evacuation site, and traffic conditions.

[1493] Step 7:

[1494] Server: Inputs prompts to the generative AI model and generates specific instructions for action, such as "An earthquake has occurred. Take shelter under a desk, and once the shaking has stopped, head to the designated evacuation site."

[1495] Step 8:

[1496] Device: Receives the generated action guide and activates the emotion engine, which analyzes the user's voice and text data to determine their emotional state.

[1497] Step 9:

[1498] Terminal: After the emotion engine determines the user's emotional state, it sends the result to the server.

[1499] Step 10:

[1500] Server: Adjust the guidance based on the received emotion data. For example, if the user expresses anxiety, include additional reassurance such as "Don't worry, these are the steps we're taking to ensure your safety."

[1501] Step 11:

[1502] Server: Sends tailored action instructions to the device, conveying them to the user via pop-up messages, audio alerts, vibrations, etc.

[1503] Step 12:

[1504] Device: Provides instructions to the user. For example, it displays something like, "The evacuation site is XX Elementary School. Follow the designated route and evacuate safely. Rest assured, the situation is understood."

[1505] Step 13:

[1506] User: Receives notifications from the device and acts accordingly. For example, in the event of an earthquake, the user may take shelter under a desk and then head to a designated evacuation site. Location information during evacuation is periodically updated by the device and sent to the server.

[1507] Step 14:

[1508] Server: Receives the user's location information and checks whether the evacuation was successful. Provides continuous support until the evacuation is complete.

[1509] Example 2

[1510] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1511] In the event of a natural disaster or other emergency, the challenge is to help users act quickly and appropriately while maintaining psychological stability. Current systems provide emergency notifications and acquire location information, but do not provide action guidance that takes into account the user's emotional state, which can lead to situations where users' anxiety and confusion cannot be completely alleviated.

[1512] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for receiving an emergency notification, a means for acquiring location information, a means for identifying a target area based on the received emergency notification and the acquired location information, a means for collecting related information from the cloud, a means for generating a prompt sentence based on the collected information, a means for generating action guidance based on the generated prompt sentence, a means for determining the user's emotional state, a means for adjusting the action guidance based on the emotional state, and a means for notifying the user of the generated action guidance. This makes it possible to provide quick and appropriate action guidance that takes the user's emotional state into consideration, and to support appropriate action in the event of a disaster while maintaining the user's psychological stability.

[1513] "Emergency Notification" refers to alerts and bulletins issued in the event of a natural disaster or other emergency.

[1514] "Location Information" means data that indicates a user's current geographic location, such as data obtained from GPS, Wi-Fi, or cell tower information.

[1515] "Target area" refers to an area that is likely to be affected by a disaster, as identified based on emergency notifications and location information.

[1516] "Cloud" refers to storage and computing resources for providing and sharing large amounts of data over the Internet.

[1517] "Relevant information" refers to auxiliary data necessary for disaster response, such as traffic conditions, evacuation sites, and the extent of impact on the area.

[1518] A "prompt" is an instruction given to a generative AI model to generate an output in response to a specific question or instruction to act.

[1519] "Action instructions" refer to specific steps of action that the user should take, generated based on the prompt text.

[1520] "User's emotional state" refers to the psychological state determined from the user's voice data, text data, etc.

[1521] "Means for adjusting action guidance based on emotional state" refers to a function for changing the content and expression of action guidance according to the user's emotional state.

[1522] The present invention relates to a system for ensuring user safety and psychological stability in emergency situations, which functions in cooperation with a server, a terminal, an emotion engine, and a user. In this system, the server receives emergency notifications, acquires location information, collects related information from the cloud, and uses a generative AI model to generate prompts and action guidance for the user. The system also includes a means for determining the user's emotional state using the emotion engine and adjusting the action guidance based on the emotional state.

[1523] server

[1524] The server receives emergency notifications from the Japan Meteorological Agency and other organizations. Emergency notifications include earthquake alerts and heavy rain and flood warnings. The server analyzes the received emergency notifications to identify the type of disaster. Next, it receives the user's location information obtained from the device and identifies the affected area. It also collects related information from the cloud, such as traffic conditions, evacuation sites, and the extent of impact on the area. A generative AI model is used based on this collected information to generate prompt text. For example, prompt texts such as "safety instructions for users near the epicenter" and "evacuation routes for users affected by flooding" are generated. Specific action instructions are created from these prompt texts, and the content is adjusted taking into account the user's emotional state.

[1525] An example prompt for a generative AI model is:

[1526] "Safety instructions for users near the epicenter"

[1527] Example prompt for a generative AI model: "What is the best course of action for a user near the epicenter? Hide under a desk until the shaking stops, then head to a designated evacuation site."

[1528] "Providing reassuring information"

[1529] Example prompt input for the generative AI model: "Please provide reassurance information to reduce user anxiety. Rest assured, these are steps to ensure your safety."

[1530] The server transmits the generated action guide to the terminal and notifies the user.

[1531] Terminal

[1532] The device obtains the user's current location using GPS, Wi-Fi, and cell tower information. When a disaster notification is received, it immediately obtains the location information and sends it to the server. It also receives action instructions sent from the server and notifies the user. Notification methods include pop-up messages, audio alerts, and vibrations. When the user begins to take action, the device periodically updates the location information and sends it to the server. This allows the server to continue to know the user's location in real time.

[1533] Emotion Engine

[1534] The emotion engine uses voice and text data to determine the user's emotional state. It uses speech recognition software and natural language processing technology to analyze the emotions expressed in what the user says and writes. For example, if the user is feeling very anxious, it will add reassuring content. The emotion engine is built into a server or device and often runs on the server to perform advanced analysis. The generated action guidance is adjusted based on the data obtained by the emotion engine.

[1535] user

[1536] Users follow the notifications from their devices and take the actions instructed. For example, in the event of an earthquake, they would follow instructions such as "Hold shelter under a desk until the shaking stops, then head to the designated evacuation site." In the event of a flood, they would follow instructions such as "The evacuation site is XX Elementary School. Please evacuate by following the designated route." While the disaster continues, the device periodically sends its location information to the server, and new instructions are sent to the user when necessary.

[1537] In this way, the system helps users act quickly and appropriately in the event of a natural disaster or other emergency, providing psychological stability. The above configuration ensures the safety and security of users.

[1538] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1539] Specific example in the case of an earthquake

[1540] Step 1:

[1541] The server receives an emergency earthquake alert from the Japan Meteorological Agency.

[1542] Input: Earthquake Early Warning data from the Japan Meteorological Agency.

[1543] Processing: The server's data receiving module analyzes the earthquake early warning data.

[1544] Output: Earthquake occurrence information (occurrence time, epicenter, seismic intensity, etc.).

[1545] Step 2:

[1546] The device receives a disaster notification and obtains the user's current location information.

[1547] Input: Emergency earthquake alert sent from the server.

[1548] Processing: The device's location acquisition module acquires the user's current location using GPS, Wi-Fi, and cell tower information.

[1549] Output: Current location of the user (latitude, longitude).

[1550] Step 3:

[1551] The current location information acquired by the terminal is sent to the server.

[1552] Input: The user's current location.

[1553] Processing: The device sends location information to the server.

[1554] Output: The location information received by the server.

[1555] Step 4:

[1556] The server uses location information to determine whether the user is close to the epicenter.

[1557] Input: Earthquake occurrence information, user's current location information.

[1558] Processing: The server's analysis module calculates the distance between the user's location and the epicenter and evaluates the impact.

[1559] Output: The result of determining whether the user is close or far from the epicenter.

[1560] Step 5:

[1561] The server collects relevant information such as traffic conditions and evacuation locations from the cloud.

[1562] Input: Information about the area of ​​interest.

[1563] Processing: The server collects the necessary information from the traffic database and evacuation site database via the cloud API.

[1564] Output: Information on traffic conditions, evacuation locations, and local impact.

[1565] Step 6:

[1566] The server generates prompt sentences for the generative AI model based on the collected information.

[1567] Input: Earthquake occurrence information, user location information, and related information.

[1568] Processing: The generative AI model is given a prompt: "Safety instructions for users near the epicenter."

[1569] Output: Prompt text "Safety instructions for users near the epicenter."

[1570] Step 7:

[1571] The server generates action guidance using the generative AI model.

[1572] Input: Prompt text "Safety instructions for users near the epicenter."

[1573] Processing: The generative AI model generates specific action instructions based on the prompt sentence.

[1574] Output: Instructions for action: "Take shelter under a desk until the shaking stops, then proceed to the designated evacuation site."

[1575] Step 8:

[1576] The server uses an emotion engine to determine the user's emotional state.

[1577] Input: User voice and text data.

[1578] Processing: The emotion engine analyzes the data and determines the user's anxiety or stress.

[1579] Output: The user's emotional state (calm, anxious, stressed, etc.).

[1580] Step 9:

[1581] The server adjusts the user's behavioral guidance based on the user's emotional state.

[1582] Input: Generated action guide, user's emotional state.

[1583] Treatment: If anxiety is recognized, add an additional reassuring message to the action guide.

[1584] Output: Action instructions: "Take shelter under a desk until the shaking stops, then proceed to a designated evacuation site. Rest assured, these are procedures to ensure your safety."

[1585] Step 10:

[1586] The server transmits the generated action guide to the terminal.

[1587] Input: Coordinated behavioral guidance.

[1588] Processing: Send action instructions to the terminal.

[1589] Output: Action instructions received by the device.

[1590] Step 11:

[1591] The device notifies the user of action instructions.

[1592] Input: Action guide.

[1593] Action: Notify action instructions using pop-up messages, audio alerts, and vibrations.

[1594] Output: The call to action received by the user.

[1595] Step 12:

[1596] The user takes the indicated action.

[1597] Input: Action guide.

[1598] Action: The user "takes shelter under a desk until the shaking stops, then heads to a designated evacuation location."

[1599] Output: The user takes action to evacuate to a safe place.

[1600] Step 13:

[1601] The device periodically updates the user's current location and sends it to the server.

[1602] Input: The user's new location.

[1603] Processing: Periodically obtain location information and send it to the server.

[1604] Output: The latest location information received by the server.

[1605] Step 14:

[1606] The server generates additional action guidance as needed and transmits it to the terminal.

[1607] Input: Latest location and emergency notification updates.

[1608] Processing: Using the generative AI model, new action guidance is generated and sent to the device.

[1609] Output: New action instructions received by the device.

[1610] In this way, a system is constructed that supports users in taking prompt and appropriate action in the event of a disaster through specific actions at each step.

[1611] (Application example 2)

[1612] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1613] In the event of a natural disaster or other emergency, users are required to take prompt and appropriate action. However, conventional systems are limited to emergency notifications and location information acquisition, and lack guidance based on the user's individual emotional state, which can lead to anxiety and panic. Furthermore, it is difficult to convert the collected information into appropriate action guidance, which can result in delayed notification to users.

[1614] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1615] In this invention, the server includes means for receiving an emergency notification, means for acquiring location information, means for identifying a target area based on the received emergency notification and the acquired location information, means for collecting related information from the cloud, means for generating a prompt sentence based on the collected information, means for generating action guidance from the prompt sentence using a generative AI model, means for detecting the user's emotional state and adjusting the action guidance according to that state, and means for notifying the user of the generated action guidance. This allows the user to receive prompt and accurate action guidance in an emergency, and by being provided with individual instructions according to the user's emotional state, the user can act safely while maintaining psychological stability.

[1616] "Emergency Notification" means a notification regarding a natural disaster or other emergency.

[1617] "Location Information" means data about a user's current location obtained using GPS, Wi-Fi, or cell tower information.

[1618] "Target Area" means an area potentially affected by a disaster, as identified based on emergency notification and location information.

[1619] The "cloud" is a distributed infrastructure of resources, data storage, and computing power delivered over the internet.

[1620] "Relevant information" is information necessary for dealing with an emergency, such as traffic conditions, evacuation sites, and the extent of impact on the area.

[1621] A "prompt sentence" is a collection of terms and sentences that are input into a generative AI model and serve as the source data for generating action guidance.

[1622] A "generative AI model" is an artificial intelligence model that generates appropriate action guidance from a prompt sentence.

[1623] "Action guidance" is information that provides specific instructions on what actions a user should take in an emergency.

[1624] "Emotional state" refers to the user's psychological state as sensed using voice data, text data, or other input means.

[1625] The "emotion engine" is a function that recognizes the user's emotional state and adjusts behavioral guidance.

[1626] The present invention is a system for ensuring the safety of users and providing psychological stability in emergency situations. The system includes the following components: a server, a terminal, an emotion engine, and a user.

[1627] server

[1628] The server has the function of receiving emergency notifications and analyzing their contents. Emergency notifications include earthquake alerts, heavy rain and flood warnings, and other warnings. When this notification is received, the server analyzes its contents and identifies the type of disaster that requires response.

[1629] The server then retrieves the user's location from the device and identifies the target area, which can be obtained using GPS, Wi-Fi, or cell tower information.

[1630] The server then uses the cloud to collect relevant information such as traffic conditions, evacuation sites, and the extent of local impact. Based on this information, the server uses a generative AI model to generate a prompt. The generated prompt is text data in the format "Notification: Earthquake Warning, Location: Tokyo, Emotion: Fear." Using this prompt as input, the generative AI model generates specific action instructions and adjusts those instructions.

[1631] Terminal

[1632] The device has the ability to obtain the user's current location. It obtains location information using GPS, Wi-Fi, or cell tower information and sends it to the server. The device also receives action instructions sent from the server and notifies the user of them. Notification methods include pop-up messages, audio alerts, and vibrations.

[1633] Emotion Engine

[1634] The emotion engine has the function of recognizing the user's emotional state and adjusting the action guidance. It determines the user's emotional state using voice data, text data, or other input means. Based on the determined emotion, it adjusts the expression and content of the generated action guidance to provide the user with appropriate instructions.

[1635] Specific examples

[1636] When an earthquake occurs, the server receives an earthquake early warning from the Japan Meteorological Agency and analyzes the information to identify the occurrence of the earthquake. The device then obtains the user's current location information and sends it to the server. The server uses this location information to determine whether the user is near the epicenter.

[1637] If the user is close to the epicenter, the server collects relevant information such as traffic conditions and evacuation sites from the cloud. The generated prompt is "Notification: Earthquake warning, Location: Tokyo, Emotion: Fear." This prompt is input into the generative AI model, and the emotion engine is used to check the user's emotional state.

[1638] By combining the generative AI model with the emotion engine, the system can generate action instructions such as, "Hold shelter under a desk until the shaking stops, then proceed to a designated evacuation site." Furthermore, if the emotion engine recognizes the user's anxiety, it can include additional reassurance information such as, "Don't worry. These are steps to ensure your safety."

[1639] In this way, the system helps users take early and appropriate action in the event of a disaster. It also provides guidance tailored to the user's emotional state, reducing psychological stress and supporting more effective evacuation.

[1640] In the embodiment of the present invention, the above elements function in cooperation with each other to provide quick and accurate evacuation instructions and ensure the safety and psychological stability of the user.

[1641] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1642] Step 1:

[1643] The server receives emergency notifications. Specifically, it receives emergency notifications such as earthquake alerts and flood warnings from the Japan Meteorological Agency and other public organizations in real time. The input data is the content of the emergency notification, and the output is the emergency notification data to be analyzed. The server analyzes the necessary information from this notification data and identifies the type and details of the disaster.

[1644] Step 2:

[1645] The device obtains the user's location information. Specifically, it obtains the current location using GPS, Wi-Fi, or cell tower information and sends it to the server. The input data is the user's location information, and the output is the user's latitude and longitude data.

[1646] Step 3:

[1647] The server identifies the affected area based on the received emergency notification and the acquired location information. The server compares the user's location with the extent of the disaster's impact and determines whether the user is in an area affected by the disaster. The input data is the emergency notification and location information, and the output is data identifying the affected area.

[1648] Step 4:

[1649] The server collects relevant information from the cloud, such as traffic conditions, evacuation sites, and the extent of impact on the area, and integrates it within the server. The input data is specific data for the target area, and the output is the collected relevant information.

[1650] Step 5:

[1651] The server generates a prompt sentence based on the collected related information. The generated prompt sentence is text data to be input into the generative AI model. An example of a prompt sentence is "Notification: Earthquake warning, Location: Tokyo, Emotion: Fear." The input data is the collected related information, and the output is the generated prompt sentence.

[1652] Step 6:

[1653] The server uses a generative AI model to generate action instructions from prompts. The input data is the generated prompt, and specific action instructions are output through data calculations. For example, "Please take shelter under a desk until the shaking stops, then head to the designated evacuation site."

[1654] Step 7:

[1655] The server detects the user's emotional state and adjusts the action guidance accordingly. Specifically, it analyzes the user's emotions from voice and text data, and the emotion engine adjusts the action guidance based on the results. The input data is the user's emotional state, and the output is the adjusted action guidance.

[1656] Step 8:

[1657] The terminal notifies the user of the generated action guidance using a pop-up message, a voice alert, and vibration. The input data is the adjusted action guidance, and the output is displayed as a notification to the user.

[1658] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1659] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1660] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1661] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1662] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1663] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1664] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1665] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1666] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1667] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1668] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1669] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1670] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1671] 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.

[1672] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1673] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1674] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1675] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1676] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1677] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1678] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1679] The following is further disclosed regarding the above embodiment.

[1680] (Claim 1)

[1681] a means for receiving emergency notifications;

[1682] A means for acquiring location information;

[1683] A means for identifying a target area based on the received emergency notification and the acquired location information;

[1684] a means for collecting relevant information from the cloud;

[1685] means for generating prompts based on the collected information;

[1686] means for generating action instructions based on the generated prompts;

[1687] means for notifying a user of the generated action guidance;

[1688] A system including:

[1689] (Claim 2)

[1690] 2. The system of claim 1, wherein the emergency notification is an earthquake alert, a heavy rain and flood warning, or an alert.

[1691] (Claim 3)

[1692] 10. The system of claim 1, wherein the location information is obtained using GPS, Wi-Fi, or cell tower information.

[1693] "Example 1"

[1694] (Claim 1)

[1695] a means for receiving emergency notifications;

[1696] A means for acquiring location information;

[1697] A means for identifying a target area based on the received emergency notification and the acquired location information;

[1698] a means for collecting relevant information from the cloud;

[1699] means for generating a prompt sentence based on the collected information;

[1700] A generation AI model means for generating an action guide based on the generated prompt sentence;

[1701] means for notifying a user of the generated action guidance;

[1702] means for monitoring user activity and updating location information;

[1703] A system including:

[1704] (Claim 2)

[1705] 2. The system of claim 1, wherein the emergency notification is an earthquake alert, a heavy rain and flood warning, or other alert.

[1706] (Claim 3)

[1707] 10. The system of claim 1, wherein the location information is obtained using GPS, Wi-Fi, or cell tower information.

[1708] "Application Example 1"

[1709] (Claim 1)

[1710] a means for receiving emergency notifications;

[1711] A means for acquiring location information;

[1712] A means for identifying a target area based on the received emergency notification and the acquired location information;

[1713] a means for collecting relevant information from the cloud;

[1714] means for generating prompts based on the collected information;

[1715] means for generating action instructions based on the generated prompts;

[1716] means for notifying a user of the generated action guidance;

[1717] A means for displaying safe actions in an emergency on a display or smartphone in an autonomous vehicle;

[1718] A system including:

[1719] (Claim 2)

[1720] 2. The system of claim 1, wherein the emergency notification is an earthquake alert, a heavy rain and flood warning, or an alert.

[1721] (Claim 3)

[1722] 10. The system of claim 1, wherein the location information is obtained using GPS, Wi-Fi, or cell tower information.

[1723] "Example 2: Combining Emotion Engines"

[1724] (Claim 1)

[1725] a means for receiving emergency notifications;

[1726] A means for acquiring location information;

[1727] A means for identifying a target area based on the received emergency notification and the acquired location information;

[1728] a means for collecting relevant information from the cloud;

[1729] means for generating a prompt sentence based on the collected information;

[1730] means for generating action guidance based on the generated prompt sentence;

[1731] means for determining the emotional state of a user;

[1732] means for adjusting behavioral guidance based on emotional state;

[1733] a means for notifying a user of the generated action guidance;

[1734] A system including:

[1735] (Claim 2)

[1736] 2. The system of claim 1, wherein the emergency notification is an earthquake alert, a heavy rain and flood warning, or an alert.

[1737] (Claim 3)

[1738] 10. The system of claim 1, wherein the location information is obtained using GPS, Wi-Fi, or cell tower information.

[1739] "Application example 2 when combining emotion engines"

[1740] (Claim 1)

[1741] a means for r...

Claims

1. a means for receiving emergency notifications; A means for acquiring location information; A means for identifying a target area based on the received emergency notification and the acquired location information; a means for collecting relevant information from the cloud; means for generating prompts based on the collected information; means for generating action instructions based on the generated prompts; means for notifying a user of the generated action guidance; A system including:

2. The system of claim 1 , wherein the emergency notification is an earthquake alert, a heavy rain and flood warning, or an alert.

3. The system of claim 1 , wherein the location information is obtained using GPS, Wi-Fi, or cell tower information.

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