system

Abstract

We provide the system. [Solution] Means for collecting network information, A means for analyzing the aforementioned information and predicting the impact on means of transportation and infrastructure facilities, A means for generating an emergency response plan based on prediction results, Means for providing the aforementioned response plan to users, A means of identifying the user's current location and guiding them along a safe route using visualization technology, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the event of a disaster, it is difficult to quickly and accurately aggregate information and predict the impact on transportation and infrastructure, and there is a problem that appropriate evacuation guidance cannot be provided. In addition, there is a possibility of exacerbating panic due to delayed or inaccurate information provided to users. For this reason, there is a need for a system that enables real-time information acquisition and presentation of an appropriate evacuation route based on prediction.

Means for Solving the Problems

[0005] The present invention solves the above problems by providing a system that includes means for collecting network data, means for analyzing the data to predict the impact on traffic and infrastructure, means for generating a disaster response plan based on the prediction results, and means for providing the response plan to a user. This system can aggregate and analyze data in real time and quickly provide users with information on safe evacuation routes. This minimizes confusion and supports effective evacuation actions.

[0006] "Network data" refers to various types of information obtained through the internet and various communication protocols.

[0007] "Analysis" refers to the process of processing and analyzing collected data to extract useful information.

[0008] "Predicting the impact on transportation and infrastructure" refers to calculating and predicting in advance the impact that transportation networks and lifelines will have on them in the event of an earthquake, disaster, or other disaster.

[0009] "Generating a countermeasure plan" refers to creating appropriate action guidelines and evacuation plans based on predicted impacts.

[0010] "Means of providing information to users" refers to interfaces used to notify and present generated information and plans to users. [Brief explanation of the drawing]

[0011] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0012] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0013] First, let's explain the terminology used in the following explanation.

[0014] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0015] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0016] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0017] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0018] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0022] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0023] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0024] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0025] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0026] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0029] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0030] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0031] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0032] This invention is a system that enables the rapid and accurate provision of information during disasters. This system operates through the coordinated efforts of a server, terminals, and users.

[0033] The server first collects real-time data via the network. This data includes disaster alerts, traffic conditions, infrastructure damage, and real-time information from social media. This data is obtained from multiple APIs and integrated on the server. Next, the acquired data is analyzed to predict the state of the transportation network and infrastructure damage. This allows for the identification of which areas will be more severely affected and which evacuation routes are safe. Based on the predicted information, the server generates a concrete and practical response plan. This plan includes information on safe evacuation routes and shelters.

[0034] The terminal's role is to provide users with response plans received from the server. Through the terminal, users can check evacuation routes displayed on a map and real-time updated traffic information. Furthermore, the terminal has a voice guidance function to navigate users to ensure their safe evacuation. It also has a function to guide users to the nearest evacuation shelter using their current location information.

[0035] This system allows users to take safe and swift evacuation actions even in the event of a disaster. Users can follow the instructions on their terminals, avoiding confusion and taking optimal evacuation actions. The system includes a function to send user feedback to a server, thereby collecting information on problems and areas for improvement that arose during the disaster.

[0036] As a concrete example, consider a scenario where a major earthquake occurs in Tokyo. The server immediately receives an earthquake alert and analyzes traffic conditions and infrastructure damage. Next, it identifies a safe evacuation route and transmits it to the terminal in real time. The terminal provides the user with specific instructions such as, "To safely evacuate to XX Park, go straight down XX Street and then turn right." This allows the user to avoid congestion and evacuate via a safe route. In this way, the present invention realizes rapid and accurate evacuation support during disasters.

[0037] The following describes the processing flow.

[0038] Step 1:

[0039] The server collects disaster-related data in real time via the network. This includes information from earthquake early warning APIs, traffic information APIs, and infrastructure operator APIs. In addition, it crawls social media to obtain damage reports and evacuation status.

[0040] Step 2:

[0041] The server analyzes the collected data. This analysis uses natural language processing to extract important information from text data and machine learning models to predict the impact on transportation networks and infrastructure. Based on these predictions, the server identifies affected areas.

[0042] Step 3:

[0043] Based on the prediction results, the server generates a response plan that includes safe evacuation routes and shelter information. Multiple plans are prepared to accommodate different scenarios (e.g., the degree of traffic congestion or infrastructure damage).

[0044] Step 4:

[0045] The terminal receives the countermeasure plan sent from the server. The received plan is presented to the user through the user interface as a map display and audio guidance. This allows the user to obtain information both visually and aurally.

[0046] Step 5:

[0047] Based on the information presented by the device, the user selects the optimal evacuation route and begins evacuating quickly. If new circumstances change during the evacuation (e.g., route closure), the device receives updates in real time and provides the user with new instructions.

[0048] Step 6:

[0049] After evacuation, users send their feedback (e.g., route safety, new obstacles, etc.) to the server via their devices. The server uses this feedback to generate future predictions and plans, thereby continuously improving the system.

[0050] (Example 1)

[0051] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0052] During disasters, it is essential to collect and analyze information quickly and accurately to support the presentation of safe evacuation routes and the appropriate use of public facilities. However, current systems have challenges such as being unable to respond to real-time changes in the situation and providing insufficient timely guidance based on the user's current location.

[0053] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0054] In this invention, the server includes means for collecting and integrating information from a network, means for analyzing the collected information to predict the impact on traffic routes and public facilities, and means for creating a countermeasure plan based on the prediction according to the disaster situation. This makes it possible to adapt to real-time changes in the situation and quickly provide users with optimized evacuation routes and guidance on the use of public facilities.

[0055] "Means of collecting and integrating information from a network" refers to methods of obtaining various data from external sources, converting them into a common data format, and compiling them into a single database.

[0056] "Methods for analyzing collected information to predict the impact on transportation routes and public facilities" refers to analytical methods that use acquired data to evaluate and predict how a disaster will affect transportation networks and public facilities.

[0057] "Means for creating disaster response plans based on predictions" refers to a process that automatically generates specific action plans to enable users to evacuate safely, based on predictive data obtained through analysis.

[0058] "Means of transmitting and presenting to the user's terminal" refers to a mechanism that provides the generated countermeasure plan to the user visually or audibly through a digital terminal.

[0059] "Means for providing visual and audio guidance on evacuation routes that are dynamically updated based on the user's location information" refers to a system that grasps the user's current location in real time and presents the optimal evacuation route, and includes functions that provide guidance using audio guidance and map display.

[0060] This invention is an information processing system for realizing rapid and accurate evacuation support during disasters. The system operates through the coordinated operation of server, terminal, and user elements.

[0061] The server first collects and integrates information from various sources via the network. Specifically, a wide variety of APIs are used for data collection, including weather data, traffic conditions, operational information for public facilities, and even real-time information from social media. This information is integrated into a database and processed on the server. Using programming languages ​​such as Python and leveraging machine learning libraries, the server analyzes the information and predicts the impact on traffic routes and public facilities. Based on these predictions, the server automatically generates an optimal evacuation plan.

[0062] The terminal acts as a user interface, providing the user with evacuation plans transmitted from the server. The terminal visually displays map information and guides the user through voice prompts. Using tools such as the Google® Maps SDK, it displays evacuation routes in detail and uses GPS functionality to track the user's location in real time. The terminal's voice assistant function allows users to confirm safe routes through voice guidance.

[0063] This system allows users to easily take swift evacuation actions during disasters. Based on the user's dynamic location information, a safe evacuation route is suggested, allowing for a more secure evacuation. Furthermore, users can send feedback to the server after completing their evacuation, and this information is used for further system improvements. For example, in the event of a major earthquake, the server immediately analyzes and provides an evacuation route, and the terminal instructs the user to "proceed straight down XX Street for safe evacuation, then turn right." By following these instructions, the user can evacuate safely.

[0064] An example of a prompt message is, "Please tell me how to identify the safest evacuation route based on earthquake reports and provide detailed navigation to the user." Thus, the present invention is a system that enables rapid and accurate evacuation support during disasters.

[0065] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0066] Step 1:

[0067] The server collects data from various sources via the network. Inputs include weather data, traffic conditions, public transport operation information, and user posts from social media, all obtained using APIs. This data is stored on the server in databases such as Google Cloud Storage. The data is received in JSON or XML format and converted to a unified format.

[0068] Step 2:

[0069] The server analyzes the collected data. The input data is processed as a dataset for machine learning, and tools such as Python and TENSORFLOW® are used to predict the usability of transportation routes and public facilities. Specifically, it utilizes models based on historical disaster data to perform impact analysis under certain disaster scenarios. The output is the result of damage prediction.

[0070] Step 3:

[0071] The server creates a disaster response plan based on the analysis results. This process uses a generative AI model to generate appropriate evacuation routes and methods for using public facilities from predictive data. The input is the predictive data from the analysis results, and the output is a concrete evacuation plan. This plan includes information on emergency evacuation roads and shelters and is structured in XML format.

[0072] Step 4:

[0073] The server sends the generated response plan to the terminal in real time. Communication methods using WebSocket or MQTT protocols are ensured, and the plan is delivered to the user's terminal immediately. The generated evacuation plan is used as input, and the output is data displayed on the terminal.

[0074] Step 5:

[0075] The device presents the received evacuation plan to the user visually and audibly. Specifically, the evacuation route is visually displayed on the device's map application using tools such as the Google Maps SDK. Furthermore, a voice assistant provides route guidance, which is updated in real time based on the user's current location. The evacuation plan is provided as input from the server, and the specific guidance information that the user receives is generated as output.

[0076] Step 6:

[0077] Users perform safe evacuation actions based on information provided by their devices. By following the instructions on their devices, users evacuate while avoiding congestion and danger. Furthermore, after the evacuation is complete, users can send feedback to the server. The feedback that users experienced during the evacuation is used as input, and improvement information is collected on the server as output.

[0078] (Application Example 1)

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

[0080] In disaster situations, conventional technologies are insufficient for providing real-time safe evacuation routes to support swift, accurate, and safe evacuation actions while avoiding chaos. Furthermore, the use of visualization technologies to support situational assessment during disasters is inadequate, creating a need for more intuitive means of indicating safe evacuation routes.

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

[0082] In this invention, the server includes means for collecting network information, means for analyzing the information to predict the impact on transportation means and infrastructure facilities, and means for generating an emergency response plan based on the prediction results. This enables the provision of the optimal evacuation route to users in real time during a disaster and provides guidance on safe evacuation routes that can be intuitively understood using visualization technology.

[0083] "Network information" refers to real-time data and information that can be obtained via the internet or communication networks.

[0084] "Means of transportation" refers to methods and devices that enable the physical movement of people and things, such as public transport and walking.

[0085] "Infrastructure facilities" refer to structures and systems that belong to the infrastructure and support the foundations of economic activity and daily life.

[0086] An "emergency response plan" refers to specific action plans and procedures formulated to ensure swift and appropriate action in the event of a disaster or emergency.

[0087] "User" refers to an individual or organization that uses the system or service.

[0088] "Visualization technology" refers to technologies that visually display information and data, making them intuitively understandable.

[0089] An "evacuation route" refers to a path or route used to move safely to avoid danger.

[0090] The system that implements this application consists of three main components: a server, a terminal, and a user. The server uses Python to collect network information in real time from multiple APIs. The collected information includes data on traffic conditions and infrastructure damage, which is analyzed using TensorFlow to predict the impact on transportation and infrastructure. Based on the prediction results, the system generates the optimal evacuation route as an emergency response plan. The information from the server is transmitted to the terminal in real time.

[0091] The device provides information to the user through an application built with React Native. The device obtains the user's current location using GPS and displays evacuation routes using visualization technology with ARKit (iOS) or ARCore (Android®). This allows the user to intuitively confirm a safe route while moving.

[0092] Users can use their smartphones or smart glasses to take quick and safe evacuation actions based on information provided by the server and guidance from their devices.

[0093] A concrete example would be when a heavy rain warning is issued, and the app provides specific instructions such as, "To evacuate to the nearest high ground from your current location, proceed along △△ Street, then turn left at the next traffic light." An example of a prompt message would be, "Output a design proposal for an AR application that predicts the optimal evacuation route in real time during a disaster and guides the user visually and audibly."

[0094] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0095] Step 1:

[0096] The server collects network information from real-time APIs via the internet. Inputs include disaster-related data and traffic data, and output is an integrated dataset. Specifically, the server uses a Python script to send requests to multiple APIs and then aggregates the obtained data.

[0097] Step 2:

[0098] The server utilizes TensorFlow based on collected data to predict the impact on transportation and infrastructure. The input is an integrated dataset, and the output is the prediction. Specifically, the server runs a machine learning model and calculates the prediction results.

[0099] Step 3:

[0100] The server generates an optimal emergency response plan based on the prediction results. The input is the prediction results, and the output is information on evacuation routes and safe locations. Specifically, the server uses an optimization algorithm to calculate safe routes and compiles them into a plan.

[0101] Step 4:

[0102] The device receives the response plan sent from the server and provides it to the user. The input is the server's response plan information, and the output is visual and audio guidance to the user. Specifically, a React Native app running on the device displays the information and provides guidance using speech synthesis technology.

[0103] Step 5:

[0104] The user takes evacuation action according to the instructions on the device. The input is guidance information from the device, and the output is the actual evacuation action. Specifically, the user follows a safe evacuation route according to visual instructions using ARKit or ARCore.

[0105] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0106] This invention combines an emotion engine with a system that provides optimal evacuation information during disasters, thereby enabling flexible information delivery tailored to the user's psychological state. The system consists of a server, a terminal, a user, and an emotion engine.

[0107] The server continues to collect network data in real time and analyze traffic conditions and infrastructure damage. Based on this analysis, it generates a countermeasure plan, but what distinguishes it from conventional systems is its integration with an emotion engine. The emotion engine recognizes the user's emotions, and the server uses this emotion data to prepare to present information in the format most appropriate for the user. For example, if the system detects high stress levels, it will provide simpler, more reassuring information.

[0108] The device is equipped with an emotion engine that analyzes the user's emotions in real time from their voice and facial expressions. The analyzed emotion results are sent to the server via the device. Based on this emotion data, the device presents the user with the most appropriate format for the response plan and evacuation route information received from the server. If the user shows surprise or anxiety, the device can also select a more polite and slow voice guidance mode.

[0109] Users can evacuate safely and quickly by utilizing the appropriate information provided through their devices. This system takes into account that users' emotional states differ significantly between normal times and disaster situations, and provides functions that allow them to evacuate with peace of mind while reducing psychological burden.

[0110] As a concrete example, suppose a major earthquake occurs in Tokyo at night. Users are often awake and confused. The device analyzes the user's state of tension using an emotion engine and guides them to the nearest evacuation center with a calming voice. If the user shows signs of panic while moving, the device will play further reassuring messages or keep the navigation simple. In this way, a system combined with an emotion engine can provide more personalized evacuation support.

[0111] The following describes the processing flow.

[0112] Step 1:

[0113] Upon receiving notification of a disaster, the server immediately begins collecting relevant data via the network. This includes real-time data such as earthquake warnings, traffic information, and infrastructure damage reports.

[0114] Step 2:

[0115] The server analyzes the collected data and predicts the impact on infrastructure and transportation networks. Based on these predictions, it identifies areas requiring evacuation and safe evacuation routes, and generates a response plan.

[0116] Step 3:

[0117] The device analyzes the user's voice and facial expressions using an emotion engine to determine the user's emotional state at that moment. This emotional data reveals whether the user is feeling anxious or stressed.

[0118] Step 4:

[0119] The device receives a response plan generated from the server and provides information optimized for the user's emotional state. For example, if the user is feeling anxious, the device will provide guidance in a calm voice and explain evacuation routes in a gentle tone.

[0120] Step 5:

[0121] The user follows instructions from the device, selects a safe evacuation route, and begins moving. The device monitors the user's location and progress, and continuously updates the information as needed.

[0122] Step 6:

[0123] After the user completes their evacuation, they send feedback to the server via their device. This feedback includes data on emotional changes recorded by the emotion engine, which is used as learning material to help with future disasters.

[0124] (Example 2)

[0125] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0126] Conventional disaster evacuation information systems have been insufficient in providing information that takes into account the psychological burden on users. As a result, users may experience increased confusion and anxiety, which can hinder swift and safe evacuation. This invention aims to solve these problems and support safer and more efficient evacuations by providing flexible and appropriate information tailored to the emotional state of individual users.

[0127] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0128] In this invention, the server includes means for collecting network data, means for analyzing the data to predict the impact on traffic and infrastructure, and means for determining the user's psychological state using an emotion engine that analyzes the user's emotional state. This makes it possible to generate countermeasures plans based on the user's emotional state and provide information in an optimal form.

[0129] "Network data" refers to information that can be obtained over a network, such as traffic conditions and infrastructure status.

[0130] An "emotion engine" is a technology that analyzes a user's emotional state in real time, evaluating their psychological state based on voice and facial expression data.

[0131] A "disaster response plan" refers to a plan or guideline developed to enable safe and rapid evacuation during a disaster.

[0132] "Psychological state" refers to the emotions and mental state a user exhibits in a particular situation, including feelings of relief, anxiety, and confusion.

[0133] A "user" refers to an entity that receives information through the system and actually takes evacuation action.

[0134] This invention combines an emotion engine with a system that provides optimal evacuation information during disasters, enabling flexible information delivery tailored to the user's psychological state. The system operates around a server, terminals, and users.

[0135] The server collects diverse network data in real time and analyzes traffic conditions and infrastructure damage. This includes centrally managing data from various sources using APIs and databases. It also utilizes machine learning algorithms to predict traffic flow and infrastructure conditions. The server's role is to build countermeasure plans based on the generated data and refine those plans using the output of an emotion engine.

[0136] The device is equipped with an emotion engine that analyzes the user's voice and facial expressions to recognize their emotional state in real time. This is achieved through an input device using a camera and microphone, which transmits the emotional data to a server. Based on the received information, the device has the function of presenting evacuation information to the user in the most appropriate format.

[0137] Users can evacuate quickly and safely using evacuation information provided through their devices. The emotional engine reduces the user's psychological burden, enabling them to evacuate with confidence.

[0138] As a concrete example, consider a scenario where a major earthquake occurs at night. When the user shows signs of panic, the device guides them along an evacuation route in a calm, easy-to-understand voice to alleviate their anxiety. This process is executed based on a generative AI model.

[0139] An example of a prompt would be, "What kind of evacuation information system takes into account how users express their emotions during a disaster?" This prompt allows the generating AI model to assess the user's emotional state and provide the necessary information in an appropriate format.

[0140] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0141] Step 1:

[0142] The server collects real-time traffic conditions and infrastructure damage data from publicly available data sources on the network. Inputs are data from traffic APIs and infrastructure monitoring systems, and outputs are datasets for analysis. The data is organized within the server and pre-processed using machine learning algorithms.

[0143] Step 2:

[0144] The server analyzes the collected dataset to identify traffic flow and damaged areas. Input consists of various sensor data and patterns based on past events. Using a predictive algorithm, it identifies congested areas and passable zones. The output is predictive information that should be incorporated into disaster response plans.

[0145] Step 3:

[0146] The device uses a built-in emotion engine to analyze the user's voice and facial expressions and evaluate their emotional state. The input is the user's real-time voice data and camera footage, and the output is an evaluation of the user's current emotional state (e.g., anxiety, relief). Based on this evaluation, the method of information provision generated by the server is determined.

[0147] Step 4:

[0148] The server uses data from the emotion engine to generate an action plan optimized for the user's psychological state. Inputs are previously collected and analyzed environmental data and user emotion evaluation data, while output is customized evacuation information provided in written and audio formats.

[0149] Step 5:

[0150] The terminal presents the user with a countermeasure plan received from the server. The input is the countermeasure plan sent from the system, and the output is the information the user receives visually or aurally. Specifically, the terminal may display a map on the screen or provide voice guidance along a specified route.

[0151] Step 6:

[0152] Users initiate swift and safe evacuation actions based on information provided through their devices. Input is the information presented by the device, and output is the physical evacuation action. User feedback is sent back to the server to help improve system performance.

[0153] (Application Example 2)

[0154] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0155] During disasters, people often experience confusion and stress, making it difficult to provide them with appropriate information for safe evacuation. Conventional evacuation information systems provide uniform information without considering the emotional state of the user, resulting in a significant psychological burden on users when receiving and understanding the information. Therefore, there is a need for a means to provide flexible and appropriate information according to the emotional state of the user, thereby supporting safe and rapid evacuation.

[0156] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0157] In this invention, the server includes means for collecting network data, means for predicting the impact on traffic and infrastructure, and means for analyzing the user's emotional state during the information provision process and optimizing the information. This makes it possible to provide information that reduces stress and provides a sense of security based on the user's emotional state.

[0158] "Means of collecting network data" refers to technologies and devices for acquiring data in real time from the internet and various other networks.

[0159] "Means for predicting the impact on transportation and infrastructure" refers to methods and systems that estimate the impact of a disaster on transportation networks and infrastructure based on collected network data.

[0160] "Methods for generating disaster response plans" refer to algorithms and processes that create appropriate disaster response measures based on predicted data.

[0161] "Means for analyzing emotional states" refer to software and hardware that use data such as voice and facial expressions to identify and analyze a user's emotions.

[0162] "Means of optimizing and delivering information" refer to engines and modules that customize information based on analyzed sentiment data to make it most easily understandable and reassuring for the user.

[0163] "A map display method that specifically shows evacuation route information" refers to a map display technology that visually presents evacuation routes to the user.

[0164] "Means of providing voice guidance" refers to a system that conveys evacuation information and instructions to users via voice.

[0165] "Means of collecting and learning from feedback" refers to data processing techniques that gather user reactions and opinions and use them to improve system performance.

[0166] "Methods for learning and optimizing the effectiveness of information presentation according to emotional state" refers to machine learning techniques that analyze the results of information presentation in response to the user's emotions and make information provision more efficient in subsequent instances.

[0167] This invention aims to construct a system equipped with an emotion engine to effectively provide evacuation support during disasters. Specifically, it involves the coordinated operation of three elements: a server, a terminal, and a user.

[0168] The server has the ability to collect network data and analyze weather, traffic conditions, and infrastructure impacts in real time. Using this analysis, the server generates optimal evacuation routes and countermeasures based on the user's location and situation, and sends them to the terminal. It also works in conjunction with the aforementioned emotion engine to optimize information based on the user's emotion data. For emotion recognition, the server utilizes generative AI models such as Google Cloud Vision API and Microsoft Cognitive Services.

[0169] The device captures the user's voice and facial expressions in real time and analyzes them using an emotion engine. The analysis results are sent to a server, and the device then presents optimized evacuation information based on this analysis. For example, if the user is feeling anxious, the device provides reassuring voice guidance and displays evacuation routes clearly using a graphical interface.

[0170] Users can receive information provided through this system and take swift and safe evacuation actions. Emotional feedback from users is used to further improve the system's performance.

[0171] For example, when an earthquake occurs, the user's device can detect the surrounding chaos, and the server can perform sentiment analysis to generate a simple yet reassuring message such as, "This is the nearest evacuation center. Please rest assured, we will guide you to a safe route," which can then be delivered via both voice and visuals.

[0172] An example of a prompt message would be: "Analyze the following user situation and provide optimal evacuation information. Use the user's facial image and voice data to assess their emotional state." This input will then activate the generative AI model.

[0173] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0174] Step 1:

[0175] The server collects network data in real time from the internet and various sensors. Inputs are data obtained through network connections and include disaster information, traffic conditions, and infrastructure health. Outputs are organized datasets for processing in the next stage.

[0176] Step 2:

[0177] The server analyzes the collected network data to predict the impact of a disaster. Specifically, it uses an analysis algorithm to estimate traffic congestion and the extent of infrastructure damage. The input for this process is the data obtained in step 1, and the output is the prediction results necessary for the user to build an evacuation plan.

[0178] Step 3:

[0179] The server generates disaster response plans and evacuation routes based on the prediction results. It utilizes a generative AI model to establish the most appropriate plan from numerous scenarios. The input is the prediction result from step 2, and the plan is the output for integration with sentiment data in the next step.

[0180] Step 4:

[0181] The device captures the user's voice and facial expressions using a camera and microphone, and inputs them into the emotion engine in real time. The emotion is then analyzed based on these prompt messages. The input is the user's raw data, and the output is the analyzed emotion data.

[0182] Step 5:

[0183] The server integrates emotional data transmitted from the terminal and processes the data to optimize the response plan for the user. Using an emotion engine, the server adjusts the information considering factors such as the user's stress level. The output is evacuation information optimized for the user.

[0184] Step 6:

[0185] The device provides users with optimized evacuation information. Using map display and voice guidance functions, it delivers information in a format that prioritizes safety and ease of understanding. Input is the output in step 5, and the final output is the visual and audio information presented to the user.

[0186] Step 7:

[0187] Users take safe and swift evacuation actions based on the information they receive. User feedback is collected by the system and contributes to future improvements. Feedback is collected from users during the evacuation and used in the system's learning process.

[0188] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0189] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0190] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0191] [Second Embodiment]

[0192] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0193] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0194] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0195] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0196] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0197] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0198] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0199] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0200] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0201] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0202] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0203] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0204] This invention is a system that enables the rapid and accurate provision of information during disasters. This system operates through the coordinated efforts of a server, terminals, and users.

[0205] The server first collects real-time data via the network. This data includes disaster alerts, traffic conditions, infrastructure damage, and real-time information from social media. This data is obtained from multiple APIs and integrated on the server. Next, the acquired data is analyzed to predict the state of the transportation network and infrastructure damage. This allows for the identification of which areas will be more severely affected and which evacuation routes are safe. Based on the predicted information, the server generates a concrete and practical response plan. This plan includes information on safe evacuation routes and shelters.

[0206] The terminal's role is to provide users with response plans received from the server. Through the terminal, users can check evacuation routes displayed on a map and real-time updated traffic information. Furthermore, the terminal has a voice guidance function to navigate users to ensure their safe evacuation. It also has a function to guide users to the nearest evacuation shelter using their current location information.

[0207] This system allows users to take safe and swift evacuation actions even in the event of a disaster. Users can follow the instructions on their terminals, avoiding confusion and taking optimal evacuation actions. The system includes a function to send user feedback to a server, thereby collecting information on problems and areas for improvement that arose during the disaster.

[0208] As a concrete example, consider a scenario where a major earthquake occurs in Tokyo. The server immediately receives an earthquake alert and analyzes traffic conditions and infrastructure damage. Next, it identifies a safe evacuation route and transmits it to the terminal in real time. The terminal provides the user with specific instructions such as, "To safely evacuate to XX Park, go straight down XX Street and then turn right." This allows the user to avoid congestion and evacuate via a safe route. In this way, the present invention realizes rapid and accurate evacuation support during disasters.

[0209] The following describes the processing flow.

[0210] Step 1:

[0211] The server collects disaster-related data in real time via the network. This includes information from earthquake early warning APIs, traffic information APIs, and infrastructure operator APIs. In addition, it crawls social media to obtain damage reports and evacuation status.

[0212] Step 2:

[0213] The server analyzes the collected data. This analysis uses natural language processing to extract important information from text data and machine learning models to predict the impact on transportation networks and infrastructure. Based on these predictions, the server identifies affected areas.

[0214] Step 3:

[0215] Based on the prediction results, the server generates a response plan that includes safe evacuation routes and shelter information. Multiple plans are prepared to accommodate different scenarios (e.g., the degree of traffic congestion or infrastructure damage).

[0216] Step 4:

[0217] The terminal receives the countermeasure plan sent from the server. The received plan is presented to the user through the user interface as a map display and audio guidance. This allows the user to obtain information both visually and aurally.

[0218] Step 5:

[0219] Based on the information presented by the device, the user selects the optimal evacuation route and begins evacuating quickly. If new circumstances change during the evacuation (e.g., route closure), the device receives updates in real time and provides the user with new instructions.

[0220] Step 6:

[0221] After evacuation, users send their feedback (e.g., route safety, new obstacles, etc.) to the server via their devices. The server uses this feedback to generate future predictions and plans, thereby continuously improving the system.

[0222] (Example 1)

[0223] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0224] During disasters, it is essential to collect and analyze information quickly and accurately to support the presentation of safe evacuation routes and the appropriate use of public facilities. However, current systems have challenges such as being unable to respond to real-time changes in the situation and providing insufficient timely guidance based on the user's current location.

[0225] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0226] In this invention, the server includes means for collecting and integrating information from a network, means for analyzing the collected information to predict the impact on traffic routes and public facilities, and means for creating a countermeasure plan based on the prediction according to the disaster situation. This makes it possible to adapt to real-time changes in the situation and quickly provide users with optimized evacuation routes and guidance on the use of public facilities.

[0227] "Means of collecting and integrating information from a network" refers to methods of obtaining various data from external sources, converting them into a common data format, and compiling them into a single database.

[0228] "Methods for analyzing collected information to predict the impact on transportation routes and public facilities" refers to analytical methods that use acquired data to evaluate and predict how a disaster will affect transportation networks and public facilities.

[0229] "Means for creating disaster response plans based on predictions" refers to a process that automatically generates specific action plans to enable users to evacuate safely, based on predictive data obtained through analysis.

[0230] "Means of transmitting and presenting to the user's terminal" refers to a mechanism that provides the generated countermeasure plan to the user visually or audibly through a digital terminal.

[0231] "Means for providing visual and audio guidance on evacuation routes that are dynamically updated based on the user's location information" refers to a system that grasps the user's current location in real time and presents the optimal evacuation route, and includes functions that provide guidance using audio guidance and map display.

[0232] This invention is an information processing system for realizing rapid and accurate evacuation support during disasters. The system operates through the coordinated operation of server, terminal, and user elements.

[0233] The server first collects and integrates information from various sources via the network. Specifically, a wide variety of APIs are used for data collection, including weather data, traffic conditions, operational information for public facilities, and even real-time information from social media. This information is integrated into a database and processed on the server. Using programming languages ​​such as Python and leveraging machine learning libraries, the server analyzes the information and predicts the impact on traffic routes and public facilities. Based on these predictions, the server automatically generates an optimal evacuation plan.

[0234] The terminal acts as a user interface, providing users with evacuation plans transmitted from the server. The terminal visually displays map information and guides users through voice prompts. Using tools such as the Google Maps SDK, it displays detailed evacuation routes and uses GPS functionality to track the user's location in real time. The terminal's voice assistant function allows users to confirm safe routes through voice guidance.

[0235] This system allows users to easily take swift evacuation actions during disasters. Based on the user's dynamic location information, a safe evacuation route is suggested, allowing for a more secure evacuation. Furthermore, users can send feedback to the server after completing their evacuation, and this information is used for further system improvements. For example, in the event of a major earthquake, the server immediately analyzes and provides an evacuation route, and the terminal instructs the user to "proceed straight down XX Street for safe evacuation, then turn right." By following these instructions, the user can evacuate safely.

[0236] An example of a prompt message is, "Please tell me how to identify the safest evacuation route based on earthquake reports and provide detailed navigation to the user." Thus, the present invention is a system that enables rapid and accurate evacuation support during disasters.

[0237] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0238] Step 1:

[0239] The server collects data from various sources via the network. Inputs include weather data, traffic conditions, public transport operation information, and user posts from social media, all obtained using APIs. This data is stored on the server in databases such as Google Cloud Storage. The data is received in JSON or XML format and converted to a unified format.

[0240] Step 2:

[0241] The server analyzes the collected data. The input data is processed as a machine learning dataset, and tools such as Python and TensorFlow are used to predict the availability of transportation routes and public facilities. Specifically, it utilizes models based on historical disaster data to perform impact analysis under certain disaster scenarios. The output is the result of damage prediction.

[0242] Step 3:

[0243] The server creates a disaster response plan based on the analysis results. This process uses a generative AI model to generate appropriate evacuation routes and methods for using public facilities from predictive data. The input is the predictive data from the analysis results, and the output is a concrete evacuation plan. This plan includes information on emergency evacuation roads and shelters and is structured in XML format.

[0244] Step 4:

[0245] The server sends the generated response plan to the terminal in real time. Communication methods using WebSocket or MQTT protocols are ensured, and the plan is delivered to the user's terminal immediately. The generated evacuation plan is used as input, and the output is data displayed on the terminal.

[0246] Step 5:

[0247] The device presents the received evacuation plan to the user visually and audibly. Specifically, the evacuation route is visually displayed on the device's map application using tools such as the Google Maps SDK. Furthermore, a voice assistant provides route guidance, which is updated in real time based on the user's current location. The evacuation plan is provided as input from the server, and the specific guidance information that the user receives is generated as output.

[0248] Step 6:

[0249] Users perform safe evacuation actions based on information provided by their devices. By following the instructions on their devices, users evacuate while avoiding congestion and danger. Furthermore, after the evacuation is complete, users can send feedback to the server. The feedback that users experienced during the evacuation is used as input, and improvement information is collected on the server as output.

[0250] (Application Example 1)

[0251] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0252] In disaster situations, conventional technologies are insufficient for providing real-time safe evacuation routes to support swift, accurate, and safe evacuation actions while avoiding chaos. Furthermore, the use of visualization technologies to support situational assessment during disasters is inadequate, creating a need for more intuitive means of indicating safe evacuation routes.

[0253] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0254] In this invention, the server includes means for collecting network information, means for analyzing the information to predict the impact on transportation means and infrastructure facilities, and means for generating an emergency response plan based on the prediction results. This enables the provision of the optimal evacuation route to users in real time during a disaster and provides guidance on safe evacuation routes that can be intuitively understood using visualization technology.

[0255] "Network information" refers to real-time data and information that can be obtained via the internet or communication networks.

[0256] "Means of transportation" refers to methods and devices that enable the physical movement of people and things, such as public transport and walking.

[0257] "Infrastructure facilities" refer to structures and systems that belong to the infrastructure and support the foundations of economic activity and daily life.

[0258] An "emergency response plan" refers to specific action plans and procedures formulated to ensure swift and appropriate action in the event of a disaster or emergency.

[0259] "User" refers to an individual or organization that uses the system or service.

[0260] "Visualization technology" refers to technologies that visually display information and data, making them intuitively understandable.

[0261] An "evacuation route" refers to a path or route used to move safely to avoid danger.

[0262] The system that implements this application consists of three main components: a server, a terminal, and a user. The server uses Python to collect network information in real time from multiple APIs. The collected information includes data on traffic conditions and infrastructure damage, which is analyzed using TensorFlow to predict the impact on transportation and infrastructure. Based on the prediction results, the system generates the optimal evacuation route as an emergency response plan. The information from the server is transmitted to the terminal in real time.

[0263] The device provides information to the user through an application built with React Native. The device obtains the user's current location using GPS and displays evacuation routes using visualization technology with ARKit (iOS) or ARCore (Android). This allows the user to intuitively confirm a safe route while moving.

[0264] Users can use their smartphones or smart glasses to take quick and safe evacuation actions based on information provided by the server and guidance from their devices.

[0265] A concrete example would be when a heavy rain warning is issued, and the app provides specific instructions such as, "To evacuate to the nearest high ground from your current location, proceed along △△ Street, then turn left at the next traffic light." An example of a prompt message would be, "Output a design proposal for an AR application that predicts the optimal evacuation route in real time during a disaster and guides the user visually and audibly."

[0266] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0267] Step 1:

[0268] The server collects network information from real-time APIs via the internet. Inputs include disaster-related data and traffic data, and output is an integrated dataset. Specifically, the server uses a Python script to send requests to multiple APIs and then aggregates the obtained data.

[0269] Step 2:

[0270] The server utilizes TensorFlow based on collected data to predict the impact on transportation and infrastructure. The input is an integrated dataset, and the output is the prediction. Specifically, the server runs a machine learning model and calculates the prediction results.

[0271] Step 3:

[0272] The server generates an optimal emergency response plan based on the prediction results. The input is the prediction results, and the output is information on evacuation routes and safe locations. Specifically, the server uses an optimization algorithm to calculate safe routes and compiles them into a plan.

[0273] Step 4:

[0274] The device receives the response plan sent from the server and provides it to the user. The input is the server's response plan information, and the output is visual and audio guidance to the user. Specifically, a React Native app running on the device displays the information and provides guidance using speech synthesis technology.

[0275] Step 5:

[0276] The user takes evacuation action according to the instructions on the device. The input is guidance information from the device, and the output is the actual evacuation action. Specifically, the user follows a safe evacuation route according to visual instructions using ARKit or ARCore.

[0277] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0278] This invention combines an emotion engine with a system that provides optimal evacuation information during disasters, thereby enabling flexible information delivery tailored to the user's psychological state. The system consists of a server, a terminal, a user, and an emotion engine.

[0279] The server continues to collect network data in real time and analyze traffic conditions and infrastructure damage. Based on this analysis, it generates a countermeasure plan, but what distinguishes it from conventional systems is its integration with an emotion engine. The emotion engine recognizes the user's emotions, and the server uses this emotion data to prepare to present information in the format most appropriate for the user. For example, if the system detects high stress levels, it will provide simpler, more reassuring information.

[0280] The terminal is equipped with an emotion engine that analyzes emotions in real time from the user's voice and expressions. The analyzed emotion results are transmitted to the server via the terminal. Based on this emotion data, the terminal presents the countermeasure plan and evacuation route information received from the server to the user in the most appropriate format. When the user shows surprise or uneasiness, the terminal can also select a more polite and slower voice guidance mode.

[0281] The user can utilize the appropriate information provided through the terminal to evacuate safely and quickly. This system takes into account that the user's emotional state differs significantly between normal times and disasters, and provides functions for taking evacuation actions with peace of mind while reducing the psychological burden.

[0282] As a specific example, for instance, suppose a major earthquake occurs at night in Tokyo. Users are often confused when waking up. The terminal analyzes the user's state of tension with the emotion engine and guides the route to the nearest evacuation shelter in a calming voice. If the user shows haste while moving, the terminal plays a reassuring message or keeps the navigation simple. In this way, the system combined with the emotion engine realizes more individualized evacuation support.

[0283] The following describes the processing flow.

[0284] Step 1:

[0285] The server receives a disaster occurrence notification and immediately starts collecting relevant data through the network. This includes real-time data such as earthquake early warnings, traffic information, and the damage status of infrastructure.

[0286] Step 2:

[0287] The server analyzes the collected data and predicts the impact on infrastructure and transportation networks. Based on this prediction, it identifies areas where evacuation is necessary and safe evacuation routes, and generates a countermeasure plan.

[0288] Step 3:

[0289] The device analyzes the user's voice and facial expressions using an emotion engine to determine the user's emotional state at that moment. This emotional data reveals whether the user is feeling anxious or stressed.

[0290] Step 4:

[0291] The device receives a response plan generated from the server and provides information optimized for the user's emotional state. For example, if the user is feeling anxious, the device will provide guidance in a calm voice and explain evacuation routes in a gentle tone.

[0292] Step 5:

[0293] The user follows instructions from the device, selects a safe evacuation route, and begins moving. The device monitors the user's location and progress, and continuously updates the information as needed.

[0294] Step 6:

[0295] After the user completes their evacuation, they send feedback to the server via their device. This feedback includes data on emotional changes recorded by the emotion engine, which is used as learning material to help with future disasters.

[0296] (Example 2)

[0297] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0298] Conventional disaster evacuation information systems have been insufficient in providing information that takes into account the psychological burden on users. As a result, users may experience increased confusion and anxiety, which can hinder swift and safe evacuation. This invention aims to solve these problems and support safer and more efficient evacuations by providing flexible and appropriate information tailored to the emotional state of individual users.

[0299] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0300] In this invention, the server includes means for collecting network data, means for analyzing the data to predict the impact on traffic and infrastructure, and means for determining the user's psychological state using an emotion engine that analyzes the user's emotional state. This makes it possible to generate countermeasures plans based on the user's emotional state and provide information in an optimal form.

[0301] "Network data" refers to information that can be obtained over a network, such as traffic conditions and infrastructure status.

[0302] An "emotion engine" is a technology that analyzes a user's emotional state in real time, evaluating their psychological state based on voice and facial expression data.

[0303] A "disaster response plan" refers to a plan or guideline developed to enable safe and rapid evacuation during a disaster.

[0304] "Psychological state" refers to the emotions and mental state a user exhibits in a particular situation, including feelings of relief, anxiety, and confusion.

[0305] A "user" refers to an entity that receives information through the system and actually takes evacuation action.

[0306] This invention combines an emotion engine with a system that provides optimal evacuation information during disasters, enabling flexible information provision according to the psychological state of the user. The system operates centering around a server, a terminal, and a user.

[0307] The server collects various network data in real time and analyzes the traffic situation and the damage situation of infrastructure. This includes centrally managing data from various locations using APIs and databases. Also, machine learning algorithms are utilized for the analysis to predict the traffic flow and the state of infrastructure. The role of the server is to construct a countermeasure plan based on the data generated here and adjust the plan using the output of the emotion engine.

[0308] The terminal is equipped with an emotion engine, analyzes the user's voice and expression, and recognizes the emotional state in real time. This is realized by input devices using a camera and a microphone, and the emotion data is transmitted to the server. The terminal has a function of presenting evacuation information to the user in the most appropriate form based on the received information.

[0309] The user utilizes the evacuation information provided through the terminal and proceeds with evacuation quickly and safely. The emotion engine reduces the user's psychological burden, making it possible to take evacuation actions with peace of mind.

[0310] As a specific example, consider the case where a major earthquake occurs at night. When the user shows confusion, the terminal guides the evacuation route in a calm and easy-to-hear voice to relieve tension. This process is executed based on a generative AI model.

[0311] An example of a prompt sentence is "What is an evacuation information provision system that takes into account how users show emotions during disasters?" With this prompt, the generative AI model can judge the user's emotional state and provide the necessary information in an appropriate form.

[0312] The flow of the specific process in Example 2 will be described using FIG. 13.

[0313] Step 1:

[0314] The server collects real-time traffic conditions and infrastructure damage data from publicly available data sources on the network. Inputs are data from traffic APIs and infrastructure monitoring systems, and outputs are datasets for analysis. The data is organized within the server and pre-processed using machine learning algorithms.

[0315] Step 2:

[0316] The server analyzes the collected dataset to identify traffic flow and damaged areas. Input consists of various sensor data and patterns based on past events. Using a predictive algorithm, it identifies congested areas and passable zones. The output is predictive information that should be incorporated into disaster response plans.

[0317] Step 3:

[0318] The device uses a built-in emotion engine to analyze the user's voice and facial expressions and evaluate their emotional state. The input is the user's real-time voice data and camera footage, and the output is an evaluation of the user's current emotional state (e.g., anxiety, relief). Based on this evaluation, the method of information provision generated by the server is determined.

[0319] Step 4:

[0320] The server uses data from the emotion engine to generate an action plan optimized for the user's psychological state. Inputs are previously collected and analyzed environmental data and user emotion evaluation data, while output is customized evacuation information provided in written and audio formats.

[0321] Step 5:

[0322] The terminal presents the user with a countermeasure plan received from the server. The input is the countermeasure plan sent from the system, and the output is the information the user receives visually or aurally. Specifically, the terminal may display a map on the screen or provide voice guidance along a specified route.

[0323] Step 6:

[0324] Users initiate swift and safe evacuation actions based on information provided through their devices. Input is the information presented by the device, and output is the physical evacuation action. User feedback is sent back to the server to help improve system performance.

[0325] (Application Example 2)

[0326] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0327] During disasters, people often experience confusion and stress, making it difficult to provide them with appropriate information for safe evacuation. Conventional evacuation information systems provide uniform information without considering the emotional state of the user, resulting in a significant psychological burden on users when receiving and understanding the information. Therefore, there is a need for a means to provide flexible and appropriate information according to the emotional state of the user, thereby supporting safe and rapid evacuation.

[0328] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0329] In this invention, the server includes means for collecting network data, means for predicting the impact on traffic and infrastructure, and means for analyzing the user's emotional state during the information provision process and optimizing the information. This makes it possible to provide information that reduces stress and provides a sense of security based on the user's emotional state.

[0330] "Means of collecting network data" refers to technologies and devices for acquiring data in real time from the internet and various other networks.

[0331] "Means for predicting the impact on transportation and infrastructure" refers to methods and systems that estimate the impact of a disaster on transportation networks and infrastructure based on collected network data.

[0332] "Methods for generating disaster response plans" refer to algorithms and processes that create appropriate disaster response measures based on predicted data.

[0333] "Means for analyzing emotional states" refer to software and hardware that use data such as voice and facial expressions to identify and analyze a user's emotions.

[0334] "Means of optimizing and delivering information" refer to engines and modules that customize information based on analyzed sentiment data to make it most easily understandable and reassuring for the user.

[0335] "A map display method that specifically shows evacuation route information" refers to a map display technology that visually presents evacuation routes to the user.

[0336] "Means of providing voice guidance" refers to a system that conveys evacuation information and instructions to users via voice.

[0337] "Means of collecting and learning from feedback" refers to data processing techniques that gather user reactions and opinions and use them to improve system performance.

[0338] "Methods for learning and optimizing the effectiveness of information presentation according to emotional state" refers to machine learning techniques that analyze the results of information presentation in response to the user's emotions and make information provision more efficient in subsequent instances.

[0339] This invention aims to construct a system equipped with an emotion engine to effectively provide evacuation support during disasters. Specifically, it involves the coordinated operation of three elements: a server, a terminal, and a user.

[0340] The server has the ability to collect network data and analyze weather, traffic conditions, and infrastructure impacts in real time. Using this analysis, the server generates optimal evacuation routes and countermeasures based on the user's location and situation, and sends them to the terminal. It also works in conjunction with the aforementioned emotion engine to optimize information based on the user's emotion data. For emotion recognition, the server utilizes generative AI models such as Google Cloud Vision API and Microsoft Cognitive Services.

[0341] The device captures the user's voice and facial expressions in real time and analyzes them using an emotion engine. The analysis results are sent to a server, and the device then presents optimized evacuation information based on this analysis. For example, if the user is feeling anxious, the device provides reassuring voice guidance and displays evacuation routes clearly using a graphical interface.

[0342] Users can receive information provided through this system and take swift and safe evacuation actions. Emotional feedback from users is used to further improve the system's performance.

[0343] For example, when an earthquake occurs, the user's device can detect the surrounding chaos, and the server can perform sentiment analysis to generate a simple yet reassuring message such as, "This is the nearest evacuation center. Please rest assured, we will guide you to a safe route," which can then be delivered via both voice and visuals.

[0344] An example of a prompt message would be: "Analyze the following user situation and provide optimal evacuation information. Use the user's facial image and voice data to assess their emotional state." This input will then activate the generative AI model.

[0345] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0346] Step 1:

[0347] The server collects network data in real time from the internet and various sensors. Inputs are data obtained through network connections and include disaster information, traffic conditions, and infrastructure health. Outputs are organized datasets for processing in the next stage.

[0348] Step 2:

[0349] The server analyzes the collected network data to predict the impact of a disaster. Specifically, it uses an analysis algorithm to estimate traffic congestion and the extent of infrastructure damage. The input for this process is the data obtained in step 1, and the output is the prediction results necessary for the user to build an evacuation plan.

[0350] Step 3:

[0351] The server generates disaster response plans and evacuation routes based on the prediction results. It utilizes a generative AI model to establish the most appropriate plan from numerous scenarios. The input is the prediction result from step 2, and the plan is the output for integration with sentiment data in the next step.

[0352] Step 4:

[0353] The device captures the user's voice and facial expressions using a camera and microphone, and inputs them into the emotion engine in real time. The emotion is then analyzed based on these prompt messages. The input is the user's raw data, and the output is the analyzed emotion data.

[0354] Step 5:

[0355] The server integrates emotional data transmitted from the terminal and processes the data to optimize the response plan for the user. Using an emotion engine, the server adjusts the information considering factors such as the user's stress level. The output is evacuation information optimized for the user.

[0356] Step 6:

[0357] The device provides users with optimized evacuation information. Using map display and voice guidance functions, it delivers information in a format that prioritizes safety and ease of understanding. Input is the output in step 5, and the final output is the visual and audio information presented to the user.

[0358] Step 7:

[0359] Users take safe and swift evacuation actions based on the information they receive. User feedback is collected by the system and contributes to future improvements. Feedback is collected from users during the evacuation and used in the system's learning process.

[0360] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0361] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0362] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0363] [Third Embodiment]

[0364] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0365] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0366] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0367] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0368] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0369] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0370] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0371] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0372] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0373] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0374] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0375] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0376] This invention is a system that enables the rapid and accurate provision of information during disasters. This system operates through the coordinated efforts of a server, terminals, and users.

[0377] The server first collects real-time data via the network. This data includes disaster alerts, traffic conditions, infrastructure damage, and real-time information from social media. This data is obtained from multiple APIs and integrated on the server. Next, the acquired data is analyzed to predict the state of the transportation network and infrastructure damage. This allows for the identification of which areas will be more severely affected and which evacuation routes are safe. Based on the predicted information, the server generates a concrete and practical response plan. This plan includes information on safe evacuation routes and shelters.

[0378] The terminal's role is to provide users with response plans received from the server. Through the terminal, users can check evacuation routes displayed on a map and real-time updated traffic information. Furthermore, the terminal has a voice guidance function to navigate users to ensure their safe evacuation. It also has a function to guide users to the nearest evacuation shelter using their current location information.

[0379] This system allows users to take safe and swift evacuation actions even in the event of a disaster. Users can follow the instructions on their terminals, avoiding confusion and taking optimal evacuation actions. The system includes a function to send user feedback to a server, thereby collecting information on problems and areas for improvement that arose during the disaster.

[0380] As a concrete example, consider a scenario where a major earthquake occurs in Tokyo. The server immediately receives an earthquake alert and analyzes traffic conditions and infrastructure damage. Next, it identifies a safe evacuation route and transmits it to the terminal in real time. The terminal provides the user with specific instructions such as, "To safely evacuate to XX Park, go straight down XX Street and then turn right." This allows the user to avoid congestion and evacuate via a safe route. In this way, the present invention realizes rapid and accurate evacuation support during disasters.

[0381] The following describes the processing flow.

[0382] Step 1:

[0383] The server collects disaster-related data in real time via the network. This includes information from earthquake early warning APIs, traffic information APIs, and infrastructure operator APIs. In addition, it crawls social media to obtain damage reports and evacuation status.

[0384] Step 2:

[0385] The server analyzes the collected data. This analysis uses natural language processing to extract important information from text data and machine learning models to predict the impact on transportation networks and infrastructure. Based on these predictions, the server identifies affected areas.

[0386] Step 3:

[0387] Based on the prediction results, the server generates a response plan that includes safe evacuation routes and shelter information. Multiple plans are prepared to accommodate different scenarios (e.g., the degree of traffic congestion or infrastructure damage).

[0388] Step 4:

[0389] The terminal receives the countermeasure plan sent from the server. The received plan is presented to the user through the user interface as a map display and audio guidance. This allows the user to obtain information both visually and aurally.

[0390] Step 5:

[0391] Based on the information presented by the device, the user selects the optimal evacuation route and begins evacuating quickly. If new circumstances change during the evacuation (e.g., route closure), the device receives updates in real time and provides the user with new instructions.

[0392] Step 6:

[0393] After evacuation, users send their feedback (e.g., route safety, new obstacles, etc.) to the server via their devices. The server uses this feedback to generate future predictions and plans, thereby continuously improving the system.

[0394] (Example 1)

[0395] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0396] During disasters, it is essential to collect and analyze information quickly and accurately to support the presentation of safe evacuation routes and the appropriate use of public facilities. However, current systems have challenges such as being unable to respond to real-time changes in the situation and providing insufficient timely guidance based on the user's current location.

[0397] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0398] In this invention, the server includes means for collecting and integrating information from a network, means for analyzing the collected information to predict the impact on traffic routes and public facilities, and means for creating a countermeasure plan based on the prediction according to the disaster situation. This makes it possible to adapt to real-time changes in the situation and quickly provide users with optimized evacuation routes and guidance on the use of public facilities.

[0399] "Means of collecting and integrating information from a network" refers to methods of obtaining various data from external sources, converting them into a common data format, and compiling them into a single database.

[0400] "Methods for analyzing collected information to predict the impact on transportation routes and public facilities" refers to analytical methods that use acquired data to evaluate and predict how a disaster will affect transportation networks and public facilities.

[0401] "Means for creating disaster response plans based on predictions" refers to a process that automatically generates specific action plans to enable users to evacuate safely, based on predictive data obtained through analysis.

[0402] "Means of transmitting and presenting to the user's terminal" refers to a mechanism that provides the generated countermeasure plan to the user visually or audibly through a digital terminal.

[0403] "Means for providing visual and audio guidance on evacuation routes that are dynamically updated based on the user's location information" refers to a system that grasps the user's current location in real time and presents the optimal evacuation route, and includes functions that provide guidance using audio guidance and map display.

[0404] This invention is an information processing system for realizing rapid and accurate evacuation support during disasters. The system operates through the coordinated operation of server, terminal, and user elements.

[0405] The server first collects and integrates information from various sources via the network. Specifically, a wide variety of APIs are used for data collection, including weather data, traffic conditions, operational information for public facilities, and even real-time information from social media. This information is integrated into a database and processed on the server. Using programming languages ​​such as Python and leveraging machine learning libraries, the server analyzes the information and predicts the impact on traffic routes and public facilities. Based on these predictions, the server automatically generates an optimal evacuation plan.

[0406] The terminal acts as a user interface, providing users with evacuation plans transmitted from the server. The terminal visually displays map information and guides users through voice prompts. Using tools such as the Google Maps SDK, it displays detailed evacuation routes and uses GPS functionality to track the user's location in real time. The terminal's voice assistant function allows users to confirm safe routes through voice guidance.

[0407] This system allows users to easily take swift evacuation actions during disasters. Based on the user's dynamic location information, a safe evacuation route is suggested, allowing for a more secure evacuation. Furthermore, users can send feedback to the server after completing their evacuation, and this information is used for further system improvements. For example, in the event of a major earthquake, the server immediately analyzes and provides an evacuation route, and the terminal instructs the user to "proceed straight down XX Street for safe evacuation, then turn right." By following these instructions, the user can evacuate safely.

[0408] An example of a prompt message is, "Please tell me how to identify the safest evacuation route based on earthquake reports and provide detailed navigation to the user." Thus, the present invention is a system that enables rapid and accurate evacuation support during disasters.

[0409] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0410] Step 1:

[0411] The server collects data from various sources via the network. Inputs include weather data, traffic conditions, public transport operation information, and user posts from social media, all obtained using APIs. This data is stored on the server in databases such as Google Cloud Storage. The data is received in JSON or XML format and converted to a unified format.

[0412] Step 2:

[0413] The server analyzes the collected data. The input data is processed as a machine learning dataset, and tools such as Python and TensorFlow are used to predict the availability of transportation routes and public facilities. Specifically, it utilizes models based on historical disaster data to perform impact analysis under certain disaster scenarios. The output is the result of damage prediction.

[0414] Step 3:

[0415] The server creates a disaster response plan based on the analysis results. This process uses a generative AI model to generate appropriate evacuation routes and methods for using public facilities from predictive data. The input is the predictive data from the analysis results, and the output is a concrete evacuation plan. This plan includes information on emergency evacuation roads and shelters and is structured in XML format.

[0416] Step 4:

[0417] The server sends the generated response plan to the terminal in real time. Communication methods using WebSocket or MQTT protocols are ensured, and the plan is delivered to the user's terminal immediately. The generated evacuation plan is used as input, and the output is data displayed on the terminal.

[0418] Step 5:

[0419] The device presents the received evacuation plan to the user visually and audibly. Specifically, the evacuation route is visually displayed on the device's map application using tools such as the Google Maps SDK. Furthermore, a voice assistant provides route guidance, which is updated in real time based on the user's current location. The evacuation plan is provided as input from the server, and the specific guidance information that the user receives is generated as output.

[0420] Step 6:

[0421] Users perform safe evacuation actions based on information provided by their devices. By following the instructions on their devices, users evacuate while avoiding congestion and danger. Furthermore, after the evacuation is complete, users can send feedback to the server. The feedback that users experienced during the evacuation is used as input, and improvement information is collected on the server as output.

[0422] (Application Example 1)

[0423] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0424] In disaster situations, conventional technologies are insufficient for providing real-time safe evacuation routes to support swift, accurate, and safe evacuation actions while avoiding chaos. Furthermore, the use of visualization technologies to support situational assessment during disasters is inadequate, creating a need for more intuitive means of indicating safe evacuation routes.

[0425] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0426] In this invention, the server includes means for collecting network information, means for analyzing the information to predict the impact on transportation means and infrastructure facilities, and means for generating an emergency response plan based on the prediction results. This enables the provision of the optimal evacuation route to users in real time during a disaster and provides guidance on safe evacuation routes that can be intuitively understood using visualization technology.

[0427] "Network information" refers to real-time data and information that can be obtained via the internet or communication networks.

[0428] "Means of transportation" refers to methods and devices that enable the physical movement of people and things, such as public transport and walking.

[0429] "Infrastructure facilities" refer to structures and systems that belong to the infrastructure and support the foundations of economic activity and daily life.

[0430] An "emergency response plan" refers to specific action plans and procedures formulated to ensure swift and appropriate action in the event of a disaster or emergency.

[0431] "User" refers to an individual or organization that uses the system or service.

[0432] "Visualization technology" refers to technologies that visually display information and data, making them intuitively understandable.

[0433] An "evacuation route" refers to a path or route used to move safely to avoid danger.

[0434] The system that implements this application consists of three main components: a server, a terminal, and a user. The server uses Python to collect network information in real time from multiple APIs. The collected information includes data on traffic conditions and infrastructure damage, which is analyzed using TensorFlow to predict the impact on transportation and infrastructure. Based on the prediction results, the system generates the optimal evacuation route as an emergency response plan. The information from the server is transmitted to the terminal in real time.

[0435] The device provides information to the user through an application built with React Native. The device obtains the user's current location using GPS and displays evacuation routes using visualization technology with ARKit (iOS) or ARCore (Android). This allows the user to intuitively confirm a safe route while moving.

[0436] Users can use their smartphones or smart glasses to take quick and safe evacuation actions based on information provided by the server and guidance from their devices.

[0437] A concrete example would be when a heavy rain warning is issued, and the app provides specific instructions such as, "To evacuate to the nearest high ground from your current location, proceed along △△ Street, then turn left at the next traffic light." An example of a prompt message would be, "Output a design proposal for an AR application that predicts the optimal evacuation route in real time during a disaster and guides the user visually and audibly."

[0438] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0439] Step 1:

[0440] The server collects network information from real-time APIs via the internet. Inputs include disaster-related data and traffic data, and output is an integrated dataset. Specifically, the server uses a Python script to send requests to multiple APIs and then aggregates the obtained data.

[0441] Step 2:

[0442] The server utilizes TensorFlow based on collected data to predict the impact on transportation and infrastructure. The input is an integrated dataset, and the output is the prediction. Specifically, the server runs a machine learning model and calculates the prediction results.

[0443] Step 3:

[0444] The server generates an optimal emergency response plan based on the prediction results. The input is the prediction results, and the output is information on evacuation routes and safe locations. Specifically, the server uses an optimization algorithm to calculate safe routes and compiles them into a plan.

[0445] Step 4:

[0446] The device receives the response plan sent from the server and provides it to the user. The input is the server's response plan information, and the output is visual and audio guidance to the user. Specifically, a React Native app running on the device displays the information and provides guidance using speech synthesis technology.

[0447] Step 5:

[0448] The user takes evacuation action according to the instructions on the device. The input is guidance information from the device, and the output is the actual evacuation action. Specifically, the user follows a safe evacuation route according to visual instructions using ARKit or ARCore.

[0449] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0450] This invention combines an emotion engine with a system that provides optimal evacuation information during disasters, thereby enabling flexible information delivery tailored to the user's psychological state. The system consists of a server, a terminal, a user, and an emotion engine.

[0451] The server continues to collect network data in real time and analyze traffic conditions and infrastructure damage. Based on this analysis, it generates a countermeasure plan, but what distinguishes it from conventional systems is its integration with an emotion engine. The emotion engine recognizes the user's emotions, and the server uses this emotion data to prepare to present information in the format most appropriate for the user. For example, if the system detects high stress levels, it will provide simpler, more reassuring information.

[0452] The device is equipped with an emotion engine that analyzes the user's emotions in real time from their voice and facial expressions. The analyzed emotion results are sent to the server via the device. Based on this emotion data, the device presents the user with the most appropriate format for the response plan and evacuation route information received from the server. If the user shows surprise or anxiety, the device can also select a more polite and slow voice guidance mode.

[0453] Users can evacuate safely and quickly by utilizing the appropriate information provided through their devices. This system takes into account that users' emotional states differ significantly between normal times and disaster situations, and provides functions that allow them to evacuate with peace of mind while reducing psychological burden.

[0454] As a concrete example, suppose a major earthquake occurs in Tokyo at night. Users are often awake and confused. The device analyzes the user's state of tension using an emotion engine and guides them to the nearest evacuation center with a calming voice. If the user shows signs of panic while moving, the device will play further reassuring messages or keep the navigation simple. In this way, a system combined with an emotion engine can provide more personalized evacuation support.

[0455] The following describes the processing flow.

[0456] Step 1:

[0457] Upon receiving notification of a disaster, the server immediately begins collecting relevant data via the network. This includes real-time data such as earthquake warnings, traffic information, and infrastructure damage reports.

[0458] Step 2:

[0459] The server analyzes the collected data and predicts the impact on infrastructure and transportation networks. Based on these predictions, it identifies areas requiring evacuation and safe evacuation routes, and generates a response plan.

[0460] Step 3:

[0461] The device analyzes the user's voice and facial expressions using an emotion engine to determine the user's emotional state at that moment. This emotional data reveals whether the user is feeling anxious or stressed.

[0462] Step 4:

[0463] The device receives a response plan generated from the server and provides information optimized for the user's emotional state. For example, if the user is feeling anxious, the device will provide guidance in a calm voice and explain evacuation routes in a gentle tone.

[0464] Step 5:

[0465] The user follows instructions from the device, selects a safe evacuation route, and begins moving. The device monitors the user's location and progress, and continuously updates the information as needed.

[0466] Step 6:

[0467] After the user completes their evacuation, they send feedback to the server via their device. This feedback includes data on emotional changes recorded by the emotion engine, which is used as learning material to help with future disasters.

[0468] (Example 2)

[0469] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0470] Conventional disaster evacuation information systems have been insufficient in providing information that takes into account the psychological burden on users. As a result, users may experience increased confusion and anxiety, which can hinder swift and safe evacuation. This invention aims to solve these problems and support safer and more efficient evacuations by providing flexible and appropriate information tailored to the emotional state of individual users.

[0471] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0472] In this invention, the server includes means for collecting network data, means for analyzing the data to predict the impact on traffic and infrastructure, and means for determining the user's psychological state using an emotion engine that analyzes the user's emotional state. This makes it possible to generate countermeasures plans based on the user's emotional state and provide information in an optimal form.

[0473] "Network data" refers to information that can be obtained over a network, such as traffic conditions and infrastructure status.

[0474] An "emotion engine" is a technology that analyzes a user's emotional state in real time, evaluating their psychological state based on voice and facial expression data.

[0475] A "disaster response plan" refers to a plan or guideline developed to enable safe and rapid evacuation during a disaster.

[0476] "Psychological state" refers to the emotions and mental state a user exhibits in a particular situation, including feelings of relief, anxiety, and confusion.

[0477] A "user" refers to an entity that receives information through the system and actually takes evacuation action.

[0478] This invention combines an emotion engine with a system that provides optimal evacuation information during disasters, enabling flexible information delivery tailored to the user's psychological state. The system operates around a server, terminals, and users.

[0479] The server collects diverse network data in real time and analyzes traffic conditions and infrastructure damage. This includes centrally managing data from various sources using APIs and databases. It also utilizes machine learning algorithms to predict traffic flow and infrastructure conditions. The server's role is to build countermeasure plans based on the generated data and refine those plans using the output of an emotion engine.

[0480] The device is equipped with an emotion engine that analyzes the user's voice and facial expressions to recognize their emotional state in real time. This is achieved through an input device using a camera and microphone, which transmits the emotional data to a server. Based on the received information, the device has the function of presenting evacuation information to the user in the most appropriate format.

[0481] Users can evacuate quickly and safely using evacuation information provided through their devices. The emotional engine reduces the user's psychological burden, enabling them to evacuate with confidence.

[0482] As a concrete example, consider a scenario where a major earthquake occurs at night. When the user shows signs of panic, the device guides them along an evacuation route in a calm, easy-to-understand voice to alleviate their anxiety. This process is executed based on a generative AI model.

[0483] An example of a prompt would be, "What kind of evacuation information system takes into account how users express their emotions during a disaster?" This prompt allows the generating AI model to assess the user's emotional state and provide the necessary information in an appropriate format.

[0484] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0485] Step 1:

[0486] The server collects real-time traffic conditions and infrastructure damage data from publicly available data sources on the network. Inputs are data from traffic APIs and infrastructure monitoring systems, and outputs are datasets for analysis. The data is organized within the server and pre-processed using machine learning algorithms.

[0487] Step 2:

[0488] The server analyzes the collected dataset to identify traffic flow and damaged areas. Input consists of various sensor data and patterns based on past events. Using a predictive algorithm, it identifies congested areas and passable zones. The output is predictive information that should be incorporated into disaster response plans.

[0489] Step 3:

[0490] The device uses a built-in emotion engine to analyze the user's voice and facial expressions and evaluate their emotional state. The input is the user's real-time voice data and camera footage, and the output is an evaluation of the user's current emotional state (e.g., anxiety, relief). Based on this evaluation, the method of information provision generated by the server is determined.

[0491] Step 4:

[0492] The server uses data from the emotion engine to generate an action plan optimized for the user's psychological state. Inputs are previously collected and analyzed environmental data and user emotion evaluation data, while output is customized evacuation information provided in written and audio formats.

[0493] Step 5:

[0494] The terminal presents the user with a countermeasure plan received from the server. The input is the countermeasure plan sent from the system, and the output is the information the user receives visually or aurally. Specifically, the terminal may display a map on the screen or provide voice guidance along a specified route.

[0495] Step 6:

[0496] Users initiate swift and safe evacuation actions based on information provided through their devices. Input is the information presented by the device, and output is the physical evacuation action. User feedback is sent back to the server to help improve system performance.

[0497] (Application Example 2)

[0498] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0499] During disasters, people often experience confusion and stress, making it difficult to provide them with appropriate information for safe evacuation. Conventional evacuation information systems provide uniform information without considering the emotional state of the user, resulting in a significant psychological burden on users when receiving and understanding the information. Therefore, there is a need for a means to provide flexible and appropriate information according to the emotional state of the user, thereby supporting safe and rapid evacuation.

[0500] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0501] In this invention, the server includes means for collecting network data, means for predicting the impact on traffic and infrastructure, and means for analyzing the user's emotional state during the information provision process and optimizing the information. This makes it possible to provide information that reduces stress and provides a sense of security based on the user's emotional state.

[0502] "Means of collecting network data" refers to technologies and devices for acquiring data in real time from the internet and various other networks.

[0503] "Means for predicting the impact on transportation and infrastructure" refers to methods and systems that estimate the impact of a disaster on transportation networks and infrastructure based on collected network data.

[0504] "Methods for generating disaster response plans" refer to algorithms and processes that create appropriate disaster response measures based on predicted data.

[0505] "Means for analyzing emotional states" refer to software and hardware that use data such as voice and facial expressions to identify and analyze a user's emotions.

[0506] "Means of optimizing and delivering information" refer to engines and modules that customize information based on analyzed sentiment data to make it most easily understandable and reassuring for the user.

[0507] "A map display method that specifically shows evacuation route information" refers to a map display technology that visually presents evacuation routes to the user.

[0508] "Means of providing voice guidance" refers to a system that conveys evacuation information and instructions to users via voice.

[0509] "Means of collecting and learning from feedback" refers to data processing techniques that gather user reactions and opinions and use them to improve system performance.

[0510] "Methods for learning and optimizing the effectiveness of information presentation according to emotional state" refers to machine learning techniques that analyze the results of information presentation in response to the user's emotions and make information provision more efficient in subsequent instances.

[0511] This invention aims to construct a system equipped with an emotion engine to effectively provide evacuation support during disasters. Specifically, it involves the coordinated operation of three elements: a server, a terminal, and a user.

[0512] The server has the ability to collect network data and analyze weather, traffic conditions, and infrastructure impacts in real time. Using this analysis, the server generates optimal evacuation routes and countermeasures based on the user's location and situation, and sends them to the terminal. It also works in conjunction with the aforementioned emotion engine to optimize information based on the user's emotion data. For emotion recognition, the server utilizes generative AI models such as Google Cloud Vision API and Microsoft Cognitive Services.

[0513] The device captures the user's voice and facial expressions in real time and analyzes them using an emotion engine. The analysis results are sent to a server, and the device then presents optimized evacuation information based on this analysis. For example, if the user is feeling anxious, the device provides reassuring voice guidance and displays evacuation routes clearly using a graphical interface.

[0514] Users can receive information provided through this system and take swift and safe evacuation actions. Emotional feedback from users is used to further improve the system's performance.

[0515] For example, when an earthquake occurs, the user's device can detect the surrounding chaos, and the server can perform sentiment analysis to generate a simple yet reassuring message such as, "This is the nearest evacuation center. Please rest assured, we will guide you to a safe route," which can then be delivered via both voice and visuals.

[0516] An example of a prompt message would be: "Analyze the following user situation and provide optimal evacuation information. Use the user's facial image and voice data to assess their emotional state." This input will then activate the generative AI model.

[0517] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0518] Step 1:

[0519] The server collects network data in real time from the internet and various sensors. Inputs are data obtained through network connections and include disaster information, traffic conditions, and infrastructure health. Outputs are organized datasets for processing in the next stage.

[0520] Step 2:

[0521] The server analyzes the collected network data to predict the impact of a disaster. Specifically, it uses an analysis algorithm to estimate traffic congestion and the extent of infrastructure damage. The input for this process is the data obtained in step 1, and the output is the prediction results necessary for the user to build an evacuation plan.

[0522] Step 3:

[0523] The server generates disaster response plans and evacuation routes based on the prediction results. It utilizes a generative AI model to establish the most appropriate plan from numerous scenarios. The input is the prediction result from step 2, and the plan is the output for integration with sentiment data in the next step.

[0524] Step 4:

[0525] The device captures the user's voice and facial expressions using a camera and microphone, and inputs them into the emotion engine in real time. The emotion is then analyzed based on these prompt messages. The input is the user's raw data, and the output is the analyzed emotion data.

[0526] Step 5:

[0527] The server integrates emotional data transmitted from the terminal and processes the data to optimize the response plan for the user. Using an emotion engine, the server adjusts the information considering factors such as the user's stress level. The output is evacuation information optimized for the user.

[0528] Step 6:

[0529] The device provides users with optimized evacuation information. Using map display and voice guidance functions, it delivers information in a format that prioritizes safety and ease of understanding. Input is the output in step 5, and the final output is the visual and audio information presented to the user.

[0530] Step 7:

[0531] Users take safe and swift evacuation actions based on the information they receive. User feedback is collected by the system and contributes to future improvements. Feedback is collected from users during the evacuation and used in the system's learning process.

[0532] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0533] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0534] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0535] [Fourth Embodiment]

[0536] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0537] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0538] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0539] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0540] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0541] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0542] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0543] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0544] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0545] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0546] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0547] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0548] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0549] This invention is a system that enables the rapid and accurate provision of information during disasters. This system operates through the coordinated efforts of a server, terminals, and users.

[0550] The server first collects real-time data via the network. This data includes disaster alerts, traffic conditions, infrastructure damage, and real-time information from social media. This data is obtained from multiple APIs and integrated on the server. Next, the acquired data is analyzed to predict the state of the transportation network and infrastructure damage. This allows for the identification of which areas will be more severely affected and which evacuation routes are safe. Based on the predicted information, the server generates a concrete and practical response plan. This plan includes information on safe evacuation routes and shelters.

[0551] The terminal's role is to provide users with response plans received from the server. Through the terminal, users can check evacuation routes displayed on a map and real-time updated traffic information. Furthermore, the terminal has a voice guidance function to navigate users to ensure their safe evacuation. It also has a function to guide users to the nearest evacuation shelter using their current location information.

[0552] This system allows users to take safe and swift evacuation actions even in the event of a disaster. Users can follow the instructions on their terminals, avoiding confusion and taking optimal evacuation actions. The system includes a function to send user feedback to a server, thereby collecting information on problems and areas for improvement that arose during the disaster.

[0553] As a concrete example, consider a scenario where a major earthquake occurs in Tokyo. The server immediately receives an earthquake alert and analyzes traffic conditions and infrastructure damage. Next, it identifies a safe evacuation route and transmits it to the terminal in real time. The terminal provides the user with specific instructions such as, "To safely evacuate to XX Park, go straight down XX Street and then turn right." This allows the user to avoid congestion and evacuate via a safe route. In this way, the present invention realizes rapid and accurate evacuation support during disasters.

[0554] The following describes the processing flow.

[0555] Step 1:

[0556] The server collects disaster-related data in real time via the network. This includes information from earthquake early warning APIs, traffic information APIs, and infrastructure operator APIs. In addition, it crawls social media to obtain damage reports and evacuation status.

[0557] Step 2:

[0558] The server analyzes the collected data. This analysis uses natural language processing to extract important information from text data and machine learning models to predict the impact on transportation networks and infrastructure. Based on these predictions, the server identifies affected areas.

[0559] Step 3:

[0560] Based on the prediction results, the server generates a response plan that includes safe evacuation routes and shelter information. Multiple plans are prepared to accommodate different scenarios (e.g., the degree of traffic congestion or infrastructure damage).

[0561] Step 4:

[0562] The terminal receives the countermeasure plan sent from the server. The received plan is presented to the user through the user interface as a map display and audio guidance. This allows the user to obtain information both visually and aurally.

[0563] Step 5:

[0564] Based on the information presented by the device, the user selects the optimal evacuation route and begins evacuating quickly. If new circumstances change during the evacuation (e.g., route closure), the device receives updates in real time and provides the user with new instructions.

[0565] Step 6:

[0566] After evacuation, users send their feedback (e.g., route safety, new obstacles, etc.) to the server via their devices. The server uses this feedback to generate future predictions and plans, thereby continuously improving the system.

[0567] (Example 1)

[0568] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0569] During disasters, it is essential to collect and analyze information quickly and accurately to support the presentation of safe evacuation routes and the appropriate use of public facilities. However, current systems have challenges such as being unable to respond to real-time changes in the situation and providing insufficient timely guidance based on the user's current location.

[0570] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0571] In this invention, the server includes means for collecting and integrating information from a network, means for analyzing the collected information to predict the impact on traffic routes and public facilities, and means for creating a countermeasure plan based on the prediction according to the disaster situation. This makes it possible to adapt to real-time changes in the situation and quickly provide users with optimized evacuation routes and guidance on the use of public facilities.

[0572] "Means of collecting and integrating information from a network" refers to methods of obtaining various data from external sources, converting them into a common data format, and compiling them into a single database.

[0573] "Methods for analyzing collected information to predict the impact on transportation routes and public facilities" refers to analytical methods that use acquired data to evaluate and predict how a disaster will affect transportation networks and public facilities.

[0574] "Means for creating disaster response plans based on predictions" refers to a process that automatically generates specific action plans to enable users to evacuate safely, based on predictive data obtained through analysis.

[0575] "Means of transmitting and presenting to the user's terminal" refers to a mechanism that provides the generated countermeasure plan to the user visually or audibly through a digital terminal.

[0576] "Means for providing visual and audio guidance on evacuation routes that are dynamically updated based on the user's location information" refers to a system that grasps the user's current location in real time and presents the optimal evacuation route, and includes functions that provide guidance using audio guidance and map display.

[0577] This invention is an information processing system for realizing rapid and accurate evacuation support during disasters. The system operates through the coordinated operation of server, terminal, and user elements.

[0578] The server first collects and integrates information from various sources via the network. Specifically, a wide variety of APIs are used for data collection, including weather data, traffic conditions, operational information for public facilities, and even real-time information from social media. This information is integrated into a database and processed on the server. Using programming languages ​​such as Python and leveraging machine learning libraries, the server analyzes the information and predicts the impact on traffic routes and public facilities. Based on these predictions, the server automatically generates an optimal evacuation plan.

[0579] The terminal acts as a user interface, providing users with evacuation plans transmitted from the server. The terminal visually displays map information and guides users through voice prompts. Using tools such as the Google Maps SDK, it displays detailed evacuation routes and uses GPS functionality to track the user's location in real time. The terminal's voice assistant function allows users to confirm safe routes through voice guidance.

[0580] This system allows users to easily take swift evacuation actions during disasters. Based on the user's dynamic location information, a safe evacuation route is suggested, allowing for a more secure evacuation. Furthermore, users can send feedback to the server after completing their evacuation, and this information is used for further system improvements. For example, in the event of a major earthquake, the server immediately analyzes and provides an evacuation route, and the terminal instructs the user to "proceed straight down XX Street for safe evacuation, then turn right." By following these instructions, the user can evacuate safely.

[0581] An example of a prompt message is, "Please tell me how to identify the safest evacuation route based on earthquake reports and provide detailed navigation to the user." Thus, the present invention is a system that enables rapid and accurate evacuation support during disasters.

[0582] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0583] Step 1:

[0584] The server collects data from various sources via the network. Inputs include weather data, traffic conditions, public transport operation information, and user posts from social media, all obtained using APIs. This data is stored on the server in databases such as Google Cloud Storage. The data is received in JSON or XML format and converted to a unified format.

[0585] Step 2:

[0586] The server analyzes the collected data. The input data is processed as a machine learning dataset, and tools such as Python and TensorFlow are used to predict the availability of transportation routes and public facilities. Specifically, it utilizes models based on historical disaster data to perform impact analysis under certain disaster scenarios. The output is the result of damage prediction.

[0587] Step 3:

[0588] The server creates a disaster response plan based on the analysis results. This process uses a generative AI model to generate appropriate evacuation routes and methods for using public facilities from predictive data. The input is the predictive data from the analysis results, and the output is a concrete evacuation plan. This plan includes information on emergency evacuation roads and shelters and is structured in XML format.

[0589] Step 4:

[0590] The server sends the generated response plan to the terminal in real time. Communication methods using WebSocket or MQTT protocols are ensured, and the plan is delivered to the user's terminal immediately. The generated evacuation plan is used as input, and the output is data displayed on the terminal.

[0591] Step 5:

[0592] The device presents the received evacuation plan to the user visually and audibly. Specifically, the evacuation route is visually displayed on the device's map application using tools such as the Google Maps SDK. Furthermore, a voice assistant provides route guidance, which is updated in real time based on the user's current location. The evacuation plan is provided as input from the server, and the specific guidance information that the user receives is generated as output.

[0593] Step 6:

[0594] Users perform safe evacuation actions based on information provided by their devices. By following the instructions on their devices, users evacuate while avoiding congestion and danger. Furthermore, after the evacuation is complete, users can send feedback to the server. The feedback that users experienced during the evacuation is used as input, and improvement information is collected on the server as output.

[0595] (Application Example 1)

[0596] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0597] In disaster situations, conventional technologies are insufficient for providing real-time safe evacuation routes to support swift, accurate, and safe evacuation actions while avoiding chaos. Furthermore, the use of visualization technologies to support situational assessment during disasters is inadequate, creating a need for more intuitive means of indicating safe evacuation routes.

[0598] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0599] In this invention, the server includes means for collecting network information, means for analyzing the information to predict the impact on transportation means and infrastructure facilities, and means for generating an emergency response plan based on the prediction results. This enables the provision of the optimal evacuation route to users in real time during a disaster and provides guidance on safe evacuation routes that can be intuitively understood using visualization technology.

[0600] "Network information" refers to real-time data and information that can be obtained via the internet or communication networks.

[0601] "Means of transportation" refers to methods and devices that enable the physical movement of people and things, such as public transport and walking.

[0602] "Infrastructure facilities" refer to structures and systems that belong to the infrastructure and support the foundations of economic activity and daily life.

[0603] An "emergency response plan" refers to specific action plans and procedures formulated to ensure swift and appropriate action in the event of a disaster or emergency.

[0604] "User" refers to an individual or organization that uses the system or service.

[0605] "Visualization technology" refers to technologies that visually display information and data, making them intuitively understandable.

[0606] An "evacuation route" refers to a path or route used to move safely to avoid danger.

[0607] The system that implements this application consists of three main components: a server, a terminal, and a user. The server uses Python to collect network information in real time from multiple APIs. The collected information includes data on traffic conditions and infrastructure damage, which is analyzed using TensorFlow to predict the impact on transportation and infrastructure. Based on the prediction results, the system generates the optimal evacuation route as an emergency response plan. The information from the server is transmitted to the terminal in real time.

[0608] The device provides information to the user through an application built with React Native. The device obtains the user's current location using GPS and displays evacuation routes using visualization technology with ARKit (iOS) or ARCore (Android). This allows the user to intuitively confirm a safe route while moving.

[0609] Users can use their smartphones or smart glasses to take quick and safe evacuation actions based on information provided by the server and guidance from their devices.

[0610] A concrete example would be when a heavy rain warning is issued, and the app provides specific instructions such as, "To evacuate to the nearest high ground from your current location, proceed along △△ Street, then turn left at the next traffic light." An example of a prompt message would be, "Output a design proposal for an AR application that predicts the optimal evacuation route in real time during a disaster and guides the user visually and audibly."

[0611] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0612] Step 1:

[0613] The server collects network information from real-time APIs via the internet. Inputs include disaster-related data and traffic data, and output is an integrated dataset. Specifically, the server uses a Python script to send requests to multiple APIs and then aggregates the obtained data.

[0614] Step 2:

[0615] The server utilizes TensorFlow based on collected data to predict the impact on transportation and infrastructure. The input is an integrated dataset, and the output is the prediction. Specifically, the server runs a machine learning model and calculates the prediction results.

[0616] Step 3:

[0617] The server generates an optimal emergency response plan based on the prediction results. The input is the prediction results, and the output is information on evacuation routes and safe locations. Specifically, the server uses an optimization algorithm to calculate safe routes and compiles them into a plan.

[0618] Step 4:

[0619] The device receives the response plan sent from the server and provides it to the user. The input is the server's response plan information, and the output is visual and audio guidance to the user. Specifically, a React Native app running on the device displays the information and provides guidance using speech synthesis technology.

[0620] Step 5:

[0621] The user takes evacuation action according to the instructions on the device. The input is guidance information from the device, and the output is the actual evacuation action. Specifically, the user follows a safe evacuation route according to visual instructions using ARKit or ARCore.

[0622] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0623] This invention combines an emotion engine with a system that provides optimal evacuation information during disasters, thereby enabling flexible information delivery tailored to the user's psychological state. The system consists of a server, a terminal, a user, and an emotion engine.

[0624] The server continues to collect network data in real time and analyze traffic conditions and infrastructure damage. Based on this analysis, it generates a countermeasure plan, but what distinguishes it from conventional systems is its integration with an emotion engine. The emotion engine recognizes the user's emotions, and the server uses this emotion data to prepare to present information in the format most appropriate for the user. For example, if the system detects high stress levels, it will provide simpler, more reassuring information.

[0625] The device is equipped with an emotion engine that analyzes the user's emotions in real time from their voice and facial expressions. The analyzed emotion results are sent to the server via the device. Based on this emotion data, the device presents the user with the most appropriate format for the response plan and evacuation route information received from the server. If the user shows surprise or anxiety, the device can also select a more polite and slow voice guidance mode.

[0626] Users can evacuate safely and quickly by utilizing the appropriate information provided through their devices. This system takes into account that users' emotional states differ significantly between normal times and disaster situations, and provides functions that allow them to evacuate with peace of mind while reducing psychological burden.

[0627] As a concrete example, suppose a major earthquake occurs in Tokyo at night. Users are often awake and confused. The device analyzes the user's state of tension using an emotion engine and guides them to the nearest evacuation center with a calming voice. If the user shows signs of panic while moving, the device will play further reassuring messages or keep the navigation simple. In this way, a system combined with an emotion engine can provide more personalized evacuation support.

[0628] The following describes the processing flow.

[0629] Step 1:

[0630] Upon receiving notification of a disaster, the server immediately begins collecting relevant data via the network. This includes real-time data such as earthquake warnings, traffic information, and infrastructure damage reports.

[0631] Step 2:

[0632] The server analyzes the collected data and predicts the impact on infrastructure and transportation networks. Based on these predictions, it identifies areas requiring evacuation and safe evacuation routes, and generates a response plan.

[0633] Step 3:

[0634] The device analyzes the user's voice and facial expressions using an emotion engine to determine the user's emotional state at that moment. This emotional data reveals whether the user is feeling anxious or stressed.

[0635] Step 4:

[0636] The device receives a response plan generated from the server and provides information optimized for the user's emotional state. For example, if the user is feeling anxious, the device will provide guidance in a calm voice and explain evacuation routes in a gentle tone.

[0637] Step 5:

[0638] The user follows instructions from the device, selects a safe evacuation route, and begins moving. The device monitors the user's location and progress, and continuously updates the information as needed.

[0639] Step 6:

[0640] After the user completes their evacuation, they send feedback to the server via their device. This feedback includes data on emotional changes recorded by the emotion engine, which is used as learning material to help with future disasters.

[0641] (Example 2)

[0642] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0643] Conventional disaster evacuation information systems have been insufficient in providing information that takes into account the psychological burden on users. As a result, users may experience increased confusion and anxiety, which can hinder swift and safe evacuation. This invention aims to solve these problems and support safer and more efficient evacuations by providing flexible and appropriate information tailored to the emotional state of individual users.

[0644] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0645] In this invention, the server includes means for collecting network data, means for analyzing the data to predict the impact on traffic and infrastructure, and means for determining the user's psychological state using an emotion engine that analyzes the user's emotional state. This makes it possible to generate countermeasures plans based on the user's emotional state and provide information in an optimal form.

[0646] "Network data" refers to information that can be obtained over a network, such as traffic conditions and infrastructure status.

[0647] An "emotion engine" is a technology that analyzes a user's emotional state in real time, evaluating their psychological state based on voice and facial expression data.

[0648] A "disaster response plan" refers to a plan or guideline developed to enable safe and rapid evacuation during a disaster.

[0649] "Psychological state" refers to the emotions and mental state a user exhibits in a particular situation, including feelings of relief, anxiety, and confusion.

[0650] A "user" refers to an entity that receives information through the system and actually takes evacuation action.

[0651] This invention combines an emotion engine with a system that provides optimal evacuation information during disasters, enabling flexible information delivery tailored to the user's psychological state. The system operates around a server, terminals, and users.

[0652] The server collects diverse network data in real time and analyzes traffic conditions and infrastructure damage. This includes centrally managing data from various sources using APIs and databases. It also utilizes machine learning algorithms to predict traffic flow and infrastructure conditions. The server's role is to build countermeasure plans based on the generated data and refine those plans using the output of an emotion engine.

[0653] The device is equipped with an emotion engine that analyzes the user's voice and facial expressions to recognize their emotional state in real time. This is achieved through an input device using a camera and microphone, which transmits the emotional data to a server. Based on the received information, the device has the function of presenting evacuation information to the user in the most appropriate format.

[0654] Users can evacuate quickly and safely using evacuation information provided through their devices. The emotional engine reduces the user's psychological burden, enabling them to evacuate with confidence.

[0655] As a concrete example, consider a scenario where a major earthquake occurs at night. When the user shows signs of panic, the device guides them along an evacuation route in a calm, easy-to-understand voice to alleviate their anxiety. This process is executed based on a generative AI model.

[0656] An example of a prompt would be, "What kind of evacuation information system takes into account how users express their emotions during a disaster?" This prompt allows the generating AI model to assess the user's emotional state and provide the necessary information in an appropriate format.

[0657] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0658] Step 1:

[0659] The server collects real-time traffic conditions and infrastructure damage data from publicly available data sources on the network. Inputs are data from traffic APIs and infrastructure monitoring systems, and outputs are datasets for analysis. The data is organized within the server and pre-processed using machine learning algorithms.

[0660] Step 2:

[0661] The server analyzes the collected dataset to identify traffic flow and damaged areas. Input consists of various sensor data and patterns based on past events. Using a predictive algorithm, it identifies congested areas and passable zones. The output is predictive information that should be incorporated into disaster response plans.

[0662] Step 3:

[0663] The device uses a built-in emotion engine to analyze the user's voice and facial expressions and evaluate their emotional state. The input is the user's real-time voice data and camera footage, and the output is an evaluation of the user's current emotional state (e.g., anxiety, relief). Based on this evaluation, the method of information provision generated by the server is determined.

[0664] Step 4:

[0665] The server uses data from the emotion engine to generate an action plan optimized for the user's psychological state. Inputs are previously collected and analyzed environmental data and user emotion evaluation data, while output is customized evacuation information provided in written and audio formats.

[0666] Step 5:

[0667] The terminal presents the user with a countermeasure plan received from the server. The input is the countermeasure plan sent from the system, and the output is the information the user receives visually or aurally. Specifically, the terminal may display a map on the screen or provide voice guidance along a specified route.

[0668] Step 6:

[0669] Users initiate swift and safe evacuation actions based on information provided through their devices. Input is the information presented by the device, and output is the physical evacuation action. User feedback is sent back to the server to help improve system performance.

[0670] (Application Example 2)

[0671] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0672] During disasters, people often experience confusion and stress, making it difficult to provide them with appropriate information for safe evacuation. Conventional evacuation information systems provide uniform information without considering the emotional state of the user, resulting in a significant psychological burden on users when receiving and understanding the information. Therefore, there is a need for a means to provide flexible and appropriate information according to the emotional state of the user, thereby supporting safe and rapid evacuation.

[0673] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0674] In this invention, the server includes means for collecting network data, means for predicting the impact on traffic and infrastructure, and means for analyzing the user's emotional state during the information provision process and optimizing the information. This makes it possible to provide information that reduces stress and provides a sense of security based on the user's emotional state.

[0675] "Means of collecting network data" refers to technologies and devices for acquiring data in real time from the internet and various other networks.

[0676] "Means for predicting the impact on transportation and infrastructure" refers to methods and systems that estimate the impact of a disaster on transportation networks and infrastructure based on collected network data.

[0677] "Methods for generating disaster response plans" refer to algorithms and processes that create appropriate disaster response measures based on predicted data.

[0678] "Means for analyzing emotional states" refer to software and hardware that use data such as voice and facial expressions to identify and analyze a user's emotions.

[0679] "Means of optimizing and delivering information" refer to engines and modules that customize information based on analyzed sentiment data to make it most easily understandable and reassuring for the user.

[0680] "A map display method that specifically shows evacuation route information" refers to a map display technology that visually presents evacuation routes to the user.

[0681] "Means of providing voice guidance" refers to a system that conveys evacuation information and instructions to users via voice.

[0682] "Means of collecting and learning from feedback" refers to data processing techniques that gather user reactions and opinions and use them to improve system performance.

[0683] "Methods for learning and optimizing the effectiveness of information presentation according to emotional state" refers to machine learning techniques that analyze the results of information presentation in response to the user's emotions and make information provision more efficient in subsequent instances.

[0684] This invention aims to construct a system equipped with an emotion engine to effectively provide evacuation support during disasters. Specifically, it involves the coordinated operation of three elements: a server, a terminal, and a user.

[0685] The server has the ability to collect network data and analyze weather, traffic conditions, and infrastructure impacts in real time. Using this analysis, the server generates optimal evacuation routes and countermeasures based on the user's location and situation, and sends them to the terminal. It also works in conjunction with the aforementioned emotion engine to optimize information based on the user's emotion data. For emotion recognition, the server utilizes generative AI models such as Google Cloud Vision API and Microsoft Cognitive Services.

[0686] The device captures the user's voice and facial expressions in real time and analyzes them using an emotion engine. The analysis results are sent to a server, and the device then presents optimized evacuation information based on this analysis. For example, if the user is feeling anxious, the device provides reassuring voice guidance and displays evacuation routes clearly using a graphical interface.

[0687] Users can receive information provided through this system and take swift and safe evacuation actions. Emotional feedback from users is used to further improve the system's performance.

[0688] For example, when an earthquake occurs, the user's device can detect the surrounding chaos, and the server can perform sentiment analysis to generate a simple yet reassuring message such as, "This is the nearest evacuation center. Please rest assured, we will guide you to a safe route," which can then be delivered via both voice and visuals.

[0689] An example of a prompt message would be: "Analyze the following user situation and provide optimal evacuation information. Use the user's facial image and voice data to assess their emotional state." This input will then activate the generative AI model.

[0690] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0691] Step 1:

[0692] The server collects network data in real time from the internet and various sensors. Inputs are data obtained through network connections and include disaster information, traffic conditions, and infrastructure health. Outputs are organized datasets for processing in the next stage.

[0693] Step 2:

[0694] The server analyzes the collected network data to predict the impact of a disaster. Specifically, it uses an analysis algorithm to estimate traffic congestion and the extent of infrastructure damage. The input for this process is the data obtained in step 1, and the output is the prediction results necessary for the user to build an evacuation plan.

[0695] Step 3:

[0696] The server generates disaster response plans and evacuation routes based on the prediction results. It utilizes a generative AI model to establish the most appropriate plan from numerous scenarios. The input is the prediction result from step 2, and the plan is the output for integration with sentiment data in the next step.

[0697] Step 4:

[0698] The device captures the user's voice and facial expressions using a camera and microphone, and inputs them into the emotion engine in real time. The emotion is then analyzed based on these prompt messages. The input is the user's raw data, and the output is the analyzed emotion data.

[0699] Step 5:

[0700] The server integrates emotional data transmitted from the terminal and processes the data to optimize the response plan for the user. Using an emotion engine, the server adjusts the information considering factors such as the user's stress level. The output is evacuation information optimized for the user.

[0701] Step 6:

[0702] The device provides users with optimized evacuation information. Using map display and voice guidance functions, it delivers information in a format that prioritizes safety and ease of understanding. Input is the output in step 5, and the final output is the visual and audio information presented to the user.

[0703] Step 7:

[0704] Users take safe and swift evacuation actions based on the information they receive. User feedback is collected by the system and contributes to future improvements. Feedback is collected from users during the evacuation and used in the system's learning process.

[0705] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0706] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0707] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0708] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0709] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0710] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0711] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0712] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0713] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0714] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0715] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0716] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0717] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0718] 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.

[0719] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0720] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0721] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0722] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0723] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0724] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0725] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0726] The following is further disclosed regarding the embodiments described above.

[0727] (Claim 1)

[0728] Means for collecting network data,

[0729] A means for analyzing the aforementioned data to predict the impact on traffic and infrastructure,

[0730] A means for generating disaster response plans based on prediction results,

[0731] A means of providing the aforementioned countermeasure plan to the user,

[0732] A system that includes this.

[0733] (Claim 2)

[0734] The system according to claim 1, further comprising a map display means that specifically shows evacuation route information.

[0735] (Claim 3)

[0736] The system according to claim 1, comprising means for collecting user feedback and learning to improve the performance of the generated plans.

[0737] "Example 1"

[0738] (Claim 1)

[0739] Means for collecting and integrating information from a network,

[0740] A means of analyzing collected information to predict the impact on transportation routes and public facilities,

[0741] A means of creating a disaster response plan based on predictions and in accordance with the disaster situation,

[0742] A means for transmitting and presenting the aforementioned plan to the user's terminal,

[0743] A means of providing visual and audio guidance for evacuation routes that are dynamically updated based on the user's location information,

[0744] A system that includes this.

[0745] (Claim 2)

[0746] The system according to claim 1, comprising a display device that provides detailed information on evacuation routes.

[0747] (Claim 3)

[0748] The system according to claim 1, comprising means for collecting user feedback and performing machine learning to improve the accuracy of disaster response plans.

[0749] "Application Example 1"

[0750] (Claim 1)

[0751] Means for collecting network information,

[0752] A means for analyzing the aforementioned information and predicting the impact on means of transportation and infrastructure facilities,

[0753] A means for generating an emergency response plan based on prediction results,

[0754] Means for providing the aforementioned response plan to users,

[0755] A means of identifying the user's current location and guiding them along a safe route using visualization technology,

[0756] A system that includes this.

[0757] (Claim 2)

[0758] The system according to claim 1, comprising a display means for specifically showing evacuation route information and utilizing visualization technology.

[0759] (Claim 3)

[0760] The system according to claim 1, comprising a learning means for collecting user feedback and improving the effectiveness of the generated plan.

[0761] "Example 2 of combining an emotion engine"

[0762] (Claim 1)

[0763] Means for collecting network data,

[0764] A means for analyzing the aforementioned data to predict the impact on traffic and infrastructure,

[0765] A means of determining a user's psychological state using an emotion engine that analyzes the user's emotional state,

[0766] A means of generating a countermeasure plan based on the determined emotional state and providing it to the user in the most optimal way,

[0767] A system that includes this.

[0768] (Claim 2)

[0769] The system according to claim 1, which includes a map display means that specifically shows evacuation route information, and which also provides information according to the user's emotional state.

[0770] (Claim 3)

[0771] The system according to claim 1, comprising means for collecting user feedback and sentiment data and learning to improve the performance of the generated plan.

[0772] "Application example 2 when combining with an emotional engine"

[0773] (Claim 1)

[0774] Means for collecting network data,

[0775] A means for analyzing the aforementioned data to predict the impact on traffic and infrastructure,

[0776] A means for generating disaster response plans based on prediction results,

[0777] A means of analyzing emotional states,

[0778] A means of optimizing and providing information based on user emotions,

[0779] A system that includes this.

[0780] (Claim 2)

[0781] The system according to claim 1, comprising a map display means that specifically shows evacuation route information and a means that provides voice guidance.

[0782] (Claim 3)

[0783] The system according to claim 1, comprising a means for collecting user feedback and learning to improve the performance of generated plans, and a means for learning and optimizing the effectiveness of information presentation according to emotional states. [Explanation of symbols]

[0784] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means for collecting network information, A means for analyzing the aforementioned information and predicting the impact on means of transportation and infrastructure facilities, A means for generating an emergency response plan based on prediction results, Means for providing the aforementioned response plan to users, A means of identifying the user's current location and guiding them along a safe route using visualization technology, A system that includes this.

2. The system according to claim 1, which includes a display means for specifically showing evacuation route information and utilizes visualization technology.

3. The system according to claim 1, comprising a learning means for collecting user feedback and improving the effectiveness of the generated plan.

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